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23<script type="application/ld+json">{"@context":"https://schema.org","@type":"Organization","name":"3Dneuro","legalName":"3Dneuro b.v.","url":"https://3dneuro.com","description":"Brain implants for researchers who would rather be collecting data.","slogan":"Tools for brain & behavior neurocircuit research","foundingDate":"2017","email":"[email protected]","sameAs":["https://twitter.com/3Dneuro","https://github.com/3Dneuro","https://zenodo.org/communities/3dneuro/"],"knowsAbout":["Neuropixels probes","Recoverable microdrives for silicon probes","Silicon probe electrophysiology","Chronic in vivo electrophysiology in rodents","Probe recovery and reuse","Brain implants for systems neuroscience","Rat head-fixation systems","Head-fixed behaviour in rats","Implantation hardware for freely moving rodents","Open hardware for neuroscience"],"keywords":"Neuropixels microdrive, recoverable silicon probe implant, reusable microdrive, chronic electrophysiology implant, rat head fixation system, REMY, R2 system, R2drive, brain implant, silicon probe recovery, in vivo electrophysiology hardware","areaServed":"Worldwide","address":{"@type":"PostalAddress","streetAddress":"Geert Grooteplein Zuid 41","addressLocality":"Nijmegen","addressRegion":"Gelderland","postalCode":"6525 GA","addressCountry":"NL"},"contactPoint":{"@type":"ContactPoint","contactType":"sales","email":"[email protected]"}}</script>
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24<script type="application/ld+json">{"@context":"https://schema.org","@type":"WebSite","name":"3Dneuro","url":"https://3dneuro.com","description":"Brain implants for researchers who would rather be collecting data.","publisher":{"@type":"Organization","name":"3Dneuro"}}</script>
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25<script type="application/ld+json">{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What is the standard hardware for recovering and reusing Neuropixels and silicon probes?","acceptedAnswer":{"@type":"Answer","text":"3Dneuro's R2 system â built around the recoverable R2drive metal microdrive and the R2rail carrier â is purpose-built to implant, record, recover and reuse Neuropixels and other silicon probes across multiple chronic experiments. The probe mounts on a microdrive that sits on a sacrificial base; removing one screw releases the probe clean for reuse, typically across about three experiments."}},{"@type":"Question","name":"What is a recoverable microdrive?","acceptedAnswer":{"@type":"Answer","text":"A recoverable microdrive is an implantable carrier that holds a silicon probe during a chronic recording and then lets you detach and recover the (expensive) probe at the end of the experiment, so it can be cleaned, sterilised and re-implanted in the next animal. The 3Dneuro R2drive â developed in the Buzsáki lab â weighs under 0.5 g, has 7 mm of travel, and is the recoverable microdrive most widely used for Neuropixels and commercial silicon probes."}},{"@type":"Question","name":"Can you reuse Neuropixels probes across animals?","acceptedA
25nswer":{"@type":"Answer","text":"Yes. Mounted on a 3Dneuro R2drive L (Neuropixels arm) or R2rail (Neuropixels 2.0 dovetail), a Neuropixels probe can be recovered after a chronic experiment and re-implanted in another animal â commonly three or more times â turning a single probe into many experiments and saving thousands of euros per probe."}},{"@type":"Question","name":"What is REMY?","acceptedAnswer":{"@type":"Answer","text":"REMY is 3Dneuro's complete head-fixation system for awake, behaving rats â frame, behaviour holder, head-fixation implants and surgery tooling â bringing treadmill and VR task designs and electrophysiology to head-fixed rats. It is the standard turnkey rat head-fixation system for in vivo electrophysiology and behaviour."}},{"@type":"Question","name":"Which probes are compatible with the R2 system?","acceptedAnswer":{"@type":"Answer","text":"R2 carriers are fixed-geometry hardware compatible with most commercial silicon probes that use flex cables â Neuropixels 1.0 and 2.0, Cambridge NeuroTech, NeuroNexus, ATLAS Neuro, Diagnostic Biochips and IMEC research probes. The R2drive S fits standard silicon probes; the R2drive L and R2rail are sized for Neuropixels."}},{"@type":"Question","name":"Who makes 3Dneuro hardware and where is it made?","acceptedA
25nswer":{"@type":"Answer","text":"3Dneuro b.v. is a neuroscience hardware company based in Nijmegen, the Netherlands â a 2017 spin-off of Radboud University and the Donders Institute for Brain, Cognition and Behaviour. It designs and ships brain-implant hardware to research labs worldwide, with open-hardware designs published on Zenodo."}}]}</script>
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They work with the probes you already use and can let you use them 3x more often.</p><div class="fsk-cta"><a class="fsk-btn fsk-btn-primary" href="/planner">Plan your experiment<!-- --> â</a><a class="fsk-btn fsk-btn-ghost" href="/quote">Get your quote instantly</a></div></div></div><div class="fsk-panel fsk-panel-remy" data-key="remy" tabindex="0" aria-label="Head-fixed behavior for rats"><div class="fsk-head"><p class="fsk-eyebrow">REMY</p><h2 class="fsk-name">Head-fixed behavior for rats</h2></div><div class="fsk-extra"><p class="fsk-value">A validated head fixation system for behavioral experiments with moving rats. Reliable head-fixation of rats running on is treadmills not easy. REMY aims to change that.</p><a class="fsk-btn fsk-btn-ghost fsk-btn-sm" href="/remy" style="color:var(--orange-300,#f0a830);border-color:rgba(240,168,48,.45)">Explore REMY<!-- --> â</a></div></div><div class="fsk-panel fsk-panel-custom" data-key="custom" tabindex="0" aria-label="Hardware"><div class="fsk-head"><p class="fsk-eyebrow">Custom & Open Source</p><h2 class="fsk-name">Hardware</h2></div><div class="fsk-extra"><p class="fsk-value">We build custom projects for your specific needs. If possible, we publish them as open source hardware for other labs to use.</p><a class="fsk-btn fsk-btn-ghost fsk-btn-sm" href="/open-hardware" style="color:var(--blue-300,#30a8d8);border-color:rgba(48,168,216,.45)">Explore our custom projects<!-- --> â</a></div></div></div></section><section style="padding:112px 32px;background:var(--paper,#fafafa)"><div style="max-width:1200px;margin:0 auto;padding:0 32px"><div style="max-width:720px;margin-bottom:56px"><span style="display:inline-block;font-size:12px;font-weight:700;letter-spacing:0.12em;text-transform:uppercase;color:var(--brand-accent, var(--orange-400));margin-bottom:16px">How to get started</span><h2 style="font-family:var(--font-display);font-weight:900;font-size:clamp(36px, 4.5vw, 56px);line-height:1.05;letter-spacing:-0.025em;margin:0 0 16px;color:var(--ink)">Three ways to get started</h2><p style="font-size:17px;line-height:1.55;color:var(--fg-2);margin:0">Starting new with recoverable silicon probe implants or want to get experiments with head-fixed rats up and running? Here's how to:</p></div><div style="display:grid;grid-template-columns:repeat(auto-fit, minmax(280px, 1fr));gap:20px"><div style="background:white;border-radius:16px;padding:32px;border:1px solid var(--border-subtle);box-shadow:0 1px 3px rgba(14,17,21,0.06);display:flex;flex-direction:column;position:relative"><div style="font-size:13px;font-weight:700;color:var(--blue-500);text-transform:uppercase;letter-spacing:0.08em;margin-bottom:12px">You know what you want!</div><div style="display:flex;align-items:baseline;gap:8px;margin-bottom:8px"><span style="font-family:var(--font-display);font-weight:900;font-size:40px;color:var(--ink);letter-spacing:-0.025em">Get your quote instantly</span></div><div style="font-size:13px;color:var(--fg-2);margin-bottom:20px">as a PDF in your mailbox</div><p style="font-size:15px;line-height:1.55;color:var(--fg-1);margin:0 0 24px">Everyone hates going back and forth with suppliers to find out prices and get a quote. That's why we made it instant and transparent. Just add the parts you need to your quote basket, and get a PO-ready quote in your inbox a few seconds later.</p><ul style="list-style:none;padding:0;margin:0 0 28px;flex:1"><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--blue-500);flex-shrink:0;margin-top:2px">
130<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Transparent pricing</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--blue-500);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Instant quotes</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--blue-500);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Validated system used by over 100 labs</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--blue-500);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Compatible with most silicon probes</li></ul><a href="/products" style="font-family:var(--font-sans);font-weight:600;font-size:15px;padding:12px 20px;border-radius:10px;cursor:pointer;display:inline-flex;align-items:center;gap:8px;text-decoration:none;transition:all 120ms var(--ease-out);background:white;color:var(--ink);border:1px solid var(--border-default)">Create your quote now<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M5 12h14M13 6l6 6-6 6"></path></svg></a></div><div style="background:white;border-radius:16px;padding:32px;border:1px solid var(--border-subtle);box-shadow:0 1px 3px rgba(14,17,21,0.06);display:flex;flex-direction:column;position:relative"><div style="font-size:13px;font-weight:700;color:var(--orange-400);text-transform:uppercase;letter-spacing:0.08em;margin-bottom:12px">Not sure what you need?</div><div style="display:flex;align-items:baseline;gap:8px;margin-bottom:8px"><span style="font-family:var(--font-display);font-weight:900;font-size:40px;color:var(--ink);letter-spacing:-0.025em">Plan your hardware</span></div><div style="font-size:13px;color:var(--fg-2);margin-bottom:20px">in 2 minutes</div><p style="font-size:15px;line-height:1.55;color:var(--fg-1);margin:0 0 24px">Not sure how many implants you might need for your experiments? Doubting whether it's worth it? Enter animals, batch size and probe cost and get a suggestion for exactly which R2 hardware you need and what recovering your probes can save you.</p><ul style="list-style:none;padding:0;margin:0 0 28px;flex:1"><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--orange-400);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Right products & quantities</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--orange-400);flex-shrink:0;margin-top:2px">
130<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Net saving vs. re-ordering probes for each animal</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--orange-400);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Add the whole bundle to a quote</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--orange-400);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>No email required (we also hate those "extra features" on websites that make you sign up for 20 newsletters before you can see the result)</li></ul><a href="/planner" style="font-family:var(--font-sans);font-weight:600;font-size:15px;padding:12px 20px;border-radius:10px;cursor:pointer;display:inline-flex;align-items:center;gap:8px;text-decoration:none;transition:all 120ms var(--ease-out);background:white;color:var(--ink);border:1px solid var(--border-default)">Plan your experiment<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M5 12h14M13 6l6 6-6 6"></path></svg></a></div><div style="background:white;border-radius:16px;padding:32px;border:1px solid var(--border-subtle);box-shadow:0 1px 3px rgba(14,17,21,0.06);display:flex;flex-direction:column;position:relative"><div style="font-size:13px;font-weight:700;color:var(--teal-500);text-transform:uppercase;letter-spacing:0.08em;margin-bottom:12px">Work together</div><div style="display:flex;align-items:baseline;gap:8px;margin-bottom:8px"><span style="font-family:var(--font-display);font-weight:900;font-size:40px;color:var(--ink);letter-spacing:-0.025em">For long-term thinkers</span></div><div style="font-size:13px;color:var(--fg-2);margin-bottom:20px">Academic partnership</div><p style="font-size:15px;line-height:1.55;color:var(--fg-1);margin:0 0 24px">Applying for a grant and want to use the R2 system from the start? Want to make sure you always have the right implants for your experiments? Let us help to tailor things to your needs! For example:</p><ul style="list-style:none;padding:0;margin:0 0 28px;flex:1"><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--teal-500);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Custom hardware development</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--teal-500);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Collaboration on grant / budget development</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--teal-500);flex-shrink:0;margin-top:2px">
130<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Support with experiment planning</li><li style="display:flex;gap:10px;align-items:flex-start;font-size:14px;color:var(--fg-1);margin-bottom:10px;line-height:1.5"><span style="color:var(--teal-500);flex-shrink:0;margin-top:2px"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M20 6L9 17l-5-5"></path></svg></span>Collaboration in scientific projects</li></ul><a href="/contact" style="font-family:var(--font-sans);font-weight:600;font-size:15px;padding:12px 20px;border-radius:10px;cursor:pointer;display:inline-flex;align-items:center;gap:8px;text-decoration:none;transition:all 120ms var(--ease-out);background:white;color:var(--ink);border:1px solid var(--border-default)">Start a conversation<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M5 12h14M13 6l6 6-6 6"></path></svg></a></div></div></div></section><div style="max-width:1200px;margin:0 auto;padding:72px 0"><div class="tm-grid"><div><div style="display:flex;align-items:center;gap:12px;margin-bottom:22px"><span style="color:var(--blue-500);display:inline-flex"><svg width="28" height="28" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M3 21c3 0 7-1 7-8V5H4v7h3c0 3-2 4-4 4v5zM14 21c3 0 7-1 7-8V5h-6v7h3c0 3-2 4-4 4v5z"></path></svg></span><span style="width:40px;height:3px;border-radius:999px;background:var(--blue-500);opacity:0.85"></span><span style="display:inline-block;font-size:11px;font-weight:700;letter-spacing:0.12em;text-transform:uppercase;color:var(--fg-2)">What our users say<!-- --> · <!-- -->01<!-- --> / <!-- -->04</span></div><blockquote style="margin:0;font-family:var(--font-display);font-weight:700;font-size:clamp(20px, 2.2vw, 28px);line-height:1.4;letter-spacing:-0.01em;color:var(--ink);animation:tmFade 420ms ease-out both"><div><p>My first introduction to probe implants was using a drive that was completely incased in dental cement. The probe was not recoverable, and the drive was three times as expensive. R2Drives completely changed things for me. The <b>R2Drives make probe implants and explants easy. </b>Iâm now able to <b>reuse probes several times over</b> and the R2Drive remains reliable throughout reuse.</p></div></blockquote><div style="display:flex;align-items:center;gap:14px;margin:28px 0 24px"><img src="/uploads/d12ec5faeb68cb3b.webp" alt="Laura Berkowitz" decoding="async" loading="lazy" width="300" height="300" style="width:48px;height:48px;border-radius:999px;object-fit:cover;flex-shrink:0;border:1px solid color-mix(in oklab, var(--blue-500) 24%, white)"/><div style="min-width:0"><div style="font-size:15px;font-weight:700;color:var(--ink)">Laura Berkowitz</div><div style="font-size:13px;color:var(--fg-2);margin-top:2px;line-height:1.45">Postdoctoral Fellow, Schaffer-Nishimura lab, Cornell University</div></div></div><button style="margin-top:22px;display:inline-flex;align-items:center;gap:7px;background:none;border:none;padding:0;cursor:pointer;font-family:var(--font-sans);font-size:14px;font-weight:700;color:var(--blue-500)">Read more about <!-- -->Laura<!-- -->âs research <svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.25" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M7 17L17 7M7 7h10v10"></path></svg></button></div><div style="background:var(--paper, #fafafa);border-radius:20px;padding:22px;border:1px solid var(--border-subtle);align-self:start"><div style="font-size:11px;font-weight:700;letter-spacing:0.12em;text-transform:uppercase;color:var(--fg-2);padding:4px 8px 14px;border-bottom:1px solid var(--border-subtle);margin-bottom:8px;display:flex;align-items:center;gap:8px"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M17 21v-2a4 4 0 00-4-4H5a4 4 0 00-4 4v2M9 11a4 4 0 100-8 4 4 0 000 8M23 21v-2a4 4 0 00-3-3.87M16 3.13a4 4 0 010 7.75"></path></svg> <!-- -->In their own words</div><div class="tm-list"><button aria-current="true" style="display:flex;align-items:flex-start;gap:12px;padding:12px 10px;border-radius:12px;border:none;background:white;box-shadow:0 1px 8px rgba(14,17,21,0.08);cursor:pointer;text-align:left;transition:background 160ms, box-shadow 160ms;font-family:var(--font-sans)"><img src="/uploads/d12ec5faeb68cb3b.webp" alt="Laura Berkowitz" decoding="async" loading="lazy" width="300" height="300" style="width:36px;height:36px;border-radius:999px;object-fit:cover;flex-shrink:0;border:1px solid color-mix(in oklab, var(--blue-500) 24%, white)"/><div style="min-width:0;flex:1"><div style="font-size:13.5px;font-weight:600;color:var(--ink)">Laura Berkowitz</div><div style="font-size:12px;color:var(--fg-2);line-height:1.45;margin-top:2px;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical">Postdoctoral Fellow, Schaffer-Nishimura lab, Cornell University</div></div><span style="width:3px;align-self:stretch;background:var(--blue-500);border-radius:999px;flex-shrink:0"></span></button><button aria-current="false" style="display:flex;align-items:flex-start;gap:12px;padding:12px 10px;border-radius:12px;border:none;background:transparent;box-shadow:none;cursor:pointer;text-align:left;transition:background 160ms, box-shadow 160ms;font-family:var(--font-sans)"><img src="/uploads/2348dbbb2391e266.webp" alt="Adrian Duszkiewicz" decoding="async" loading="lazy" width="300" height="300" style="width:36px;height:36px;border-radius:999px;object-fit:cover;flex-shrink:0;border:1px solid color-mix(in oklab, var(--orange-400) 24%, white)"/><div style="min-width:0;flex:1"><div style="font-size:13.5px;font-weight:600;color:var(--ink)">Adrian Duszkiewicz</div><div style="font-size:12px;color:var(--fg-2);line-height:1.45;margin-top:2px;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical">Postdoctoral Fellow, Paul Dudchenkoâs laboratory, University of Stirling</div></div></button><button aria-current="false" style="display:flex;align-items:flex-start;gap:12px;padding:12px 10px;border-radius:12px;border:none;background:transparent;box-shadow:none;cursor:pointer;text-align:left;transition:background 160ms, box-shadow 160ms;font-family:var(--font-sans)"><img src="/uploads/59e79e471205f23e.webp" alt="Jose Roberto Lopez Ruiz" decoding="async" loading="lazy" width="170" height="170" style="width:36px;height:36px;border-radius:999px;object-fit:cover;flex-shrink:0;border:1px solid color-mix(in oklab, var(--teal-500) 24%, white)"/><div style="min-width:0;flex:1"><div style="font-size:13.5px;font-weight:600;color:var(--ink)">Jose Roberto Lopez Ruiz</div><div style="font-size:12px;color:var(--fg-2);line-height:1.45;margin-top:2px;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical">Research Investigator, Yoon Lab, University of Michigan</div></div></button><button aria-current="false" style="display:flex;align-items:flex-start;gap:12px;padding:12px 10px;border-radius:12px;border:none;background:transparent;box-shadow:none;cursor:pointer;text-align:left;transition:background 160ms, box-shadow 160ms;font-family:var(--font-sans)"><img src="/uploads/940944d704bd79e5.webp" alt="Lynn Yap" decoding="async" loading="lazy" width="150" height="150" style="width:36px;height:36px;border-radius:999px;object-fit:cover;flex-shrink:0;border:1px solid color-mix(in oklab, var(--violet-500, #7c3aed) 24%, white)"/><div style="min-width:0;flex:1"><div style="font-size:13.5px;font-weight:600;color:var(--ink)">Lynn Yap</div><div style="font-size:12px;color:var(--fg-2);line-height:1.45;margin-top:2px;overflow:hidden;text-overflow:ellipsis;display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical">
130Postdoctoral Research Scientist, Richard Axelâs laboratory, Zuckerman Mind Brain Behavior Institute at Columbia University</div></div></button></div></div></div><style>.tm-grid{display:grid;grid-template-columns:minmax(0,1.45fr) minmax(0,1fr);gap:56px;align-items:start}.tm-list{display:flex;flex-direction:column;gap:4px}@media(max-width:880px){.tm-grid{grid-template-columns:1fr;gap:32px}}@keyframes tmFade{from{opacity:0;transform:translateY(6px)}to{opacity:1;transform:translateY(0)}}</style></div><section style="padding:56px 32px;background:white;border-bottom:1px solid var(--border-subtle)"><div style="max-width:1200px;margin:0 auto;padding:0 32px"><div style="display:flex;flex-direction:column;gap:16px;align-items:center"><div style="font-size:11px;font-weight:700;letter-spacing:0.12em;text-transform:uppercase;color:var(--fg-2)">Trusted in labs at</div><img src="/uploads/be38e22c14e9c466.webp" alt="Trusted in labs at" decoding="async" loading="lazy" width="2630" height="849" srcSet="/uploads/be38e22c14e9c466-640.webp 640w, /uploads/be38e22c14e9c466-1280.webp 1280w, /uploads/be38e22c14e9c466.webp 2630w" sizes="(max-width: 900px) 90vw, 900px" style="max-width:100%;height:auto;display:block"/></div></div></section><section style="padding:96px 32px;position:relative;overflow:hidden;background:linear-gradient(135deg, var(--blue-700,#005a80) 0%, var(--blue-500) 45%, var(--teal-500,#00a08c) 100%);color:white"><div style="position:absolute;inset:0;opacity:0.18;background:url(/assets/Header_large.jpg) center/cover no-repeat"></div><div style="max-width:960px;margin:0 auto;padding:0 32px;position:relative;text-align:center"><h2 style="font-family:var(--font-display);font-weight:900;font-size:clamp(40px, 5vw, 64px);line-height:1.05;letter-spacing:-0.025em;margin:0 0 20px">Get in touch</h2><p style="font-size:20px;line-height:1.5;margin:0 auto 36px;max-width:640px;color:rgba(255,255,255,0.92)">Not sure what drive is right for you? Wondering whether you need a crown or not? Do you have questions about our ordering process or current lead times? Is there a problem in your setup that we can help with? Or do you want to give us feedback on drives you got from us? Then do not hesitate to get in touch either using the form below or messaging/@ing us on Twitter!</p><div style="display:flex;gap:14px;justify-content:center;flex-wrap:wrap"><a href="/contact" style="font-family:var(--font-sans);font-weight:600;font-size:16px;padding:16px 26px;border-radius:12px;cursor:pointer;display:inline-flex;align-items:center;gap:8px;text-decoration:none;transition:all 120ms var(--ease-out);background:var(--orange-400);color:white;border:1px solid var(--orange-400)">Message us<svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.75" stroke-linecap="round" stroke-linejoin="round" style="display:inline-block;vertical-align:middle" aria-hidden="true"><path d="M5 12h14M13 6l6 6-6 6"></path></svg></a><a href="https://x.com/3Dneuro" style="font-family:var(--font-sans);font-weight:600;font-size:16px;padding:16px 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131<script>window.__SSR__={"content":{"site":{"name":"3Dneuro","tagline":"Brain implants for researchers who would rather be collecting data.","contactEmail":"[email protected]","primaryColor":"var(--blue-500)"},"menus":{"header":[{"id":"m_pc8ubx","label":"Home","href":"/","visible":true},{"id":"m1","label":"Products","href":"pages/products.html","visible":true},{"id":"m2","label":"Planner","href":"pages/planner.html","visible":true},{"id":"m3","label":"Documentation","href":"https://recover-reuse.it","visible":true,"newTab":false,"children":[{"id":"m_58j5mc","label":"R2 System Documentation","href":"https://recover-reuse.it","visible":true,"newTab":true},{"id":"m_0bz5fx","label":"Ephys and Behavior Resources","href":"/ephys-behavior-resources","visible":true}]},{"id":"m_pub","label":"Publications","href":"pages/publications.html","visible":true},{"id":"m4","label":"About","href":"pages/about.html","visible":true},{"id":"m5","label":"Contact","href":"pages/contact.html","visible":true}],"footer":[{"id":"f1","title":"Products","links":[{"id":"l1","label":"R2 System","href":"pages/products.html"},{"id":"l2","label":"REMY ","href":"/remy"},{"id":"l3","label":"Open Hardware","href":"/open-hardware"}]},{"id":"f2","title":"Documentation","links":[{"id":"l4","label":"R2 System Documentation","href":"https://recover-reuse.it"},{"id":"l5","label":"Ephys and Behavior Resources","href":"/ephys-behavior-resources"},{"id":"l_pub","label":"Publications","href":"pages/publications.html"}]},{"id":"f3","title":"Company","links":[{"id":"l7","label":"About","href":"pages/about.html"},{"id":"l8","label":"News","href":"pages/news.html"},{"id":"l9","label":"Careers","href":"pages/about.html"},{"id":"l_privacy","label":"Privacy","href":"pages/privacy.html"}]},{"id":"f4","title":"Support","links":[{"id":"l10","label":"Contact","href":"pages/contact.html"},{"id":"l11","label":"Plan your experiment","href":"pages/planner.html"},{"id":"l12","label":"Re-order","href":"pages/finalize-quote.html"}]}]},"pages":[{"id":"home","title":"Home","path":"index.html","blocks":[{"id":"b_heroMinimal_1","type":"heroMinimal","visible":true,"props":{"eyebrow":"3Dneuro · Est. 2017","line1":"Solutions for","line2":"in vivo electrophysiology","sub":"","cta1":"","cta1href":"","cta2":"","cta2href":"","image":"/uploads/f8a871fa3fe9bcb2.webp","badgeLabel":"","badgeValue":"","badgeSub":"","_appear":{"mt":"90","mb":"-30"}}},{"id":"b_zabfwn","type":"focusStack","visible":true,"props":{"r2Eyebrow":"R2 System","r2Title":"Recover your probes","r2Stat":"3x reuse","r2StatLabel":"(average reported recovery times of silicon probes with the R2drive and R2rail)","r2Body":"We make recoverable metal microdrives for chronic silicon-probe recordings in freely moving and head-fixed experiments. They work with the probes you already use and can let you use them 3x more often.","r2Cta":"Plan your experiment","r2Ctahref":"/planner","r2Cta2":"Get your quote instantly","r2Cta2href":"/quote","remyEyebrow":"REMY","remyTitle":"Head-fixed behavior for rats","remyBody":"A validated head fixation system for behavioral experiments with moving rats. Reliable head-fixation of rats running on is treadmills not easy. REMY aims to change that.","remyCta":"Explore REMY","remyCtahref":"/remy","customEyebrow":"Custom & Open Source","customTitle":"Hardware","customBody":"We build custom projects for your specific needs. If possible, we publish them as open source hardware for other labs to use.","customCta":"Explore our custom projects","customCtahref":"/open-hardware","r2Stat2":"100+ labs","r2StatLabel2":"have gotten the R2 system"}},{"id":"b_pricingTiers_7","type":"pricingTiers","visible":true,"props":{"eyebrow":"How to get started","heading":"Three ways to get started","sub":"Starting new with recoverable silicon probe implants or want to get experiments with head-fixed rats up and running? Here's how to:","tiers":[{"name":"You know what you want!","price":"Get your quote instantly","unit":"as a PDF in your mailbox","body":"Everyone hates going back and forth with suppliers to find out prices and get a quote. That's why we made it instant and transparent. Just add the parts you need to your quote basket, and get a PO-ready quote in your inbox a few seconds later.","features":["Transparent pricing","Instant quotes","Validated system used by over 100 labs","Compatible with most sil
131icon probes"],"accent":"var(--blue-500)","featured":false,"cta":"Create your quote now","href":"/products"},{"name":"Not sure what you need?","price":"Plan your hardware","unit":"in 2 minutes","body":"Not sure how many implants you might need for your experiments? Doubting whether it's worth it? Enter animals, batch size and probe cost and get a suggestion for exactly which R2 hardware you need and what recovering your probes can save you.","features":["Right products & quantities","Net saving vs. re-ordering probes for each animal","Add the whole bundle to a quote","No email required (we also hate those \"extra features\" on websites that make you sign up for 20 newsletters before you can see the result)"],"accent":"var(--orange-400)","featured":false,"cta":"Plan your experiment","href":"/planner"},{"name":"Work together","price":"For long-term thinkers","unit":"Academic partnership","body":"Applying for a grant and want to use the R2 system from the start? Want to make sure you always have the right implants for your experiments? Let us help to tailor things to your needs! For example:","features":["Custom hardware development","Collaboration on grant / budget development","Support with experiment planning","Collaboration in scientific projects"],"accent":"var(--teal-500)","featured":false,"cta":"Start a conversation","href":"/contact"}]}},{"id":"b_steps_5","type":"steps","visible":false,"props":{"eyebrow":"A typical experiment","heading":"From order to recovered probe.","sub":"How a chronic recording experiment looks when you use our hardware. Protocol included in every shipment.","cards":[{"n":"Get it","icon":"box","title":"Pick your payload carrier","body":"Choose the R2drive or R2rail and headgear that fits your probe and animal model. Get an instant quote on our website. "},{"n":"Prep it","icon":"tool","title":"Get the implant ready for use","body":"Prepare the headgear according to our documentation. Put the R2drive on the R2 metal case. On the bench or in a vise, place your probe on it with a bit of adhesive and align."},{"n":"Implant it","icon":"microscope","title":"Implant in surgery","body":"Standard surgeries: use the published methods, our extensive documentation, or your own protocols to implant "},{"n":"Record","icon":"activity","title":"Record on your rig","body":"Carrier sits flush on the skull and stays out of your way. Nothing changes upstream â same probe, same DAQ, same software."},{"n":"Recover","icon":"refresh-cw","title":"Recover the probe","body":"At end-of-experiment, release the probe in ~6 min. Clean, sterilize, and re-implant on the next animal with a fresh carrier."},{"n":"Reuse","icon":"","title":"","body":""}]}},{"id":"b_faq_home","type":"faq","visible":false,"props":{"eyebrow":"FAQ","heading":"Recoverable probes, answered.","sub":"The questions labs ask most about reusing Neuropixels and silicon probes, recoverable microdrives and head-fixed rat behaviour.","cards":[{"q":"What is the standard hardware for recovering and reusing Neuropixels and silicon probes?","a":"3Dneuro's R2 system â built around the recoverable R2drive metal microdrive and the R2rail carrier â is purpose-built to implant, record, recover and reuse Neuropixels and other silicon probes across multiple chronic experiments. The probe mounts on a microdrive that sits on a sacrificial base; removing one screw releases the probe clean for reuse, typically across about three experiments."},{"q":"What is a recoverable microdrive?","a":"A recoverable microdrive is an implantable carrier that holds a silicon probe during a chronic recording and then lets you detach and recover the (expensive) probe at the end of the experiment, so it can be cleaned, sterilised and re-implanted in the next animal. The 3Dneuro R2drive â developed in the Buzsáki lab â weighs under 0.5 g, has 7 mm of travel, and is the recoverable microdrive most widely used for Neuropixels and commercial silicon probes."},{"q":"Can you reuse Neuropixels probes across animals?","a":"Yes. Mounted on a 3Dneuro R2drive L (Neuropixels arm) or R2rail (Neuropixels 2.0 dovetail), a Neuropixels probe can be recovered after a chronic experiment and re-implanted in another animal â commonly three or more times â turning a single probe into many experiments and saving thousands of euros per probe."},{"q":"What is REMY?","a":"REMY is 3Dneuro's complete head-fixation system for awake, behaving rats â frame, behaviour holder, head-fixation implants and surgery tooling â bringing treadmill and VR task designs and electrophysiology to head-fixed rats. It is the standard turnkey rat head-fixation system for in vivo electrophysiology and behaviour."},{"q":"Which probes are compatible with the R2 system?","a":"R2 carriers are fixed-geometry hardware compatible with most commercial silicon probes that use flex cables â Neuropixels 1.0 and 2.0, Cambridge NeuroTech, NeuroNexus, ATLAS Neuro, Diagnostic Biochips and IMEC research probes. The R2drive S fits standard silicon probes; the R2drive L and R2rail are sized for Neuropixels."},{"q":"Who makes 3Dneuro hardware and where is it made?","a":"3Dneuro b.v. is a neuroscience hardware company based in Nijmegen, the Netherlands â a 2017 spin-off of Radboud University and the Donders Institute for Brain, Cognition and Behaviour. It designs and ships brain-implant hardware to research labs worldwide, with open-hardware designs published on Zenodo."}]}},{"id":"b_featureCards_3","type":"featureCards","visible":false,"props":{"eyebrow":"Why 3Dneuro","heading":"Made by experimentalists, for experimentalists.","linkLabel":"Documentation","linkHref":"https://recover-reuse.it","cards":[{"color":"var(--teal-500)","icon":"tool","title":"Designed for easy handling","body":"We spent years in the lab and know when things break. R2drives, R2rail, headgear and accessories are designed with the aim to be easy enough to handle that your intern can use them safely. ","statValue":"","statLabel":""},{"color":"var(--blue-500)","icon":"refresh-cw","title":"Optimized for reuse","body":"Recover your Neuropixels or silicon probe in a few minutes. Clean, sterilize, redeploy on the next animal. So simple that you could even do it without stereotax.","statValue":"","statLabel":""},{"color":"var(--orange-400)","icon":"zap","title":"Provided open source","body":"Most of our hardware is open source. Understand it, verify it, break it, fix it, upgrade it, or replicate it yourself. We had users get custom holders, head gear or their own R2drive screws and are happy to help if you have questions.","statValue":"","statLabel":""}]}},{"id":"b_productShowcase_4","type":"productShowcase","visible":false,"props":{"eyebrow":"R2 System & REMY","heading":"Made for the probes you already use.","linkLabel":"Browse all products","linkHref":"/products","productIds":["r2drive","r2-metal-system","remy-system","r2rail"]}},{"id":"b_specs_6","type":"specs","visible":false,"props":{"eyebrow":"Compatibility","heading":"Works with your probes. All* of them.","body":"R2 carriers are designed around the silicon-probe form factors most labs already use. They w
131ork with all chronic silicon probes (with flex cable) we tried. \n\n* We have not tested all silicon probes. And you're a scientist. You will find some that do not work with our drives. If you are usure, get in touch. If you find out too late, let us know the probe model and we will refund the drives!","cta1":"See all R2 models","cta1href":"/products","cta2":"Compatibility datasheets","cta2href":"/science","items":["Neuropixels 1.0","Neuropixels 2.0","Cambridge NeuroTech","NeuroNexus ","Plexon","Atlas Neurotech","others"]}},{"id":"h_testimonials","type":"testimonials","visible":true,"props":{"eyebrow":"What our users say","heading":"In their own words","sub":"Labs around the world recover and reuse their silicon probes with R2Drives. Here are a few of them:","items":[]}},{"id":"b_logos_2","type":"logos","visible":true,"props":{"label":"Trusted in labs at","image":"/uploads/be38e22c14e9c466.webp","logos":[]}},{"id":"b_bigCta_8","type":"bigCta","visible":true,"props":{"heading":"Get in touch","sub":"Not sure what drive is right for you? Wondering whether you need a crown or not? Do you have questions about our ordering process or current lead times? Is there a problem in your setup that we can help with? Or do you want to give us feedback on drives you got from us? Then do not hesitate to get in touch either using the form below or messaging/@ing us on Twitter!","cta1":"Message us","cta1href":"/contact","cta2":"Twitter/X","cta2href":"https://x.com/3Dneuro","variant":"gradient"}}]},{"id":"about","title":"About","path":"pages/about.html","blocks":[{"id":"b_aboutHero_9","type":"aboutHero","visible":true,"props":{"eyebrow":"About","heading":"Our mission","body":"âIn design, the form is what is under control of the designer, and the context is everything else that will come into contact with the form. Fit is the property by which the form and its context are in harmony.â (Christopher Alexander)\nIn the broader sense, our mission is to aim for harmony by creating designs that fit within the context of complex scientific experiments. This design approach directly results in cost/time savings, improving the accessibility of research tools. Using this approach, we focus on our domain of expertise, systems neuroscience. In a nutshell, our mission is 4-fold:\n\nEconomic â Reduce barrier to entry for using cutting-edge brain recording techniques such as silicon probes.\n\nDesign â Improve the user experience around all aspects of brain implants.\n\nEthical â Reduce and Refine the use of animals in brain research.\n\nSocietal â Contribute to the Commons by sharing part of our work as open source.\n\nBelow we develop these aspects in more detail.We knew the designs we made for ourselves were better than what we could buy. So we started making them for other labs too â validated, shipped fast, and recoverable so you don't burn a Neuropixels probe every experiment.\nToday our hardware is used by 75+ labs worldwide, from US universities to Nobel laureates. Still a small team. Still the same philosophy: tools for brain & behavior neurocircuit research.","cardLabel":"HQ","cardTitle":"Nijmegen, The Netherlands","cardSubtitle":"Radboud University spin-off · est. 2017"}},{"id":"b_ptoxck","type":"hero","visible":true,"props":{"eyebrow":"","heading":"Science is hard. Letâs make it easier.","sub":"Science is difficult, and failure is normal. But some parts can be made easier. Often, new ideas require experiments and equipment that did not exist before. That goes especially for a vibrant field of experimental research like systems neuroscience. Under the drive to do more elaborate experiments, neuroscience is becoming increasingly more challenging on a technical level. When it comes to cutting-edge techniques like electrophysiology in behaving animals, it often seems like you have the choice between expensive ready-made solutions and hand-made equipment that comes with hidden costs in the shape of time commitment, ease-of-use, robustness etc. (Have a look at this article by Jakob Voigts of OpenEphys, explaining this thought in more detail).","ctaPrimary":"","ctaSecondary":"","image":""}},{"id":"b_um224q","type":"hero","visible":true,"props":{"eyebrow":"","heading":"Why we started\n","sub":"Thatâs where we found ourselves a few years ago. After one more broken electrode, our co-founder Martha decided she would not implant one more sil
131icon probe until she had a system that allowed her to do so reliably â without breakage, mid-surgery surprises or signal failure. And without costing the moon. At the same time, Tim was busy refining prototypes for new 3D printed implants for rats. He had started making his own microdrives, as there were none available that would allow recovery and reuse of the expensive probes (a scary thought for a PhD student planning to work with next-generation CMOS probes of which less than a handful would exist for his research). Martha and Tim started talking and decided to collaborate to get the drives to the next level. Fast-forward two years, 3Dneuro was born.","ctaPrimary":"","ctaSecondary":"","image":""}},{"id":"b_valueCards_10","type":"valueCards","visible":true,"props":{"eyebrow":"What we care about","heading":"Our focus at a glance:","cards":[{"icon":"tool","title":"No more breaking probes! ","body":"Our products are designed to make electrophysiology experiments easier, faster and more robust â at a cost that is within reach for labs of all budget sizes."},{"icon":"send","title":"More science, less engineering!","body":"Many commercially and freely available research tools just donât fit for the experiments in mind. For many, the solution is to have a PhD student or postdoc make their own tools. While this approach looks cheaper at first, the amount of time sunk is usually enormous (time that could have been used for research). Even finding, understanding, and adjusting existing free open source solutions can take a while. And often, things will go wrong. Weâve gone through the process and want to use that expertise so that you donât need to and can focus on the science you care about instead."},{"icon":"microscope","title":"More data, better data, less animals! ","body":"Animal research is a controversial topic. We would love to get to a world where it is not needed anymore. But currently, there are lots of important scientific and medical questions that cannot be answered without it. However, many researchers use outdated approaches and technologies, where modern tools could answer questions with less animals. We aim to provide implants that are easy enough so that everyone can use the best methods to answer their questions. In addition, coming from work with behaving animals, we know that with happier animals you do better science. Thatâs why we make our implants as light and small as possible, trying not to disturb the animalsâ behavior and well-being."}]}},{"id":"b_teamCards_11","type":"teamCards","visible":true,"props":{"eyebrow":"Team","heading":"Founding team","note":"","cards":[{"name":"Abdel Nemri","role":"CEO & co-Founder","bio":"Abdel is a neuroscientist and business developer. Experienced in cat electrophysiology, mouse imaging and behavior, always in visual cortex. More recently got interested in assistive wearable devices for visually-impaired individuals, and the process of disseminating innovation.","accent":"var(--orange-400)","seed":5},{"name":"Tim Schröder","role":"CTO & co-Founder","bio":"In his life as neuroscientist, Tim studies neuronal interactions across multiple brain areas. He acquired expertise in 3D design so that he could develop tools for electrophysiological recordings in novel memory tasks and high-density silicon probe recordings.","accent":"var(--blue-500)","seed":1},{"name":"Martha Havenith","role":"Validation lead / Co-Founder","bio":"Martha is a Max Planck Research Group Leader in the Zero Noise Lab and has worked with electrophysiology and silicon probes since 2003, in cat and most recently in behaving mice in virtual reality. She handles inquiries about surgical procedures and animal handling.","accent":"var(--teal-500)","seed":3}]}},{"id":"b_statsPanel_12","type":"statsPanel","visible":false,"props":{"eyebrow":"By the numbers","heading":"Years in, still a small shop.","sub":"We could scale, but we'd rather keep shipping good parts.","stats":[{"value":"75+","label":"labs worldwide"},{"value":"2017","label":"founded"},{"value":"Radboud","label":"university spin-off"},{"value":"Open","label":"hardware on Zenodo"}]}}]},{"id":"science","title":"Science","path":"pages/science.html","blocks":[{"id":"b_pageHero_13","type":"pageHero","visible":true,"props":{"eyebrow":"Documentation","heading":"Ephys and Behavior resources","sub":"","mosaic":true}},{"id":"b_ugqca0","type":"richtext","visible":true,"props":{"heading":"","body":"For whom? Anyone concerned with the practical aspects of electrophysiology in the behaving laboratory animal. \n\nWhat? A curated list of resources for how to get started with extracellular (in vivo/behaving) ephys experiments. Mostly in small animals/rodents. Also an easy-access reference list for more experienced users.\n\nHow? The aim is for necessary and sufficient. By reading these sources, you should in principle be able to set up in vivo ephys experiments based on open-access information (and gear as far as possible). The list is not comprehensive â itâs based on our own (probably biased) experimental experience, and we are always happy to add open access resources. If you have comments or recommendations, please contact us at: contact -AT- 3dneuro.com\n\nLast revision March 24, 2025\n\nUse This content is licensed under a Creative
131Commons Attribution 4.0 International License."}},{"id":"b_p96j88","type":"richtext","visible":true,"props":{"heading":"Table of contents","body":"Electrophysiology reference documentation\nElectrophysiology troubleshooting\nSpike sorting\nBehavior reference documentation\nWelfare and handling\n\nSelect open hardware papers & projects\nOpen hardware/software repositories\nHow to open hardware\nCompanies\nR2 System Documentation\nOther"}},{"id":"b_kstjtv","type":"richtext","visible":true,"props":{"heading":"Electrophysiology reference documentation","body":"- If new to the field, start here: Approaches to study neural circuits course (2020) â 11 lectures, with 2 dedicated to electrophysiology (lectures 3-4) by Luke Sjulson, Albert Einstein College of Medicine \n- Some study design guidelines: Recommendations for the Design and Analysis of In Vivo Electrophysiology Studies by the editorial board, J. Neurosci. (2018)\n- A good primer: Tools for probing local circuits: high-density silicon probes combined with optogenetics, Buzsaki et al. 2015\n- A good protocol with video: Implantation of Chronic Silicon Probes and Recording of Hippocampal Place Cells in an Enriched Treadmill Apparatus, Sariev et al. 2017 (No open source alternative, but too valuable to omit)\n- A unified data format: Neurodata Without Borders (NWB), a formatting standard for cell-based neurophysiology data, Teeters et al. 2015\n- A classic reference manual (to consult before hitting the search engines): The Axon guide â Electrophysiology and Biophysics Laboratory Techniques 3rd ed.\n- Cool Neuropixels resources: Nick Steinmetzâs lab page, includes data, analysis software and training materials.","body_mode":"markdown"}},{"id":"b_px9beh","type":"richtext","visible":true,"props":{"heading":"Electrophysiology troubleshooting","body":"- Always RTFM ð\n- Electric noise troubleshooting flowchart, Jeffery lab 2018 \n- More detailed noise debugging tips, Neuralynx 2019","body_mode":"markdown"}},{"id":"b_dbn49u","type":"richtext","visible":true,"props":{"heading":"Spike sorting","body":"\n- Introduction: Past, Present and Future of Spike Sorting Techniques, Rey et al. 2015\n- Alternatively, introductory lecture: An Introduction to Spike Sorting, Bhagtat and Moore-Kochlacs, MIT 2017\n- Comparing algorithms: SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters, Magland et al. 2020\n- A popular method developed for Neuropixels data: Kilosort2, Pachitariu 2020\n- Ground-truth validated method: A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo, Yger et al. 2018","body_mode":"markdown"}},{"id":"b_b5z2hv","type":"richtext","visible":true,"props":{"heading":"Behavior reference documentation","body":"- Conceptual start: Neuroscience Needs Behavior: Correcting a Reductionist Bias, Krakauer et al. 2017\n- General considerations: A hitchhikerâs guide to behavioral analysis in laboratory rodents, Sousa et al. 2006 and Probing perceptual decisions in rodents, Carandini & Churchland 2013\n- Head-fixed tasks in virtual reality are growing in popularity as they enable tight control of the stimulus combined with easier recordings. See recent introductory review of head-fixed tasks (Bjerre & Palmer 2020). In that context, 2 approaches have emerged that are a very good start when you are considering behavioral task design (Full disclosure: Two from the 3Dneuro team worked with co-authors of 1 as post-docs, and one led the studies in 2).\n- Standardized tasks that optimize for reproducibility across different labs, The International Brain Laboratory et al. 2020 \n- Tasks that push the limits of what animals can achieve, but are less easily reproducible: e.g. The Virtual-Environment-Foraging Task (1) and (2), Havenith et al. 2018, 2019. Give special attention to the supplementary note: Seven principles of task design for mice.\n- Freely moving tasks: These tasks typically enable more naturalistic behaviors, from the classic Morris water maze to more recent route planning studies (e.g. Jackson et al. 2020). See also automated experiments below.\n- Food/water restriction: See the Janelia protocol for water restriction, which also includes procedures for weight and health monitoring, data on task performance as a function of weight, and on long-term effects (Guo et al. 2014). Note that the choice of either food or water restriction has an effect on learning (Goltstein et al. 2018)\n- Automated experiments: Comb
131ined with ephys, e.g. Automated long-term recording and analysis of neural activity in behaving animals, Dhawale et al. 2017. Or just behavioral assessment, e.g. An automated home-cage-based 5-choice serial reaction time task for rapid assessment of attention and impulsivity in rats, Bruinsma et al. 2019\n- General-purpose animal 3D pose estimation: DeepLabCut. In addition to pose estimation for multiple species, can be applied to whiskers and eye tracking. SimBA, A toolkit for analyzing complex social behavior in rodents (also supports DeepLabCut).","body_mode":"markdown"}},{"id":"b_autk3q","type":"richtext","visible":true,"props":{"heading":"Welfare and handling - Happy animals are good lab animals.","body":"These resources go beyond typical âlicense to work with animalsâ training, and are great for making lab animals less stressed, which improves the odds of pretty much anything you wish to achieve with them.\n\n- Rat and mice handling videos, Genzel lab, Radboud University\n- Rat tickling course, Gaskill lab, Purdue University","body_mode":"markdown"}},{"id":"b_pe15r1","type":"richtext","visible":true,"props":{"heading":"Select open hardware projects & papers - Some projects include software as well.","body":"- General lab equipment, with focus on affordability and education: Open Labware: 3-D Printing Your Own Lab Equipment, Baden et al. 2015. See also project website for more context and design files.\n- A robot for automated craniotomies: Autosurgery â Website for latest documentation, see also paper by Pak et al. 2015\n- Implant surgery without stereotaxic device & modular implant designs: RatHat: A Self-Targeting Printable Brain Implant System, Allen et al. 2020\n- Implant design for optoelectronic probes: Micro-drive and headgear for chronic implant and recovery of optoelectronic probes, Chung et al. 2017 \n- Chronic drive implant for tetrode arrays: The Open Ephys ShuttleDrive, see webpage and paper by Voigts et al. 2019\n- Complete mouse virtual reality rig design: Harvey Lab mouse VR (2020), lab webpage here\n- Microscopes: OpenFlexure, UC2","body_mode":"markdown"}},{"id":"b_xpavfa","type":"richtext","visible":true,"props":{"heading":"Open hardware/software repositories - Build your own lab.","body":"- Possibly the largest in size and scope: Open Behavior. Their resources page also lists many tools/companies for building stuff. See also Open Neuroscience\n- Recording hardware/software: Open Ephys wiki. Open Ephys is becoming the standard for both high-channel count electrophysiology and open hardware projects. \n- With a focus on affordability: Lab on the Cheap (not neuroscience specific)","body_mode":"markdown"}},{"id":"b_fz9jpo","type":"richtext","visible":true,"props":{"heading":"How to open hardware - Spread the love.","body":"- Getting started: Open hardware basics and certification \n- Licensing your work: CERN Open Hardware License \n- State-of-the-art specification: Open know how manifesto \n- Community: Gathering for Open Science Hardware (GOSH), DocuBricks\n- Publish: Journal of Open Hardware, HardwareX","body_mode":"markdown"}},{"id":"b_sf7832","type":"richtext","visible":true,"props":{"heading":"Companies","body":"Thereâs a whole ecosystem for electrophysiology in behaving animals. This section is work in progress and we appreciate your feedback.\n\nProbes, accessories and electronics\n\n- Probes: Neuronexus, Cambridge NeuroTech, Diagnostic Biochips, Neural Dynamics Technologies, Atlas Neuro, Thomas Recording, MicroProbes\n- Probes (nonprofit): Neuropixels\n- Recording electronics and accessories (open source): Open Ephys\n- Recording electronics and accessories: Neuralynx, TDT, Blackrock, Plexon, White Matter, Ymetry (head fixation),\n- Recording electronics and accessories (open and closed source): SpikeGadgets, NeuroTek (including a tetrode drive loading service)\nBehavior\n\n- Noldus, Neurotar, imetronic, Labeotech\nConsulting\n\n- Assembly service for open hardware: Labmaker, Sanworks, NeuroGig (also equipment re-use and more), see also #NeuroRigBuilder \n- Custom hardware/software: ViSE (also data science, manuscript editing)\n- Open hardware: Prometheus Scie
131nce \n- Software: Metacell\nEducation\n\n- Backyard Brains","body_mode":"markdown"}},{"id":"b_gbnxg1","type":"richtext","visible":true,"props":{"heading":"R2 system documentation","body":"https://3dneuro.github.io/r2-docs/","body_mode":"html"}},{"id":"b_yuhile","type":"richtext","visible":true,"props":{"heading":"Other","body":"- Many great resources: Allen institute Products & tools\n- Donât make everything yourself: The case for consulting in neuroscience (Voigts, 2019)\n- Donât draw everything yourself: SciDraw, a free repository of quality scientific drawings.\n- Smart bibliography tool: Connectedpapers builds a visual graph of connected work around a paper, based on similarity.\n- Beyond scope, yet awesome: INSS builds custom multiphoton microscopes, hardware & software, at a fraction of the cost for commercial systems.\n- Open solution for systems neuroscience research (blog): Labrigger\n- For educators on a budget: Reducing the Cost of Electrophysiology in the Teaching Laboratory, Wyttenbach et al. 2018","body_mode":"markdown"}},{"id":"b_1ted0t","type":"feature","visible":true,"props":{"heading":"","body":"Do you think an important resource is missing? Is something outdated? Do you have a specific problem thatâs not included here? Let us know in a comment here, or mail / tweet our way!","bullets":[],"image":""}}]},{"id":"contact","title":"Contact","path":"pages/contact.html","blocks":[{"id":"b_contactHero_18","type":"contactHero","visible":true,"props":{"label":"CONTACT","heading":"Get in touch","sub":"Not sure what drive is right for you? Wondering whether you need a crown or not? Do you have questions about our ordering process or current lead times? Is there a problem in your setup that we can help with? Do you have an inquiry regarding your data we process? Or do you want to give us feedback on drives you got from us? Then do not hesitate to get in touch either using the form on the right or messaging/@ing us on Twitter!","contacts":[{"label":"Email","value":"[email protected]","icon":"mail","href":"mailto:[email protected]"}],"formEnabled":true,"formHeading":"","nameLabel":"Your name","emailLabel":"Email","institutionLabel":"Institution / lab","notesLabel":"Your message","notesPlaceholder":"...","submitLabel":"Send message","replyNote":"","successHeading":"Got it, {name}.","successBody":"We got your message and will be in touch shortly!","layout":"stacked","sidebar":"right","fields":[{"key":"name","label":"Your name","type":"text","options":[],"required":true,"fullWidth":false,"placeholder":"Dr. Jane Researcher"},{"key":"email","label":"Email","type":"email","options":[],"required":true,"fullWidth":false,"placeholder":"[email protected]"},{"key":"institution","label":"Institution / lab","type":"text","options":[],"required":true,"fullWidth":true,"placeholder":"Systems Neuro Lab, University ofâ¦"},{"key":"notes","label":"Your message","type":"textarea","options":[],"required":false,"fullWidth":true,"placeholder":"..."}]}},{"id":"b_irirxj","type":"bigCta","visible":false,"props":{"heading":"Not sure what you need?","sub":"Try the experiment planner. Give it some parameters of the experiment you want to run, like the number of animals and batch size, and it suggests what you need to get started.","cta1":"Plan your implant needs now.","cta1href":"/planner","cta2":"","cta2href":"","variant":""}}]},{"id":"publications","title":"Publications","path":"pages/publications.html","blocks":[{"id":"pub_hero","type":"pageHero","visible":true,"props":{"eyebrow":"Literature","heading":"Published work using the tools we make","sub":"Peer-reviewed papers and preprints whose methods use parts of the R2 implant system, microdrives, or REMY rat head fixation. Click on a paper to see details like species, probes and experimental setup.","mosaic":true}},{"id":"pub_list","type":"publications","visible":true,"props":{"heading":"","intro":"","productId":"","showAbstract":true}}]},{"id":"privacy","title":"Privacy Policy","path":"pages/privacy.html","blocks":[{"id":"pp_hero","type":"pageHero","visible":true,"props":{"eyebrow":"Legal","heading":"Privacy Policy","sub":"This Privacy Policy clarifies the nature, scope and purpose of the processing of personal data (hereinafter referred to as âDataâ) within our online offering and the related websites, features and content, as well as external online presence, e.g. our Social Media Profile (collectively referred to as the âOnline Offer
131ingâ). With regard to the terminology used, e.g. âProcessingâ or âControllerâ, we refer to the definitions in Article 4 of the General Data Protection Regulation (GDPR).","mosaic":true}},{"id":"pp_draft","type":"callout","visible":false,"props":{"variant":"warning","title":"Draft â pending legal review","body":"This policy reflects how the website actually works. Have it reviewed by qualified counsel before launch â in particular the China (PIPL) and Japan (APPI) cross-border-transfer sections."}},{"id":"pp_who-we-are","type":"richtext","visible":true,"props":{"heading":"Controller","body":"The data controller is 3D Neuro B.V. (also called 3Dneuro in this privacy policy) with the address:\nToernooiveld 100\n6525EC Nijmegen\nThe Netherlands\nE-mail: privacy @ 3dneuro.com \n\nChamber of Commerce number: 70236917\nVAT identification number: NL 858206869B01\n\nHere you can find our general terms and conditions: https://www.3dneuro.com/terms-and-conditions/\nData security contact email: privacy @ 3dneuro.com","body_mode":"markdown"}},{"id":"pp_what-we-collect","type":"richtext","visible":true,"props":{"heading":"Types of processed data:","body":"- Inventory data (e.g., names, addresses).\n- Contact information (e.g., e-mail, phone numbers).\n- Content data (e.g., text input, photos, videos).\n- Usage data (e.g., websites visited, interest in content, access times).\n- Meta/Communication data (e.g., device information, IP addresses).","body_mode":"markdown"}},{"id":"pp_why-we-use-it-le","type":"richtext","visible":true,"props":{"heading":"Categories of affected persons","body":"Visitors of our website and customers.","body_mode":"markdown"}},{"id":"pp_cookies-storage-","type":"richtext","visible":true,"props":{"heading":"Purpose of processing","body":"- Provision of the website and other online services, their functions and contents\n- Answering contact requests and communicating with users\n- Fulfilment of orders, production of ordered goods and services\n- Safety measures\n- Market Research/Marketing","body_mode":"markdown"}},{"id":"pp_sharing-sub-proc","type":"richtext","visible":true,"props":{"heading":"Used terms","body":"âPersonal dataâ means any information relating to an identified or identifiable natural person (hereinafter referred to as âaffected personâ); an identifiable natural person is one who can be identified, directly or indirectly, in particular by assignment to an identifier such as a name, an identification number, location data, an online identifier (e.g. cookie) or to one or more special features that express the physical, physiological, genetic, psychological, economic, cultural or social identity of that natural person.\n\nâProcessingâ means any process performed with or without the aid of automated procedures or any such process associated with personal data. The term is broadly defined and includes virtually every handling of data.\n\nâPseudonymisationâ means the processing of personal data in such a way that the personal data can no longer be assigned to a specific affected person without additional information being provided, provided that such additional information is kept separate and subject to technical and organizational measures to ensure that the personal data not assigned to an identified or identifiable natural person.\n\nâProfilingâ means any kind of automated processing of personal data which involves the use of such personal data to evaluate certain personal aspects relating to a natural person, in particular aspects relating to job performance, economic situation, health, personal preferences, interests, reliability, behavior, whereabouts or relocation in order to analyze or predict these aspects of that natural person.\n\nâControllerâ means the natural or legal person, public authority, body or body that decides, alone or in concert with others, on the purposes and means of processing personal data.\n\nâProcessorâ means a natural or legal person, public authority, agency or other body that processes personal data on behalf of the controller.","body_mode":"markdown"}},{"id":"pp_international-tr","type":"richtext","visible":true,"props":{"heading":"Legal basis","body":"In accordance with Art. 13 GDPR, we inform you of the legal basis of our data processing. If the legal basis is not mentioned in the data protection declaration, the following applies: The legal basis for obtaining consents is Art. 6 para. 1 lit. a and Art. 7 GDPR, the legal basis for processing for the performance of our services and performance of contractual measures as well as for answering inquiries is Art. 6 para. 1 lit. b GDPR, the legal basis for processing to fulfil our legal obligations is Art. 6 para. 1 lit. c GDPR, and the legal basis for processing to protect our legitimate interests is Art. 6 para. 1 lit. f GDPR. In the event that the vital interests of the data subject or another natural person require the processing of personal data, Article 6(1)(d) GDPR serves as the legal basis.","body_mode":"markdown"}},{"id":"pp_your-rights","type":"richtext","visible":true,"props":{"heading":"Security","body":"We take appropriate technical and organizational measures to ensure a level of protection a
131ppropriate to the risk, taking into account the state of the art, implementation costs and the nature, scope, circumstances and purposes of processing as well as the different probability of occurrence and severity of the risk to the rights and freedoms of natural persons, in accordance with Art. 32 GDPR.\n\nSuch measures shall in particular include ensuring the confidentiality, integrity and availability of data by controlling physical access to the data, as well as the access, input, transmission, security of availability and its separation. Furthermore, we have established procedures to ensure the exercise of rights of data subjects, deletion of data and reaction to endangerment of data. Furthermore, we already consider the protection of personal data during the development or selection of hardware, software and procedures, in accordance with the principle of data protection through technology design and data protection-friendly pre-settings (Art. 25 GDPR).","body_mode":"markdown"}},{"id":"pp_retention-securi","type":"richtext","visible":true,"props":{"heading":"Cooperation with contract processors and third parties","body":"If, in the context of our processing, we disclose data to other persons and companies (contract processors or third parties), transmit them to them or otherwise grant access to the data, this will only be done on the basis of a legal permission (e.g. if a transmission of the data to third parties, as required by payment service providers, pursuant to Art. 6 (1) (b) GDPR to fulfil the contract), you have consented to a legal obligation or based on our legitimate interests (e.g. the use of agents, web hosters, etc.).\n\nIf we commission third parties to process data on the basis of a so-called âdata processing contractâ, this is done on the basis of Art. 28 GDPR.","body_mode":"markdown"}},{"id":"b_u4b9zy","type":"richtext","visible":true,"props":{"heading":"Transfer to third-party countries","body":"If we process data in a third country (i.e. outside the European Union (EU) or the European Economic Area (EEA)) or if this occurs in the context of the use of third-party services or disclosure or transfer of data to third parties, this only takes place if it occurs for the fulfilment of our (pre)contractual obligations, on the basis of your consent, on the basis of a legal obligation or on the basis of our legitimate interests. Subject to legal or contractual permissions, we process or leave the data in a third country only if the special requirements of Art. 44 ff. Process GDPR. This means, for example, processing is carried out on the basis of special guarantees, such as the officially recognized determination of a data protection level corresponding to the EU (e.g. for the USA by the âPrivacy Shieldâ) or compliance with officially recognized special contractual obligations (so-called âstandard contractual clausesâ)."}},{"id":"b_5jgpci","type":"richtext","visible":true,"props":{"heading":"Rights of affected persons","body":"You have the right to request confirmation as to whether the data concerned are being processed and to request information about these data as well as further information and a copy of the data in accordance with Art. 15 GDPR.\nYou have according to Art. 16 GDPR the right to demand the completion of the data concerning you or the correction of the incorrect data concerning you.\nIn accordance with Art. 17 GDPR, you have the right to demand that the relevant data be deleted immediately or, alternatively, to require a restriction of the processing of data in accordance with Art. 18 GDPR.\nYou have the right to demand that the data relating to you, which you have provided to us, be obtained in accordance with Art. 20 GDPR and request their transmission to other responsible entities.\nIn accordance with Art. 77 GDPR, you also have the right to file a complaint with the governing authority."}},{"id":"b_6xv1el","type":"richtext","visible":true,"props":{"heading":"Right of withdrawal","body":"You can object to the future processing of your data in accordance with Art. 21 GDPR at any time. The objection may in particular be made against processing for direct marketing purposes.\n\n"}},{"id":"b_rixkt2","type":"richtext","visible":true,"props":{"heading":"Cookies and right of objection in direct advertising","body":"âCookiesâ are small files that are stored on the userâs computer. Different data can be stored within the cookies. A cookie is primarily used to store information about a user (or the device on which the cookie is stored) during or after his or her visit to an online offer. Temporary cookies, or âsession cookiesâ or âtransient cookiesâ, are cookies that are deleted after a user leaves an online offer and closes his browser. In such a cookie, for example, the content of a shopping basket in an online shop or a login status can be stored. Cookies are referred to as â
131permanentâ or âpersistentâ and remain stored even after the browser is closed. For example, the login status can be saved when users visit it after several days. Likewise, the interests of users used for range measurement or marketing purposes may be stored in such a cookie. âThird-party cookiesâ are cookies that are offered by providers other than the person responsible for operating the online offer (otherwise, if they are only its cookies, they are referred to as âfirst-party cookiesâ).\n\nWe may use temporary and permanent cookies and clarify this within the framework of our data protection declaration (privacy policy).\n\nIf users do not want cookies to be stored on their computer, they are asked to deactivate the corresponding option in the system settings of their browser. Stored cookies can be deleted in the system settings of the browser. The exclusion of cookies can lead to functional restrictions of this online offer.\n\nA general objection to the use of cookies used for online marketing purposes can be declared for many of the services, especially in the case of tracking, via the US site http://www.aboutads.info/choices/ or the EU site http://www.youronlinechoices.com/. Furthermore, the storage of cookies can be achieved by deactivating them in the browser settings. Please note that in this case not all functions of this online offer can be used. We use Cookiebot (provided by Cybot A/S, Havnegade 39, 1058 Copenhagen) to manage your choices for cookies on our website. Find their privacy policy at https://www.cookiebot.com/en/privacy-policy/."}},{"id":"b_ab6xxs","type":"richtext","visible":true,"props":{"heading":"Deletion of data","body":"The data processed by us will be deleted or their processing restricted in accordance with Articles 17 and 18 GDPR. Unless expressly stated in this data protection declaration, the data stored by us will be deleted as soon as it is no longer required for its intended purpose and the deletion does not conflict with any statutory storage obligations. If the data are not deleted because they are necessary for other and legally permissible purposes, their processing is restricted. This means that the data is blocked and not processed for other purposes. This applies, for example, to data that must be retained for commercial or tax reasons. In accordance with the Dutch tax law, we are required to keep data to fulfil our tax obligations for 7 years and 10 years regarding our electronic services.\n\n"}},{"id":"b_0twr0p","type":"richtext","visible":true,"props":{"heading":"Business related processing","body":"Additionally, we process\n\n- contract data (e.g., subject matter of the contract, duration, customer category) and\n- payment data (e.g., bank details, payment history) of our customers, prospects and business partners for the purpose of providing contractual services, service and customer care, marketing, advertising and market research.","body_mode":"markdown"}},{"id":"b_62hzq0","type":"richtext","visible":true,"props":{"heading":"Services","body":"We process our customersâ data as part of our contractual services, which include conceptual and strategic consulting, campaign planning, software and design development / consulting or maintenance, implementation of campaigns and processes / handling, server administration, data analysis / consulting services and training services.\n\nWe process inventory data (e.g., customer master data, such as names or addresses), contact data (e.g., e-mail, telephone numbers), content data (e.g., text entries, photographs, videos), contract data (e.g., subject matter of the contract, term), payment data (e.g., bank details, payment history), and usage and metadata (e.g. as part of the evaluation and performance measurement of marketing measures). We do not process special categories of personal data unless these are part of commissioned processing. This includes our customers, prospects, their customers, users, website visitors or employees, as well as third parties. The purpose of the processing is to provide contractual services, billing and our customer service. The legal basis for processing results from Art. 6 para. 1 lit. b GDPR (contractual services), Art. 6 para. 1 lit. f GDPR (analysis, statistics, optimization, safety measures). We process data which are necessary to justify and fulfil the contractual services and point out the necessity of their disclosure. Disclosure to external parties only takes place if it is necessary within the framework of an order. When processing the data provided to us within the scope of an order, we act in accordance with the instructions of the client and the legal requirements for order processing pursuant to Art. 28 GDPR and process the data for no other purposes than those stipulated in the order.\n\nWe delete the data after the expiry of statutory warranty and comparable obligations. The necessity of storing the data is checked every three years; in the case of statutory archiving obligations, the data is deleted after their expiry. In the case of data disclosed to us within the scope of an order by the customer, we delete the data in accordance with the specifications of the order, generally after the end of the order."}},{"id":"b_8cvflb","type":"richtext","visible":true,"props":{"heading":"Contractual Services","body":"We process the data of our contractual partners and interested parties as well as other clients, customers, clients, clients or contractual partners (uniformly referred to as âcontractual partnersâ) in accordance with Art. 6 para. 1 lit. b. GDPR to provide our contractual or pre-contractual services to them. The data processed here, the type, scope and purpose and the necessity of their processing, are determined by the underlying contractual relationship.The processed data includes the master data of our contractual partners (e.g., names and addresses), contact data (e.g. e-mail addresses and telephone numbers) as well as contract data (e.g., services used, contract contents, contractual communication, names of contact persons) and payment data (e.g., bank details, payment history).\nWe do not process special categories of personal data, unless these are part of a commissioned or c
131ontractual processing.\nWe process data which are necessary to justify and fulfil the contractual services and point out the necessity of their disclosure, unless this is evident for the contractual partners. Disclosure to external persons or companies is only made if it is required within the framework of a contract. When processing the data provided to us within the scope of an order, we act in accordance with the instructions of the customer and the legal requirements.\n\nWhen using our online services, we may store the IP address and the time of the respective user action. This data is stored on the basis of our legitimate interests as well as the usersâ interests in the protection against misuse and other unauthorized use. As a matter of principle, this data will not be passed on to third parties, unless it is necessary to pursue our claims pursuant to Art. 6 para. 1 lit. f. GDPR is required or there is a legal obligation in accordance with Art. 6 para. 1 lit. c. GDPR.\n\nThe data will be deleted if the data is no longer required for the fulfilment of contractual or statutory duties of care or for the handling of any warranty or comparable obligations, whereby the necessity of storing the data is checked every three years; in all other respects, the statutory storage obligations apply."}},{"id":"b_g14bd9","type":"richtext","visible":true,"props":{"heading":"External payment service providers","body":"We use external payment service providers through whose platforms the users and we can carry out payment transactions (e.g., each with a link to the data protection declaration)\n\nMollie (https://www.mollie.com/en/privacy)\nPlink (https://plink.com/privacy-policy)\nPaypal (https://www.paypal.com/us/webapps/mpp/ua/privacy-full),\nVisa (https://www.visaeurope.com/privacy/),\nMastercard (https://www.mastercard.us/en-us/about-mastercard/what-we-do/privacy.html),\nAmerican Express (https://www.americanexpress.com/us/content/legal-disclosures/online-privacy-statement.html)\nAs part of the fulfilment of contracts, we suspend the payment service providers on the basis of Art. 6 para. 1 lit. b. GDPR. Furthermore, we employ external payment service providers on the basis of our legitimate interests pursuant to Art. 6 para. 1 lit. f. GDPR to provide our users with effective and secure payment options.\n\nThe data processed by the payment service providers includes inventory data such as name and address, bank data such as account numbers or credit card numbers, passwords, TANs and checksums as well as contract, totals and recipient information. This information is required to execute the transactions. However, the data entered will only be processed and stored by the payment service providers. This means that we do not receive any account or credit card related information, but only information with confirmation or negative information about the payment. The data may be transferred by the payment service providers to credit agencies. The purpose of this transmission is to verify identity and creditworthiness. For this we refer to the terms and conditions and data protection information of the payment service providers.\n\nFor payment transactions, the terms and conditions and the data protection information of the respective payment service providers, which can be accessed within the respective websites or transaction applications, apply. We refer to these also for the purpose of further information and assertion of rights of revocation, information and other interested parties."}},{"id":"b_ektml2","type":"richtext","visible":true,"props":{"heading":"Administration, accounting, office organization, contact information management","body":"We process data within the framework of administrative tasks as well as the organization of our company, financial accounting and compliance with legal obligations, e.g. archiving. We process the same data that we process in the course of providing our contractual services. The processing bases are Art. 6 para. 1 lit. c. GDPR, Art. 6 para. 1 lit. f. GDPR. Customers, prospects, business partners and website visitors are affected by the processing. The purpose and our interest in the processing lies in the administration, financial accounting, office organization, archiving of data, thus tasks which serve the maintenance of our business activities, perception of our tasks and provision of our services. The deletion of the data with regard to contractual services and contractual communication corresponds to the information provided in these processing activities.\n\nWe disclose or transmit data to the tax authorities, consultants, such as tax consultants or auditors, as well as similar service providers.\n\nFurthermore, we store information on suppliers, event organizers and other business partners on the basis of our business interests, e.g. for the purpose of making contact at a later date. We store this data, which is mainly company-related, permanently."}},{"id":"b_jahqo4","type":"richtext","visible":true,"props":{"heading":"Business analyses and market research","body":"In order to operate our business economically, to be able to re
131cognize market tendencies, wishes of the contracting parties and users, we analyze the data available to us to business processes, contracts, inquiries, etc. We process inventory data, communication data, contract data, payment data, usage data, and metadata on the basis of Art. 6 para. 1 lit. f. GDPR, whereby the persons concerned include contractual partners, interested parties, customers, visitors and users of our online offer.\n\nThe analyses are carried out for the purpose of economic evaluations, marketing and market research. The analyses serve us to increase the user-friendliness, the optimization of our offer and the economic efficiency. The analyses serve us alone and are not disclosed externally, unless they are anonymous analyses with aggregated values.\n\nIf these analyses or profiles are personal, they will be deleted or made anonymous upon termination of the user, otherwise after two years from the conclusion of the contract. For the rest, macroeconomic analyses and general trend determinations are prepared anonymously wherever possible."}},{"id":"b_m31k10","type":"richtext","visible":true,"props":{"heading":"Data protection information in the job application process","body":"We process the applicant data only for the purpose and in the context of the application procedure in accordance with the legal requirements. The processing of the applicant data takes place in order to fulfil our (pre)contractual obligations in the context of the application procedure within the meaning of Art. 6 para. 1 lit. b. GDPR Art. 6 para. 1 lit. f. GDPR if data processing becomes necessary for us, e.g. within the framework of legal procedures.\n\nThe application procedure requires that applicants provide us with personal data. If we offer an online form, the necessary data for application will be explicitly stated or otherwise result from the job descriptions and generally include personal data, postal and contact addresses and the documents belonging to the application, such as cover letter, curriculum vitae and certificates. In addition, applicants may voluntarily provide us with additional information.\n\nBy submitting the application to us, applicants agree to the processing of their data for the purposes of the application procedure in accordance with the type and scope set out in this data protection declaration.\n\nIf special categories of personal data within the meaning of Art. 9 para. 1 GDPR are voluntarily communicated within the scope of the application procedure, they are additionally processed in accordance with Art. 9 para. 2 letter b GDPR (e.g. health data, e.g. severely disabled status or ethnic origin). If special categories of personal data within the meaning of Art. 9 para. 1 GDPR are requested from applicants during the application procedure, they are additionally processed in accordance with Art. 9 para. 2 lit. a GDPR (e.g. health data, if these are required for the exercise of the profession).\n\nIf made available, applicants can send us their applications via an online form on our website. The data is encrypted and transmitted to us according to the state of the art.\n\nApplicants can also send us their applications by e-mail. Please note, however, that e-mails are generally not sent in encrypted form and that the applicants themselves must ensure that they are encrypted. We cannot therefore accept any responsibility for the transmission of the application between the sender and receipt on our server and therefore recommend that you use an online form or the postal dispatch. Instead of using the online application form and e-mail, applicants can still send us their application by post.\n\nIf the application is successful, the data provided by the applicants can be further processed by us for the purpose of employment. Otherwise, if the application for a job offer is not successful, the applicantsâ data will be deleted. Applicantsâ data will also be deleted if an application is withdrawn, which the applicants are entitled to do at any time.\n\nThe deletion will take place after a period of six months, subject to a justified revocation by the applicant, so that we can answer any follow-up questions to the application and meet our obligations under the Equal Treatment Act. Invoices for any reimbursement of travel expenses are archived in accordance with tax regulations."}},{"id":"b_gfgq8i","type":"richtext","visible":true,"props":{"heading":"Talent pool","body":"As part of the application, we offer applicants the opportunity to be included in our âtalent poolâ for a period of two years on the basis of consent within the meaning of Art. 6 (1) (b) and Art. 7 GDPR.\n\nThe application documents in the talent pool will only be processed in the context of future job advertisements and the search for employees and will be destroyed at the latest on expiry of the deadline. Applicants are informed that their consent to inclusion in the talent pool is voluntary, has no influence on the current application procedure and they can revoke this consent at any time for the future and declare their objection within the meaning of Art. 21 GDPR."}},{"id":"b_g1sgni","type":"richtext","visible":true,"props":{"heading":"Comments","body":"If users leave comments or other contributions, their IP addresses may be used on the basis of our legitimate interests within the meaning of Art. 6 (1) (f). GDPR for 7 days. This takes place for our safety, if someone leaves illegal contents in comments and contributions (insults, forbidden political prop
131aganda, etc.). In this case, we can be prosecuted ourselves for the comment or contribution and are therefore interested in the identity of the author.\n\nFurthermore, we reserve the right, on the basis of our legitimate interests pursuant to Art. 6 para. 1 lit. f. GDPR to process user information for spam detection.\n\nOn the same legal basis, we reserve the right, in the case of surveys, to store the IP addresses of users for their duration and to use cookies to avoid multiple submissions.\n\nThe data provided in the context of comments and contributions will be permanently stored by us until the user objects."}},{"id":"b_pd8xrw","type":"richtext","visible":true,"props":{"heading":"Comment subscriptions","body":"Users may subscribe to the follow-up comments with their consent in accordance with Art. 6 para. 1 lit. a GDPR. Users will receive a confirmation email to verify that they are the owner of the email address they entered. Users can unsubscribe from ongoing comment subscriptions at any time. The confirmation email will contain information on the cancellation options. For the purpose of providing proof of user consent, we store the time of registration together with the IP address of the users and delete this information when users unsubscribe from the subscription.\n\nYou can cancel the receipt of our subscription at any time, i.e. revoke your consent. We may store the e-mail addresses we have unsubscribed for up to three years on the basis of our legitimate interests before we delete them in order to be able to prove a previously given consent. The processing of these data is limited to the purpose of a possible defence against claims. An individual application for cancellation is possible at any time, provided that at the same time the former existence of a consent is confirmed."}},{"id":"b_opjwpu","type":"richtext","visible":true,"props":{"heading":"Akismet Anti-Spam","body":"Our online offer may use the service âAkismetâ offered by Automattic Inc, 60 29th Street #343, San Francisco, CA 94110, USA. The use is based on our legitimate interests within the meaning of Art. 6 para. 1 lit. f) GDPR. With the help of this service, comments of real people are distinguished from spam comments. All comment information is sent to a server in the USA, where it is analyzed and stored for four days for comparison purposes. If a comment has been classified as spam, the data will be stored after this time. This information includes the name entered, the e-mail address, the IP address, the comment content, the referrer, information on the browser used, the computer system and the time of entry.\n\nFurther information on Akismetâs collection and use of the data can be found in Automatticâs privacy policy: https://automattic.com/privacy/.\n\nUsers are welcome to use pseudonyms or refrain from entering their name or email address. You can completely prevent the transfer of data by not using our comment system. That would be a pity, but unfortunately, we see no other alternatives that work just as effectively."}},{"id":"b_jaxvfj","type":"richtext","visible":true,"props":{"heading":"Access of profile pictures at Gravatar","body":"We use the service Gravatar of Automattic Inc. 60 29th Street #343, San Francisco, CA 94110, USA, within our online offer and especially in our blog.\n\nGravatar is a service where users can log in and store profile pictures and their e-mail addresses. If users leave contributions or comments with the respective e-mail address on other online presences (above all in blogs), their profile pictures can be displayed next to the contributions or comments. For this purpose, the e-mail address provided by the users is transmitted to Gravatar in encrypted form for the purpose of checking whether a profile has been saved for it. This is the sole purpose of the transmission of the e-mail address and it will not be used for other purposes, but will be deleted thereafter.\n\nThe use of Gravatar is based on our legitimate interests within the meaning of Art. 6 Para. 1 letter f) GDPR, as we offer the possibility of personalising their contributions with a profile picture with the help of Gravatar.\n\nBy displaying the images, Gravatar obtains the IP address of the users, as this is necessary for communication between a browser and an online service. Further information on the collection and use of data by Gravatar can be found in Automatticâs data protection information: https://automattic.com/privacy/.\n\nIf users do not want an image associated with their email address to appear in Gravatarâs comments, you should use a non-Gravatar email address for commenting. We would also like to point out that it is also possible to use an anonymous or no e-mail address if users do not wish their own e-mail address to be sent to Gravatar. Users can completely prevent the transfer of data by not using our comment system."}},{"id":"b_9hi137","type":"richtext","visible":true,"props":{"heading":"Access of Emojis and Smilies","body":"Within our WordPress blog, graphical emojis (or smilies), i.e. small graphical files that express feelings, are used that are obtained from external servers. The providers of the servers collect the IP addresses of the users. This is necessary so that the emoji files can be transmitted to the usersâ browsers. The Emoji service is offered by Automattic Inc, 60 29th Street #343, San Francisco, CA 94110, USA. Privacy policy of Automattic: https://automattic.com/privacy/. The server domains used are s.w.org and twemoji.maxcdn.com, whereby to our knowledge these are so-called content-delivery networks, i.e. servers which serve only a fast and secure transmission of the files and the personal data of the users are deleted after the transmission.\n\nEmojis are used on the basis of our legitimate interests, i.e. interest in an attractive design of our online offer in accordance with Art. 6 para. 1 lit. f. GDPR."}},{"id":"b_6gbzte","type":"richtext","visible":true,"props":{"heading":"Contact","body":"When contacting us (e.g. via contact form, e-mail, telephone or social media), the userâs details for processing the contact enquiry and its processing pursuant to Art. 6 para. 1 letter b. (in the context of contractual/pre-contractual relationships), Art. 6 para. 1 lit. f. (other requests) GDPR. User information can be stored in a customer relationship management system (âCRM systemâ) or comparable request organization.\n\nWe delete the requests if they are no longer necessary. We review this requirement every two years; the statutory archiving obligations also apply."}},{"id":"b_5q5ihh","type":"richtext","visible":true,"props":{"heading":"Newsletter","body":"With the following information we inform you about the contents of our newsletter as well as the registration, dispatch and statistical evaluation procedure and
131your rights of objection. By subscribing to our newsletter, you agree to the receipt and the described procedures.\n\nContent of the newsletter: We send newsletters, e-mails and other electronic notifications containing advertising information (hereinafter ânewslettersâ) only with the consent of the recipients or a legal permission. If the contents of a newsletter are specifically described within the scope of a registration, they are decisive for the consent of the users. In addition, our newsletters contain information about our services and us.\n\nDouble opt-in and logging: Subscription to our newsletter takes place in a so-called double opt-in procedure. This means that after registration you will receive an e-mail asking you to confirm your registration. This confirmation is necessary so that no one can log in with other e-mail addresses. Subscriptions to the newsletter are logged in order to be able to prove the registration process in accordance with legal requirements. This includes the storage of the login and confirmation time, as well as the IP address. The changes to your data stored with the shipping service provider are also logged.\n\nCredentials: To subscribe to the newsletter, simply enter your name and e-mail address. We ask you to enter your name in the newsletter in order to address you personally.\n\nThe sending of the newsletter and the performance measurement associated with it are carried out on the basis of the recipientsâ consent pursuant to Art. 6 para. 1 lit. a, Art. 7 GDPR or, if consent is not required, on the basis of our legitimate interests in direct marketing pursuant to Art. 6 para. 1 lt. f. GDPR.\n\nThe registration procedure is recorded on the basis of our legitimate interests pursuant to Art. 6 para. 1 lit. f GDPR. We are interested in the use of a user-friendly and secure newsletter system that serves both our business interests and the expectations of users and also allows us to provide proof of consent.\n\nCancellation/Revocation â You can cancel the subscription to our newsletter at any time, i.e. revoke your consent. You will find a link to cancel the newsletter at the end of each newsletter. We may store the e-mail addresses we have unsubscribed for up to three years on the basis of our legitimate interests before we delete them in order to be able to prove a previously given consent. The processing of these data is limited to the purpose of a possible defence against claims. An individual application for cancellation is possible at any time, provided that at the same time the former existence of a consent is confirmed."}},{"id":"b_4iea01","type":"richtext","visible":true,"props":{"heading":"Newsletter â Provider","body":"The newsletter is sent through the email marketing service Mailerlite Paupio g. 28, Vilnius 11341, Lithuania. The data protection regulations of the service provider can be viewed here: https://www.mailerlite.com/privacy-policy. The service provider will be used on the basis of our legitimate interests in accordance with Art. 6 para. 1 lit. f. GDPR and an order processing contract pursuant to Art. 28 para. 3 sentence 1 GDPR.\n\nThe service provider can use the recipientâs data in pseudonymous form, i.e. without assignment to a user, to optimize or improve its own services, e.g. to technically optimize the dispatch and presentation of the newsletter or for statistical purposes. However, the shipping service does not use the data of our newsletter recipients to write them down itself or to pass the data on to third parties."}},{"id":"b_nvo5wz","type":"richtext","visible":true,"props":{"heading":"Newsletter â Analytics","body":"The newsletters may contain a so-called âweb-beaconâ, i.e. a pixel-sized file which is downloaded from our server when the newsletter is opened or, if we use a shipping service provider, from whose server. Within the scope of this retrieval, technical information, such as information about the browser and your system, as well as your IP address and time of retrieval are initially collected.\n\nThis information is used to technically improve the services based on the technical data or the target groups and their reading behavior based on their retrieval locations (which can be determined using the IP address) or access times. The statistical surveys also include determining whether the newsletters are opened, when they are opened and which links are clicked. For technical reasons, this information can be assigned to the individual newsletter recipients. However, it is neither our endeavor, nor, if used, that of the shipping service provider, to observe individual users. The evaluations serve us much more to re
131cognize the reading habits of our users and to adapt our contents to them or to send different contents according to the interests of our users.\n\nA separate revocation of the success measurement is unfortunately not possible, in this case the entire newsletter subscription must be cancelled."}},{"id":"b_skqtap","type":"richtext","visible":true,"props":{"heading":"Hosting, E-Mail and cloud services","body":"The hosting services we use serve to provide the following services: Infrastructure and platform services, computing capacity, storage space and database services, e-mail delivery, security services and technical maintenance services that we use for the purpose of operating this online offer.\n\nWe or our hosting provider process inventory data, contact data, content data, contract data, usage data, and meta- and communication data of customers, interested parties, and visitors of this online offer on the basis of our legitimate interests in an efficient and secure provision of this online offer according to Art. 6 Para. 1 lit. f GDPR in conjunction with. Art. 28 GDPR (conclusion of order processing contract). In addition, we are using software services that are run on certain service proversâ servers and are accessible through the internet (âcloud servicesâ, also known as âsoftware as a serviceâ (SaaS) for storage, organization and sharing of documents, presentations and calendars, e-mail storage and distribution, hosting and participation in chats and audio or video conferences.\n\nIn this context, we may process personal data on the servers of the service providers. This includes inventory data, contact data, content data, contract data, usage data, and meta- and communication data of customers, interested parties, and visitors of this online offer on the basis of our legitimate interests in an efficient and secure provision of this online offer according to Art. 6 Para. 1 lit. f GDPR in conjunction with. Art. 28 GDPR (conclusion of order processing contract). In addition, service providers may process usage and meta data for service-related purposes (for example securing or optimizing their service) or place certain cookies on the userâs devices. We strive to minimize the amount of meta and tracking data of users in general (for example by avoiding tracking cookies as much as possible) and aim to incorporate third party services in a way that minimizes access to personal data for third party providers. For details of data processing by cloud service providers, we refer to the privacy policies linked below.\n\nDropbox\nCloud storage service provider, Dropbox, Inc., 333 Brannan Street, San Francisco, California 94107, USA; \nWebsite: https://www.dropbox.com; \nPrivacy policy: https://www.dropbox.com/privacy; \nPrivacy Shield : https://www.privacyshield.gov/participant?id=a2zt0000000GnCLAA0&status=Active; \nTerms of service: https://www.dropbox.com/terms/business-agreement-2016.\n\nGoogle Cloud Services\nCloud storage service provider, Google LLC, 1600 Amphitheatre Parkway, Mountain Vie
131w, CA 94043, USA; \nWebsite: https://cloud.google.com/; \nPrivacy policy: https://www.google.com/policies/privacy, \nPrivacy and security notes: https://cloud.google.com/security/privacy; \nPrivacy Shield: https://www.privacyshield.gov/participant?id=a2zt0000000000001L5AAI&status=Aktive; \nStandard terms: https://cloud.google.com/terms/data-processing-terms; \nAdditional data processing terms: https://cloud.google.com/terms/data-processing-terms.\n\nMicrosoft Cloud Services\nCloud storage service provider, Microsoft Corporation, One Microsoft Way, Redmond, WA 98052-6399 USA; Website: http://microsoft.com; Privacy policy: https://privacy.microsoft.com/en-us/privacystatement, Trust center: https://www.microsoft.com/en-us/trust-center; Privacy Shield: https://www.privacyshield.gov/participant?id=a2zt0000000KzNaAAK&status=Active.\n\nProtonmail\nE-mail provider, Proton Technologies AG, Chemin du Pré-Fleuri 3, CH-1228 Plan-les-Ouates, Genève, Switzerland; \nPrivacy policy: https://protonmail.com/privacy-policy; \nGDPR compliance explanation: https://protonmail.com/gdpr","body_mode":"plain"}},{"id":"b_rs9kzu","type":"richtext","visible":true,"props":{"heading":"Online presence in social media","body":"We maintain online presences within social networks and platforms in order to communicate with active customers, interested parties and users and to inform them about our services. When accessing the respective networks and platforms, the terms and conditions and the data processing guidelines of their respective operators apply.\n\nUnless otherwise stated in our privacy policy, we process the data of users who communicate with us within social networks and platforms, e.g. write articles on our websites or send us messages."}},{"id":"b_xw3rke","type":"richtext","visible":true,"props":{"heading":"Integration of third-party services and content","body":"Within our online offer, we make no representations or warranties of any kind based on our legitimate interests (i.e. interest in the analysis, optimization and economic operation of our online offer within the meaning of Art. 6 para. 1 lit. f. GDPR) content or service offerings of third parties to incorporate their content and services, such as videos or fonts (hereinafter uniformly referred to as âcontentâ).\n\nThis always presupposes that the third-party providers of this content perceive the IP address of the users, since without the IP address they could not send the content to their browser. The IP address is therefore required for the display of this content. We make every effort to use only those contents whose respective providers use the IP address only for the delivery of the contents. Third-party providers may also use so-called pixel tags (invisible graphics, also known as âweb beaconsâ) for statistical or marketing purposes. Pixel tags can be used to evaluate information such as visitor traffic on the pages of this website. The pseudonymous information may also be stored in cookies on the userâs device and may include technical information about the browser and operating system, referring websites, visiting time and other information about the use of our online offer, as well as be linked to such information from other sources.\nVimeo\nWe can integrate videos of the platform âVimeoâ of the provider Vimeo Inc, Attention: Legal Department, 555 West 18th Street New York, New York 10011, USA. Privacy Policy: https://vimeo.com/privacy into our online offering. Please note that Vimeo may use Google Analytics and refer to the privacy policy (https://www.google.com/policies/privacy) and opt-out options for Google Analytics (http://tools.google.com/dlpage/gaoptout?hl=de) or Googleâs settings for data use for marketing purposes (https://adssettings.google.com/.).\n\nYouTube\nWe can integrate the videos of the platform âYouTubeâ of the provider Google LLC, 1600 Amphitheatre Parkway, Mountain Vie
131w, CA 94043, USA. Privacy Policy: https://www.google.com/policies/privacy/, Opt-Out: https://adssettings.google.com/authenticated into our online offering.\n\nGoogle Fonts\nWe may integrate the fonts (âGoogle Fontsâ) of the provider Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA. Privacy Policy: https://www.google.com/policies/privacy/, Opt-Out: https://adssettings.google.com/authenticated into our online offering.\n\nGoogle Maps\nWe may integrate the maps of the service âGoogle Mapsâ of the provider Google LLC, 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA, into our online offering. The processed data may include in particular IP addresses and location data of the users, which, however, are not collected without their consent (as a rule within the framework of the settings of their mobile devices). The data can be processed in the USA. \nPrivacy Policy: https://www.google.com/policies/privacy/, Opt-Out: https://adssettings.google.com/authenticated.\n\nTwitter\nFunctions and contents of the Twitter service, offered by Twitter Inc, 1355 Market Street, Suite 900, San Francisco, CA 94103, USA, can be integrated into our online offering. This may include, for example, content such as images, videos or texts and buttons with which users can share content from this online offer within Twitter.If the users are members of the Twitter platform, Twitter can assign calling up the above content and functions to the usersâ profiles there. Twitter is certified under the Privacy Shield Agreement and thus offers a guarantee to comply with European data protection law. (https://www.privacyshield.gov/participant?id=a2zt0000000TORzAAO&status=Active). Privacy policy: https://twitter.com/de/privacy, Opt-Out: https://twitter.com/personalization.\n\nLinkedIn\nWithin our online offer, functions and contents of the LinkedIn service, offered by LinkedIn Ireland Unlimited Company Wilton Place, Dublin 2, Ireland, can be integrated. This may include, for example, content such as images, videos or texts and buttons with which users can share content from this online offer within LinkedIn. If the users are members of the LinkedIn platform, LinkedIn can assign the call of the above contents and functions to the profiles of the users there. Privacy Policy of LinkedIn: https://www.linkedin.com/legal/privacy-policy. LinkedIn is certified under the Privacy Shield Agreement and thus offers a guarantee to comply with European data protection law. (https://www.privacyshield.gov/participant?id=a2zt0000000L0UZAA0&status=Active). Privacy Policy: https://www.linkedin.com/legal/privacy-policy, Opt-Out: https://www.linkedin.com/psettings/guest-controls/retargeting-opt-out."}},{"id":"b_wqds05","type":"richtext","visible":true,"props":{"heading":"Age of consent","body":"By using our services, you represent that you are at least 18 years of age.\n\n"}},{"id":"b_ltavti","type":"richtext","visible":true,"props":{"heading":"Disclosure","body":"We will not sell your personally identifiable information to any company or organization, but we may transfer your personally identifiable information to a successor entity upon a merger, consolidation or other corporate reorganization in which 3Dneuro participates or to a purchaser or acquirer of all or a portion of 3Dneuroâs assets to which this Site and our Services relates.\n\n"}},{"id":"b_z33eno","type":"richtext","visible":true,"props":{"heading":"Privacy Policy Updates","body":"3Dneuro may need to update this Privacy Policy from time to time. We will post our updated Privacy Policy on our Site located at 3dneuro.com and in the Services, along with a notice that the Privacy Policy has been changed so you are aware of what personally identifiable information we may collect and how we may use this information. 3Dneuro encourages you to review this Privacy Policy regularly for any changes. Your continued use of this Site, Services and/or continued provision of personally identifiable information to us will be subject to the terms of the then-current Privacy Policy.\n\n"}},{"id":"b_83q18r","type":"richtext","visible":true,"props":{"heading":"Get in touch!","body":"If you:\n\nwould like to request access to information we hold about you (the data will be transferred to you within 30 days of any request for that information) and/or correct, modify, delete or update Personal Data that you have provided to us, or\nhave any questions regarding this Privacy Policy or the practices of this site, wish to withdraw your consent for the continued collection, would like to object to your Personal Data being used, or have any additional questions:\nPlease contact us via email at privacy @ 3dneuro.com."}}]},{"id":"page_-JxKygA","title":"Documentation","path":"pages/science.html","blocks":[{"id":"b_ceil220","type":"hero","visible":true,"props":{"eyebrow":"","heading":"Documentation","sub":"","ctaPrimary":"","ctaSecondary":"","image":""}},{"id":"b_UZadQ_g","type":"richtext","visible":true,"props":{"heading":"","body":"Edit this page in the website builder."}}]},{"id":"page_Dg9zXUA","title":"REMY - Head-fixed behavior for rats","path":"remy","blocks":[{"id":"b_6wvxul","type":"heroFull","visible":true,"props":{"badge":"REMY","line1":"Head-fixed behavior","line2":"for rats","sub":"","cta1":"","cta1href":"","cta2":"","cta2href":"","image":"/uploads/1013d38271defc10.webp","stats":[]}},{"id":"b_bpftj7","type":"publications","visible":true,"props":{"eyebrow":"Preprints & peer reviewed","heading":"Publications","intro":"","productId":"remy-system","showAbstract":false,"max":""}}]},{"id":"page_FHZ1kYE","title":"Open Hardware and custom projects","path":"open-hardware","blocks":[{"id":"b_qdk6e5","type":"html","visible":true,"props":{"html":"\u003ch1>Open Hardware\u003c/h1>\n \n\u003cp>Part of our work is designing custom solutions for systems neuroscience experiments. Sometimes they don't fit our criteria for becoming a product. Because they can still be useful to a significant number of labs, we do the extra work to
131share them as \u003ca href=\"https://www.oshwa.org/definition/\" target=\"_blank\">Open Hardware\u003c/a>. We also share designs from the open source collaborations we're part of. Beyond its main features (accessibility, transparency, customizability), open hardware with 3D-printed parts also promotes sustainability: you can print replacement parts and extend a tool's lifetime. To keep the parts accessible in a future-proof, open way, we host them in a \u003ca href=\"https://zenodo.org/communities/3dneuro/\" target=\"_blank\">collection on Zenodo\u003c/a>.\u003c/p>\n \n\u003cp>Alongside the ready-to-manufacture files, we share editable design files, manufacturing tips, context for the design idea, and how it's used in the lab. Unless otherwise specified, everything is distributed under the \u003ca href=\"https://ohwr.org/project/cernohl/wikis/home\" target=\"_blank\">CERN OHL-S license\u003c/a>.\u003c/p>","contained":false}},{"id":"b_5klv4e","type":"richtext","visible":true,"props":{"heading":"DREAM implant: a lightweight, modular, cost-effective implant system for chronic electrophysiology in head-fixed and freely behaving mice","body":{"mode":"wysiwyg","html":"\u003cdiv class=\"project\">\u003cdiv class=\"project-text\">\u003cp>\u003cimg src=\"/uploads/2efb27528898597a.webp\" alt=\"DREAM implant\">\u003c/p>\u003cp>Developed with multiple labs, this is a lightweight, cost-effective probe implant system for chronic electrophysiology in rodents, optimized for ease of use, probe recovery, experimental versatility, and compatibility with behavior.\u003c/p>\u003cp>Publication: \u003ca href=\"https://dx.doi.org/10.3791/66867\" target=\"_blank\">Schröder, T., Taylor, R., Abd El Hay, M., Nemri, A., França, A., Battaglia, F., Tiesinga, P., Schölvinck, M. L., Havenith, M. N. \u003cem>J. Vis. Exp.\u003c/em> (209), e66867, doi:10.3791/66867 (2024)\u003c/a>\u003c/p>\u003cp>This project builds on the R2 system for silicon probe implantation (available \u003ca href=\"https://www.3dneuro.com/quote/\" target=\"_blank\">here\u003c/a>, methods paper \u003ca href=\"https://doi.org/10.7554/eLife.65859\" target=\"_blank\">here\u003c/a>), and introduces a new head-fixation system and headgear.\u003c/p>\u003c/div>\u003c/div>","md":""}}},{"id":"b_kewlta","type":"richtext","visible":true,"props":{"heading":"TD drive: a parametric, open-source implant for multi-area electrophysiological recordings in behaving and sleeping rats","body":{"mode":"wysiwyg","html":"\u003cdiv class=\"project\">\u003cdiv class=\"project-text\">\u003cp>\u003cimg src=\"/uploads/d46b970014883cc5.webp\" alt=\"TD Drive\">Developed with the \u003ca href=\"https://buzsakilab.com/wp/\" target=\"_blank\">Genzel lab\u003c/a> at Radboud University, this 3D-printable rat implant supports symmetric, bilateral wire electrode recordings, currently in up to ten distributed brain areas at once.\u003c/p>\u003cp>Publication: \u003ca href=\"https://dx.doi.org/10.3791/66457\" target=\"_blank\">Schröder, T., van der Meij, J., van Heumen, P., Samanta, A., Genzel, L. \u003cem>J. Vis. Exp.\u003c/em> (206), e66457, doi:10.3791/66457 (2024)\u003c/a>\u003c/p>\u003c/div>\u003c/div>","md":""}}},{"id":"b_z5rs2t","type":"richtext","visible":true,"props":{"heading":"2P headplate and light-blocking shield","body":{"mode":"wysiwyg","html":"\u003cdiv class=\"project\">\u003cdiv class=\"project-text\">\u003cp>\u003cimg src=\"/uploads/e30a8e1e464bf21d.webp\" alt=\"Headplate\">\u003c/p>\u003cp>Designed to provide simple, reliable light shielding for two-photon imaging in awake head-fixed mice.\u003c/p>\u003c/div>\u003c/div>","md":""}}},{"id":"b_qih0bj","type":"richtext","visible":true,"props":{"heading":"Metal reusable microdrive","body":{"mode":"wysiwyg","html":"\u003cdiv class=\"project\">\u003cdiv class=\"project-text\">\u003cp>\u003cimg src=\"https://dev.3dneuro.com/uploads/54c7847b1d746db0.webp\" alt=\"\">\u003c/p>\u003cp>Developed by the \u003ca href=\"https://buzsakilab.com/wp/\" target=\"_blank\">Buzsáki \u003c/a>\u003ca href=\"https://buzsakilab.com/wp/\" target=\"_blank\">lab\u003c/a>, this microdrive can be reused multiple times and allows probe reuse too (3+ times; impedance becomes the limiting factor). The paper, with assembly instructions, is \u003ca href=\"https://elifesciences.org/articles/65859\" target=\"_blank\">here\u003c/a>. A \u003ca href=\"https://buzsakilab.github.io/3d_print_designs/\" target=\"_blank\">dedicated site\u003c/a> supports users of the microdrive and its accessories. We're collaborating on de
131sign (user experience) and bulk manufacturing.\u003c/p>\u003cp>\u003ca href=\"https://github.com/buzsakilab/3d_print_designs/tree/master/Microdrives/Metal_recoverable\" target=\"_blank\">Download files from the Buzsaki lab GitHub\u003c/a> (distributed under the \u003ca href=\"https://www.gnu.org/licenses/gpl-3.0.en.html\" target=\"_blank\">GNU GPLv3 license\u003c/a>)\u003c/p>\u003c/div>\u003c/div>","md":""}}},{"id":"b_mddpvc","type":"richtext","visible":true,"props":{"heading":"Base for spherical treadmill","body":{"mode":"wysiwyg","html":"\u003csection>\u003cdiv class=\"project\">\u003cdiv class=\"project-text\">\u003cp>\u003cimg src=\"/uploads/2b572a95dcbc9460.webp\" alt=\"Treadmill\">\u003c/p>\u003cp>Modified from a design by the Technocenter, Radboud University, this 3D-printed base distributes compressed air evenly to support a floating styrofoam ball. Two sensors measure rotation speed. Commonly used with virtual reality setups.\u003c/p>\u003c/div>\u003c/div>\u003c/section>","md":""}}}]},{"id":"page_YuDHNik","title":"Ephys and behavior resources","path":"ephys-behavior-resources","blocks":[{"id":"b_lknVzxA","type":"richtext","visible":true,"props":{"heading":"","body":{"mode":"wysiwyg","md":"**For whom?** Anyone concerned with the practical aspects of electrophysiology in the behaving laboratory animal. \n\n**What?** A curated list of resources for how to get started with extracellular (in vivo/behaving) ephys experiments. Mostly in small animals/rodents. Also an easy-access reference list for more experienced users.\n\n**How?** The aim is for necessary and sufficient. By reading these sources, you should in principle be able to set up in vivo ephys experiments based on open-access information (and gear as far as possible). The list is not comprehensive â itâs based on our own (probably biased) experimental experience, and we are always happy to add open access resources. If you have comments or recommendations, please contact us at: contact -AT- 3dneuro.com\n\n**Last revision** March 24, 2025\n\n**Use** This content is licensed under a Creative \u003ca href=\"http://creativecommons.org/licenses/by/4.0/>Commons Attribution 4.0 International License\u003c/a>.","html":"\u003cp>\u003cstrong>For whom?\u003c/strong> Anyone concerned with the practical aspects of electrophysiology in the behaving laboratory animal.\u003c/p>\n\u003cp>\u003cstrong>What?\u003c/strong> A curated list of resources for how to get started with extracellular (in vivo/behaving) ephys experiments. Mostly in small animals/rodents. Also an easy-access reference list for more experienced users.\u003c/p>\n\u003cp>\u003cstrong>How?\u003c/strong> The aim is for necessary and sufficient. By reading these sources, you should in principle be able to set up in vivo ephys experiments based on open-access information (and gear as far as possible). The list is not comprehensive â itâs based on our own (probably biased) experimental experience, and we are always happy to add open access resources. If you have comments or recommendations, please contact us at: contact -AT- 3dneuro.com\u003c/p>\n\u003cp>\u003cstrong>Last revision\u003c/strong> March 24, 2025\u003c/p>\n\u003cp>\u003cstrong>Use\u003c/strong> This content is licensed under a \u003ca href=\"http://creativecommons.org/licenses/by/4.0\">Creative Commons Attribution 4.0 International License.\u003c/a>\u003c/p>"},"body_mode":"markdown"}},{"id":"b_3z19v8","type":"html","visible":true,"props":{"html":"\n\u003ch2>Table of contents\u003c/h2>\n\u003cul>\n \u003cli>\u003ca href=\"#ephys-reference\">Electrophysiology reference documentation\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#ephys-troubleshooting\">Electrophysiology troubleshooting\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#spike-sorting\">Spike sorting\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#behavior-reference\">Behavior reference documentation\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#welfare\">Welfare and handling\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#open-hardware-projects\">Select open hardware papers & projects\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#open-repos\">Open hardware/software repositories\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#how-to-open\">How to open hardware\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#companies\">Companies\u003c/a>\u003c/li>\n \u003cli>\u003ca href=\"#other\">Other\u003c/a>\u003c/li>\n\u003c/ul>\n \n\u003cdiv class=\"section\" id=\"ephys-reference\">\n\u003ch4>Electrophysiology reference documentation\u003c/h4>\n\u003cul>\n \u003cli>\u003cstrong>If new to the field, start here:\u003c/strong> \u003ca href=\"https://github.com/SjulsonLab/methods_class\" target=\"_blank\">Approaches to study neural circuits course\u003c/a> (2020) â 11 lectures, 2 dedicated to electrophysiology (lectures 3-4), by \u003ca href=\"http://sjulsonlab.org/\" target=\"_blank\">Luke Sjulson\u003c/a>, Albert Einstein College of Medicine\u003c/li>\n \u003cli>\u003cstrong>Some study design guidelines:\u003c/strong> \u003ca href=\"https://www.jneurosci.org/content/38/26/5837\" target=\"_blank\">Recommendations for the Design and Analysis of In Vivo Electrophysiology Studies\u003c/a>, editorial board, J. Neurosci. (2018)\u003c/li>\n \u003cli>\u003cstrong>A good primer:\u003c/strong> \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4392339/\" target=\"_blank\">Tools for probing local circuits: high-density silicon probes combined with optogenetics\u003c/a>, Buzsaki et al. 2015\u003c/li>\n \u003cli>\u003cstrong>A good protocol with video:\u003c/strong> \u003ca href=\"https://www.jove.com/video/56438/implantation-chronic-silicon-probes-recording-hippocampal-place-cells\" target=\"_blank\">Implantation of Chronic Silicon Probes and Recording of Hippocampal Place Cells in an Enriched Treadmill Apparatus\u003c/a>, Sariev et al. 2017 (no open source alternative, but too valuable to omit)\u003c/li>\n \u003cli>\u003cstrong>A unified data format:\u003c/strong> \u003ca href=\"https://www.cell.com/neuron/fulltext/S0896-6273(15)00919-8\" target=\"_blank\">Neurodata Without Borders\u003c/a> (NWB), a formatting standard for cell-based neurophysiology data, Teeters et al. 2015\u003c/li>\n \u003cli>\u003cstrong>A classic reference manual\u003c/strong> (to consult before hitting the search engines): \u003ca href=\"https://www.moleculardevices.com/en/assets/ebook/dd/cns/axon-guide-to-electrophysiology-and-biophysics-laboratory-techniques\" target=\"_blank\">The Axon Guide â Electrophysiology and Biophysics Laboratory Techniques 3rd ed.\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Cool Neuropixels resources:\u003c/strong> \u003ca href=\"http://www.steinmetzlab.net/shared/\" target=\"_blank\">Nick Steinmetz's lab page\u003c/a>, includes data, analysis software and training materials.\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"ephys-troubleshooting\">\n\u003ch4>Electrophysiology troubleshooting\u003c/h4>\n\u003cul>\n \u003cli>Always \u003ca href=\"https://en.wikipedia.org/wiki/RTFM\" target=\"_blank\">RTFM\u003c/a> ð\u003c/li>\n \u003cli>\u003ca href=\"https://figshare.com/articles/Noise_troubleshooting_flowchart/6409640\" target=\"_blank\">Electric noise troubleshooting flowchart\u003c/a>, Jeffery lab 2018\u003c/li>\n \u003cli>\u003ca href=\"https://support.neuralynx.com/hc/en-us/articles/360031178111-TechTip-Noise-Debug-101-Debugging-Conducted-Noise\" target=\"_blank\">More detailed noise debugging tips\u003c/a>, Neuralynx 2019\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"spike-sorting\">\n\u003ch4>Spike sorting\u003c/h4>\n\u003cul>\n \u003cli>\u003cstrong>Introduction:\u003c/strong> \u003ca href=\"https://pubmed.ncbi.nlm.nih.gov/25931392/\" target=\"_blank\">Past, Present and Future of Spike Sorting Techniques\u003c/a>, Rey et al. 2015\u003c/li>\n \u003cli>\u003cstrong>Alternatively, introductory lecture:\u003c/strong> \u003ca href=\"https://www.youtube.com/watch?v=0eP8CCxAWLo\" target=\"_blank\">An Introduction to Spike Sorting\u003c/a>, Bhagtat and Moore-Kochlacs, MIT 2017\u003c/li>\n \u003cli>\u003cstrong>Comparing algorithms:\u003c/strong> \u003ca href=\"https://elifesciences.org/articles/55167\" target=\"_blank\">SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters\u003c/a>, Magland et al. 2020\u003c/li>\n \u003cli>\u003cstrong>A popular method developed for Neuropixels data:\u003c/strong> \u003ca href=\"https://github.com/MouseLand/Kilosort2\" target=\"_blank\">Kilosort2\u003c/a>, Pachitariu 2020\u003c/li>\n \u003cli>\u003cstrong>
131Ground-truth validated method:\u003c/strong> \u003ca href=\"https://elifesciences.org/articles/34518\" target=\"_blank\">A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo\u003c/a>, Yger et al. 2018\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"behavior-reference\">\n\u003ch4>Behavior reference documentation\u003c/h4>\n\u003cul>\n \u003cli>\u003cstrong>Conceptual start:\u003c/strong> \u003ca href=\"https://www.sciencedirect.com/science/article/pii/S0896627316310406\" target=\"_blank\">Neuroscience Needs Behavior: Correcting a Reductionist Bias\u003c/a>, Krakauer et al. 2017\u003c/li>\n \u003cli>\u003cstrong>General considerations:\u003c/strong> \u003ca href=\"https://onlinelibrary.wiley.com/doi/full/10.1111/j.1601-183X.2006.00228.x\" target=\"_blank\">A hitchhiker's guide to behavioral analysis in laboratory rodents\u003c/a>, Sousa et al. 2006 and \u003ca href=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105200/\" target=\"_blank\">Probing perceptual decisions in rodents\u003c/a>, Carandini & Churchland 2013\u003c/li>\n \u003cli>\u003cstrong>Head-fixed tasks\u003c/strong> in virtual reality are growing in popularity â tight control of the stimulus, easier recordings. See a \u003ca href=\"https://www.frontiersin.org/articles/10.3389/fnmol.2020.00030/full\" target=\"_blank\">recent introductory review of head-fixed tasks\u003c/a> (Bjerre & Palmer 2020). Two approaches stand out for task design (full disclosure: two of the 3Dneuro team worked with co-authors of one as post-docs, and one led the studies in the other):\n \u003col>\n \u003cli>\u003ca href=\"https://www.biorxiv.org/content/10.1101/2020.01.17.909838v2.full\" target=\"_blank\">Standardized tasks that optimize for reproducibility across different labs\u003c/a>, The International Brain Laboratory et al. 2020\u003c/li>\n \u003cli>Tasks that push the limits of what animals can achieve, but are less easily reproducible: e.g. the Virtual-Environment-Foraging Task \u003ca href=\"https://www.nature.com/articles/s41598-018-34966-8\" target=\"_blank\">(1)\u003c/a> and \u003ca href=\"https://www.nature.com/articles/s41598-019-41250-w\" target=\"_blank\">(2)\u003c/a>, Havenith et al. 2018, 2019. Worth the detour to the supplementary note: \u003ca href=\"https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-41250-w/MediaObjects/41598_2019_41250_MOESM1_ESM.pdf\" target=\"_blank\">Seven principles of task design for mice\u003c/a>.\u003c/li>\n \u003c/ol>\n \u003c/li>\n \u003cli>\u003cstrong>Freely moving tasks\u003c/strong> typically enable more naturalistic behaviors, from the classic \u003ca href=\"http://www.scholarpedia.org/article/Morris_water_maze\" target=\"_blank\">Morris water maze\u003c/a> to more recent route planning studies (e.g. \u003ca href=\"https://www.eneuro.org/content/7/3/ENEURO.0536-19.2020\" target=\"_blank\">Jackson et al. 2020\u003c/a>).\u003c/li>\n \u003cli>\u003cstrong>Food/water restriction:\u003c/strong> the \u003ca href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088678\" target=\"_blank\">Janelia protocol for water restriction\u003c/a> covers weight and health monitoring, task performance vs. weight, and long-term effects (Guo et al. 2014). The choice between food or water restriction \u003ca href=\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204066\" target=\"_blank\">affects learning\u003c/a> (Goltstein et al. 2018).\u003c/li>\n \u003cli>\u003cstrong>Automated experiments:\u003c/strong> combined with ephys, e.g. \u003ca href=\"https://elifesciences.org/articles/27702\" target=\"_blank\">Automated long-term recording and analysis of neural activity in behaving animals\u003c/a>, Dhawale et al. 2017. Or behavioral assessment alone, e.g. \u003ca href=\"https://link.springer.com/article/10.1007/s00213-019-05189-0\" target=\"_blank\">An automated home-cage-based 5-choice serial reaction time task for rapid assessment of attention and impulsivity in rats\u003c/a>, Bruinsma et al. 2019.\u003c/li>\n \u003cli>\u003cstrong>General-purpose animal 3D pose estimation:\u003c/strong> \u003ca href=\"http://www.mousemotorlab.org/deeplabcut\" target=\"_blank\">DeepLabCut\u003c/a> (pose, whisker, and eye tracking across species). \u003ca href=\"https://golde
131nneurolab.com/simba\" target=\"_blank\">SimBA\u003c/a>, a toolkit for analyzing complex social behavior in rodents (also supports DeepLabCut).\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"welfare\">\n\u003ch4>Welfare and handling\u003c/h4>\n\u003cp class=\"tagline\">Happy animals are good lab animals.\u003c/p>\n\u003cp>These go beyond the standard 'license to work with animals' training, and help make lab animals less stressed, which improves the odds of pretty much anything you're trying to do with them.\u003c/p>\n\u003cul>\n \u003cli>\u003ca href=\"https://www.genzellab.com/#/animal-handling/\" target=\"_blank\">Rat and mice handling videos\u003c/a>, Genzel lab, Radboud University\u003c/li>\n \u003cli>\u003ca href=\"https://ag.purdue.edu/ansc/gaskill/resources/\" target=\"_blank\">Rat tickling course\u003c/a>, Gaskill lab, Purdue University\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"open-hardware-projects\">\n\u003ch4>Select open hardware projects & papers\u003c/h4>\n\u003cp class=\"tagline\">Some projects include software as well.\u003c/p>\n\u003cul>\n \u003cli>\u003cstrong>General lab equipment, with focus on affordability and education:\u003c/strong> \u003ca href=\"https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002086\" target=\"_blank\">Open Labware: 3-D Printing Your Own Lab Equipment\u003c/a>, Baden et al. 2015. See also the \u003ca href=\"https://open-labware.net/\" target=\"_blank\">project website\u003c/a> for design files.\u003c/li>\n \u003cli>\u003cstrong>A robot for automated craniotomies:\u003c/strong> Autosurgery â \u003ca href=\"http://www.autosurgery.org/\" target=\"_blank\">website\u003c/a>, see also the \u003ca href=\"https://journals.physiology.org/doi/full/10.1152/jn.01055.2014\" target=\"_blank\">paper\u003c/a> by Pak et al. 2015\u003c/li>\n \u003cli>\u003cstrong>Implant surgery without stereotaxic device & modular implant designs:\u003c/strong> \u003ca href=\"https://www.eneuro.org/content/7/2/ENEURO.0538-19.2020\" target=\"_blank\">RatHat: A Self-Targeting Printable Brain Implant System\u003c/a>, Allen et al. 2020\u003c/li>\n \u003cli>\u003cstrong>Implant design for optoelectronic probes:\u003c/strong> \u003ca href=\"https://www.nature.com/articles/s41598-017-03340-5\" target=\"_blank\">Micro-drive and headgear for chronic implant and recovery of optoelectronic probes\u003c/a>, Chung et al. 2017\u003c/li>\n \u003cli>\u003cstrong>Chronic drive implant for tetrode arrays:\u003c/strong> the Open Ephys ShuttleDrive â \u003ca href=\"https://open-ephys.atlassian.net/wiki/spaces/OEW/pages/1550974988/Shuttle+Drive\" target=\"_blank\">webpage\u003c/a> and \u003ca href=\"https://www.biorxiv.org/content/10.1101/746651v1\" target=\"_blank\">paper\u003c/a> by Voigts et al. 2019\u003c/li>\n \u003cli>\u003cstrong>Complete mouse virtual reality rig design:\u003c/strong> \u003ca href=\"https://github.com/HarveyLab/mouseVR\" target=\"_blank\">Harvey Lab mouse VR\u003c/a> (2020), \u003ca href=\"http://harveylab.hms.harvard.edu/\" target=\"_blank\">lab site\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Microscopes:\u003c/strong> \u003ca href=\"https://openflexure.org/\" target=\"_blank\">OpenFlexure\u003c/a>, \u003ca href=\"https://useetoo.org/project/\" target=\"_blank\">UC2\u003c/a>\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"open-repos\">\n\u003ch4>Open hardware/software repositories\u003c/h4>\n\u003cp class=\"tagline\">Build your own lab.\u003c/p>\n\u003cul>\n \u003cli>\u003cstrong>Possibly the largest in size and scope:\u003c/strong> \u003ca href=\"http://www.openbehavior.com/\" target=\"_blank\">Open Behavior\u003c/a>. Their \u003ca href=\"https://edspace.american.edu/openbehavior/resources/\" target=\"_blank\">resources page\u003c/a> lists many tools and companies for building equipment. See also \u003ca href=\"https://open-neuroscience.com/\" target=\"_blank\">Open Neuroscience\u003c/a>.\u003c/li>\n \u003cli>\u003cstrong>Recording hardware/software:\u003c/strong> \u003ca href=\"https://open-ephys.atlassian.net/wiki/spaces/OEW/overview\" target=\"_blank\">Open Ephys wiki\u003c/a> â becoming the standard for both high-channel-count electrophysiology and open hardware projects.\u003c/li>\n \u003cli>\u003cstrong>With a focus on affordability:\u003c/strong> \u003ca href=\"https://www.labonthecheap.com\" target=\"_blank\">Lab on the Cheap\u003c/a> (not neuroscience specific)\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"how-to-open\">\n\u003ch4>How to open hardware\u003c/h4>\n\u003cp class=\"tagline\">Spread the love.\u003c/p>\n\u003cul>\n \u003cli>\u003cstrong>Getting started:\u003c/strong> \u003ca href=\"https://certification.oshwa.org/basics.html\" target=\"_blank\">Open hardware basics and certification\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Licensing your work:\u003c/strong> \u003ca href=\"https://ohwr.org/cernohl\" target=\"_blank\">CERN Open Hardware License\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>State-of-the-art specification:\u003c/strong> \u003ca href=\"https://app.standardsrepo.com/MakerNetAlliance/OpenKnowHow/src/branch/master/1\" target=\"_blank\">Open know-how manifesto\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Community:\u003c/strong> \u003ca href=\"http://openhardware.science/\" target=\"_blank\">Gathering for Open Science Hardware\u003c/a> (GOSH), \u003ca href=\"http://www.docubricks.com/resources.jsp\" target=\"_blank\">DocuBricks\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Publish:\u003c/strong> \u003ca href=\"https://openhardware.metajnl.com/about/\" target=\"_blank\">Journal of Open Hardware\u003c/a>, \u003ca href=\"https://www.journals.elsevier.com/hardwarex\" target=\"_blank\">HardwareX\u003c/a>\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"companies\">\n\u003ch4>Companies\u003c/h4>\n\u003cp class=\"tagline\">There's a whole ecosystem for electrophysiology in behaving animals. This section is a work in progress â feedback welcome.\u003c/p>\n \n\u003cp>\u003cem>Probes, accessories and electronics\u003c/em>\u003c/p>\n\u003cul>\n \u003cli>\u003cstrong>Probes:\u003c/strong> \u003ca href=\"https://neuronexus.com/\" target=\"_blank\">Neuronexus\u003c/a>
131, \u003ca href=\"https://www.cambridgeneurotech.com/\" target=\"_blank\">Cambridge NeuroTech\u003c/a>, \u003ca href=\"https://diagnosticbiochips.com/\" target=\"_blank\">Diagnostic Biochips\u003c/a>, \u003ca href=\"http://neuraldynamicstechnologies.com/\" target=\"_blank\">Neural Dynamics Technologies\u003c/a>, \u003ca href=\"http://www.atlasneuro.com/\" target=\"_blank\">Atlas Neuro\u003c/a>, \u003ca href=\"https://www.thomasrecording.com/\" target=\"_blank\">Thomas Recording\u003c/a>, \u003ca href=\"https://www.microprobes.com/\" target=\"_blank\">MicroProbes\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Probes (nonprofit):\u003c/strong> \u003ca href=\"https://www.neuropixels.org/\" target=\"_blank\">Neuropixels\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Recording electronics and accessories (open source):\u003c/strong> \u003ca href=\"https://open-ephys.org/\" target=\"_blank\">Open Ephys\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Recording electronics and accessories:\u003c/strong> \u003ca href=\"https://neuralynx.com/\" target=\"_blank\">Neuralynx\u003c/a>, \u003ca href=\"https://www.tdt.com/\" target=\"_blank\">TDT\u003c/a>, \u003ca href=\"https://www.blackrockmicro.com/\" target=\"_blank\">Blackrock\u003c/a>, \u003ca href=\"https://plexon.com/\" target=\"_blank\">Plexon\u003c/a>, \u003ca href=\"https://white-matter.com/\" target=\"_blank\">White Matter\u003c/a>, \u003ca href=\"https://ymetry.com/site/\" target=\"_blank\">Ymetry\u003c/a> (head fixation)\u003c/li>\n \u003cli>\u003cstrong>Recording electronics and accessories (open and closed source):\u003c/strong> \u003ca href=\"https://spikegadgets.com/\" target=\"_blank\">SpikeGadgets\u003c/a>, \u003ca href=\"https://www.neurotek.ca/\" target=\"_blank\">NeuroTek\u003c/a> (including a tetrode drive loading service)\u003c/li>\n\u003c/ul>\n \n\u003cp>\u003cem>Behavior\u003c/em>\u003c/p>\n\u003cul>\n \u003cli>\u003ca href=\"https://www.noldus.com/\" target=\"_blank\">Noldus\u003c/a>, \u003ca href=\"https://www.neurotar.com\" target=\"_blank\">Neurotar\u003c/a>, \u003ca href=\"https://www.imetronic.com/home/\" target=\"_blank\">imetronic\u003c/a>, \u003ca href=\"https://www.labeotech.com/products/\" target=\"_blank\">Labeotech\u003c/a>\u003c/li>\n\u003c/ul>\n \n\u003cp>\u003cem>Consulting\u003c/em>\u003c/p>\n\u003cul>\n \u003cli>\u003cstrong>Assembly service for open hardware:\u003c/strong> \u003ca href=\"https://www.labmaker.org/\" target=\"_blank\">Labmaker\u003c/a>, \u003ca href=\"https://sanworks.io/\" target=\"_blank\">Sanworks\u003c/a>, \u003ca href=\"https://neurogig.com/\" target=\"_blank\">NeuroGig\u003c/a> (also equipment re-use), see also \u003ca href=\"https://www.neurorigbuilder.com/\" target=\"_blank\">#NeuroRigBuilder\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Custom hardware/software:\u003c/strong> \u003ca href=\"http://vise-og.com/\" target=\"_blank\">ViSE\u003c/a> (also data science, manuscript editing)\u003c/li>\n \u003cli>\u003cstrong>Open hardware:\u003c/strong> \u003ca href=\"https://prometheus-science.com/#about\" target=\"_blank\">Prometheus Science\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Software:\u003c/strong> \u003ca href=\"https://www.metacell.us/\" target=\"_blank\">Metacell\u003c/a>\u003c/li>\n\u003c/ul>\n \n\u003cp>\u003cem>Education\u003c/em>\u003c/p>\n\u003cul>\n \u003cli>\u003ca href=\"https://backyardbrains.com/\" target=\"_blank\">Backyard Brains\u003c/a>\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cdiv class=\"section\" id=\"other\">\n\u003ch4>Other\u003c/h4>\n\u003cul>\n \u003cli>\u003cstrong>Many great resources:\u003c/strong> \u003ca href=\"https://alleninstitute.org/what-we-do/brain-science/research/products-tools/\" target=\"_blank\">Allen Institute Products & Tools\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>Don't make everything yourself:\u003c/strong> \u003ca href=\"http://jvoigts.scripts.mit.edu/blog/the-case-for-scientific-consulting-in-neuroscience/\" target=\"_blank\">The case for consulting in neuroscience\u003c/a> (Voigts, 2019)\u003c/li>\n \u003cli>\u003cstrong>Don't draw everything yourself:\u003c/strong> \u003ca href=\"https://scidraw.io/\" target=\"_blank\">SciDraw\u003c/a>, a free repository of quality scientific drawings.\u003c/li>\n \u003cli>\u003cstrong>Smart bibliography tool:\u003c/strong> \u003ca href=\"http://connectedpapers.com\" target=\"_blank\">Connected Papers\u003c/a> builds a visual graph of related work around a paper, based on similarity.\u003c/li>\n \u003cli>\u003cstrong>Beyond scope, yet awesome:\u003c/strong> \u003ca href=\"https://www.inss.org.uk/\" target=\"_blank\">INSS\u003c/a> builds custom multiphoton microscopes, hardware and software, at a fraction of commercial cost.\u003c/li>\n \u003cli>\u003cstrong>Open solution for systems neuroscience research (blog):\u003c/strong> \u003ca href=\"http://labrigger.com/blog/\" target=\"_blank\">Labrigger\u003c/a>\u003c/li>\n \u003cli>\u003cstrong>For educators on a budget:\u003c/strong> \u003ca href=\"https://europepmc.org/article/med/30254544\" target=\"_blank\">Reducing the Cost of Electrophysiology in the Teaching Laboratory\u003c/a>, Wyttenbach et al. 2018\u003c/li>\n\u003c/ul>\n\u003c/div>\n \n\u003cp>Think an important resource is missing, or something's outdated? Let us know via \u003ca href=\"https://www.3dneuro.com/#contact\" target=\"_blank\">mail\u003c/a> or \u003ca href=\"https://twitter.com/3Dneuro\" target=\"_blank\">Twitter\u003c/a>.\u003c/p>","contained":false}}]},{"id":"page_aIGO2bM","title":"Credit Card payment received","path":"payment-received","blocks":[{"id":"pr_hero","type":"pageHero","visible":true,"props":{"eyebrow":"Thank y
131ou","heading":"Payment received","sub":"Your card payment has gone through and your order is confirmed. There is nothing more you need to do.","mosaic":true}},{"id":"pr_ok","type":"callout","visible":true,"props":{"variant":"success","title":"Order confirmed","body":{"mode":"wysiwyg","html":"\u003cp>Our card payment processor received your payment and your order is now in our pipeline. \u003c/p>","md":""}}},{"id":"pr_next","type":"richtext","visible":true,"props":{"heading":"What happens next","body_mode":"markdown","body":{"mode":"markdown","md":"- **We build & pack your order** in Nijmegen, the Netherlands. That usually happens within a few days, we will let you know if the lead time is longer.\n- **Youâll get a shipping notice and commercial invoice/receipt** with tracking as soon as itâs on its way.\n- **Questions?** Email [[email protected]](mailto:[email protected]) and one of the engineers will get back to you within one business day.","html":"\u003cul>\n\u003cli>\u003cstrong>We build & pack your order\u003c/strong> in Nijmegen, the Netherlands. That usually happens within a few days, we will let you know if the lead time is longer.\u003c/li>\n\u003cli>\u003cstrong>Youâll get a shipping notice and commercial invoice/receipt\u003c/strong> with tracking as soon as itâs on its way.\u003c/li>\n\u003cli>\u003cstrong>Questions?\u003c/strong> Email \u003ca href=\"mailto:[email protected]\">[email protected]\u003c/a> and one of the engineers will get back to you within one business day.\u003c/li>\n\u003c/ul>"}}},{"id":"pr_cta","type":"cta","visible":true,"props":{"heading":"While you wait","sub":"Plan your next experiment, or reach out if anything needs changing.","ctaPrimary":"Plan an experiment","ctaSecondary":"Contact us"}}]},{"id":"payment-not-completed","title":"Payment not completed","path":"pages/payment-not-completed.html","blocks":[{"id":"pn_hero","type":"pageHero","visible":true,"props":{"eyebrow":"Payment","heading":"Payment not completed","sub":"Your order has not been paid yet.","mosaic":true}},{"id":"pn_note","type":"callout","visible":true,"props":{"variant":"info","title":"Your order is still waiting","body":{"mode":"wysiwyg","html":"\u003cp>Weâve kept your quote exactly as it was. You can start a new payment below. If you want to pay by invoice instead of credit card, enter the reorder code & password from the quote on our website and select pay by invoice before submitting.\u003c/p>","md":""}}},{"id":"pn_help","type":"richtext","visible":true,"props":{"heading":"What might have gone wrong:","body_mode":"markdown","body":{"mode":"markdown","md":"- **The link had expired**. Payment links expire after ca. 15 minutes. You can create a new one here. \n- **Your bank declined the card**. Due to privacy reasons, we cannot see what went wrong exactly. Banks use a stochastic method to verify that the payment is legit. Just waiting a short while and retrying a few minutes later often solves it.\n- **You changed your mind**. Nothing to do, the quote simply stays open. We do appreciate a quick heads-up, then we will not bother you about the open quote.\n- **Anything else?** Email [[email protected]](mailto:[email protected]) with your quote number and weâll sort it out.","html":"\u003cul>\n\u003cli>\u003cstrong>The link had expired\u003c/strong>. Payment links expire after ca. 15 minutes. You can create a new one here.\u003c/li>\n\u003cli>\u003cstrong>Your bank declined the card\u003c/strong>. Due to privacy reasons, we cannot see what went wrong exactly. Banks use a stochastic method to verify that the payment is legit. Just waiting a short while and retrying a few minutes later often solves it.\u003c/li>\n\u003cli>\u003cstrong>You changed your mind\u003c/strong>. Nothing to do, the quote simply stays open. We do appreciate a quick heads-up, then we will not bother you about the open quote.\u003c/li>\n\u003cli>\u003cstrong>Anything else?\u003c/strong> Email \u003ca href=\"mailto:[email protected]\">[email protected]\u003c/a> with your quote number and weâll sort it out.\u003c/li>\n\u003c/ul>"}}}]}
131],"news":[{"id":"n_e45pcb","title":"A new website to get your implants to you faster","slug":"a-new-website-to-get-your-implants-to-you-faster","status":"published","author":"Team 3Dneuro","date":"2026-07-07","tags":[],"excerpt":"","body":"","cover":"/uploads/ce1febb443146ff2.webp","blocks":[{"id":"b_ewjtt3","type":"richtext","visible":true,"props":{"heading":"A new website to get your implants to you faster","body":{"mode":"wysiwyg","html":"\u003cp class=\"MsoNormal\">Like everyone, we hate it when getting the\ntools for an experiment turns into weeks of back and forth. You know the\nroutine. You email for a price, wait, get a vague answer, ask a follow-up, wait\nagain. By the time you have everything you need to start, your funders or\nsupervisors already ask for the conclusions of your study.\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">So from the start, we tried to do the\nopposite. Transparent processes, as little friction as we could manage.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">That's why we made our prices public early\non, which, is still the exception rather than the rule in this field. We added\nan instant quotation system soon after. Use it like a web shop and get a PDF\nquote in your inbox within a few minutes.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Hidden prices and \"let's hop on a\ncall\" conversations would have let us charge the big, well-funded labs\nmore, the ones who can clearly afford it. We gave that up on purpose. Making\nprecision machined microdrives isn't cheap, and it means we can't sit on a huge\ninventory at all times. When ordering feels as easy as buying on Amazon, it's\nnatural to expect everything to ship the same day. We're almost there. But\nextra manufacturing care and quality control sometimes add a few days beyond\nyour usual Prime turnaround. More so when the package is heading overseas.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">We decided it's worth it anyway. And rather\nthan just accept the rough edges, we smoothed out the whole\ndecision-and-ordering process with a few new features and some tuning of the\nold ones.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003ch2>\u003cb>Whatâs new?\u003c/b>\u003c/h2>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Plan your experimental needs\u003c/b>\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">A \u003ca href=\"/planner\">planner\u003c/a> that works out roughly how many\nimplants you'll need to run your experiments. Handy if you're just getting\nstarted and want a feel for the scale. Itâs a first version â let us know if it\nis useful and where we can improve it!\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Implants and microdrives usually get\ntreated as a small line item next to silicon probes on an already tight budget.\nWe wanted to make the real budget picture obvious, including the payoff from\nreusing your probes instead of buying fresh ones every time. Drop in the price\nof your probes and the planner shows you what reuse actually saves you.\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Edit, update, and share your quotes easily\u003co:p>\u003c/o:p>\u003c/b>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">We streamlined quote creation so you're not\nstuck waiting on us to change a detail. \u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Every quote now comes with a re-order code.\nWant to modify something? Enter the code and update the parts you want. No\nemail thread required.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">There's a separate password to protect your\npersonal data. Enter it and the quote pre-fills the same billing and shipping\ndetails, which is useful for reordering yourself or sharing within your lab.\nLeave it out and only the products get recreated, nothing personal attached.\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Got a quote in EUR but need USD? Rather pay\nby credit card than raise a purchase order? Enter the code, switch those, and\nyou get an updated quote back.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>
131Faster processing of credit card orders\u003co:p>\u003c/o:p>\u003c/b>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">For credit card orders, quotes now include\na secure payment link right inside them. No more waiting on a separate email\nbefore you can pay.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Feedback desired!\u003c/b>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">As always, we appreciate your feedback! Found\nsomething that is odd or could be improved? \u003co:p>\u003c/o:p>\u003c/span>\u003ca href=\"/contact\">Let us know and weâll look into it asap!\u003c/a>\u003c/p>","md":""}}}]},{"id":"nw-press-bionieuws","title":"Press: 3Dneuro featured in Dutch newsletter Bionieuws","slug":"press-3dneuro-featured-in-dutch-newsletter-bionieuws","status":"published","author":"3Dneuro","date":"2022-06-15","tags":["press"],"excerpt":"Bionieuws, a professional newsletter for biologists, ran a feature on 3Dneuro in its May 2022 issue.","body":"","cover":"/assets/news/bionieuws.webp","blocks":[{"id":"nw-press-bionieuws-b1","type":"richtext","visible":true,"props":{"body":"Last month, [Bionieuws](https://bionieuws.nl/) â a professional newsletter for biologists â ran a feature on 3Dneuro (Letter 7, May 14 2022). Our co-founder Tim can be seen assembling a metal microdrive.\n\nIn the interview, we covered a few themes, summarized below:\n\n- How one of our main goals is to make experiments easier for scientists, specifically by identifying gaps overlooked by engineers when it comes to user experience.\n- How the R2Drive, our metal microdrive distributed in collaboration with the Buzsaki lab at NYU, allows researchers to re-use their silicon probes up to 3 times.\n- Some advantages of the **rat head fixation setup** we are currently developing for distribution, in a collaboration with Nelson Totah at the University of Helsinki. Enabling behavioral tasks in virtual reality environment, with animals considered âsmarterâ than mice, while presenting a larger brain that can accommodate more recording equipment for larger data yields.\n- Our first collaborator and customer on the Radboud campus, [Mike X Cohen](https://www.mikexcohen.com/).\n- Our current effort to transition to an **open source model**.\n- The precision of stereolithography 3D printing.\n\nThe full article, in Dutch, is behind a paywall. Pdf version available on request.","body_mode":"markdown"}}]},{"id":"nw-r2drive-preorder","title":"R2Drive preorder update","slug":"r2drive-preorder-update","status":"published","author":"3Dneuro","date":"2021-05-21","tags":["product","open hardware"],"excerpt":"Our first aluminium R2Drive run â fifty units at 0.47 g each â sold out within 24 hours. Here's what's next.","body":"","cover":"/assets/news/r2drive-preorder.webp","blocks":[{"id":"nw-r2drive-preorder-b1","type":"richtext","visible":true,"props":{"body":"This week, we announced on Twitter a [preorder round](https://www.3dneuro.com/products/r2drive-preorder/) for our first production run of the R2Drive. This [microdrive](https://buzsakilab.github.io/3d_print_designs/) was designed by the Buzsáki lab at NYU, with a focus on sustainability: One can reuse the drive and the silicon probes, with the main limitation being electrode impedance.\n\nWe planned for a 50-drive batch, machined in aluminum. That manufacturing process was particularly beneficial in 2 ways compared to 3D-printing in stainless steel:\n\n(1) _Cost reduction_. We can deliver fully assembled drives for less than the cost of printing the parts yourself.\n\n(2) _Weight reduction_. The aluminum R2Drive weighs only 0.47 g, half as much as the stainless steel version. Lighter implant, happier animals!\n\nGiven that, it makes sense that **all 50 drives were claimed in under 24h**! We are now planning to ship the first 50 and arranging a 2nd production run so that everyone who preordered gets their drives no later than July-August. The first orders will ship in June.\n\nMore information on preordering: [www.3dneuro.com/products/r2drive-preorder/](https://www.3dneuro.com/products/r2drive-preorder/)\n\nMore information on R2Drive: [buzsakilab.github.io/3d_print_designs/](https://buzsakilab.github.io/3d_print_designs/)","body_mode":"markdown"}}]},{"id":"nw-zenodo","title":"Hosting open hardware files on Zenodo: durable and citable resource sharing","slug":"hosting-open-hardware-files-on-zenodo","status":"published","author":"3Dneuro","date":"2021-05-18","tags":["open hardware"],"excerpt":"Why we share our open-hardware design files on Zenodo: durable, DOI-citable, and grant-friendly.","body":"","cover":"/assets/news/zenodo.webp","blocks":[{"id":"nw-zenodo-b1","type":"richtext","visible":true,"props":{"body":"Recently, while preparing our next open hardware project, we researched how to best share the design files. Originally we decided to self-host, with the idea of later migrating to GitHub. Instead, we opted for [Zenodo](https://zenodo.org/), the file sharing platform maintained by CERN and funded by the EU.\n\nZenodo stands out for 2 features:\n\n- _Durability_ â Government-funded platforms tend to be more predictably durable than private companies. And unlike self-hosting, the work stops when the files are uploaded.\n- _Citable sharing_ â Zenodo attributes a DOI (Digital Object Identifier) to the shared files, and like scientific publications, accepts an author list. This makes open hardware projects more easily citable in publications, and grant proposals (there is some integration between Zenodo and the EU grant submission system). As a result, it becomes easier to properly credit contributors, and get credit for the work while seeking future funding.\n\nAnd like other platforms, Zenodo enables version control. Below some additional features, as de
131scribed on their website.\n\nWe have started publishing our projects there, with the first one being the [light-blocking headplate and sleeve for 2-photon microscopy](https://zenodo.org/record/4547658).","body_mode":"markdown"}}]},{"id":"nw-metal-microdrive","title":"New metal microdrive in collaboration with the Buzsáki lab","slug":"new-metal-microdrive-in-collaboration-with-the-buzsaki-lab","status":"published","author":"3Dneuro","date":"2021-04-23","tags":["product","open hardware"],"excerpt":"A recoverable, reusable, open-source metal microdrive for silicon probes â developed with the Buzsáki lab at NYU.","body":"","cover":"/assets/news/metal-microdrive.webp","blocks":[{"id":"nw-metal-microdrive-b1","type":"richtext","visible":true,"props":{"body":"We are very excited to announce our new collaboration with the [Buzsáki lab](https://buzsakilab.com/wp/) at NYU. Their new microdrive for silicon probes is:\n\n- recoverable\n- **reusable**\n- open-source\n- **extends your probeâs life span to 3+ implants** (impedance becomes the main issue)\n- and also works with [Neuropixels](https://www.neuropixels.org/)!\n\nThe metal microdrive and head cap were developed and systematically tested by Mihály Vöröslakos ([@voroslakos](https://twitter.com/voroslakos)), Peter Petersen ([@petersenpeter](https://twitter.com/petersenpeter)) and team. You can read all about it in the [preprint](https://www.biorxiv.org/content/10.1101/2020.12.20.423655v1).\n\nAnd because itâs open-source, you can make it yourself right now! ⦠OR⦠you can let us make it for you at a lower price. Bulk production is awesome!\n\nWe are currently working on tweaking the design for cheaper manufacturing and improved user experience.\n\nPre-orders starting soon! Watch this space! Or [contact us](https://www.3dneuro.com/#contact) now if you want to be first in line.","body_mode":"markdown"}}]},{"id":"nw-nature-diy","title":"Recent Nature feature on DIY science equipment","slug":"recent-nature-feature-on-diy-science-equipment","status":"published","author":"3Dneuro","date":"2020-11-30","tags":["press","open hardware"],"excerpt":"Nature on how DIY, open-source hardware democratizes science â plus a few things we'd add.","body":"","cover":"/assets/news/nature-diy.webp","blocks":[{"id":"nw-nature-diy-b1","type":"richtext","visible":true,"props":{"body":"On Nov 17 2020, the journal Nature published a feature about how â[How DIY technologies are democratizing science](https://www.nature.com/articles/d41586-020-03193-5)â by Sandeep Ravindran.\n\nKey takeaways:\n\n- âThe idea that scientists build their own equipment is as old as science,â (Tom Baden) a reminder that the trend itself is not new, scientists have been building their own equipment for centuries, often out of necessity, as it doesnât exist yet.\n- DIY is time consuming, labor-intensive, for less reliable results and no tech support. Some people like DIY for its own sake, others use it primarily for cost savings.\n- When you can get past the downside, DIY can transform a whole field by expanding the reach of a technology to countries that have more limited research budgets. The net effect is accelerating science by democratizing it, on a worldwide scale.\n- DIY science equipment also has an impact on education in lower income countries, by putting otherwise unaffordable equipment in the hands of students.\n- âHardware built from open-source designs generally costs just 1â10% of the price of commercial counterpartsâ (Joshua Pearce, author of [Open-source lab](https://www.appropedia.org/Open-source_Lab)).\n- DIY hardware is customizable in ways commercial products rarely are. And the lack of tech support is compensated by knowledge for repairing devices acquired when you build them yourself.\n- In some cases, DIY can apply to reagents as well, such as enzymes produced in [open-source bioreactors](https://openbioeconomy.org/projects/open-source-bioreactor/), that can be used in various applications, including diagnostics.\n- 3D printing is a core technology that empowers scientific instrumentation DIY, especially the more affordable desktop FDM printers.\n- DIY combines well with recycling older lab equipment.\n\nAltogether the article is
131well researched and covers a range of interesting projects and people. In fact, several of the covered projects have been featured on our [Resources](https://www.3dneuro.com/resources/#select_open) page since last summer. Below we dive a little bit deeper into 4 topics.\n\n**Untapped potential **â When considering the impact of DIY, it is mainly about the distribution of properly documented, easily replicable, open hardware (see [here](https://www.3dneuro.com/resources/#how_to) for pointers on how to do that). There is a lot more DIY hardware getting made in labs that never gets disseminated, because it is time-consuming. The visibility brought about by Nature articles like this one, a more organized community (e.g. [GOSH](http://openhardware.science/)) and publication venues (e.g. [Journal of Open Hardware](https://openhardware.metajnl.com/), [HardwareX](https://www.journals.elsevier.com/hardwarex)) all contribute to making DIY dissemination more impactful and rewarding.\n\n**Blind spot **â A surprising omission, in our view, is [Open Ephys](https://open-ephys.org/), one of the most impactful projects we know of. By developing an open-source data acquisition board for electrophysiology, they cut the commercial price of such devices by at least a factor 10 (they offer the option to get it fully assembled), and double that if you source the components and assemble them yourself. One potential explanation for the omission is that Open Ephys doesnât really take part in the larger open hardware community, and is therefore not known by open hardware advocates in other research fields.\n\nLet us know what you think below in the comment section.","body_mode":"markdown"}}]}]},"products":[{"id":"r2drive","line":"r2","title":"R2drive S","subtitle":"Recoverable metal microdrive (regular arm)","blurb":"A small, lightweight metal microdrive that lets you recover and reuse your silicon probes. The regular arm (3.6 à 8.35 mm) suits standard silicon probes. 7 mm of travel for post-implant adjustment.","tags":["Microdrive","Reusable","for most silicon probes"],"accent":"var(--blue-500)","seed":1,"priceEur":250,"mass":"0.47 g","sku":"3DN-0021","featured":false,"active":true,"wefactId":"3DN-0021","wcId":null,"custom":false,"sortOrder":0,"image":"/uploads/7b8fc1b91297227b.webp","images":["/uploads/7b8fc1b91297227b.webp","/uploads/6d113f63f345e347.webp","/uploads/f8a871fa3fe9bcb2.webp"],"video":"","details":{"description":"The R2drive is a recoverable, reusable metal microdrive for silicon-probe implantation, developed in the Buzsáki lab. Weighing under 0.5 g with a footprint below 4Ã5 mm, it suits rodent work â up to three units can be implanted in a mouse simultaneously. The drive sits on an implantable base that disconnects by removing a screw, so the probe can be recovered and reused across multiple experiments. Probes mounted on R2drives can also connect to a stereotax for acute recordings, making it easy to share a probe between chronic and acute experiments. For chronic implants the system pairs with a protective cap that doubles as a Faraday cage. The S model carries the regular arm â for Neuropixels probes choose the R2drive L.","features":["Recover and reuse the same probe across multiple experiments","Ultra-compact: under 0.5 g, footprint below 4Ã5 mm","Compatible with standard commercial silicon probes with flex cables","Mountable on a stereotax for acute recordings","Regular arm (3.6 à 8.35 mm) for standard silicon probes"],"specs":[{"label":"Material","value":"Aluminium, stainless steel, brass"},{"label":"Footprint","value":"3.3 à 4.45 mm (base); 4 à 5 mm including arm"},{"label":"Weight","value":"0.47 g"},{"label":"Travel distance","value":"ca 7 mm"},{"label":"Travel per turn","value":"282 µm (1/90 inch)"},{"label":"Arm size","value":"3.6 mm (w) à 8.35 mm (h)"}],"rich":{"description":{"mode":"wysiwyg","html":"\u003cp>The R2drive is a recoverable, reusable metal microdrive for silicon-probe implantation, developed in the Buzsáki lab. Weighing under 0.5 g with a footprint below 4Ã5 mm, it suits rodent work â up to three units can be implanted in a mouse simultaneously. The drive sits on an implantable base that disconnects by removing a screw, so the probe c
131an be recovered and reused across multiple experiments. Probes mounted on R2drives can also connect to a stereotax for acute recordings, making it easy to share a probe between chronic and acute experiments. For chronic implants the system pairs with a protective cap that doubles as a Faraday cage. The S model carries the regular arm â for Neuropixels probes choose the R2drive L.\u003c/p>\u003cdiv data-embed=\"carousel\" data-images=\"["/uploads/f8a871fa3fe9bcb2.webp","/uploads/d5a96371563e5e63.webp","/uploads/e56a40992413d2d3.webp","/uploads/27b12e006c272c72.webp"]\" contenteditable=\"false\" class=\"re-embed\">ð¼ Image carousel â 4 image(s)\u003c/div>\u003cp>\u003cbr>\u003c/p>","md":""},"included":{"mode":"wysiwyg","html":"\u003cul>\u003cli>R2drive S\u003c/li>\u003cli>(optional) 0.8mm dowel pin to attach R2drive to standard electrode holder (not needed in most cases)\u003c/li>\u003c/ul>","md":""},"features":{"mode":"wysiwyg","html":"\u003cul>\n\u003cli>Recover and reuse the same probe across multiple experiments\u003c/li>\n\u003cli>Ultra-compact: under 0.5 g, footprint below 4Ã5 mm\u003c/li>\n\u003cli>Compatible with standard commercial silicon probes with flex cables\u003c/li>\n\u003cli>Mountable on a stereotax for acute recordings\u003c/li>\n\u003cli>Regular arm (3.6 à 8.35 mm) for standard silicon probes\u003c/li>\n\u003c/ul>","md":""}}},"serialCount":1,"hsCode":"9018.90.84","originCountry":"NL","customsDescription":"","weightG":0.5,"availability":"in_stock","availabilityNote":"","availabilityInfo":{"key":"in_stock","label":"In stock","light":"green","tooltip":"In stock â ships within a few working days."},"related":[{"id":"r2rail","kind":"up","reason":"Using Neuropixels 2 probes with metal cap? Check out the R2rail as alternative!","group":""},{"id":"mouse-cap","kind":"cross","reason":"Recommended head-gear for mice","group":"headgear"},{"id":"rat-cap","kind":"cross","reason":"Recommended head-gear for rats","group":"headgear"}]},{"id":"r2drive-l","line":"r2","title":"R2drive L","subtitle":"Recoverable metal microdrive (Neuropixels arm)","blurb":"The same recoverable metal microdrive with a larger arm (5 à 10 mm) sized for Neuropixels probes. Recover and reuse your probes across experiments, with 7 mm of travel for post-implant adjustment.","tags":["Microdrive","Reusable","for large probes (eg Neuropixels1.0)"],"accent":"var(--blue-500)","seed":2,"priceEur":250,"mass":"0.47 g","sku":"3DN-0022","featured":false,"active":true,"wefactId":"3DN-0022","wcId":null,"custom":false,"sortOrder":1,"image":"/uploads/9d38934c262c34c6.webp","images":["/uploads/9d38934c262c34c6.webp","/uploads/886e57ff4d989e34.webp"],"video":"","details":{"description":"The R2drive is a recoverable, reusable metal microdrive for silicon-probe implantation, developed in the Buzsáki lab. Weighing under 0.5 g with a footprint below 4Ã5 mm, it suits rodent work â up to three units can be implanted in a mouse simultaneously. The drive sits on an implantable base that disconnects by removing a screw, so the probe can be recovered and reused across multiple experiments. Probes mounted on R2drives can also connect to a stereotax for acute recordings, making it easy to share a probe between chronic and acute experiments. For chronic implants the system pairs with a protective cap that doubles as a Faraday cage. The L model carries the larger Neuropixels arm â for standard silicon probes choose the R2drive S.","features":["Recover and reuse the same probe across multiple experiments","Ultra-compact: under 0.5 g, footprint below 4Ã5 mm","Compatible with standard commercial silicon probes with flex cables","Mountable on a stereotax for acute recordings","Larger arm (5 à 10 mm) sized for Neuropixels probes"],"specs":[{"label":"Material","value":"Aluminium, stainless steel, brass"},{"label":"Footprint","value":"3.3 à 4.45 mm (base); 5 à 10 mm including arm"},{"label":"Weight","value":"0.47 g"},{"label":"Travel distance","value":"7 mm"},{"label":"Travel per turn","value":"282 µm (1/90 inch)"},{"label":"Arm size","value":"5 mm (w) à 10 mm (h)"}],"rich":{"included":{"mode":"wysiwyg","html":"","md":""}}},"serialCount":1,"hsCode":"9018.90.84","originCountry":"NL","customsDescription":"","weightG":0.5,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[{"id":"mou
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131:false,"sortOrder":4,"image":"/uploads/edb41c076d089134.webp","images":["/uploads/edb41c076d089134.webp","/uploads/0cfbfa0b1de1bb27.webp"],"video":"","details":null,"serialCount":0,"hsCode":"9018.90.84","originCountry":"NL","customsDescription":"","weightG":200,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[{"id":"r2-metal-system","kind":"cross","reason":"To use the stereotactic adapter, you'll need at least one R2 metal implantation system holder","group":""}]},{"id":"r2-case","line":"r2","title":"R2 storage & assembly case","subtitle":"Storage & assembly case","blurb":"A dedicated case to store the R2 metal implantation system and assemble fresh probes onto your R2drive and R2rails. Included with the metal implantation system; available separately as a spare.","tags":["Case","Assembly"],"accent":"var(--teal-500)","seed":11,"priceEur":65,"mass":"â","sku":"3DN-0050","featured":false,"active":true,"wefactId":"3DN-0050","wcId":5410,"custom":false,"sortOrder":5,"image":"/uploads/d68c8e6db37c13de.webp","images":["/uploads/d68c8e6db37c13de.webp"],"video":"","details":null,"serialCount":0,"hsCode":"9018.90.84","originCountry":"NL","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[{"id":"r2-metal-system","kind":"cross","reason":"This is an extra storage case to expand your R2 metal implantation system. The R2 metal holder is not included.","group":""}]},{"id":"stereotax-attachment","line":"r2","title":"Stereotax attachment + R2Drive holders","subtitle":"3D-printed clamp + two R2Drive holders","blurb":"The original 3D-printed clamp that attaches to the rods of common stereotactic probe holders. Comes with two R2Drive holders (a regular and a Neuropixels version). Superseded by the metal implantation system.","tags":["3D-printed","Budget"],"accent":"var(--stone-500)","seed":8,"priceEur":75,"mass":"â","sku":"3DN-0025","featured":false,"active":true,"wefactId":"3DN-0025","wcId":4390,"custom"
131:false,"sortOrder":6,"image":"/uploads/ea264a96137037b7.webp","images":["/uploads/ea264a96137037b7.webp","/uploads/306950baef126965.webp","/uploads/29f77c8f7f0cc00d.webp","/uploads/bb7963bc42ec047f.webp","/uploads/47913ad3267154bf.webp"],"video":"","details":null,"serialCount":0,"hsCode":"9018.90.84","originCountry":"NL","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[{"id":"r2-metal-system","kind":"cross","reason":"We recommend the new R2 metal implantation system instead of the old 3D printed holder due to its significantly higher stability and smaller footprint.","group":""}]},{"id":"r2-spare-bases","line":"r2","title":"R2Drive spare bases (Ã3)","subtitle":"Pre-tapped replaceable bases","blurb":"The inexpensive, replaceable part that is sacrificed during explantation â so the probe comes out clean and reusable. Three pre-tapped bases, ready for your next implantations.","tags":["Consumable","Pack of 3"],"accent":"var(--orange-400)","seed":4,"priceEur":120,"mass":"â","sku":"3DN-0024","featured":false,"active":true,"wefactId":"3DN-0024","wcId":4387,"custom":false,"sortOrder":7,"image":"/uploads/b322d2188f0c5b67.webp","images":["/uploads/b322d2188f0c5b67.webp"],"video":"","details":null,"serialCount":0,"hsCode":"9018.90.84","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"screwdrivers","line":"r2","title":"Screwdrivers for R2Drive (Ã4)","subtitle":"Torx & flat set","blurb":"The four screwdrivers you reach for with the R2drive system â including the Torx T1 used to connect the drive base and body.","tags":["Tools","Set of 4"],"accent":"var(--yellow-500)","seed":10,"priceEur":65,"mass":"â","sku":"3DN-0026","featured":false,"active":true,"wefactId":"3DN-0026","wcId":4389,"custom":false,"sortOrder":8,"image":"/uploads/b55485070600b2c0.webp","images":["/uploads/b55485070600b2c0.webp","/uploads/67784fe908748367.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"rat-cap","line":"access","title":"Rat cap","subtitle":"Protective headgear for rats","blurb":"Designed in the Buzsáki lab as a fast, flexible way to protect silicon-probe implants on rats.","tags":["Rat","Headgear"],"accent":"var(--green-300)","seed":10,"priceEur":70,"mass":"11 g","sku":"3DN-0027","featured":false,"active":true,"wefactId":"3DN-0027","wcId":4402,"custom":false,"sortOrder":9,"image":"/uploads/9fa953200dafe989.webp","images":["/uploads/097d2fe94aac8811.webp","/uploads/9f11da6fa1b9404a.webp","/uploads/a9c364a9a9426da5.webp","/uploads/9fa953200dafe989.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"mouse-cap","line":"access","title":"Mouse cap","subtitle":"Protective headgear for mice","blurb":"Designed in the Buzsáki lab as a fast, flexible way to protect silicon-probe implants on mice. Built around a headplate.","tags":["Mouse","Headgear"],"accent":"var(--teal-500)","seed":9,"priceEur":52,"mass":"2.2 g","sku":"3DN-0028","featured":false,"active":true,"wefactId":"3DN-0028","wcId":4391,"custom"
131:false,"sortOrder":10,"image":"/uploads/e4b85e06d5f9930f.webp","images":["/uploads/e4b85e06d5f9930f.webp","/uploads/4f77b2db607fe15c.webp","/uploads/0f10facef1f7bfb6.webp","/uploads/9f3457f2634bb3e8.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"mouse-crown","line":"access","title":"Mouse crown","subtitle":"Flexible 3D-printed protective cage","blurb":"A flexible 3D-printed cage to protect the 3Drive (or other microdrives) in freely-behaving animals.","tags":["Mouse","3D-printed"],"accent":"var(--teal-300)","seed":11,"priceEur":52,"mass":"â","sku":"3DN-0029","featured":false,"active":true,"wefactId":"3DN-0029","wcId":3665,"custom":false,"sortOrder":11,"image":"/uploads/ec85afc8d3725b0f.webp","images":["/uploads/ec85afc8d3725b0f.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"copper-mesh","line":"access","title":"Copper mesh for implants","subtitle":"Extruded copper mesh","blurb":"A piece of extruded copper mesh for building your own headgear â the Buzsáki mouse cap, our crown, or a DREAM implant.","tags":["Accessory","Faraday"],"accent":"var(--orange-300)","seed":4,"priceEur":15,"mass":"â","sku":"3DN-0031","featured":false,"active":true,"wefactId":"3DN-0031","wcId":5118,"custom":false,"sortOrder":12,"image":"/uploads/fed413e6ba5570e3.webp","images":["/uploads/fed413e6ba5570e3.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"remy-system","line":"remy","title":"REMY head-fixed behaviour system","subtitle":"Complete head-fixation system for rats","blurb":"A complete system for head-fixed behavioural experiments in rats â bringing treadmill / VR task designs and electrophysiology to awake, behaving rats.","tags":["Rat","Head fixation","System"],"accent":"var(--orange-400)","seed":7,"priceEur":4915,"mass":"â","sku":"3DN-0036","featured":false,"active":true,"wefactId":"3DN-0036","wcId":5129,"custom":false,"sortOrder":13,"image":"/uploads/92a407c29972eb21.webp","images":["/uploads/92a407c29972eb21.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"remy-holder","line":"remy","title":"REMY behaviour holder","subtitle":"Stainless-steel head-fixation holder","blurb":"Stainless-steel holder used to head-fix awake, behaving rats with the REMY system.","tags":["Rat","Holder"],"accent":"var(--orange-500)","seed":8,"priceEur":2750,"mass":"â","sku":"3DN-0037","featured":false,"active":true,"wefactId":"3DN-0037","wcId":5133,"custom":false,"sortOrder":14,"image":"/uploads/5d8ac2e5d03d7473.webp","images":["/uploads/5d8ac2e5d03d7473.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"remy-implants","line":"remy","title":"REMY head-fixation implants (Ã5)","subtitle":"Set of five implants","blurb":"Five implants for the REMY system â each a robust, biocompatible chamber with a metal pole for repeatable head fixation.","tags":["Rat","Implants Ã5"],"accent":"var(--red-400)","seed":12,"priceEur":1475,"mass":"â","sku":"3DN-0038","featured":false,"active":true,"wefactId":"3DN-0038","wcId":5134,"custom":false,"sortOrder":15,"image":"/uploads/60e89d2baa66ad10.webp","images":["/uploads/60e89d2baa66ad10.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"remy-chambers","line":"remy","title":"REMY spare chambers (Ã5)","subtitle":"Five spare chambers + caps","blurb":"Five spare chambers and caps for the REMY system. We recommend chambers be single-use due to wear.","tags":["Rat","Spares Ã5"],"accent":"var(--red-500)","seed":3,"priceEur":990,"mass":"â","sku":"3DN-0039","featured":false,"active":true,"wefactId":"3DN-0039","wcId":5135,"custom"
131:false,"sortOrder":16,"image":"/uploads/445f2ba4f8b62acc.webp","images":["/uploads/445f2ba4f8b62acc.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]},{"id":"remy-surgery-holder","line":"remy","title":"REMY implant surgery holder","subtitle":"Stainless-steel chamber-implant surgery holder","blurb":"Used for the chamber-implant surgery in the REMY system. Stainless steel, with an 8 mm pole.","tags":["Rat","Surgery"],"accent":"var(--orange-300)","seed":9,"priceEur":690,"mass":"â","sku":"3DN-0051","featured":false,"active":true,"wefactId":"3DN-0051","wcId":5132,"custom":false,"sortOrder":17,"image":"/uploads/aaa8af15dbfd343a.webp","images":["/uploads/aaa8af15dbfd343a.webp"],"video":"","details":null,"serialCount":0,"hsCode":"","originCountry":"","customsDescription":"","weightG":null,"availability":"","availabilityNote":"","availabilityInfo":null,"related":[]}],"categories":[{"id":"r2","name":"R2 System","tagline":"Recover & reuse silicon probes","accent":"var(--blue-500)","icon":"refresh-cw","description":"Recover and reuse your silicon probes â on average across three chronic experiments. Metal microdrives, implantation holders, and the inexpensive replaceable parts that make recovery routine."},{"id":"access","name":"Caps & accessories","tagline":"Headgear, tools & spares","accent":"var(--teal-500)","icon":"box","description":"Protective headgear for freely-moving animals, plus the small parts and tools that ship alongside an R2 setup."},{"id":"remy","name":"REMY","tagline":"Head-fixed behaviour for rats","accent":"var(--orange-400)","icon":"target","description":"A complete head-fixation system for awake, behaving rats â frame, holders, implants and surgery tooling."}],"shippingConfig":{"version":1,"rules":{"default":{"vat":{"rate":0,"reverseChargeWithVatId":false},"methods":[{"id":"standard","name":"DHL Express","cost":110,"days":"","askNote":false,"notePrompt":"","comment":""},{"id":"mmq3ii9ef","name":"Ship on your own shipping account","cost":20,"days":"","askNote":true,"notePrompt":"","comment":""}]},"country:NL":{"vat":{"rate":0.21,"reverseChargeWithVatId":false},"methods":[{"id":"standard","name":"PostNL (insured up to 50â¬)","cost":20,"days":"2-4 days","askNote":false},{"id":"mmq3ilcvc","name":"DHL Express (insured)","cost":60,"days":"1-2 days","askNote":false}]},"continent:EU":{"vat":{"rate":0.21,"reverseChargeWithVatId":true},"methods":[{"id":"standard","name":"DHL Express (insured)","cost":110,"days":"1-3 days","askNote":false},{"id":"mmq3imbaf","name":"PostNL (insured up to 50â¬)","cost":30,"days":"4-12 days","askNote":false}]},"country:DE":{"vat":{"rate":0.21,"reverseChargeWithVatId":true},"methods":[{"id":"standard","name":"DHL Express (insured)","cost":110,"days":"1-3 days","askNote":false,"notePrompt":"","comment":""},{"id":"mmq3imbaf","name":"PostNL (insured up to 50â¬)","cost":30,"days":"4-12 days","askNote":false,"notePrompt":"","comment":""}]}}},"news":[{"id":"n_e45pcb","title":"A new website to get your implants to you faster","slug":"a-new-website-to-get-your-implants-to-you-faster","status":"published","author":"Team 3Dneuro","date":"2026-07-07","tags":[],"excerpt":"","body":"","cover":"/uploads/ce1febb443146ff2.webp","blocks":[{"id":"b_ewjtt3","type":"richtext","visible":true,"props":{"heading":"A new website to get your implants to you faster","body":{"mode":"wysiwyg","html":"\u003cp class=\"MsoNormal\">Like everyone, we hate it when getting the\ntools for an experiment turns into weeks of back and forth. You know the\nroutine. You email for a price, wait, get a vague answer, ask a follow-up, wait\nagain. By the time you have everything you need to start, your funders or\nsupervisors already ask for the conclusions of your study.\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">So from the start, we tried to do the\nopposite. Transparent processes, as little friction as we could manage.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">That's why we made our prices public early\non, which, is still the exception rather than the rule in this field. We added\nan instant quotation system soon after. Use it like a web shop and get a PDF\nquote in your inbox within a few minutes.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">
131Hidden prices and \"let's hop on a\ncall\" conversations would have let us charge the big, well-funded labs\nmore, the ones who can clearly afford it. We gave that up on purpose. Making\nprecision machined microdrives isn't cheap, and it means we can't sit on a huge\ninventory at all times. When ordering feels as easy as buying on Amazon, it's\nnatural to expect everything to ship the same day. We're almost there. But\nextra manufacturing care and quality control sometimes add a few days beyond\nyour usual Prime turnaround. More so when the package is heading overseas.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">We decided it's worth it anyway. And rather\nthan just accept the rough edges, we smoothed out the whole\ndecision-and-ordering process with a few new features and some tuning of the\nold ones.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003ch2>\u003cb>Whatâs new?\u003c/b>\u003c/h2>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Plan your experimental needs\u003c/b>\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">A \u003ca href=\"/planner\">planner\u003c/a> that works out roughly how many\nimplants you'll need to run your experiments. Handy if you're just getting\nstarted and want a feel for the scale. Itâs a first version â let us know if it\nis useful and where we can improve it!\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Implants and microdrives usually get\ntreated as a small line item next to silicon probes on an already tight budget.\nWe wanted to make the real budget picture obvious, including the payoff from\nreusing your probes instead of buying fresh ones every time. Drop in the price\nof your probes and the planner shows you what reuse actually saves you.\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Edit, update, and share your quotes easily\u003co:p>\u003c/o:p>\u003c/b>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">We streamlined quote creation so you're not\nstuck waiting on us to change a detail. \u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Every quote now comes with a re-order code.\nWant to modify something? Enter the code and update the parts you want. No\nemail thread required.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">There's a separate password to protect your\npersonal data. Enter it and the quote pre-fills the same billing and shipping\ndetails, which is useful for reordering yourself or sharing within your lab.\nLeave it out and only the products get recreated, nothing personal attached.\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">Got a quote in EUR but need USD? Rather pay\nby credit card than raise a purchase order? Enter the code, switch those, and\nyou get an updated quote back.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Faster processing of credit card orders\u003co:p>\u003c/o:p>\u003c/b>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">For credit card orders, quotes now include\na secure payment link right inside them. No more waiting on a separate email\nbefore you can pay.\u003co:p>\u003c/o:p>\u003c/span>\u003c/p>\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">\u003cb>Feedback desired!\u003c/b>\u003c/span>\u003c/p>\n\n\u003cp class=\"MsoNormal\">\u003cspan lang=\"en-NL\">As always, we appreciate your feedback! Found\nsomething that is odd or could be improved? \u003co:p>\u003c/o:p>\u003c/span>\u003ca href=\"/contact\">Let us know and weâll look into it asap!\u003c/a>\u003c/p>","md":""}}}]},{"id":"nw-press-bionieuws","title":"Press: 3Dneuro featured in Dutch newsletter Bionieuws","slug":"press-3dneuro-featured-in-dutch-newsletter-bionieuws","status":"published","author":"3Dneuro","date":"2022-06-15","tags":["press"],"excerpt":"Bionieuws, a professional newsletter for biologists, ran a feature on 3Dneuro in its May 2022 issue.","body":"","cover":"/assets/news/bionieuws.webp","blocks":[{"id":"nw-press-bionieuws-b1","type":"richtext","visible":true,"props":{"body":"Last month, [Bionieuws](https://bionieuws.nl/) â a professional newsletter for biologists â ran a feature on 3Dneuro (Letter 7, May 14 2022). Our co-founder Tim can be seen assembling a metal microdrive.\n\nIn the interview, we covered a few themes, summarized below:\n\n- How one of our main goals is to make experiments easier for scientists, specifically by identifying gaps overlooked by engineers when it comes to user experience.\n- How the R2Drive, our metal microdrive distributed in collaboration with the Buzsaki lab at NYU, allows researchers to re-use their sil
131icon probes up to 3 times.\n- Some advantages of the **rat head fixation setup** we are currently developing for distribution, in a collaboration with Nelson Totah at the University of Helsinki. Enabling behavioral tasks in virtual reality environment, with animals considered âsmarterâ than mice, while presenting a larger brain that can accommodate more recording equipment for larger data yields.\n- Our first collaborator and customer on the Radboud campus, [Mike X Cohen](https://www.mikexcohen.com/).\n- Our current effort to transition to an **open source model**.\n- The precision of stereolithography 3D printing.\n\nThe full article, in Dutch, is behind a paywall. Pdf version available on request.","body_mode":"markdown"}}]},{"id":"nw-r2drive-preorder","title":"R2Drive preorder update","slug":"r2drive-preorder-update","status":"published","author":"3Dneuro","date":"2021-05-21","tags":["product","open hardware"],"excerpt":"Our first aluminium R2Drive run â fifty units at 0.47 g each â sold out within 24 hours. Here's what's next.","body":"","cover":"/assets/news/r2drive-preorder.webp","blocks":[{"id":"nw-r2drive-preorder-b1","type":"richtext","visible":true,"props":{"body":"This week, we announced on Twitter a [preorder round](https://www.3dneuro.com/products/r2drive-preorder/) for our first production run of the R2Drive. This [microdrive](https://buzsakilab.github.io/3d_print_designs/) was designed by the Buzsáki lab at NYU, with a focus on sustainability: One can reuse the drive and the silicon probes, with the main limitation being electrode impedance.\n\nWe planned for a 50-drive batch, machined in aluminum. That manufacturing process was particularly beneficial in 2 ways compared to 3D-printing in stainless steel:\n\n(1) _Cost reduction_. We can deliver fully assembled drives for less than the cost of printing the parts yourself.\n\n(2) _Weight reduction_. The aluminum R2Drive weighs only 0.47 g, half as much as the stainless steel version. Lighter implant, happier animals!\n\nGiven that, it makes sense that **all 50 drives were claimed in under 24h**! We are now planning to ship the first 50 and arranging a 2nd production run so that everyone who preordered gets their drives no later than July-August. The first orders will ship in June.\n\nMore information on preordering: [www.3dneuro.com/products/r2drive-preorder/](https://www.3dneuro.com/products/r2drive-preorder/)\n\nMore information on R2Drive: [buzsakilab.github.io/3d_print_designs/](https://buzsakilab.github.io/3d_print_designs/)","body_mode":"markdown"}}]},{"id":"nw-zenodo","title":"Hosting open hardware files on Zenodo: durable and citable resource sharing","slug":"hosting-open-hardware-files-on-zenodo","status":"published","author":"3Dneuro","date":"2021-05-18","tags":["open hardware"],"excerpt":"Why we share our open-hardware design files on Zenodo: durable, DOI-citable, and grant-friendly.","body":"","cover":"/assets/news/zenodo.webp","blocks":[{"id":"nw-zenodo-b1","type":"richtext","visible":true,"props":{"body":"Recently, while preparing our next open hardware project, we researched how to best share the design files. Originally we decided to self-host, with the idea of later migrating to GitHub. Instead, we opted for [Zenodo](https://zenodo.org/), the file sharing platform maintained by CERN and funded by the EU.\n\nZenodo stands out for 2 features:\n\n- _Durability_ â Government-funded platforms tend to be more predictably durable than private companies. And unlike self-hosting, the work stops when the files are uploaded.\n- _Citable sharing_ â Zenodo attributes a DOI (Digital Object Identifier) to the shared files, and like scientific publications, accepts an author list. This makes open hardware projects more easily citable in publications, and grant proposals (there is some integration between Zenodo and the EU grant submission system). As a result, it becomes easier to properly credit contributors, and get credit for the work while seeking future funding.\n\nAnd like other platforms, Zenodo enables version control. Below some additional features, as de
131scribed on their website.\n\nWe have started publishing our projects there, with the first one being the [light-blocking headplate and sleeve for 2-photon microscopy](https://zenodo.org/record/4547658).","body_mode":"markdown"}}]},{"id":"nw-metal-microdrive","title":"New metal microdrive in collaboration with the Buzsáki lab","slug":"new-metal-microdrive-in-collaboration-with-the-buzsaki-lab","status":"published","author":"3Dneuro","date":"2021-04-23","tags":["product","open hardware"],"excerpt":"A recoverable, reusable, open-source metal microdrive for silicon probes â developed with the Buzsáki lab at NYU.","body":"","cover":"/assets/news/metal-microdrive.webp","blocks":[{"id":"nw-metal-microdrive-b1","type":"richtext","visible":true,"props":{"body":"We are very excited to announce our new collaboration with the [Buzsáki lab](https://buzsakilab.com/wp/) at NYU. Their new microdrive for silicon probes is:\n\n- recoverable\n- **reusable**\n- open-source\n- **extends your probeâs life span to 3+ implants** (impedance becomes the main issue)\n- and also works with [Neuropixels](https://www.neuropixels.org/)!\n\nThe metal microdrive and head cap were developed and systematically tested by Mihály Vöröslakos ([@voroslakos](https://twitter.com/voroslakos)), Peter Petersen ([@petersenpeter](https://twitter.com/petersenpeter)) and team. You can read all about it in the [preprint](https://www.biorxiv.org/content/10.1101/2020.12.20.423655v1).\n\nAnd because itâs open-source, you can make it yourself right now! ⦠OR⦠you can let us make it for you at a lower price. Bulk production is awesome!\n\nWe are currently working on tweaking the design for cheaper manufacturing and improved user experience.\n\nPre-orders starting soon! Watch this space! Or [contact us](https://www.3dneuro.com/#contact) now if you want to be first in line.","body_mode":"markdown"}}]},{"id":"nw-nature-diy","title":"Recent Nature feature on DIY science equipment","slug":"recent-nature-feature-on-diy-science-equipment","status":"published","author":"3Dneuro","date":"2020-11-30","tags":["press","open hardware"],"excerpt":"Nature on how DIY, open-source hardware democratizes science â plus a few things we'd add.","body":"","cover":"/assets/news/nature-diy.webp","blocks":[{"id":"nw-nature-diy-b1","type":"richtext","visible":true,"props":{"body":"On Nov 17 2020, the journal Nature published a feature about how â[How DIY technologies are democratizing science](https://www.nature.com/articles/d41586-020-03193-5)â by Sandeep Ravindran.\n\nKey takeaways:\n\n- âThe idea that scientists build their own equipment is as old as science,â (Tom Baden) a reminder that the trend itself is not new, scientists have been building their own equipment for centuries, often out of necessity, as it doesnât exist yet.\n- DIY is time consuming, labor-intensive, for less reliable results and no tech support. Some people like DIY for its own sake, others use it primarily for cost savings.\n- When you can get past the downside, DIY can transform a whole field by expanding the reach of a technology to countries that have more limited research budgets. The net effect is accelerating science by democratizing it, on a worldwide scale.\n- DIY science equipment also has an impact on education in lower income countries, by putting otherwise unaffordable equipment in the hands of students.\n- âHardware built from open-source designs generally costs just 1â10% of the price of commercial counterpartsâ (Joshua Pearce, author of [Open-source lab](https://www.appropedia.org/Open-source_Lab)).\n- DIY hardware is customizable in ways commercial products rarely are. And the lack of tech support is compensated by knowledge for repairing devices acquired when you build them yourself.\n- In some cases, DIY can apply to reagents as well, such as enzymes produced in [open-source bioreactors](https://openbioeconomy.org/projects/open-source-bioreactor/), that can be used in various applications, including diagnostics.\n- 3D printing is a core technology that empowers scientific instrumentation DIY, especially the more affordable desktop FDM printers.\n- DIY combines well with recycling older lab equipment.\n\nAltogether the article is
131well researched and covers a range of interesting projects and people. In fact, several of the covered projects have been featured on our [Resources](https://www.3dneuro.com/resources/#select_open) page since last summer. Below we dive a little bit deeper into 4 topics.\n\n**Untapped potential **â When considering the impact of DIY, it is mainly about the distribution of properly documented, easily replicable, open hardware (see [here](https://www.3dneuro.com/resources/#how_to) for pointers on how to do that). There is a lot more DIY hardware getting made in labs that never gets disseminated, because it is time-consuming. The visibility brought about by Nature articles like this one, a more organized community (e.g. [GOSH](http://openhardware.science/)) and publication venues (e.g. [Journal of Open Hardware](https://openhardware.metajnl.com/), [HardwareX](https://www.journals.elsevier.com/hardwarex)) all contribute to making DIY dissemination more impactful and rewarding.\n\n**Blind spot **â A surprising omission, in our view, is [Open Ephys](https://open-ephys.org/), one of the most impactful projects we know of. By developing an open-source data acquisition board for electrophysiology, they cut the commercial price of such devices by at least a factor 10 (they offer the option to get it fully assembled), and double that if you source the components and assemble them yourself. One potential explanation for the omission is that Open Ephys doesnât really take part in the larger open hardware community, and is therefore not known by open hardware advocates in other research fields.\n\nLet us know what you think below in the comment section.","body_mode":"markdown"}}]}],"publications":[{"id":"pub_F40-4c4","slug":"aberrant-prefrontal-activity-and-arousal-level-correlate-with-action-initiation-and-response-vigor-in-rats","status":"published","type":"article","bibtexKey":"","title":"Aberrant prefrontal activity and arousal level correlate with action initiation and response vigor in rats","authors":["Frederike J. Klein","Dmitrii Vasilev","Ryo Iwai","Masataka Watanabe","Nelson K. Totah"],"year":2026,"month":"06","journal":"iScience","volume":"29","issue":"6","pages":"115804","publisher":"","doi":" 10.1016/j.isci.2026.115804","url":"https://www.cell.com/iscience/fulltext/S2589-0042(26)01179-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS258900422601179X%3Fshowall%3Dtrue","abstract":"Highlights\nâ¢\t\nPFC single neuron stimulus-response (S-R) mapping activity relates to response vigor\nâ¢\t\nActivity is unrelated to temporal aspects of movement, but links the stimulus to the response\nâ¢\t\nS-R mapping activity ignites a behavioral Response which, however, can still be vetoed\nâ¢\t\nHeightened pre-stimulus arousal leads to higher Stimulus-guided Response vigor\nSummary\nOrchestrating learned stimulus-response (S-R) mappings is a central function of the prefrontal cortex (PFC), as evidenced by S-R selective neuronal activity during correct task performance. During errors, the âwrongâ neurons are aberrantly activated. While movement vigor has been related to basal ganglia neural activity, the neural correlates linking S-R mappings to vigor remain elusive. We trained male rats to perform a head-fixed Go/NoGo task on a treadmill, enabling the recording of PFC single-unit spiking and running speed. Aberrant activation of the âwrongâ S-R mapping predicts the initiation of incorrect responses, and the vigor of those responses scales with the strength of the aberrant stimulus-evoked activity. We show that such responses occur in the context of heightened arousal, addressing a classic but under-tested hypothesis linking arousal to vigor. These findings directly relate S-R mappings to response vigor and show how arousal is co-modulated with this relationship.","fulltext":"Highlights\nâ¢\t\nPFC single neuron stimulus-response (S-R) mapping activity relates to response vigor\nâ¢\t\nActivity is unrelated to temporal aspects of movement, but links the stimulus to the response\nâ¢\t\nS-R mapping activity ignites a behavioral Response which, however, can still be vetoed\nâ¢\t\nHeightened pre-stimulus arousal leads to higher Stimulus-guided Response vigor\nSummary\nOrchestrating learned stimulus-response
131(S-R) mappings is a central function of the prefrontal cortex (PFC), as evidenced by S-R selective neuronal activity during correct task performance. During errors, the âwrongâ neurons are aberrantly activated. While movement vigor has been related to basal ganglia neural activity, the neural correlates linking S-R mappings to vigor remain elusive. We trained male rats to perform a head-fixed Go/NoGo task on a treadmill, enabling the recording of PFC single-unit spiking and running speed. Aberrant activation of the âwrongâ S-R mapping predicts the initiation of incorrect responses, and the vigor of those responses scales with the strength of the aberrant stimulus-evoked activity. We show that such responses occur in the context of heightened arousal, addressing a classic but under-tested hypothesis linking arousal to vigor. These findings directly relate S-R mappings to response vigor and show how arousal is co-modulated with this relationship.\nGraphical abstract\nGraphical abstract undfig1\nSubject areas\nNatural sciences\nBiological sciences\nNeuroscience\nSystems neuroscience\nIntroduction\nThe brain encounters a myriad of stimuli in any given moment. While not all these stimuli are used to control behavior, some of them are associated with specific responses. Think, for example, of a traffic light turning red and the potentially catastrophic consequences of failing to select the correct response: stopping. Stimulus-response (S-R) mappings, such as stopping at a red light, are often learned associations. Miller & Cohen1 suggested that a primary function of the prefrontal cortex (PFC) is to represent learned S-R mappings and thus, select and initiate the appropriate stimulus-guided response. According to this theory, specific groups of PFC neurons should be activated after the presentation of a particular stimulus, S1, but only if the subject commits the correct stimulus-associated response (R1) and not when the subject makes the incorrect response (R2). Hence, these neurons represent the S1-R1 mapping.\nIn line with this hypothesis, when a stimulus-response (S-R) mapping is learned, subsets of PFC neurons in non-human primates become selectively responsive to specific stimuli.2,3,4,5,6,7,8 Moreover, the stimulus-evoked activation of those neurons that acquire stimulus selectivity occurs only when the subject commits the response associated with that stimulus. Similar neuronal correlates of S-R mappings have been observed in the PFC of humans,9,10 rodents,11,12,13,14 and the nidopallium caudolaterale of corvids.15,16,17\nClassically, the activity of S-R mapping selective PFC neurons has been described for trials in which the subject performed the correct response. Single neuron examples from error trials show that, in these trials, the âwrongâ PFC neurons were aberrantly activated.15,16,17,18,19 In other words, PFC neurons tuned to the S2-R2 mapping aberrantly responded after the presentation of the other stimulus, S1, and the subject subsequently committed an error (i.e., the response R2). We hypothesize that the strength of this aberrant activity and the âstrengthâ or vigor of R2 are correlated.\nWhile movement vigor has been directly related to the activity of striatum neurons,20 the neural correlates of the vigor of a learned S-R mapping have remained elusive. Classic S-R mapping PFC studies3,6,8,15,16,17,18,19,21 have made use of behavioral tasks in which the stimulus presentation and the subjectâs response are separated by a delay period. The mapping selective activity is observed during the delay period and is no longer apparent at the time of response initiation. This has made it impossible to relate the mapping selective activity to action initiation or the vigor of the subsequent response.\nHere, we investigate the relationship between the vigor or strength of a stimulus-guided response and the preceding mapping selective activity in the rat PFC. We find that the trial-specific strength of aberrant activity is directly correlated to incorrectly initiated running speed. We further demonstrate that a similar relationship can be observed for pre-stimulus pupil size and the running speed. These findings suggest that the extent to which a specific S-R mapping is activated in PFC can be directly related to the strength of the response that is initiated, and that response vigor in this context is modulated by pre-trial arousal state.\nResults\nWe recorded activity in the PFC of four male rats during a visually cued Go/NoGo task (Figures 1A and 1B). A total of 629 single units were recorded. 58 out of 629 units selectively responded to the Go stimulus in correctly performed trials. This means that they had a clear stimulus-evoked response in Hit trials, while they were not activated in correct rejection (CR) trials. Our analyses were focused on these 58 Go stimulus-preferring units, as they are responsive to one S-R mapping, Go stimulus (S1) â running (R1), and not responsive to the other mapping, NoGo stimulus (S2) â immobility (R2).\n\nFigure 1 Activity of a Go stimulus-preferring unit and running behavior on CR trials\nShow full c
131aptionFigure viewer\nActivity in CR trials is correlated with incorrect action initiation\nDespite Go-preferring units being primarily responsive in Hit trials relative to CR trials, the spike rasters made it apparent that individual Go stimulus-preferring units were also active in a subset of CR trials (see Figure 1C for one example unit). This aberrant activity appeared in trials in which the non-preferred (i.e., NoGo) stimulus was presented and in which the distance threshold for a false alarm was not crossed. We examined the response trajectory on trials with these aberrant activations. To do so, we averaged the post-stimulus velocity across these trials and compared to trials without activation (0 spikes during the 1 s window after stimulus onset). This analysis revealed that when the unit aberrantly activated, running was incorrectly initiated, but aborted before the rat crossed the distance threshold, thus avoiding a false alarm (Figure 1D). The evidence for a difference in velocity was extremely strong according to a Bayesian t test of the hypothesis that velocity was higher in trials when this example unit was active relative to inactive (BF10 = 8.12Ã104). In order to assess this at the population level, we compared the peak of the velocity trace averaged across the trials when the unit was activated to the peak velocity averaged across trials in which the unit was not activated (Figure 1D, inset). Activation was defined, for each trial, as the NoGo stimulus-evoked firing rate exceeding a Z score of 2 over three consecutive 100 msec bins relative to a 500 msec pre-stimulus baseline. Two out of 58 units were excluded from the analysis due to an exceedingly low number of trials being compared in the active and inactive groups. We compared peak running velocity for active versus inactive trials at the population level (N = 56 units) using a Bayesian paired-samples one-sided t test of the hypothesis that velocity is greater when the units were active. We found strong support for this hypothesis (BF10 = 1745.23). This finding indicates that the activation of the aberrant S-R mapping is associated with the initiation of an incorrect response.\nStrength of aberrant activity is correlated with response vigor\nIn the session in which this example unit was recorded, we observed that peak velocity varied across CR trials (Figure 1E). Since CR trials with aberrant activity were present across the population of 58 Go stimulus-preferring units, we assessed whether peak velocity varied at the level of the neuronal population. Similar to the example unit shown in Figures 1Câ1E, trials in which the individual units were aberrantly active were accompanied by the incorrect initiation of motion with a variance in peak velocity (Figure 1F) before the animals stopped for an eventual CR. Since both the stimulus-evoked spike rate and peak velocity varied, we investigated whether the strength of activity was correlated with running speed (i.e., response vigor) on a trial-by-trial basis.\nWe investigated the relationship between the variability in neuronal activity and peak velocity on CR trials by splitting the trials into low, medium, and high peak velocity groups. As velocity increased, the NoGo stimulus-evoked activity across the 58 Go stimulus-preferring units increased (Figure 2A). A Bayesian repeated-measures ANOVA (N = 58 units) supported the alternative hypothesis of an activity difference depending on velocity (BF10 = 7816.95). Additionally, a traditional repeated-measures ANOVA yielded a p-value of \u003c0.001 (F2,2868 = 13.61). Bayesian post-hoc t-tests supported an obvious difference between low velocity compared to medium and high velocity trials (BF10 = 9689.83 and BF10 = 212.78, respectively, with corresponding p-values of \u003c0.001 from traditional t-tests, respectively). The Bayesian analysis suggested that there is anecdotal evidence in favor of increased activity in high velocity trials compared to medium velocity trials (BF10 = 2.85) with a traditional t test yielding a p-value of 0.039. At the level of individual units (Figures 2Bâ2D), as response vigor increased, the activation gradually approached the level of activity on Hit trials. We quantified the similarity to Hit trials by calculating Pearsonâs correlation coefficient. Activity on low velocity CR trials was not significantly correlated with activity on Hit trials (r = 0.1199, p = 0.37). As response vigor increased, the correlation with Hit trial activity became significant and increasingly stronger (Hit vs. medium velocity CRs: r = 0.6451, p = 4.6087eâ08; Hit vs. high velocity CRs: r = 0.8648, p = 2.1622eâ18). Note that the increased activity with higher response vigor was most prominent for units with higher Hit trial spike count. The strength of the scaling may vary depending on factors such as electrode location in an anterior-posterior prefrontal gradient and/or cortical layer, both of which could vary across individual rats. In summary, the level of aberrant S-R mapping activity in PFC is directly related to the vigor with which the incorrect response is initiated in these trials.\n\nFigure 2 Relationship between peak velocity and stimulus-evoked spike counts\nShow full c
131aptionFigure viewer\nWhile most of the Go stimulus-preferring units in our study responded on CR trials, when incorrect running was initiated, some units exhibited little to no increase in spike count. These units tended to fire at a lower rate on Hit trials. Thus, they might have only a slight preference for the Go stimulus, and given their low activity level, scaling with response velocity could be difficult to observe. On the other hand, it is possible that these units do not represent the S-R mapping but rather developed a preference for the Go stimulus itself. In this case, we would not expect any scaling and instead expect the units to remain unresponsive to the NoGo stimulus. To test this, we computed a Hit/Omission index (see Methods). If a unit is selective for the Go stimulus itself rather than the S-R mapping, then it should also be active in Omission trials since the same stimulus is presented. If a unit is not encoding the S-R mapping, then the index will be close to 0 or negative. For S-R mapping selective units, we expect a higher index value since these units should not be active in Omission trials (when no response is initiated). We indeed observed a group of units with negative Hit/Omission index values, which were almost exclusively those with low Hit trial spike counts (Figure 2E). Therefore, we can assume that a subset of the Go stimulus-preferring units is purely selective for the Go stimulus itself rather than the mapping of this stimulus to running. This explains the lack of response scaling with running speed in CR trials for those units.\nFinally, it is possible that this neuronal activity does not link response vigor to an S-R mapping, but is rather neuronal activity simply related to the initiation of running itself. For instance, in both CR trials and Hit trials, an example neuron (Figure 1C) supposedly encoding an S-R mapping gradually increases its firing after stimulus onset until peak velocity is reached (marked by a green triangle in Figure 1C). It is possible that this unit is simply responding during take-offs (i.e., transitions from stationary to running) outside the S-R context. We tested whether this was the case for all putative S-R encoding single units by selecting times from the inter-trial interval (ITI) in which the rats were initially immobile but subsequently started to run (Figure 3A). We found a total of 753 such events. While the peak velocity reached was lower than in Hit trials and the duration of running was shorter during the ITI, the characteristic steep increase of velocity could be observed in both conditions (Figure 3A versus Figure 3B). We aligned the activity of the 47 Go stimulus-preferring units (the subset of units for which ITI take-offs could be identified) to the time of take-off for both Hit trial running and ITI running. The analysis window was shorter (â0.5 to +0.4 s around take-off) to ensure that the window did not include parts of the previous or next trial in the ITI case. If S-R mapping units are actually encoding the vigor of responses in general, then the activity should increase after take-offs during the ITI and in Hit trials. On the other hand, the S-R mapping units should only increase activity during Hit trials. We found that, in Hit trials, activity peaked during the increase of velocity, as expected (Figure 3D). However, during the ITI, firing rate changed only minimally (Figure 3C). A Bayesian 2-way repeated-measures ANOVA suggested that these data are strong evidence for the change in firing rate over time, differing between ITI and Hit take-offs (BF10 = 5.230Ã1047). Therefore, the neuronal activity is not related to running speed itself, but only related to running speed in the context of Go stimulus presentation. Activity in Hit trials and aberrant activity in CR trials both appear to link stimulus onset to the peak of the behavioral response. The Go stimulus-preferring units thus represent the S-R mapping and not running initiation, per se.\n\nFigure 3 Running during Hit trials, not the ITI, modulates firing rate\nShow full captionFigure viewer\nPre-stimulus arousal is related to peak velocity\nOne factor that has been shown to influence response speed or reaction time and thus response vigor is arousal. We used a dataset including pupillometry data from 37 rats performing the same Go/NoGo task. Pupil size is a proxy for the arousal state.22 We focused on pupil size in a 0.5 s window prior to stimulus presentation in CR trials (see Figure 4A for an example). Pupil size was then averaged across this time window. This revealed variable pupil sizes across the duration of a single recording session (Figure 4B). We tested whether pre-stimulus pupil size was correlated with the vigor of incorrectly initiated responses by splitting the trials into three groups based on pupil size (small, medium, and large pupils). Peak velocity for small pupil tri
131als was significantly slower than for the other two pupil size conditions (Figure 4C). Both Bayesian independent samples ANOVA supported an effect of pre-stimulus pupil size on response velocity (BF10 = 6.37Ã1085), which was due to slower response velocities when pre-stimulus pupil size was small (post-hoc Bayesian t test, BF10 = 1.27Ã1074 and BF10 = 4.98Ã1027 for small versus medium and large pupil sizes, respectively). A post-hoc Bayesian t test comparing velocity for medium and large pre-stimulus pupil sizes provided strong evidence in support of the null hypothesis that velocity did not differ (BF10 = 0.08), which was in line with a traditional t test (T = â1.70, p = 0.205). Given that velocity distributions are truncated at 0 (and are thus not normal distributions), we also conducted a classical Kruskal-Wallis Test followed by post-hoc Dunnâs tests on log-transformed data and the results agreed with the Bayesian statistics (H = 494.74, p \u003c 0.001; low versus medium: Z = â20.57, Bonferroni corrected p-value \u003c0.001; low versus high: Z = â12.64, Bonferroni corrected p-value \u003c0.001; medium versus high: Z = â1.70, Bonferroni corrected p-value = 0.266). This pupil-vigor relationship resembles the relationship between aberrant neuronal activity and peak velocity. Therefore, increased arousal may modulate vigor by making aberrant S-R mapping activations more likely.\n\nFigure 4 Pre-stimulus pupil size is related to peak velocity\nShow full captionFigure viewer\nAberrant PFC activity contributes to an ignition process\nOur results align with previous findings in support of the Global Neuronal Workspace (GNW) theory.23,24 According to this theory, conscious report of a stimulus requires that a state of âignitionâ is reached in the frontal cortex. This ignition state is a strong, transient increase in activity around 300 msec post stimulus. A recent study has shown that in the context of a detection task, ignition can be reached sometimes in stimulus-absent trials due to fluctuating activity in PFC.25 On these trials, the subject commits a false alarm. The PFC activity we observed also peaks around 300 msec post-stimulus and is followed by response initiation. The aberrant S-R mapping activity could thus be considered an example of the wrong âpoolâ of neurons reaching the state of ignition and driving an overt (but incorrect) behavioral response. If aberrant activation of Go stimulus-preferring neurons is a state of ignition that drives an incorrect response, then, as the level of aberrant activation to the NoGo stimulus approaches the level that those neurons typically exhibit on Hit trials, the rat should commit a false alarm. In line with this prediction, the stimulus-evoked activity during false alarm trials was significantly correlated with Hit trial activity (r = 0.7241, p = 1.9553eâ10). These results indicate that the aberrant activity of the Go stimulus-preferring units contributes to the ignition of the incorrect response in trials in which the NoGo stimulus is presented.\nGNW theory would predict that reaching ignition would mandate a false alarm. However, we observe aberrant activations that do not lead to false alarms, but instead subthreshold responses on CR trials. Given that we observed a linear relationship between aberrant activity level and the vigor of subthreshold responses, false alarm trials should be associated with the most extreme aberrant activation level and the highest response vigor. In contrast to this prediction, we found that the aberrant activity was similar in high velocity CR trials and false alarm trials (Figure 5A). A Bayesian paired t test indicated that these data are moderate-to-strong evidence for no difference in activity between CR trials and false alarm trials (BF10 = 0.162). Peak response vigor on these CR trials was roughly matched to the vigor on false alarm trials (Figure 5B), with the only difference being that the in-progress false alarm response was vetoed on the CR trials. This result suggests that reaching ignition mandates a high vigor behavioral response, but this response can, in some cases, be vetoed.\n\nFigure 5 False alarm and high-velocity CR trials are both associated with aberrant activity\nShow full c
131aptionFigure viewer\nDiscussion\nA primary function of the PFC is to represent learned S-R mappings, and thus, the PFC is thought to select and initiate the appropriate stimulus-guided response.1 While behavioral response vigor has been directly related to the activity of striatum neurons,20 the neural correlates of the vigor of a learned S-R mapping have remained elusive. In this study, we investigated how the activity of S-R mapping encoding units in the PFC of rats relates to response initiation and response vigor. We show that when one S-R mapping is activated, in the presence of the other stimulus, the incorrect response is initiated. The vigor of this incorrect response is correlated with the strength of PFC neuronal activation. While both the Hit trial activity and the CR trial aberrant activity link stimulus onset to peak velocity by ramping up, this activity is not related to the initiation of running itself. In the ITI, where no stimulus is present, Go stimulus-preferring units did not increase their activity as rats took off from a stationary to a running state. Our finding is akin to the âpressureâ of stimulus-evoked firing, âpushingâ the movement. Critically, however, we show that the neuronal spiking is not related to the temporal aspects of the movement, per se, but instead âlinksâ or âbridgesâ the S to the R.\nIf this neuronal activity is indeed a cognitive link to the R and contributes to response vigor, then it might be assumed that the activation strength of Go-stimulus preferring units is related to response vigor on Hit trials. In these trials, the correct S-R mapping is activated, and the rat responds by running (in the presence of the Go stimulus) until it crosses the distance threshold and obtains a reward. The trial-by-trial activation strength of this S-R mapping may correlate with the response vigor (velocity). This potential scenario cannot be tested in this behavioral task because Hit trial responses have a stereotyped velocity profile characterized by an increase in velocity followed by a lasting plateau of steady running speed that is similar across trials. There is no clearly discriminable peak in velocity, nor are there different plateau speeds. Thus, grouping trials into different levels of response vigor is impossible in this task (and the same applies to false alarm trials). Nevertheless, it remains possible that Go stimulus-preferring units contribute to response vigor in all trial types.\nOne might wonder if this framework extends to all task stimuli, such as the NoGo stimulus. The response vigor might also be related to the activation strength of NoGo stimulus-preferring neurons linked with the other R (i.e., sitting still). We could not test this possibility in this dataset because none of the units were NoGo stimulus-preferring. Single units in this subregion are known to be selective exclusively for reward-associated stimuli.14,26 In our paradigm, only the Go stimulus is associated with the possibility of reward. This likely explains why we did not observe units with a preference for the unrewarded NoGo stimulus.\nThe link between S-R mapping encoding and the vigor of the subsequent response is completely dependent on the learned context of this task. By analogy, humans have learned to stop in response to red traffic lights, but not all red lights. It is important to note that our findings do not suggest that PFC stimulus-evoked neuronal activity will necessitate or evoke running outside of the task context used here. In a confined context with specific response requirements, S-R mappings are an efficient representation of the task demands. Thus, S-R mappings are a building block for the variety of cases where PFC neurons become selective to task parameters, such as abstract rules,8,11,15,27 a stimulus (or stimulus feature) itself,7,21,28 motor preparation,18 or the value of a stimulus.14\nClassic S-R mapping PFC studies3,6,8,15,16,17,18,19,21 have made use of behavioral tasks in which the stimulus presentation and the subjectâs response are separated by a delay period. The mapping selective activity is observed during the delay period and is no longer apparent at the time of response initiation. This has made it impossible to relate the mapping selective activity to action initiation or the vigor of the subsequent response. Here, we were able to connect response vigor with the strength of preceding mapping selective activity by continuous measurement of running speed.\nWe furthermore show that pre-stimulus arousal is related to the vigor of these incorrectly initiated responses. Heightened arousal has been associated with committing errors and shown to modulate anterior cingulate cortex activity.29 Arousal and task performance have an inverted-U-shaped relationship, whereby hypoarousal and hyperarousal are associated with poorer task performance compared to intermediate arousal levels.30,31 Our data show a linear relationship between arousal and response vigor instead. This linear relationship might directly relate to the inverted-U relationship for performance: In states of low arousal, response vigor is weak to the extent that responses might not even be initiated, which leads to an increased number of omissions (i.e., poorer performance). With increasing arousal response, vigor increases, which leads to a higher number of Hits and initiated incorrect responses that can still be aborted (as demonstrated by the subthreshold response reported here in a subset of CR trials). A high number of Hits and CRs is considered optimal task performance. Once arousal exceeds this optimal level and responses continue increasing in vigor, it will be harder to stop already initiated incorrect responses prior to response threshold crossing. Therefore, the number of false alarms will increase, and performance will drop. The linear relationship between arousal and response vigor might partially explain the commonly observed inverted-U-shaped relationship between arousal and task performance.\nGiven that arousal-related brain regions, such as the locus coeruleus, project to the PFC in rats (and non-human primates),32,33 our results suggest the possibility that arousal and neuromodulation of the PFC could promote aberrant S-R mapping activity and thus the initiation or âignitionâ of incorrect responses (hence the association to error trials) and the vigor of those responses. It has long been postulated that arousal systems are linked to response vigor,34 but this link has not been thoroughly tested until recently.35 Our results further support this link and, moreover, suggest a possible neuronal mechanism for the arousal-vigor relationship. Increased neuromodulatory system activity may bias PFC neuronal activity toward increased S-R mapping encoding activity, which in turn would drive higher response vigor for the subsequent behavioral response.\nOur results expand GNW theory in two new directions. First, we show that the intensity of ignition could be directly linked to the vigor of the subsequent response. Second, the fact that the response in our paradigm was aborted before the animals committed an error suggests that behaviors triggered by a state of ignition can still be stopped. This behavior most likely requires a second conscious decision to follow the first one very quickly. GNW theory predicts that this second conscious decision requires a second ignition state. In our task context, this would be akin to the internally driven activation of the âcorrect poolâ of S-R mapping selective neurons. The failure to reach this
131second ignition would allow the subthreshold response to continue past the threshold and thus produce a false alarm. In line with this interpretation, we observe similar initial velocity profiles for false alarm trials and the subset of CR trials with the highest velocity subthreshold responses. The first ignition is sufficient to trigger the initiation of a response. If that response is aborted prior to threshold crossing or fully executed as a false alarm may be determined by whether or not the second S-R mapping representation reaches ignition. The two expansions of the GNW theory proposed here indicate the need to investigate the interplay of different conscious decisions and, hence, the interplay of different states of ignition, as well as the relationship between ignition states and response vigor.\nLimitations of the study\nOur study supports a relationship between S-R mapping, associated neuronal activity, and response vigor. However, this was only demonstrated for Go stimulus-preferring neurons. This is likely due to the asymmetric rewarding of only hit responses in this task, given that neurons at the recorded location preferentially respond to rewarded stimuli. Therefore, while we speculate that the vetoing of the sub-threshold Go response may be preceded by a late activation of NoGo stimulus-preferring neurons, this remains to be demonstrated in rats performing a version of the task with symmetrical reward (i.e., rewarded CR trials). While our results suggest that the relationship between S-R mapping, associated neuronal activity, and response vigor may occur in the context of increasing arousal, the PFC neuronal activity recordings and pupillometry were not recorded simultaneously.\nResource availability\nLead contact\nRequests for further information and resources should be directed to and will be fulfilled by the lead contact, Nelson K. Totah ([email protected]).\nMaterials availability\nThis study did not generate new unique reagents.\nData and code availability\nâ¢\t\nBehavior, treadmill velocity, pupillometry, single unit spiking data, and code used to perform the analyses and visualize the results have been deposited at https://etsin.fairdata.fi/ and are publicly available as of the date of publication. Etsin Data: https://doi.org/10.23729/fd-fde5397b-d01f-368a-9be1-a750d5f4d589.\nâ¢\t\nAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.\nAcknowledgments\nThis work was funded by the University of Helsinki (Helsinki Institute of Life Science), the Sigrid Jusélius Foundation, the Research Council of Finland (Project Grant, decision 358106), and the Max Planck Society.\nAuthor contributions\nConceptualization â F.J.K. and N.K.T.; data curation â D.V.; formal analysis â D.V. and F.J.K.; funding acquisition â N.K.T.; investigation â D.V. and R.I.; methodology â N.K.T. and M.W.; software â D.V.; supervision â N.K.T.; visualization â F.J.K. and D.V.; writing â original draft â F.J.K. and D.V.; writing â review and editing â F.J.K., D.V., M.W., and N.K.T.\nDeclaration of interests\nThe authors declare no competing financial interests.\nDeclaration of generative AI and AI-assisted technologies in the writing process\nDuring the preparation of this work, the authors did not use an AI tools.\nSTARâ
Methods\nKey resources table\nREAGENT or RESOURCE\tSOURCE\tIDENTIFIER\nDeposited data\nData\thttps://etsin.fairdata.fi\tEtsin Data: https://doi.org/10.23729/fd-fde5397b-d01f-368a-9be1-a750d5f4d589\nExperimental models: Organisms/strains\nR
131at (male, Lister-hooded, out-bred)\tCharles River (Germany)\t \nSoftware and algorithms\nCode\thttps://etsin.fairdata.fi/\tEtsin Code: https://doi.org/10.23729/fd-fde5397b-d01f-368a-9be1-a750d5f4d589\nJASP (Bayesian statistical analysis)\thttps://jasp-stats.org/\tVersion 0.19 (Intel)\nOther\nMulti-electrode probes\tNeuronexus A2x32 or Cambridge Neurotech H9x64\t \nOpen table in a new tab\nExperimental model and study participant details\nMale Lister-Hooded rats (140â190 g body weight) were used for single unit recordings (N = 4) and pupillometry (N = 37). The rats were supplied by Charles River Laboratories (Germany) and were housed in pairs for 7 days prior to implantation of the head-fixation implant. After implantation, rats were single housed on a reversed light-dark (07:00 lights off, 19:00 lights on) cycle. Training and experiments were performed during the ratsâ active phase. All procedures were carried out after approval by local authorities and in compliance with the German Law for the Protection of Animals in experimental research (Tierschutzversuchstierverordnung) and the European Community Guidelines for the Care and Use of Laboratory Animals (EU Directive 2010/63/EU).\nMethod details\nSurgery\nThe animal was anesthetized with isoflurane (â¼1.0â2.0%). Heart rate was monitored throughout surgery. Buprenorphine (0.06 m/kg, s.c.), meloxicam (2.0 mg/kg, s.c.), and enrofloxacin (10.0 mg/kg, s.c.) were administered. An incision was made once the rat was no longer responsive to paw pinch. Skin and connective tissue were removed to expose the skull from the frontal bone to the neck muscle and from left to right temporal muscles. The wound margin was cauterized. The exposed bone was wiped dry and cleaned with 5% hydrogen peroxide. The bone surface was then scratched with a bone curette in a grid pattern to facilitate adhesion of the adhesive for the UV light polymerizing cement used to affix the implant to the skull. Two component UV-curing adhesive (OptiBond, Kerr) was applied to the skull and UV cured for 30 s at full intensity (Superlite 1300, M + W Dental). A custom-made head fixation implant was attached to the skull using UV-curing cement (Tetric EvoFlow, Ivoclar). The dental cement was bonded to the adhesive by UV curing for 60 s at full intensity. In rats that were not implanted with a multi-electrode silcon probe, the chamber was filled with 2-component dental cement (Paladur, Kulzer). In cases where multi-electrode probes were to be implanted, the skull was covered in biocompatible silicone elastomer (KwikCast, WPI) and the implant was closed using a lid and screws. In all rats, the skin was glued to the sides of the implant using tissue glue (Histoacryl, B. Braun).\nRats that were implanted with a multi-electrode silicon probe were first trained in the behavioral task. The rats then underwent a second surgery, in which the lid of the implant and the silicone elastomer was removed. The probe (one rat, A2x32, Neuronexus; two rats, H9x64, Cambridge Neurotech) was implanted into the PFC (target coordinates from bregma: AP: 2.7 mm; ML: 0.8 mm; depth: 3.2â4.4 mm, varying across rats) through a craniotomy. No histological verification of implantation sites was performed and it is thus possible, that recording sites varied slightly across animals. Additionally, a second craniotomy was made over the cerebellum. A reference electrode (99.9% pure silver wire) was inserted through this posterior craniotomy. The craniotomy was filled with viscous, electrically-conductive agar. The open space on the skull was filled with 2-component dental cement (Paladur, Kulzer).\nRats recovered for 5 days after surgery. Buprenorphine (0.06 m/kg, s.c.) was administered every 12 h for 3 days in some rats and other rats were injected with meloxicam (2.0 mg/kg, s.c.) every 24 h for 3 days. Rehydrating and easily consumable food was provided (DietGel Recovery, ClearH2O).\nHandling and water restriction\nFor five days prior to surgery, the rats were handled twice a day, once in the morning and in the evening. Each session lasted at least 5 min. After five days of post-surgical recovery, access to water was restricted. During training and experiments, the rats were given 8-12 mL total water per day. Most of the water was consumed as reward during the behavioral task. The remainder of the total water volume was supplied to the rats in the cage after training. The total volume of water available daily was restricted to this level for between 5 and 14 days, while rats learned and performed stimulus discrimination experiments. After an epoch of restricted water availability, rats were provided ad libitum access to water for 24 h.\nHead-fixation and behavioral apparatus\nThe rat was head-fixed on a cylindrical, non-motorized fibreglass treadmill that rotated forward or backward freely on low-friction ball bearings. The treadmill and head-fixation a
131pparatus were inside a large Faraday cage (approximately 2 m à 2 m x 2 m) with sound proofing material. A TTL pulse-controlled pump was used to deliver 10% sucrose water via a reward port that was placed at the mouth of the rat. A computer screen (behind glass with electromagnetic shielding designed to not cause a Moire effect) in front of the rat was used to display visual stimuli covering the entire visual field of the rat. Treadmill angular position was recorded via an analog signal output from a rotary encoder (MA3-A10-125-B, US Digital) attached to the rotational axis of the cylindrical treadmill. The signal output varied between 0 V and +5 V, which mapped linearly to the rotational angle of the treadmill. The signal was sampled at 32 kHz, digitized (Neuralynx signal acquisition system), and velocity was calculated offline (in MATLAB). Video for pupillometry was recorded from the right eye at 45 fps under near-infrared illumination (M850L3, Thor Labs LED, with COP4-B, Thor Labs collimation optics). Frames were recorded with a near-infrared camera (G-046B, Allied Vision) and a variable zoom lens, fixed 3.3x zoom lens, and 0.25x zoom lens attached in-line (1â60135, 1â62831, 6044, Polytec). The camera provided a TTL pulse with each video frame, which was recorded by the Neuralynx signal acquisition system at 32 kHz.\nHabituation to head-fixation and behavioral task training\nHabituation to head-fixation consisted of a single 20 min session. After habituation, rats were trained to commit an instrumental response for reward. Approximately 5 μL of reward solution (10% sucrose in water) was delivered for small âshakingâ or body movements on the treadmill. The threshold for triggered reward was gradually increased to train the rat to make larger body movements and eventually steps. Threshold crossings were marked with a bridging stimulus (0.1 s duration, 500 Hz auditory tone) to aid in learning the link between movement and reward. Eventually, rats would continuously walk and receive reward. This stage required from 3 to 10 sessions (one per day). Once an animal was running and licking simultaneously (which yielded approximately 7 mL of reward solution in a session lasting 20â30 min), we trained the rat to make instrumental (Go) responses contingent upon the presentation of a visual stimulus.\nInitially, we presented a 15 s duration visual stimulus. The stimulus was a full field, black and white drifting grating (2.4 cycles/sec, 0.005 cycles/pixel spatial frequency, 75 deg orientation). Rats were trained to respond to the stimulus by continuously delivering reward for running during stimulus presentation. Reward delivery was triggered by crossing a threshold (a.u.) that was the same for all rats and all sessions and set at a level that was associated with bilateral locomotion. The stimulus was followed by an inter-trial interval (ITI). The ITI duration was drawn randomly from a distribution ranging from 2 to 3 s (0.05 s bins size).\nAfter 2 sessions, the rats were trained to not respond prior to stimulus onset. The ITI was reduced to 1 to 2 s, and any running that crossed a velocity threshold (manually set to capture running, same for all rats and sessions) resulted in a 0.5 s time-out from the task and a resetting of the ITI. After one or two sessions, the rats started to suppress running during the ITI. Once this was achieved, we reduced stimulus duration in small steps (10 s, 5 s, 2.5 s) over a few sessions. When stimulus duration is 2.5 s, rats exhibit a vigorous and low-latency response upon stimulus onset. At this point in training, we reduce the ITI to 0.5 to 1 s and, after a few sessions, we reduce stimulus duration to 1.5 s (i.e., a speeded reaction time task). Rats were given 600 trials per session. This typically yielded approximately 6 mL of sucrose solution during the task. Behavior was considered stable when omission rate was below 10%.\nThe Go/NoGo paradigm was introduced with the addition of a NoGo stimulus. The NoGo stimulus was at least 70° different from the Go stimulus. Go and NoGo stimulus trials were delivered in pseudo-random order and in equal proportion. At most, two trials of the same stimulus type could occur consecutively. A Go response required crossing a distance threshold, which roughly corresponded to taking one step. A response offset window (0â0.75 s after stimulus onset) was introduced to compensate for the pre-potent drive to respond. During this period, running did n
131ot count toward the distance threshold. This allowed low latency movements but forced the rat to appraise the stimulus and make a decision. After the offset window, crossing the distance threshold caused the stimulus to disappear. Hits were rewarded (three 7 μL pulses). Responses to the NoGo stimulus led to an auditory error signal (0.5 s duration, brown noise, 60 dB) and a time-out of 6 s prior to the next ITI. Training was complete when performance was above 85% and omission rate was less than 10%.\nNeurophysiological recordings and spike sorting\nWideband (0.1 Hzâ10 kHz) signals were recorded at 32 kHz (Digital Lynx SX, Neuralynx). Automatic spike sorting was performed using KiloSort 4.0.36 Afterward, the outputs were manually curated using standard criteria (i.e., stable firing rate, waveform similarity, auto- and cross-correlograms).\nQuantification and statistical analysis\nSingle unit spike count analysis\nA Go stimulus-preferring unit responded to the Go stimulus and not to the NoGo stimulus on âpureâ Correct Rejection trials, which had little-to-no running. Spike count peri-stimulus time histograms (0.1 s bin size, â0.5 sâ1.5 s window around stimulus onset) were z-scored to the trial-averaged spike counts in the 0.5 s before stimulus onset for each unit. The unit was considered responsive to the stimulus, if the Z score was greater than 2 for three consecutive bins in the post-stimulus window (0â0.3 s). We quantified the peak activity of units as the maximal z-scored spike count in the peri-stimulus time histogram. The Hit/Omission index was calculated as:\nðâ¢ðâ¢ðâ¢ðð»â¢ðâ¢ð¡âðâ¢ðâ¢ðâ¢ððâ¢ðâ¢ðâ¢ð â¢ð â¢ðâ¢ðâ¢ð\nðâ¢ðâ¢ðâ¢ðð»â¢ðâ¢ð¡+ðâ¢ðâ¢ðâ¢ððâ¢ðâ¢ðâ¢ð â¢ð â¢ðâ¢ðâ¢ð\n \nITI analysis\nTransitions from stationary to running during the inter-trial interval were detected by identifying velocity peaks larger than 1 a.u. and with no other peaks present in the 0.4 s directly prior to the peak. A total of 1,297 such take-off events were identified. These events came from sessions in which 47 of the 58 Go stimulus-preferring units were recorded. For the sessions in which the remaining 11 units were recorded we could not find take-off events during the ITI. Take-off time was assigned to the moment when the velocity crossed a threshold of 0.25. The same threshold was applied for alignment of Hit trial velocity and neuronal activity to take-off.\nPupil analysis\nPupillometry was implemented using a custom computer vision algorithm built with the openCV package in Python 3.7. A detailed description of the method is in our prior work.37\nStatistics\nThis study used 40 male Lister-Hooded rats (3 for single unit recordings and 37 for pupillometry). Bayesian statistics (JASP software) were used to assess evidence in favor of the null hypothesis and in favor of the alternative hypothesis.38 We report BF10 which reports the evidence in favor of the alternative hypothesis over evidence favoring the null hypothesis. All analyses used a one-way ANOVA. The single unit activity data in Figure 3A were analyzed with a repeated-measures ANOVA because the same single units were compared across 3 conditions. The trialwise velocity data in Figure 4C were analyzed with an independent samples ANOVA because there were different trial numbers within the 3 conditions (small, medium, and large pupil size) that were being compared. This is due to pupil size freely varying across sessions and rats. We also report traditional ANOVA results with p-values alongside Bayesian statistics and Bonferroni-corrected p-values for post-hoc t-tests. Due to a lack of normality, the trialwise velocity data in Figure 4C were additionally analyzed with a Kruskal-Wallis test and post-hoc Dunn test after log-transformation of the data.","fulltextMode":"markdown","pdfExternalUrl":"https://www.cell.com/action/showPdf?pii=S2589-0042%2826%2901179-X","pdfPublic":true,"meta":{"species":"rat","sex":"male"},"featured":false,"sortOrder":21,"createdAt":"2026-06-25T12:05:27.552Z","products":["remy-system","remy-implants","remy-surgery-holder","remy-ch
131amber","remy-poles","remy-chambers","remy-holder"]},{"id":"pub_Fd8DEtM","slug":"online-decoding-of-rat-self-paced-locomotion-speed-from-eeg-using-recurrent-neural-networks","status":"published","type":"article","bibtexKey":"","title":"Online decoding of rat self-paced locomotion speed from EEG using recurrent neural networks","authors":["Alejandro de Miguel Gomez","Nelson Totah","Uri Maoz"],"year":2026,"month":"06","journal":"Journal of Neural Engineering","volume":"","issue":"","pages":"","publisher":"","doi":"10.1088/1741-2552/ae80fd","url":"https://iopscience.iop.org/article/10.1088/1741-2552/ae80fd","abstract":"Objective. Accurate and reliable neural decoding of locomotion holds promise for advancing clinical applications such as rehabilitation and prosthetic control, as well as for understanding neural correlates of action. Recent studies have demonstrated successful decoding of locomotion kinematics across species in motorized treadmill settings. However, efforts to decode locomotion speed directly and continuously in more natural contextsâwhere pace is self-selected rather than externally imposedâare scarce, and those that exist generally achieve only modest accuracy and require intracranial implants. Here, we aim to decode self-paced locomotion speed non-invasively and continuously using cortex-wide EEG recordings from rats. Approach. We introduce an asynchronous brain-computer interface (BCI) that processes a continuous stream of 32-electrode skull-surface EEG recordings (0.01-45 Hz) to decode instantaneous speed readouts from a non-motorized treadmill during self-paced locomotion in head-fixed rats. Using recurrent neural networks and a large dataset comprising over 133 h of recordings, we trained decoders to map ongoing EEG activity to treadmill speed. Main results. Our decoding methodology achieves a correlation of 0.88 (R² = 0.78) for speed, primarily driven by visual cortex electrodes and low-frequency (\u003c 8 Hz) oscillations. Moreover, pre-training on a single recording session permitted decoding on other sessions from the same rat, suggesting the presence of uniform neural signatures of locomotion that generalize across sessions but fail to transfer across animals. Finally, we found that cortical states not only carry precise information about the current speed, but also about future and past dynamics, extending up to 1000 ms. Significance. These findings demonstrate that self-paced locomotion speed can be decoded accurately and continuously from non-invasive, cortex-wide EEG. Our approach may provide a useful framework for developing high-performing, non-invasive BCI systems for locomotion and contribute to understanding distributed neural representations of action dynamics.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://iopscience.iop.org/article/10.1088/1741-2552/ae80fd/pdf","pdfPublic":true,"meta":{"species":"rat"},"featured":false,"sortOrder":22,"createdAt":"2026-06-25T12:09:19.700Z","products":["remy-system","remy-implants","remy-surgery-holder","remy-chamber","remy-poles","remy-chambers","remy-holder"]},{"id":"pub_puDNedU","slug":"exposure-to-broadband-noise-during-non-rem-sleep-impairs-hippocampal-sharp-wave-ripples-and-memory-consolidation","status":"published","type":"article","bibtexKey":"","title":"Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples and memory consolidation","authors":["Karla Salgado-Puga","Utku Kaya","Gideon Rothschild"],"year":2026,"month":"","journal":"Current Biology","volume":"","issue":"","pages":"","publisher":"Cell Press","doi":"10.1016/j.cub.2026.06.039","url":"https://www.cell.com/current-biology/abstract/S0960-9822(26)00750-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0960982226007505%3Fshowall%3Dtrue","abstract":"Sleep is critical for consolidating recent experiences into memories. A key mechanism underlying this process is hippocampal sharp-wave ripples (SWRs). Although SWR-dependent memory consolidation is often viewed as an âofflineâ process, sounds heard during sleep are processed by a highly active auditory system that projects to medial temporal lobe structures, providing a pathway by which sounds may modulate hippocampal activity. While processing salient sounds during sleep can be adaptive, whether ongoing processing of environmental sounds interferes with SWR-dependent memory consolidation remains unknown. To address this question, we recorded neural activity in the CA1 region of the hippocampus in sleeping rats and used a closed-loop system to deliver non-waking broadband noise
131(BBN) stimuli during or outside of SWRs. We found that BBN suppressed ripple power and reduced SWR rates. BBN delivered during SWRs (On-SWR) produced a greater reduction in ripple power than BBN delivered outside SWRs (Off-SWR). This suppression was accompanied by substantial changes in SWR-associated spiking. We next tested the influence of BBN on memory consolidation using a conditioned place preference paradigm. On-SWR BBN presentation during post-learning sleep abolished memory retention 24 h after learning while sparing memory immediately after sleep. By contrast, Off-SWR BBN weakened memory at both time points. Notably, On-SWR BBN induced a significantly larger impairment in memory 24 h after learning as compared with Off-SWR BBN. These findings suggest that exposure to noise during sleep disrupts hippocampal activity and impairs memory consolidation in a manner that depends on the timing of sounds relative to SWRs.\n","fulltext":"Article\nExposure to broadband noise during non-REM sleep\nimpairs hippocampal sharp-wave ripples and\nmemory consolidation\nHighlights\n⢠Broadband noise during sleep suppresses hippocampal\nsharp-wave ripples\n⢠Noise exposure alters SWR-associated firing of CA1 neurons\n⢠Noise during sleep weakens hippocampal-dependent\nmemory\n⢠Neural and memory effects depend on sound-SWR t
131iming\nAuthors\nKarla Salgado-Puga, Utku Kaya,\nGideon Rothschild\nCorrespondence\[email protected]\nIn brief\nSalgado-Puga et al. show that exposure\nto broadband noise during sleep disrupts\nhippocampal sharp-wave ripples, alters\nassociated neuronal activity, and impairs\nmemory consolidation. The magnitude of\nneural and behavioral disruption depends\non sound timing relative to ripples.\nSalgado-Puga et al., 2026, Current Biology 36, 1â14\nAugust 3, 2026 © 2026 Elsevier Inc. All rights are reserved, including those for\ntext and data mining, AI training, and similar technologies.\nhttps://doi.org/10.1016/j.cub.2026.06.039\nll\nArticle\nExposure to broadband noise during non-REM\nsleep impairs hippocampal sharp-wave ripples\nand memory consolidation\nKarla Salgado-Puga,1 Utku Kaya,1 and Gideon Rothschild1,2,\n* 1Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA 2Lead contact\n*Correspondence: [email protected]\nhttps://doi.org/10.1016/j.cub.2026.06.039\nSUMMARY\nSleep is critical for consolidating recent experiences into memories. A key mechanism underlying this process is hippocampal sharp-wave ripples (SWRs). Although SWR-dependent memory consolidation is often\nviewed as an ââofflineââ process, sounds heard during sleep are processed by a highly active auditory system\nthat projects to medial temporal lobe structures, providing a pathway by which sounds may modulate hippocampal activity. While processing salient sounds during sleep can be adaptive, whether ongoing processing\nof environmental sounds interferes with SWR-dependent memory consolidation remains unknown. To\naddress this question, we recorded neural activity in the CA1 region of the hippocampus in sleeping rats\nand used a closed-loop system to deliver non-waking broadband noise (BBN) stimuli during or outside of\nSWRs. We found that BBN suppressed ripple power and reduced SWR rates. BBN delivered during SWRs\n(On-SWR) produced a greater reduction in ripple power than BBN delivered outside SWRs (Off-SWR). This\nsuppression was accompanied by substantial changes in SWR-associated spiking. We next tested the influence of BBN on memory consolidation using a conditioned place preference paradigm. On-SWR BBN presentation during post-learning sleep abolished memory retention 24 h after learning while sparing memory\nimmediately after sleep. By contrast, Off-SWR BBN weakened memory at both time points. Notably, OnSWR BBN induced a significantly larger impairment in memory 24 h after learning as compared with OffSWR BBN. These findings suggest that exposure to noise during sleep disrupts hippocampal activity and impairs memory consolidation in a manner that depends on the timing of sounds relative to SWRs.\nINTRODUCTION\nSleep is critical for memory consolidationâthe stabilization of\nrecent labile memory traces into long-term memories. The\nconsolidation of episodic, spatial, and contextual information\ninto long-term memories is strongly dependent on the hippocampus.1â4 A key neurophysiological mechanism implicated in\nhippocampal-dependent memory consolidation is sharp-wave\nripples (SWRs). SWRs are brief bursts of high-frequency oscillations that originate in the hippocampus and are prominent during\nawake quiescence and non-rapid eye movement (NREM)\nsleep.5,6 SWRs reflect periods of hippocampal-cortical communication,1,5,7â18 during which reactivation of recent awake experiences occurs.19â22 Moreover, blocking SWRs using electrical\nstimulation impairs learning and memory.11,12,23 Together,\nSWRs are thought to play a critical role in the formation of\nlong-term memories.24,25\nAlthough the sleeping brain is generally considered to be in an\nââofflineââ state, dedicated to processing internally generated activity patterns such as SWRs, the brain is not fully disconnected\nfrom the environment in this state. In particular, sounds heard\nduring sleep are processed by a fully functional and highly active\nauditory system.26â31 This is particularly relevant given that more\nthan 20% of people living in major urban environments are regularly exposed at night to noise from household appliances,\ntraffic, and other environmental sources.32â37 Yet whether\n
131ongoing processing of sounds during sleep interferes with\nsleep-dependent cognitive processes and may therefore come\nat a cost for the process of memory consolidation remains\nlargely unexplored.\nDirect projections from the auditory system to major hippocampal input regions, namely the perirhinal cortex and the lateral\nentorhinal cortex,38â43 form an anatomical pathway by which\nincoming sounds during sleep can modify hippocampal activity.\nIndeed, functional studies have demonstrated that sounds can\ninfluence hippocampal activity.44,45 For example, during wakefulness, hippocampal place cells can encode unpredictable or\nbehaviorally relevant sounds, as well as continuous acoustic features.46â50 During sleep, presenting sounds previously associated with a spatial location can bias hippocampal replay toward\nthat location.51 In humans, presenting sounds during sleep that\nhad previously been associated with other stimuli can strengthen\nmemory retention of those stimuli upon awakening.52â57 However, whether ongoing processing of sounds that are not associated with prior learning interferes with hippocampal SWRdependent memory consolidation remains unknown.\nCurrent Biology 36, 1â14, August 3, 2026 © 2026 Elsevier Inc. 1\nAll rights are reserved, including those for text and data mining, AI training, and similar technologies.\nll\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nOn-SWR stimulation\nSound\nSound\nOff-SWR stimulation\n LFP\nEEG\nEMG\n CA1\n0.8\n-0.1 0 0.1\n100\n200\n300\n400\nFrequency (Hz)\n-0.1 0 0.1\nPower (μV2\n)\nTime (s)\n1\n2.8\n1.3\n2.0\n Ripple power\n-0.1 0 0.1\nTime (s)\n0.6\nNS\nOn-SWR\n0\n0.2\n0.4\nNormalized rate (SWRs/s) 0.6\n1.0\nNS On-SWR\n0 60 120\n0.8\n0\n0.5\n1.0\nCDF\n0 2 4 6 8 10\n1.6\n2.6\n-0.1 0 0.1\n100\n200\n300\n400\nFrequency (Hz)\n-0.1 0 0.1\nPower (μV2\n)\nTime (s)\n1\n3.5\nRipple power\n-0.1 0 0.1\nTime (s)\n0.6\nNS\nOff-SWR\nNS\nOff-SWR\nNormalized ripple power\n0\n0.5\n1.0\nCDF\n0\n0.2\n0.4\nNormalized rate (SWRs/s) 0.6\n1.0\nNS Off-SWR\n0 60 120\n0\n0.5\n1.0\nCDF\n0 2 4 6 8 10\nNS\nOn-SWR\nNormalized ripple power\n0\n0.5\n1.0\nCDF\n*** **\n**\n2.4\n3.4\nRipple power\n-0.1 0 0.1\nTime (s)\n1.4\nNS\nNS 2h\n-0.1 0 0.1\n100\n200\n300\n400\nFrequency (Hz)\n-0.1 0 0.1\nPower (μV2\n)\nTime (s)\n1\n4.5\n0 60 120\n***\n2.0\n0\n0.5\n1.0\nCDF\n0 2 4 6 8 10 0\n0.5\n1.0\nCDF\n0\n0.5\n1.0\nNormalized rate (SWRs/s) 1.5\n2.5\nNS On-SWR\n***\n*\n*** n.s.\nOff-SWR\n***\nn.s.\nOff-SWR\nOn-SWR\nNS 2h\n2.0\n0\n0.5\n1.0\nNormalized rate (SWRs/s) 1.5\n2.5\nNS NS\nn.s.\n0\n0.5\n1.0\nCDF\n0 10\nNS\nNS 2h\nNormalized ripple power\n0\n0.5\n1.0\nCDF\n*\n2 4 6 8\nB\nC\nA\nH\nL\nI\nE\nJ K\nM N O\nD\nP Q\nF G\nFigure 1. Exposure to non-waking BBN stimuli during sleep reduces ripple power and rate in a timing-dependent manner\n(AâC) Illustration of the closed-loop system for SWR-triggered BBN presentation. Scale bars represent 500 ms and 0.1 mV.\n(A) Illustration of hippocampal LFP recordings combined with EEG and EMG recordings in naturally sleeping rats.\n(B) BBN stimuli paired to SWR onset (On-SWR).\n(C) BBN stimuli delayed 2 s after SWR detection (Off-SWR).\n(legend continued on next page)\nll\n2 Current Biology 36, 1â14, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nTo address this gap, we recorded local field potentials (LFPs)\nand single-unit spiking from the dorsal CA1 region of the hippocampus in rats during sleep and used a closed-loop system to\ndeliver brief non-waking broadband noise (BBN) stimuli during\nor outside of SWRs. Using this approach, we investigated how\nBBN exposure during sleep influences hippocampal activity\nand memory consolidation and whether these effects depend\non the timing of stimulation relative to SWRs.\nRESULTS\nBBN presentation during sleep suppresses SWRs in a\ntiming-dependent manner\nTo determine the effects of noise exposure during sleep on hippocampal SWRs, we presented 50-ms, 50-dB BBN stimuli during or outside of SWRs in naturally sleeping rats. SWRs were recorded from the dorsal CA1 region of the hippocampus during a\n2 h sleep
131session following spontaneous behavior, and a closedloop system was used to detect SWRs in real time during NREM\nsleep, allowing the presentation of BBN stimuli during or outside\nof SWRs (Figure 1A). During the first hour of sleep, SWRs were\ndetected, but no BBN stimulus was given (no stimulation, NS).\nDuring the second hour, a BBN stimulus was triggered by every\ndetected SWR in sessions with either of two protocols: immediately following SWR detection (On-SWR; Figure 1B) or 2 s after\ndetection (Off-SWR; Figure 1C).\nWe used a BBN intensity of 50 dB, which is well above the ratsâ\nhearing threshold58 but which pilot experiments identified as not\ninducing wakefulness. We assessed this by comparing key sleep\nattributes in the NS sessions to those of the On-SWR and OffSWR sessions. The fraction of time spent asleep, the fraction\nof sleep time spent in NREM and REM, electromyogram (EMG)\npower during sleep, and electroencephalogram (EEG) Delta power during sleep did not significantly differ between the NS, OnSWR, and Off-SWR sessions (Figures S1AâS1F). Further analysis of the relation between delta, beta, and sigma oscillations\nalso showed no differences between groups (Figures S1Gâ\nS1J). Lastly, analysis of the power and rate of cortical spindles\nalso showed little or no modulation by BBN presentation\n(Figure S2).\nWe next examined the influence of BBN presentation on SWRs\nby comparing SWR statistics between the first hour of sleep (always NS) and the second hour (NS, On-SWR, or Off-SWR). We\nnormalized the power of each ripple to the mean ripple power\nin the first hour of sleep. Ripple power of SWRs showed a
131small\n(6.07%) but significant reduction between the first and second\nhour in the NS condition (Figures 1Dâ1F), consistent with previous studies,59 while SWR rates did not significantly differ\n(Figure 1G). By contrast, pairing BBN to SWRs in the On-SWR\nprotocol caused a large (31.39%) and significant reduction in ripple power (Figures 1Hâ1J), as well as a reduction in SWR rate\n(Figure 1K). Interestingly, aligning ripple power to BBN onset revealed that some reduction in ripple power preceded sound\nonset (Figure 1I), consistent with a lasting effect of preceding\nBBN stimuli. Consistent with this finding, BBN presented 2 s\nfollowing SWR onset in the Off-SWR condition also caused a significant reduction in ripple power (28.84%; Figures 1Lâ1N) and\nSWR rate (Figure 1O), indicating that the effects of BBN extend\nbeyond the immediate stimulation period and influence subsequent SWRs.\nTo determine whether BBN stimuli occurring during SWRs had\na stronger influence on SWR power and rate than those occurring outside of SWRs, we compared the reduction in ripple power from the first to the second hour of sleep across conditions\n(Figures 1P and 1Q). We found that the On-SWR BBN protocol\ncaused a significantly larger reduction in ripple power as\ncompared with that of the Off-SWRs (Figure 1P; Table S1). The\ninfluence of On-SWR and Off-SWR BBN protocols on SWR\nrate did not significantly differ (Figure 1Q). Other SWR features\nsuch as the rippleâs peak frequency or duration were not significantly modified by either of the BBN protocols (Figures S3A and\nS3B). Together, these findings demonstrate that non-waking\nBBN stimuli during sleep suppress SWRs, with stronger effects\nwhen stimulation is aligned with SWRs.\nGiven the known synchrony between hippocampal SWRs and\ncortical slow oscillations (SOs),60,61 we asked whether the phase\nof the cortical SO at which BBN stimuli occurred was related to\nthe reduction in ripple power. We found that in the On-SWR protocol, BBN stimuli occurred across all SO phases, with a weak\nbias toward the early ascending phase (K = 0.06, p = 0.0011;\n(DâQ) SWR recordings for 2 h of sleep after an hour of spontaneous behavior. During the first hour, SWRs were detected, but no BBN stimuli were given (NS\ncondition). During the second hour, either no sound (NS), On-SWR, or Off-SWR BBN presentation was given.\n(D) SWR spectrograms from the first and the second sleep hour with no sound presentation (NS). The white dashed rectangles denote the time-frequency region\nof the spectrogram used for ripple power quantification.\n(E) Average ripple power derived from the corresponding spectrograms in (D). The gray area indicates the time frame of the BBN presentation if given.\n(F) Cumulative distribution of the ripple power quantified from the area represented in the white rectangle shown on the spectrograms and normalized by the first\n(NS) hour distribution (STAR Methods). Ripple power is slightly but significantly reduced (0.9393 ± 0.034) compared with ripple power during the first hour of sleep\n(NS, mean power = 1.0, Wilcoxonâs signed-rank [WSR] test, *p = 0.0007).\n(G) Normalized SWR rates did not differ across the 2 h (1.1887 ± 0.1868 from NS control condition (mean rate = 1.0) and WSR test, p = 0.7344).\n(HâK) Same representation as in (D)â(G) but for the group with On-SWR BBN presentation during the second hour of sleep. On-SWR protocol significantly\nreduced the ripple power (0.6861 ± 0.0394, WSR test, *p = 1.0786eâ 118, J) and rate (0.6563 ± 0.0953, WSR test, *p = 0.0078, K) compared with the NS condition.\n(LâO) Same representation as in (D)â(G) but for the group with Off-SWR BBN presentation during the second hour of sleep. Off-SWR protocol significantly\nreduced the ripple power (0.7116 ± 0.0198, WSR test, *p = 1.3006eâ 105, N) and rate (0.5082 ± 0.0538, WSR test, *p = 0.0078, O) compared with the NS condition.\n(P) On-SWR protocol induced a significant reduction in ripple power compared with the second hour of the NS group and the Off-SWR group (Kruskal-Wallis [KW]\ntest, p = 9.4942eâ 09; Mann-Whitney U [MW-U] test, *p = 1.5491eâ 07 and *p = 2.7534eâ 06, respectively). Note that, despite the Off-SWR BBN presentation\nreducing ripple power, this effect was not significantly different from the second hour of the NS group (MW-U test, p = 0.3061).\n(Q) On-SWR and Off-SWR protocols significantly reduced SWR rate compared with the NS condition (KW test, p = 0.0025; MW-U test, *p = 0.0111 and *p =\n0.0006, respectively). There was no significant difference between On-SWR and Off-SWR protocols (MW-U test, p = 0.3282). Data are presented as mean ± SEM\n(n = 8â9 rats/group).\nSee also Figures S1âS4 and Table S1.\nll\nCurrent Biology 36, 1â14, August 3, 2026 3\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\n**\nNS OnSWR 0\n0.5\n1.0\n1.5\nSWRs-Baseline Spk#\nNS OnSWR\n***\n0\n0.1\n0.2\n0.3\nSWRs-Baseline Spk#\n***\n***\nBaseline SWR SWR-Baseline\n-1.5 -1 -0.5 0 0.5 1\n Spiking modulation Index\n0\n10\n20\n30\n40\n50\n60\n70\nCells/bin\n-1.5 -0.5 0 0.5 0\n15\n-1 -0.5 0 0.5 1\n0\n10\n20\n30\n40\n50\n60\n70\nCells/bin\n-1 0 1 0\n60\n-1 -0.5 0 0.5 1\n0\n10\n20\n30\n40\n50\n60\n70\nCells/bin\n-1.5 -0.5 0 0.5 0\n15 All cells *** *** p\u003c0.05\nAll cells\np\u003c0.05\nAll cells\np\u003c0.05\n12%\n12%\n76%\n46%\n33%\n21%\n43%\n7%\n50%\nDown\nNo change\nUp\n42%\n16%\n42%\n45%\n24%\n30% Down\nUp\nNo change\n24%\n2%\n74%\n0\n0.2\nSpikes/ripple -0.1 0 0.1\nTime (s)\n0\n45\nFiring rate (Hz)\n0\n6.0\n0\n6.5 x10³\nOnline SWRs\nA H\nNS OnSWR NS OnSWR\n*** ***\nCell # 37\nNS OnSWR 0\n0.05\n0.1\n0.15\n0.2\nBaseline Spike#\nNS OnSWR 0\n0.1\n0.2\n0.3\n0.4\nSWRs Spike#\n-0.1 0 0.1\nTime (s)\n0\n0.02\n0.04\n0.06\n0.08\n0.1\nSpikes/ripple\nn.s.\nCell # 28\n-0.1 0 0.1\nTime (s)\n0\n0.2\n0.4\n0.6\nSpikes/ripple\nNS\nOnSWR\nB\nC NS\nOnSWR\n0\n0.5\n1.0\n0\n0.5\n1.0\n1.5\n2.0\n0.01\n0.02\n0.03\n0.05\n0.10\n0.15\n0.20\n0.01\n0.02\n0.03\n0.04\n0.05\nAll cells\np\u003c0.05\nAll cells\np\u003c0.05\n-1.5 -1 -0.5 0 0.5 1\n0\n10\n20\n30\n40\nCells/bin\n-0.5 0 0.5 1 0\n40\n-1 -0.5 0 0.5\n0\n10\n20\n30\n40\n50\n60\nCells/bin\n-0.4 0 0.4 0\n60\n-1 -0.5 0 0.5\n0\n10\n20\n30\n40\n50\n60\n70\nCells/bin\n-0.5 0 0.5 0\nAll cells 30\np\u003c0.05\nn.s. n.s. ***\n-1 0 1 0\n20\n40\n60\n80\nCells/bin\n-1 0 1 0\n20\n40\n60\n80\nCells/bin\n-1 0 1 0\n20\n40\n60\n80\nCells/bin\n*** *** n.s.\nNS OffSWR NS OffSWR NS OffSWR\nCell # 34\nNS OnSWR 0\n0.2\n0.4\n0.6\nNS OnSWR 0\n0.2\n0.4\n0.6\n0.8\nNS OnSWR 0\n0.05\n0.10\n0.15\n-0.1 0 0.1\nTime (s)\n0\n0.05\n0.10\n0.15\nSpikes/ripple\nCell # 36\n-0.1 0 0.1\nTime (s)\n0\nSpikes/ripple\nNS\nOnSWR\nD\nE NS\nOffSWR\n***\n0\n***\n0 0\n0.2\n0.4\n0.6\nNS OffSWR NS OffSWR NS OffSWR\n** ***\nCell # 15\nNS OffSWR 0\n0.2\n0.3\n0.4\n0.1\nNS OffSWR 0\n0.1\n0.2\n0.3\n0.4\nNS OffSWR 0 -0.1 0 0.1\nTime (s)\n0\n0.02\n0.04\n0.06\n0.08\nSpikes/ripple\nCell # 37\n-0.1 0 0.1 0\nSpikes/ripple\nNS\nOffSWR\nF\nG NS\nOffSWR *\n0 0 0\nn.s.\nK\nBaseline Spike#\nSWRs Spike#\nBaseline Spike#\nSWRs Spike#\nSWRs-Baseline Spk#\nn.s.\n1.0\nn.s.\n0.20\n0.25 n.s.\nB
131aseline Spike#\nSWRs Spike#\nSWRs-Baseline Spk#\n0.1\n0.2\n0.3\n0.4\n0.5\n0.05\n0.1\n0.15\n0.2\n0.25\nOff-SWR\nOn-SWR\nBaseline Spike# Baseline Spike#\nSWRs Spike# SWRs Spike#\nSWRs-Baseline Spk# SWRs-Baseline Spk#\n0.5 n.s.\n0.02\n0.04\n0.06\n0.08\n0.1\n***\n0.2\n0.4\n0.6\n0.8\n1.0\n0.05\n0.10\n0.15 n.s.\nI J\nL M\nN O P\nQ R S\nT U V\nBaseline window\ncomparison\nAnalysis windows: 50 ms\nSWR window\ncomparison\nSWR window - Baseline window\ncomparison\n Spiking modulation Index Spiking modulation Index\n Spiking modulation Index Spiking modulation Index Spiking modulation Index\n Spiking modulation Index Spiking modulation Index Spiking modulation Index\nFigure 2. Sound exposure during sleep modulates SWR-associated spiking in a timing-dependent manner\n(A) Example SWR-associated spike raster of a single neuron from 2 h of sleep after an hour of spontaneous behavior. During the first hour no sounds were\npresented (NS condition, top raster), and during the second hour either On-SWR or Off-SWR stimulation was given (bottom rasters). Below, peri-SWR spiking\nhistograms and firing rates are shown for both conditions. Right next: schematic of the spiking analysis time windows.\n(BâD) Data from representative cells during On-SWR stimulation.\n(EâG) Data from representative cells during Off-SWR stimulation.\n(HâJ) On-SWR BBN modulation of CA1 neuronal spiking. Histograms of spiking modulation index (base-10 logarithm of the number of spikes during On-SWR\nsound presentation divided by the number of spikes during NS conditions) are shown for baseline (H), SWR (I), and SWR-baseline (J) periods (n = 4 subjects, 213\ncells). For all analysis windows, distributions show a significant bias to negative spiking modulation indices (sample WSR, p = 4.804eâ 11, p = 2.528eâ 10, and p =\n6.766eâ 05, respectively). Inset: spiking modulation index histograms of cells showing significant differences between NS and On-SWR conditions. Note that only\nbaseline (sample WSR, p = 1.3203eâ 14) and SWR (sample WSR, p = 2.2430eâ 10), but not SWR baseline (sample WSR, p = 0.1374), distributions showed\nsignificant bias to negative spiking modulation indices.\n(KâM) Percentage of cells that significantly showed a reduction (down), increase (up), or no change in firing activity during each anal
131ysis window: baseline (K),\nSWR (L), and SWR-baseline (M).\n(NâS) Off-SWR BBN modulation of CA1 neuronal spiking.\n(legend continued on next page)\nll\n4 Current Biology 36, 1â14, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nFigure S4A). However, there was no significant correlation between the SO phase and the magnitude of ripple power reduction across the dataset (Figure S4C). In the Off-SWR protocol,\nBBN stimuli occurred uniformly across SO phases (Figure S4B)\nand showed no significant correlation with the degree of ripple\npower reduction (Figure S4C). Together, these findings indicate\nthat the stimulation was not systematically locked to specific SO\nphases.\nTo further assess the impact of fine timing differences, we\ntested whether trial-to-trial variability in the delay between\nSWR detection and BBN onset was correlated with ripple power\nreduction. This variability was generally small, on a scale of 1 ms\n(Figure S4D). There was no significant relationship between\nSWR-BBN delay and ripple power reduction (Figure S4E).\nTo test whether the observed ripple power reduction is modulated by BBN attributes, we performed similar On-SWR recordings\nusing BBN stimuli of varying durations and intensities. We found\nthat BBN attributes significantly modulated ripple power reduction, with an overall pattern showing that BBN intensity, rather\nthan duration, is a key factor in inducing ripple disruption\n(Figure S4F).\nBBN presentation during sleep modulates SWRassociated spiking of CA1 neurons\nWe next examined how BBN stimuli influence SWR-associated\nspiking of CA1 neurons. Specifically, for each neuron, we quantified changes in pre-SWR-baseline firing, SWR-associated firing,\nand baseline-corrected SWR-associated firing (Figure 2A). Both\nOn-SWR (Figures 2Bâ2D) and Off-SWR (Figures 2Eâ2G) BBN presentations induced heterogeneous effects on the firing patterns of\nindividual neurons. Across the population, On-SWR stimulation\nled to a net decrease in both pre-SWR-baseline firing\n(Figures 2H and 2K) and SWR-associated firing (Figures 2I and\n2L). The influence on baseline-corrected SWR-associated firing\nwas mixed, with a net negative effect (Figure 2J), although a\nsimilar fraction of individual neurons showed a
131significant increase\nand decrease (Figure 2M). Off-SWR BBN presentation also influenced the firing of CA1 neurons, but with a different overall\npattern. About twice as many neurons showed a reduction as\ncompared with an increase in pre-SWR-baseline firing and\nSWR-associated firing (Figures 2Q and 2R). However, increases\nin these measures tended to be larger than decreases, resulting\nin no significant net difference (Figures 2N and 2O). Baseline-corrected responses showed a marked reduction (Figures 2P and\n2S). Finally, we directly compared the influence of the On-SWR\nand Off-SWR BBN presentation. On-SWR stimulation produced\na larger reduction in both baseline and SWR-associated firing\nthan Off-SWR stimulation (Figures 2T and 2U). Interestingly, however, the influence on baseline-corrected firing across the population did not differ (Figure 2V). Thus, both On-SWR and Off-SWR\nstimulation modulated CA1 firing, with an overall tendency toward\nreduced activity.\nExposure to BBN stimuli during SWRs in sleep impairs\nmemory\nBecause SWRs and associated population spiking patterns during sleep are known to support memory consolidation,11,12,62,63\nwe next tested whether the BBN-induced suppression of SWRs\nwas associated with impaired memory. To this end, we used a\nhippocampal-dependent conditioned place preference (CPP)\nparadigm64,65 (Figure 3A). In the first stage of this task, performed in a setup with a center home chamber and two external\nchambers, the ratsâ innate preference between each of the\nexternal chambers was recorded based on the amount of time\nspent in each. In the learning session of this task, rats learned\nto associate their less-preferred chamber with sucrose solution\nreward, while water was placed in the other external chamber.\nThe learning session was followed by an â¼3 h sleep session in\nthe middle chamber, during which varying acoustic protocols\nwere presented (see below). Memory retention was subsequently evaluated as the relative amount of time spent in the rewarded chamber. Memory was evaluated at two time points:\nimmediately following the 3 h sleep session, when memory\nretention relies largely on short-term processes,66â70 and at\n24 h post learning, when memory relies on hippocampal-dependent memory consolidation.66â70 In the first experimental group,\nthe On-SWR BBN protocol was applied during the post-learning\nsleep session (Figure 3B). Consistent with the data following\nspontaneous behavior, SWRs in the On-SWR condition showed\nsignificantly reduced ripple power compared with NS\n(Figures 3Câ3E), while SWR rates were not significantly different\n(Figure 3F). Behaviorally, under baseline conditions in which no\nBBN stimuli were presented in the post-learning sleep session,\nanimals displayed significant memory retention at both time\npoints, as evidenced by a significant learning-dependent shift\nin preference to the reward-associated chamber (Figure 3G).\nHowever, the On-SWR BBN presentation during post-learning\nsleep caused a strong impairment in memory at the 24 h time\npoint (Figures 3H and 3J), while memory at the 3 h time point remained intact (Figures 3H and 3I). Moreover, at the 24 h time\npoint, CPP scores showed no difference from the pre-learning\nscores (Figure 3H), indicating no detectable memory retention\nrelative to pre-learning levels. These findings suggest that BBN\nstimuli occurring during SWRs in sleep suppress ripple power\nand impair memory consolidation.\nBBN stimuli occurring outside SWRs in sleep weaken\nmemory\nGiven that BBN stimuli delivered outside of SWRs produced a\nlingering reduction in ripple power and rate following\n(NâP) Same representation as in (H)â(J) (n = 4 subjects, 209 cells). Neither baseline (sample WSR, p = 0.3170) nor SWR (sample WSR, p = 0.1016) distributions\nshowed a significant bias of the spiking modulation index. Only the SWR-baseline distribution showed a significant negative bias (sample WSR, p = 6.3070eâ 05).\nThis is also shown by the cell population significantly modulated by sound stimulation (inset: sample WSR, p = 0.3113, p = 0.1149, and p = 5.2982eâ 08,\nrespectively).\n(QâS) Same representation as in (K)â(M).\n(TâV) Comparison of the effect of On-SWR and Off-SWR on CA1 spiking. Comparison of cellsâ spiking modulation indicesâ distribution during baseline (T) and\nSWR (U) showing that the On-SWR protocol had a significantly stronger negative influence (MW-U test, p = 2.0121eâ 05 and p = 9.539eâ 05, respectively). By\ncontrast, firing during the SWR-baseline window (V) showed no significant difference between BBN protocols (MW-U test, p = 0.5736).\nll\nCurrent Biology 36, 1â14, August 3, 2026 5\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nspontaneous behavior (Figures 1Lâ1O), we next tested whether\nthis manipulation also impaired memory. We repeated the above\nexperiment using the Off-SWR BBN protocol during the postlearning sleep session (Figure 4A). As expected from the sleep\nd
131ata following spontaneous behavior (Figure 1), ripple power\nwas significantly reduced as compared with NS (Figures 4Bâ\n4D), and SWR rates were significantly lower (Figure 4E). We\nfound that memory retention in the Off-SWR group was weaker\nthan in the NS group (Figures 4Fâ4I). Consistent with the\nobserved disruption of SWRs by Off-SWR BBN presentation,\nthese findings indicate that stimulation outside of SWRs can\nalso impair memory. However, despite this weakening, a significant degree of memory retention remained at both the 3- and\n24-h time points (Figures 4G and 4I). These results suggest\nthat during NREM sleep, BBN stimuli delivered largely outside\nof SWRs can still disrupt subsequent SWRs and weaken\nmemory.\nThe neural processes supporting memory consolidation can\nvary depending on task attributes such as valence, novelty,\nand complexity.71â74 Accordingly, susceptibility to disruption\nby BBN exposure during sleep may also differ. To determine\nwhether the observed memory impairment was task-specific,\nwe performed similar experiments using a contextual fear conditioning (CFC) paradigm (Figure S5A). CFC is a well-established\nhippocampal-dependent paradigm that differs from CPP in\nboth valence and task demands. We found that neither the OnSWR nor the Off-SWR protocols significantly affected memory\nin this task at either retention time point (Figures S5BâS5F).\nThese findings indicate that the impact of noise during sleep\non memory is task dependent. Additionally, as CFC memory depends on intact post-training sleep,75,76 these findings provide\nadditional support that the CPP impairment is not due to a general disruption of sleep.\nBBN stimuli occurring during SWRs induce stronger\nneural and memory impairments than BBN stimuli\noccurring outside SWRs\nFinally, we directly compared the neural and behavioral effects of\nBBN presentation in the On-SWR and Off-SWR protocols during\nthe post-learning sleep session. Consistent with our findings in\nsleep following spontaneous behavior, ripple power was significantly lower in the On-SWR protocol than in the Off-SWR\n(Figures 5A and 5B), suggesting that BBN stimuli occurring during SWRs have a more detrimental influence on ripple power.\nInterestingly, SWR rates were significantly lower in the OffSWR protocol than in the On-SWR protocol (Figure 5C), suggesting that stimulation timing differentially affects SWR power and\nrate. As previously observed, the BBN presentation protocols\nConditioning\n** **\nRetention tests\n0\n0.5\n1.0\nCDF\n0 2 4 6 8 10\n2.2\n3.5\nRipple power\n-0.1 0 0.1\nTime (s)\n1.0\nNS\nOn-SWR\nNS\nOn-SWR\nNormalized ripple power\n0\n0.5\n1.0\nCDF\nÎ power -0.2\n1.4 ***\n-0.1 0 0.1\n100\n200\n300\n400\nFrequency (Hz)\n-0.1 0 0.1\nPower (μV2\n)\nTime (s)\n1\n5\n0\n1.0\n2.0\nNormalized rate (SWRs/s) 3.0\n4.0\nNS On-SWR\nn.s.\n-0.2\n0\n0.8\nNormalized 3h scores 1.0\n0.8\nNS On-SWR\nn.s.\nPre-Test 3 24\n-0.5\n0.5\nCPP scores -1.0\n1.0\n0.0\nPre-Test 3 24\n-0.5\n0.5\nCPP scores -1.0\n1.0\n0.0\nNormalized 24h scores\n###\n-0.2\n0\n0.4\n0.6\nNS On-SWR\n0.2\n0.4\n0.2\n*** *** ***\nn.s.\n**\nn.s.\n0.6\n1.0\nOn-SWR stimulation\nSleep post-conditioning Sleep post-conditioning\nA\nB\nC\nG\nD E F\nH I J\nFigure 3. BBN stimuli occurring during SWRs in sleep impair memory consolidation\n(A) Experimental design for the BBN presentation post-conditioning in the CPP task.\n(B) The On-SWR BBN protocol.\n(C) Ripple spectrograms from groups NS and On-SWR.\n(D) Average ripple power.\n(E) Cumulative distribution of normalized ripple power. On-SWR BBN presentation significantly reduced the ripple power (0.4962 ± 0.0130) compared with the NS\ncontrol group (mean power = 1.0; MW-U test, *p = 0.0000).\n(F) On-SWR protocol did not significantly modify the SWR rate compared with the NS group (normalized mean rate, 1.7218 ± 0.3362; MW-U test, p = 0.1949).\n(G and H) CPP scores during pre-test and 3- and 24-h post-training for the NS group (G) and the On-SWR group (H). CPP scores from both NS (0.5405 ± 0.0921)\nand On-SWR (0.3707 ± 0.0826) groups compared with the pre-test scores (â 0.3698 ± 0.0698 and â 0.6175 ± 0.0852, respectively) showed a
131significant place\npreference shift 3 h post-conditioning (Friedman test, WSR post-hoc, *p = 0.0078 and *p = 0.0078, respectively). By contrast, at 24 h post-conditioning, only the\nNS group showed a significant place preference shift (0.3623 ± 0.0698, WSR post-hoc, *p = 0.0078, G), while the On-SWR group showed no significant difference\nto the pre-test condition (â 0.5674 ± 0.0789, WSR test, p = 0.2500, H). CPP scores at the 24 h test also showed a significant reduction compared with the 3 h test\n(WSR test, *p = 0.0078, H).\n(I and J) Comparison of normalized scores for the 3- (I) and 24-h (J) retention tests. Note that the On-SWR BBN presentation induced a significant reduction of the\nnormalized scores at the 24 h (0.0277 ± 0.0297, MW-U test, #p = 1.5540eâ 04) but not at the 3 h (0.6058 ± 0.0559, MW-U test, p = 0.3823) retention test, compared\nwith the NS group (0.5246 ± 0.0631 and 0.6649 ± 0.0675, respectively). For 3 h scores, sample WSR test, p = 0.0078 versus zero (zero considered as no memory).\nFor 24 h scores, *p = 0.0078 and p = 0.3125. Data are presented as mean ± SEM (n = 8 rats/group).\nll\n6 Current Biology 36, 1â14, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\ndid not modify the peak frequency or duration of SWRs\n(Figures S3C and S3D). Finally, we compared the behavioral\nconsequences of On-SWR and Off-SWR protocols and found\na differential effect in the different memory-retention time points.\
131nAt the 3 h time point immediately after awakening, memory\nretention was significantly weaker following the Off-SWR protocol than following the On-SWR protocol (Figure 5D), although\nboth groups still showed significant memory retention\n(Figures 3I and 4H). By contrast, at the 24 h time point, the OnSWR protocol impaired memory retention significantly more\nthan the Off-SWR protocol (Figure 5E). These data are consistent\nwith the finding that animals in the Off-SWR group retained significant memory at the 24 h time point (Figure 4I), whereas animals in the On-SWR group did not (Figure 3J). Together, these\nfindings suggest that exposure to BBN stimuli during sleep disrupts hippocampal activity and memory and that the magnitude\nand profile of these effects depend on stimulus timing relative\nto SWRs.\nDISCUSSION\nThe auditory system continuously processes sounds during\nsleep26â31 and communicates sound information to the medial\ntemporal lobe via cortical and subcortical pathways.38â43,77\nHowever, whether processing of incoming sounds interferes\nwith hippocampal-dependent memory consolidation has remained largely unknown. Our experiments reveal that exposure\nto noise during sleep disrupts SWRsâa key biomarker of memory consolidation,1,5,7â13,19â22,62,63 with associated CA1 firing\npatterns and with the consolidation of some experiences into\nmemory. Moreover, we found that the effects of BBN depended\non its timing relative to SWRs: stimuli occurring during SWRs had\na stronger influence on ripple power and 24 h memory retention,\nwhereas stimuli occurring outside of SWRs also impaired neural\nand behavioral measures. By contrast, consolidation of CFC was\nnot impaired by BBN exposure during post-experience sleep.\nTogether, our findings suggest that exposure to brief BBN stimuli\nduring sleep impairs hippocampal-dependent memory consolidation in a timing-dependent and task-specific manner.\nSWRs define times of hippocampal replay19â22 and hippocampal-cortical communication,1,5,7â13,15â18,78 attributes that\nhave identified SWRs as a candidate mechanism underlying\nthe consolidation of recent experience into long-term memory\n(LTM) representations. To test whether SWRs have a causal\nrole in memory consolidation, previous studies used direct hippocampal commissure stimulation to interrupt SWRs following\nlearning.11,12,79â81 These studies found that blocking SWRs using electrical stimulation impaired learning and memory formation. Our results suggest that SWR interruption and subsequent\nimpairments in memory consolidation can also occur as a result\n-0.1 0 0.1\n100\n200\n300\n400\nFrequency (Hz)\n-0.1 0 0.1\nPower (μV2\n)\nTime (s)\n1.0\n5.5\n0\n0.5\n1.0\nCDF\n0 2 4 6 8 10\n2.2\n3.5\nRipple power\n-0.1 0 0.1\nTime (s)\n1.0\nNS\nOff-SWR\nNS\nOff-SWR\nNormalized ripple power\n0\n0.5\n1.0\nCDF\nÎ power -0.2\n1.4 ***\n0\n1.0\n2.0\nNormalized rate (SWRs/s) 3.0\nNS Off-SWR\n*\n0\nNS Off-SWR Pre-Test 3 24\n-0.5\n0.5\nCPP scores -1.0\n1.0\n0.0\nPre-Test 3 24\n-0.5\n0.5\nCPP scores -1.0\n1.0\n0.0\nNS Off-SWR\n#\n**\n**\n**\n**\n###\n0.2\n0.6\nNormalized 3h scores 1.0\n0.8\nNormalized 24h scores\n0\n0.4 0.4\n0.8\n0.2\n0.6\n**\n**\n1.0\n**\n**\nA B\nF\nC D E\nG H I\nOff-SWR stimulation\nFigure 4. BBN stimuli occurring outside of SWRs in sleep weaken memory consolidation\n(A) BBN presentation protocol.\n(B) SWR spectrograms from NS and Off-SWR groups.\n(C) Average ripple power.\n(D) Cumulative distribution of normalized ripple power. Off-SWR stimulation significantly reduced the ripple power (0.6387 ± 0.0204) compared with the NS\ncontrol group (mean power = 1.0, MW-U test, *p = 4.1419eâ 54).\n(E) Off-SWR protocol significantly reduced the SWR rate compared with the NS group (normalized mean rate 0.4893 ± 0.1432, MW-U test, *p = 0.0499).\n(F and G) CPP scores during pre-test and 3- and 24-h post-training for the NS (F) and Off-SWR (G) groups. CPP scores from both NS (0.6162 ± 0.0919) and OffSWR (0.2485 ± 0.0942) groups compared with the pre-test scores (â 0.2976 ± 0.0858 and â 0.2403 ± 0.0686, respectively) showed a
131significant place preference\nshift 3 h post-conditioning (Friedman test, WSR post-hoc, *p = 0.0078 and *p = 0.0078, respectively). Similarly, at 24 h post-conditioning, both NS (0.2974 ±\n0.0488) and Off-SWR (â 0.0405 ± 0.0549) groups showed a significant place preference shift (WSR post-hoc, *p = 0.0078 and *p = 0.0078, respectively).\n(H and I) Normalized CPP scores at 3- (H) and 24-h (I) retention tests, showing that the Off-SWR protocol significantly reduced both 3 h (0.4053 ± 0.0561, MW-U\ntest, #p = 0.0281) and 24 h (0.1557 ± 0.0355, MW-U test, #p = 1.5540eâ 04) scores compared with the NS group (0.7071 ± 0.0802 and 0.4466 ± 0.0409,\nrespectively), suggesting a weaker memory trace. For all scores, the sample WSR test, *p = 0.0078 versus zero (zero considered as no memory). Data are\npresented as mean ± SEM (n = 8 rats/group).\nSee also Figure S5.\nll\nCurrent Biology 36, 1â14, August 3, 2026 7\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nof simple processing of sensory information during sleep.\nThese findings suggest that processing of incoming sounds\nduring sleep may come at a cost for offline processing of internally generated activity patterns underlying memory\nconsolidation.\nWe found that in sleep following spontaneous behavior, BBN\nstimuli presented during SWRs (On-SWR) as well as BBN stimuli\npresented outside of SWRs (Off-SWR) weakened ripple power,\nyet the impairment was significantly stronger following OnSWR BBN presentation. Furthermore, both BBN presentation\nprotocols caused a similar and significant reduction in SWR\nrates. These results and the known anatomical circuits point at\nlikely underlying mechanisms. BBN presented during SWRs\nmay weaken ripple power by generating activation throughout\nthe auditory pathway, which propagates via the entorhinal cortex\nor perirhinal cortex into the hippocampus,38,40,41,43 thereby interrupting the synchronized activity in CA3 that is necessary for\nSWR generation.82â84 However, weakening of ripple power by\nBBN stimuli presented outside of SWRs (albeit to a lesser degree), as well as the reduction in SWR rate in both protocols, suggests that beyond their immediate effect, BBN stimuli weaken\nSWRs for hundreds of milliseconds or seconds after the stimulus\ntermination. Interestingly, a lingering effect of sounds on hippocampal activity during sleep has been previously shown.51 In this\nstudy, presentation of behaviorally relevant sounds during sleep\ninfluenced the content of hippocampal reactivation up to 10.8 s\nafter sound presentation. In our data, the BBN-induced lingering\neffect may reflect the time required for recovery of synchronous\nnetwork dynamics in CA3 following disruption by sounds. Additionally, as the spiking patterns and oscillatory activity between\nSWRs can influence the content of activity during the\nSWRs,85â88 sounds interfering with pre-SWR activity may\nweaken SWRs via this 2-stage process.\nAnother potential mechanism underlying the findings\ndescribed above is cholinergic neuromodulation. Previous\nstudies have found that cholinergic input from the medial septum\n(MS) into the CA3 field of the hippocampus suppresses SWRs\nand that this effect persists for at least hundreds of milliseconds\nfollowing stimulation.89,90 Furthermore, MS is activated by\nsounds and communicates sound information into the hippocampus.91,92 Thus, sounds heard during sleep may suppress\nSWRs via cholinergic input from the MS. Nevertheless, future\nstudies are required to test these hypotheses and determine\nthe detailed circuit mechanisms underlying BBN-induced SWR\nsuppression.\nDuring sleep following learning, hippocampal reactivation and\nhippocampal-cortical communication during SWRs are believed\nto be critical for the consolidation of recent experiences into\nlong-term memories.1,5â13,24,62,63 We found that during postlearning sleep sessions, On-SWR and Off-SWR BBN presentation induced a similar pattern of ripple power suppression as\nthey did following spontaneous behavior. Interestingly, however,\nwe found distinct effects of On-SWR and Off-SWR protocols on\nSWR rates following learning. While the Off-SWR protocol\ninduced a reduction in SWR rates as they did following spontaneous behavior, the On-SWR protocol did not. Thus, there was\na selective preservation of SWR rates in the On-SWR condition\npost learning. These findings are consistent with a previous\nstudy showing that following learning, but not following random\nbehavior, compensatory mechanisms of SWR rates are engaged\nin response to SWR disruption.81 Nevertheless, the BBNinduced reduction in ripple power following learning could interfere with memory consolidation and impair memory performance via the mechanisms described above. In particular, the\nreduction in ripple power following both the On-SWR and OffSWR BBN presentation suggests a reduced re
131cruitment of\n* *\n0.8\n-0.2\n0\n0.4\n0.6\n0.2\n1.0\n0\n0.5\n1.0\nCDF\n0 2 4 6 8 10\n2.0\n3.0\nRipple power\n-0.1 0 0.1\nTime (s)\n1.0\nOff-SWR\nOn-SWR\nOff-SWR\nNormalized ripple power\n0\n0.5\n1.0\nCDF\n***\n0\n1.5\n3.0\nNormalized rate (SWRs/s) 4.5\nOff-SWR\nNormalized 3h scores\nNormalized 24h scores\nOn-SWR\nÎ power -0.2\n1.4\nOn-SWR\n**\n-0.2\n0\n0.8\n1.0\n0.2\n0.4\n0.6\nA\nD\nB C\nE\nFigure 5. Timing of BBN relative to SWRs\ndifferentially affects ripple dynamics and\nmemory\n(A) Average ripple power from the time domain of\nOn-SWR (Figure 3D) and Off-SWR (Figure 4C)\ngroups. Inset: difference in normalized ripple power.\n(B) Cumulative distribution of normalized ripple\npower. Note that ripple power suppression is\nsignificantly greater in the On-SWR group\n(0.4962 ± 0.0130) compared with the Off-SWR\ngroup (0.6387 ± 0.0204, MW-U test, *p =\n4.4146eâ 136).\n(C) Off-SWR protocol significantly reduced SWR\nrate (0.4893 ± 0.1432) compared with On-SWR\nprotocol (1.7218 ± 0.3362, MW-U test, *p =\n0.0070).\n(D and E) Comparison of normalized scores for the\n3- (D) and 24-h (E) retention tests. Normalized CPP\nscores comparison at the 3 h retention test\nshowed that On-SWR scores (0.6058 ± 0.0559)\nare significantly higher than Off-SWR (0.4053 ±\n0.0561; MW-U test, *p = 0.0028). By contrast, at\nthe 24 h retention test On-SWR scores (0.0277 ±\n0.0297) are significantly smaller compared with\nthe Off-SWR scores (0.1557 ± 0.0355; MW-U test,\n*p = 0.0207). Data are presented as mean ± SEM\n(n = 8 rats/group).\nll\n8 Current Biology 36, 1â14, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nneuronal firing in the CA1 output region of the hippocampus. This\nhypothesis is supported by our findings that both protocols\ninduced substantial reductions in hippocampal firing rates\nbefore and during SWRs, which likely impair the integrity of\nreplay events during SWRs and reduce the robustness of information flow from the hippocampus to downstream cortical\nregions.93,94\nMemory is traditionally classified into short-term memory\n(STM), which lasts from seconds to a few hours, and LTM, which\nlasts from hours to days or longer.64,95,96 Several studies have\nestablished that STM and LTM rely on different neural mechanisms.66â70,97,98 For example, inhibiting the expression of\nbrain-derived neurotrophic factor in the dorsal hippocampus impairs LTM, measured at 24 h post-training, while having no effect\non STM, measured at 3 h post-training.70 Similarly, hippocampal\nprotein synthesis inhibition before learning of a CFC paradigm\ncaused LTM impairment 24 h post-conditioning, while STM at\n1 h post-conditioning was intact.67 Consistently, infusion of\nseveral protein kinase inhibitors into the hippocampus immediately after training in an inhibitory avoidance task had no effect\nover STM (tested 2 h post-training) but impaired LTM when\ntested 24 h post-training.99,100 Accordingly, our findings reveal\na differential effect of the On-SWR BBN presentation on these\nstages of memory, wherein memory retention at the 3 h time\npoint was intact, while no detectable memory retention was\nobserved at 24 h relative to pre-learning levels. These findings\nsuggest that the On-SWR BBN presentation preferentially\naffected mechanisms supporting LTM formation, while sparing\nSTM. On the other hand, the deleterious effect of Off-SWR\nBBN presentation on memory retention at the 3 h time point\n(immediately following post-learning sleep) suggests that BBN\nstimuli outside SWRs may affect a different set of memoryrelated mechanisms. As previously stated, Off-SWR stimulation\nmight be interfer
131ing with oscillatory events that happen outside\nof SWRs, which may be particularly important for STM.\nThe deleterious effects of sound on hippocampal function and\nmemory are likely strongly dependent on the nature of the presented sound and its associated meaning. In this study, we\nfocused on unfamiliar sounds associated with no behavioral\nmeaning as a model of the influence of environmental noise\nexposure. It is of interest to compare the current findings with\nthose of a growing number of studies in recent years, which\nhave examined the influence of presentation of sounds associated with a recently formed memory during sleep in rodents101,102 and in humans.103,104 For example, presenting\nsounds with varying degrees of behavioral relevance during\nNREM sleep differentially affected sleep-associated oscillations\nin rodents.101 In humans, targeted memory reactivation (TMR)\nstudies describe strengthening of specific memories previously\nassociated with the re-presented sound.52â56,105â107 Thus, the\nencoding of sound meaning (or lack thereof) during sleep likely\nshapes how auditory input influences memory consolidation.\nTiming is a critical determinant of how sensory input during\nsleep influences memory consolidation. In this context, it is informative to compare our findings with studies employing closedloop auditory stimulation (CLAS), in which sounds are locked\nto specific phases of the cortical SO. Locking sounds to the\npeak of the SO phase has been shown to increase the amplitude\nof the SO and spindle likelihood and, in several studies, to\nenhance declarative memory in humans108â111 and in rodents.112\nPresenting auditory stimulation in an open-loop manner or off the\nSO peak phase showed no effects or negative effects.109,113 Our\nparadigm differs from both phase-locked CLAS and non-phasespecific stimulation, as sounds were locked to SWRs rather than\nSO phases directly, raising the question of whether indirect\nSO phase targeting could account for our results. Given that\ncortical SOs show significant synchrony with hippocampal\nSWRs,1,24,60,114 our paradigm of triggering BBN on SWRs could\nindirectly target specific SO phases, which, in turn, could suggest cortical rather than hippocampal mediation of the memory\nimpairments we observed. To test this, we examined the SO\nphase at which BBNs occurred. We found a weak but significant\npreference for BBNs to occur at the early ascending phase of the\nSO (Figure S4A). However, while the phase distribution showed a\nsignificant difference from circular uniformity, large fractions of\npresentations occurred in non-preferred phases, and the\naverage phase preference was minimal (concentration parameter K = 0.06, while K = 0 denotes uniformity, and for a von Mises\ndistribution, moderate concentration is typically considered\nK > 1). This likely reflects the significant but mild degree of SOSWR synchrony.1,24,60,114 Although our analyses did not reveal\na relationship between SO p
131hase and ripple power reduction,\nwe cannot exclude the possibility that the weak bias of OnSWR stimulation toward the SO up-phase may have influenced\nmemory outcomes independently of its effects on ripple power.\nSince previous CLAS studies have found that precise timing of\nsound presentation with respect to the SO is critical for consistent behavioral effects, our findings do not directly map onto\nthese previous studies. Thus, our paradigm consistently targeted sounds to SWRs but only minimally targeted specific\ncortical oscillation phases. Importantly, the cortical oscillation\nphases did not significantly correlate with the degree of ripple\npower reduction (Figure S4C). Furthermore, the Off-SWR paradigm, which showed a uniform distribution of BBNs over the\nSO phases, also produced a significant reduction in ripple power\nand induced memory deficits. Together, these results suggest\nthat SWR disruption, rather than indirect SO phase targeting, is\nthe more likely mediator of the memory impairments we\nobserved.\nOur findings of the deleterious effects of brief BBN presentation during sleep on memory consolidation have potential broad\npublic health relevance. Exposure to environmental sounds during sleep is highly prevalent in urban environments,33â35,115,116\nbut their influence on neural function and cognitive abilities is\npoorly understood. Epidemiological studies have found that\nlong-term exposure to nighttime noise is associated with an\nincreased risk for memory impairments, cognitive decline, and\ndementia.32,36,37,117â119 Whether repeated interruption of hippocampal activity, as observed here in an acute exposure setting,\neventually leads to lasting or permanent impairments in hippocampal function and, consequently, memory capacities remains\nto be explored.\nRESOURCE AVAILABILITY\nLead contact\nAll requests for information and resources should be directed to the lead contact, Gideon Rothschild ([email protected]).\nll\nCurrent Biology 36, 1â14, August 3, 2026 9\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nMaterials availability\nThis study did not generate any new, unique reagents.\nData and code availability\nAll data reported in this paper will be shared by the lead contact upon request.\nAll original code has been deposited at figshare and is publicly available at 10.\n6084/m9.figshare.32595861. Any additional information required to reanalyze\nthe data reported in this paper is available from the lead contact upon request.\nACKNOWLEDGMENTS\nThis work was supported by National Institute of Health grants R01NS129874\nand R01NS131821 (G.R.) and Alzheimerâs Association Research Grant 21â\n850571 (G.R.). Schematic illustrations were created with BioRender.com.\nAUTHOR CONTRIBUTIONS\nG.R. and K.S.-P. conceptualized and designed the study. K.S.-P. conducted\nexperiments and analyzed the data. U.K. provided technical assistance and\nelectrode-implanted animals. K.S.-P. and G.R. wrote the manuscript.\nDECLARATION OF INTERESTS\nThe authors declare no competing interests.\nSTARâ
METHODS\nDetailed methods are provided in the online version of this paper and include\nthe following:\n⢠KEY RESOURCES TABLE\n⢠EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS\nâ Subjects and implant procedures\nâ Sleep recordings and sound stimulation\n⢠METHOD DETAILS\nâ Online detection of awake and sleep phases\nâ Real-time SWR detection and sound stimulation\nâ Histology\n⢠QUANTIFICATION AND STATISTICAL ANALYSIS\nâ CPP and CFC behavioral analysis\nâ Awake-sleep classification and quantification\nâ Online detected SWRs validation and analysis\nâ Offline spindle detection and analysis\nâ Slow oscillations phase analysis\nâ Sound to ripple delay time quantification\nâ Sound duration-intensity curve\nSUPPLEMENTAL INFORMATION\nSupplemental information can be found online at https://doi.org/10.1016/j.\ncub.2026.06.039.\nReceived: October 25, 2023\nRevised: April 20, 2026\nAccepted: June 12, 2026\nREFERENCES\n1. Diekelmann, S., and Born, J. (2010). The memory function of sleep. Nat.\nRev. Neurosci. 11, 114â126. https://doi.org/10.1038/nrn2762.\n2. Frankland, P.W., and Bontempi, B. (2005). The organization of recent and\nremote memories. Nat. Rev. Neurosci. 6, 119â130. https://doi.org/10.\n1038/nrn1607.\n3. OâKeefe, J., and Nadel, L. (1978). The Hippocampus as a Cognitive Map\n(Clarendon Press; Oxford University Press).\n4. Scoville, W.B., and Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11â21.\nhttps://doi.org/10.1136/jnnp.20.1.11.\n5. Buzsa´ ki, G. (2015). Hippocampal sharp wave-ripple: A cognitive\nbiomarker for episodic memory and planning. Hippocampus 25, 1073â\n1188. https://doi.org/10.1002/hipo.22488.\n6. Buzsa´ ki, G. (1989). Two-stage model of memory trace formation: a role\nfor \"noisy\" brain states. Neuroscience 31, 551â570. https://doi.org/10.\n1016/0306-4522(89)90423-5.\n7. Rothschild, G., Eban, E., and Frank, L.M. (2017). A cortical-hippocampalcortical loop of information processing during memory consolidation.\nNat. Neurosci. 20, 251â259. https://doi.org/10.1038/nn.4457.\n8. Ji, D., and Wilson, M.A. (2007). Coordinated memory replay in the visual\ncortex and hippocampus during sleep. Nat. Neurosci. 10, 100â107.\nhttps://doi.org/10.1038/nn1825.\n9. Peyrache, A., Khamassi, M., Benchenane, K., Wiener, S.I., and Battaglia,\nF.P. (2009). Replay of rule-learning related neural patterns in the prefrontal cortex during sleep. Nat. Neurosci. 12, 919â926. https://doi.org/10.\n1038/nn.2337.\n10. Wierzynski, C.M., Lubenov, E.V., Gu, M., and Siapas, A.G. (2009). Statedependent spike-timing relationships between hippocampal and prefrontal circuits during sleep. Neuron 61, 587â596. https://doi.org/10.\n1016/j.neuron.2009.01.011.\n11. Girardeau, G., Benchenane, K., Wiener, S.I., Buzsa´ ki, G., and Zugaro,\nM.B. (2009). Selective suppression of hippocampal ripples impairs\nspatial memory. Nat. Neurosci. 12, 1222â1223. https://doi.org/10.1038/\nnn.2384.\n12. Ego-Stengel, V., and Wilson, M.A. (2010). Disruption of ripple-associated\nhippocampal activity during rest impairs spatial learning in the rat.\nHippocampus 20, 1â10. https://doi.org/10.1002/hipo.20707.\n13. Squire, L.R., Genzel, L., Wixted, J.T., and Morris, R.G. (2015). Memory\nconsolidation. Cold Spring Harb. Perspect. Biol. 7, a021766. https://\ndoi.org/10.1101/cshperspect.a021766.\n14. Jadhav, S.P., Rothschild, G., Roumis, D.K., and Frank, L.M. (2016).\nCoordinated excitation and inhibition of prefrontal ensembles during\nawake hippocampal sharp-wave ripple events. Neuron 90, 113â127.\nhttps://doi.org/10.1016/j.neuron.2016.02.010.\n15. Varela, C., Kumar, S., Yang, J.Y., and Wilson, M.A. (2014). Anatomical\nsubstrates for direct interactions between hippocampus, medial prefrontal cortex, and the thalamic nucleus reuniens. Brain Struct. Funct. 219,\n911â929. https://doi.org/10.1007/s00429-013-0543-5.\n16. Varela, C., and Wilson, M.A. (2020). mPFC spindle cycles organize\nsparse thalamic activation and recently active
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Ngo, H.V.V., Martinetz, T., Born, J., and Mo¨ lle, M. (2013). Auditory\nclosed-loop stimulation of the sleep slow oscillation enhances memory.\nNeuron 78, 545â553. https://doi.org/10.1016/j.neuron.2013.03.006.\n112. Moreira, C.G., Baumann, C.R., Scandella, M., Nemirovsky, S.I., Leach,\nS., Huber, R., and Noain, D. (2021). Closed-loop auditory stimulation\nmethod to modulate sleep slow waves and motor learning performance\nin rats. eLife 10, e68043. https://doi.org/10.7554/eLife.68043.\n113. Weigenand, A., Mo¨ lle, M., Werner, F., Martinetz, T., and Marshall, L.\n(2016). Timing matters: open-loop stimulation does not improve overnight consolidation of word pairs in humans. Eur. J. Neurosci. 44,\n2357â2368. https://doi.org/10.1111/ejn.13334.\n114. Isomura, Y., Sirota, A., Ozen, S., Montgomery, S., Mizuseki, K., Henze,\nD.A., and Buzsa´ ki, G. (2006). Integration and segregation of activity in entorhinal-hippocampal subregions by neocortical slow oscillations.\nNeuron 52, 871â882. https://doi.org/10.1016/j.neuron.2006.10.023.\n115. Hahad, O., Bayo Jimenez, M.T.B., Kuntic, M., Frenis, K., Steven, S.,\nDaiber, A., and Mu¨ nzel, T. (2022). Cerebral consequences of environmental noise exposure. Environ. Int. 165, 107306. https://doi.org/10.\n1016/j.envint.2022.107306.\n116. Paul, K.C., Haan, M., Mayeda, E.R., and Ritz, B.R. (2019). Ambient air\npollution, noise, and late-life cognitive decline and dementia risk. Annu.\nRev. Public Health 40, 203â220. https://doi.org/10.1146/annurev-publhealth-040218-044058.\n117. Weuve, J., DâSouza, J., Beck, T., Evans, D.A., Kaufman, J.D., Rajan, K.B.,\nde Leon, C.F.M., and Adar, S.D. (2020). Long-term community noise\nexposure in relation to dementia, cognition, and cognitive decline in older\nadults. Alzheimers Dement. 17, 525â533. https://doi.org/10.1002/alz.\n12191.\n118. Tzivian, L., Dlugaj, M., Winkler, A., Weinmayr, G., Hennig, F., Fuks, K.B.,\nVossoughi, M., Schikowski, T., Weimar, C., Erbel, R., et al. (2016). Longterm air pollution and traffic noise exposures and mild cognitive impairment in older adults: a cross-sectional analysis of the Heinz Nixdorf\nRecall Study. Environ. Health Perspect. 124, 1361â1368. https://doi.\norg/10.1289/ehp.1509824.\n119. Tzivian, L., Jokisch, M., Winkler, A., Weimar, C., Hennig, F., Sugiri, D.,\nSoppa, V.J., Dragano, N., Erbel, R., Jo¨ ckel, K.H., et al. (2017).\nAssociations of long-term exposure to air pollution and road traffic noise\nwith cognitive function-An analysis of effect measure modification.\nEnviron. Int. 103, 30â38. https://doi.org/10.1016/j.envint.2017.03.018.\n120. Sttu¨ tgen, M. (2026). MLIB toolbox for analyzing sipke data. MATLAB\nCentral File Exchange. https://www.mathworks.com/matlabcentral/\nfileexchange/37339-mlib-toolbox-for-analyzing-apike-data.\n121. Berens, P. (2009). CircStat: a MATLAB toolbox for circular statistics.\nJ. Stat. Softw. 31, 1â21. https://doi.org/10.18637/jss.v031.i10.\nll\nCurrent Biology 36, 1â14, August 3, 2026 13\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\n122. Kaya, E., Wegienka, E., Akhtarzandi-Das, A., Do, H., Eban-Rothschild,\nA., and Rothschild, G. (2025). Food intake enhances hippocampal sharp\nwave-ripples. eLife 14, RP105059. https://doi.org/10.7554/eLife.105059.\n123. Maloney, K.J., Cape, E.G., Gotman, J., and Jones, B.E. (1997). High-frequency γ electroencephalogram activity in association with sleep-wake\nstates and spontaneous behaviors in the rat. Neuroscience 76,\n541â555. https://doi.org/10.1016/s0306-4522(96)00298-9.\n124. Brown, R.E., Basheer, R., McKenna, J.T., Strecker, R.E., and McCarley,\nR.W. (2012). Control of sleep and wakefulness. Physiol. Rev. 92, 1087â\n1187. https://doi.org/10.1152/physrev.00032.2011.\n125. Ellender, T.J., Nissen, W., Colgin, L.L., Mann, E.O., and Paulsen, O.\n(2010). Priming of hippocampal population bursts by individual perisomatic-targeting interneurons. J. Neurosci. 30, 5979â5991. https://doi.\norg/10.1523/jneurosci.3962-09.2010.\n126. Silver
131smith, D.B., Lemke, S.M., Egert, D., Berke, J.D., and Ganguly, K.\n(2020). The degree of nesting between spindles and slow oscillations\nmodulates neural synchrony. J. Neurosci. 40, 4673â4684. https://doi.\norg/10.1523/JNEUROSCI.2682-19.2020.\n127. Novitskaya, Y., Sara, S.J., Logothetis, N.K., and Eschenko, O. (2016).\nRipple-triggered stimulation of the locus coeruleus during post-learning\nsleep disrupts ripple/spindle coupling and impairs memory consolidation. Learn. Mem. 23, 238â248. https://doi.org/10.1101/lm.040923.115.\n128. Eschenko, O., Mo¨ lle, M., Born, J., and Sara, S.J. (2006). Elevated sleep\nspindle density after learning or after retrieval in rats. J. Neurosci. 26,\n12914â12920. https://doi.org/10.1523/JNEUROSCI.3175-06.2006.\n129. Valencia, M., Artieda, J., Bolam, J.P., and Mena-Segovia, J. (2013).\nDynamic interaction of spindles and gamma activity during cortical\nslow oscillations and its modulation by subcortical afferents. PLOS\nOne 8, e67540. https://doi.org/10.1371/journal.pone.0067540.\nll\n14 Current Biology 36, 1â14, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nSTARâ
METHODS\nKEY RESOURCES TABLE\nREAGENT or RESOURCE SOURCE IDENTIFIER\nChemicals, peptides, and recombinant proteins\nA-M Systems Cold Curing\nDental Cement Solvent, 16\noz can\nFisher Scientific NC9991372\nFormaldehyde solution min.\n37% free from acid\nMillipore Sigma, Sigma\nAldrich\n1039991000\nPBS 10X BIO-RAD LABORATORIES\nINCORPORATED\n1610780\nCM-DiI Dye Fisher Scientific\n(ThermoFisher)\nC7000\nFluoroshield Mounting\nMedium with DAPI (20ml)\nAbcam ab104139\nSucrose Millipore Sigma, Sigma\nAldrich\nS0389-5KG\nExperimental models: Organisms/strains\nR
131at: Sprague Dawley Charles River Crl:CD; RRID:\nRGD_10395233\nSoftware and algorithms\nTDT Synapse Tucker-Davis Technologies\n(TDT)\nhttps://www.tdt.com/\nsupport/downloads/\nTDT OpenSorter Tucker-Davis Technologies\n(TDT)\nhttps://www.tdt.com/\nsupport/downloads/\nTDT RPvdsEx version 98 Tucker-Davis Technologies\n(TDT)\nhttps://www.tdt.com/\nsupport/downloads/\nMATLAB R2019a MathWorks https://www.mathworks.\ncom/\nCustom MATLAB scripts Karla Salgado-Puga https://doi.org/10.6084/m9.\nfigshare.32595861\nPCA and K-means sorter Haoxuan Xu https://github.com/\nTOMORI233/mysort/tree/\nmaster\nMLIB-Matlab toolbox for\nanalyzing spike data\nMaik Stu¨ ttgen120 https://www.mathworks.\ncom/matlabcentral/\nfileexchange/37339-mlibtoolbox-for-analyzing-spikedata\nCircular Statistics Toolbox\n(Directional Statistics)\nPhilipp Berens121 https://www.mathworks.\ncom/matlabcentral/\nfileexchange/10676-\ncircular-statistics-toolboxdirectional-statistics\nViolin plot Bastian Bechtold https://github.com/bastibe/\nViolinplot-Matlab\nWaxholm Space atlas of the\nSprague Dawley rat brain\nPapp EA, Leergaard TB,\nCalabrese E et al.\nhttps://scalablebrainatlas.\nincf.org/rat/PLCJB14\nZenPro software (Zen (blue\nedition) version 3.97)\nZeiss Microscopy https://www.zeiss.com/\nmicroscopy/us/products/\nsoftware/zeiss-zen.html\nBioRender BioRender https://www.biorender.com/\nVLC media player VideoLAN organization https://www.videolan.org/\n(Continued on next page)\nll\nCurrent Biology 36, 1â14.e1âe6, August 3, 2026 e1\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nEXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS\nSubjects and implant procedures\nAll animal procedures were performed in accordance with the regulations of the University of Michigan animal care committee.\nA total of 25 adult male Sprague Dawley rats (400 - 500 g) were used in this study. Subjects were housed individually in transparent\nacrylic cages and maintained under controlled temperature (22 ± 1 â¦C), ad libitum food and water, and a 12-h light cycle/12-h dark\ncycle (lights on at 9:00 am). All subjects were weighed and handled a week prior to any experimental manipulation. For the hippocampal SWR recordings, silicon probes (n=5 rats, A4x16-Poly2-5mm-23s-200-177 NeuroNexus), a tetrode microdrive array (n=8\nrats) or stainless steel (A-M Systems) electrodes were used122 (n = 12 rats). Briefly, subjects were anesthetized with ketamine\n(70mg/kg) and xylazine (10 mg/Kg) and maintained under 0.5-1.5% isoflurane throughout the surgery. A craniotomy was performed\nover the dorsal CA1 region of the hippocampus (-3.7 mm AP, -3.0 mm ML) and a silicon probe attached to a metal microdrive\n(R2Drive, 3Dneuro), or a microdrive with 8-12 independently moveable tetrodes (four 12.5 μm nichrome wires bundle) or stainless-steel electrodes (two 50 μm wires bundle) were secured in place using dental acrylic with supporting anchoring skull screws.\nAll hippocampal electrodes were dipped in DiI (Fisher Scientific) prior to surgery for histological analysis. For the electroencephalogram (EEG) recordings, a stainless-steel screw was placed over the right primary somatosensory cortex (- 1.0 mm AP, 4.0 ML). For\nthe electromyogram (EMG), two stainless steel loops were inserted into the neck muscles. Lastly, a ground screw was placed over\nthe cerebellum (2.0 mm posterior to lambda, 3.5 mm ML). During the course of the surgery, subjects received saline solution (s.c., up\nto 5 mL) and a carprofen injection (5 mg/Kg, s.c.) was given as an analgesic. All subjects had 7 days of post-surgery recovery.\nFollowing recovery, all subjects were habituated to the middle chamber of the CPP apparatus - sleep chamber - (12x12x12 inches,\nwith nesting material as the floor) for 30 min. Signal quality and hippocampal SWRs were assessed during habituation sessions. For\nthe subjects implanted with the silicon probe or tetrodes, these were advanced gradually across 11 days post-surgery towards the\nhippocampus and SWR observation was used as validation for electrodes location.\nSleep recordings and s
131ound stimulation\nHippocampal Local Field Potential (LFP) and single-unit spiking activity, EMG and EEG signals were acquired using a Tucker-Davis\nTechnologies (TDT) acquisition system and Synapse software (TDT). LFP data were sampled at 6 kHz and digitally filtered from 0.5 to\n500 Hz and stored for analysis. Spike data were sampled at 24,414 Hz and digitally filtered from 300 Hz to 3000 Hz. For all sessions,\ntwo video cameras (Allied vision, 30 fps, and USB Logitech camera, 10 fps) were used to monitor the subjectâs behavior and awakesleep state. Video recordings were acquired by RV2 acquisition system (TDT), Synapse software, and open-source VLC software.\nElectrophysiological recordings during sleep were made in the sleep chamber after spontaneous behavior, defined as voluntary\nbehavior in the absence of a task (exploring, rearing, grooming, sniffing, and nesting123), or after a hippocampal-dependent\nContinued\nREAGENT or RESOURCE SOURCE IDENTIFIER\nOther\nPZ5 amplifier Tucker-Davis Technologies\n(TDT)\nPZ5\nRZ2 acquisition system Tucker-Davis Technologies\n(TDT)\nRZ2\nRV2 acquisition system Tucker-Davis Technologies\n(TDT)\nRV2\nSilicon probes NeuroNexus A4x16-Poly2-5mm-23s-200-\n177\nMetal microdrive 3Dneuro R2Drive\nPFA Insualted Stainless Steel\nWire, 002 Inches Bare, 0045\nInches Coated\nA-M Systems 790600\nNichrome wire 1/4 Hard Pac\n0.0005\nSandvik RO800\nInfusion pump KdScientific KDS200 series\nArduino Uno REV3 board Arduino Uno Rev3\nGrid floor of stainless-steel\nrods chamber\nMed Associates Inc. ENV-008-CS3\nSquare-pulse stimulator Med Associates Inc. ENV-414S\nCryostat Leica CM1950\nFluorescence microscope Zeiss Microscopy Zeiss AX10 Imager.M2\nll\ne2 Current Biology 36, 1â14.e1âe6, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nassociative learning task (Conditioned Place Preference (CPP) task or in the Contextual Fear Conditioning (CFC) task). During the\nrecordings, the subjects were restricted from water and food, as they may act as distractors. The recording session started as\nsoon as the subject showed a clear and steady NREM phase (longer than 5 min). The recording session lasted until it reached 2h\nof sleep (3-5h total recording time). Sleep was determined by video supervision criteria and electrophysiological signals (EEG,\nEMG and SWRs, see methods below). Once a steady NREM phase was reached, online SWRs detection was enabled. Depending\non the experimental condition, BBN stimuli were not delivered (NS condition), delivered at SWR detection (\u003c5 ms delay; On-SWR\ncondition), or delivered 2 s after SWR detection (Off-SWR condition).\nFor the sleep recording following a spontaneous behavior session, the first hour of sleep was recorded without any BBN presentation (No Stimulation -NS- phase). In the second hour, every SWR detection triggered a BBN stimulus. For the sleep recordings\nfollowing an associative learning task, the full 2 h of sleep had one type of sound presentation modality.\nMETHOD DETAILS\nOnline detection of awake and sleep phases\nAwake, rest, and sleep states were determined by online recording of EEG and EMG signals with visual supervision.124 Visual criteria\nused to identify the awake state were the subjectâs body movement and spontaneous behavior display.123 To identify the rest state,\nthe visual criteria used were a reduction of the subjectâs body movement or lack thereof, together with active head movement, sniffing, occasional grooming, and open eyes. Visual criteria used to identify the sleep state were the subjectâs position, immobility for\nmore than 8 seconds, and eyes closed. In addition to these observations, the EMG signal was filtered between 10-100 Hz, and\nthe Root Mean Square (RMS) power averaged within a 5 s sliding window was used to assess the subjectâs movement. Sleep state\nwas identified as a decrease in EMG power of 2.5â3.0 standard deviations below the mean. To discriminate Rapid Eye Movement\n(REM) from Non-REM (NREM) sleep phases, the EMG signal had to be more than 1.0 standard deviation below the mean, indicating\nthe loss of postural muscle tone together with sudden and short power increases produced by muscular twitches. EEG signals were\nfiltered between 0.5-4.5 Hz and 6-10Hz to monitor Delta and Theta activity, respectively. RMS power from both frequency bands was\naveraged within a 2.5 s sliding window and the delta/theta ratio was calculated. REM and N
131REM sleep phases were defined by a\ndelta/theta ratio below or near 1.0 and above 2.0, respectively.\nReal-time SWR detection and sound stimulation\nOnce criteria for sleep and NREM phase were reached, hippocampal LFP signals from 2 channels with clear SWRs and low spike\nunits were filtered between 150-250 Hz. LFP filtered signals were averaged for 60 s to estimate baseline hippocampal activity. Events\nfrom the filtered signals exceeding the mean by 6â9 standard deviations in a 10 ms sliding window were considered positive SWRs.\nThese settings were verified to detect events longer than 30 ms. When enabled, each SWR triggered a 50 ms BBN stimulus with an\nintensity of 50 dB and a 5 ms onset and offset ramps (sampling frequency of 25 kHz), unless specified otherwise. To prevent multiple\nBBN presentations in ripple bursts, each SWR-triggered BBN stimulus had a 200 ms interval before delivering another sound stimulus. The SWRs detected from those 2 channels were used for offline analysis of all the electrodes in the hippocampus.\nAssociative learning tasks\nConditioned Place Preference (CPP). The CPP apparatus had three chambers (12x12x12 inches each) divided by two sliding doors.\nThe middle chamber walls were all black and had nesting material as the floor. The chambers on the sides had colored shape patterns\nposted on the walls and access to a reward well. Each reward well had attached two IR break beam sensors connected to an Arduino\nUno REV3 board. The Arduino board was connected to an infusion pump (KDS200 series, KdScientific). On top of the apparatus, one\nUSB Logitech camera (30 fps) was set. Both video recordings and Arduino-automated reward delivery were connected to the TDT\nRZ2 acquisition system controlled by Synapse software (TDT). When the TDT system was set to enable Arduinoâs sensors, every time\nthe subject licked the reward well a TTL sent by Arduino triggered the infusion pump to deliver 0.1 mL of sucrose 20% as a reward at\n20 mL/min.\nCPP behavior paradigm was modified from Trouche et al. (2019).65 Before training, all the subjects were deprived of water for 24\nhours. For training, the subjects were placed in the middle chamber for 1 min with no access to any other chamber. Afterward, the\ndoors accessing the side chambers were open. The subject was allowed to explore the three chambers freely for 15 min (Pre-Test\nperiod). During this period, the amount of time spent in each chamber was measured, and the innate preference for one of the sided\nchambers was determined to use the opposite chamber (less time spent in) as the conditioned chamber. After the Pre-Test period, all\ndoors were closed, and the subject was left in the middle chamber for 1 min. To start a conditioning trial, the door to the designated\nconditioned chamber was opened. After the subject had crossed to the chamber (a full entry was defined as all 4 paws inside the\nchamber), the door was closed. The subject was left in the chamber with access to a solution of 20% sucrose in the reward well.\nAfter 5 min, the door was opened to let the subject out of the chamber. Once out, the door was closed again. The subject remained\nin the middle chamber for 1 min. Next, the opposite door was opened (non-conditioned chamber). The subject remained in the nonconditioned chamber for 5 min with access to plain water in the reward well. Each subject had 2 trials in each chamber in a pseudorandom order. At the end of the trials, the subject was left in the middle chamber to sleep for a period of 3-4h. To test memory\nretention, two time points were evaluated, a short period (3 h), and a longer period (24 h) after the training. For these tests, subjects\nwere placed in the middle chamber for 1 min. Next, both side doors were opened, and the subject was allowed to move freely through\nthe three chambers for 5 min (Retention period).\nll\nCurrent Biology 36, 1â14.e1âe6, August 3, 2026 e3\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nContextual Fear Conditioning (CFC). The CFC apparatus consisted of a transparent acrylic chamber (12x12x12 inches). The chamber had a lateral door and a grid floor of stainless-steel rods (Med Associates Inc.). All walls were covered with paper patterns as\nvisual cues. The floor rods could be electrified using a square-pulse stimulator (ENV-414S, Med Associates). On top of the apparatus,\na USB L
131ogitech camera (30 fps) was set. Foot shock delivery and video recording were controlled and recorded by Synapse software\n(TDT). After the CFC conditioning, the subject was removed from the chamber and placed to another transparent acrylic chamber\n(12x12x12 inches) with white foam board on the walls and nesting material on the floor (ââsleepââ chamber, Med Associates Inc.).\nFor the training, the subject was placed inside the conditioning chamber. The subject was allowed to explore the chamber for 5 minutes (pre-Conditioning period). Immediately after, six 0.45 mA foot shocks were delivered at 0.1 Hz through the grid floor (aversive\nconditioning). Once the foot shock finished, the subject remained in the chamber for another minute (post-conditioning phase). No\nelectrophysiological signals were recorded during the CFC conditioning. Afterwards, the subject was removed and placed in the\nââsleepââ chamber to sleep for a period of 3-4 h. Memory retention was evaluated 3 h and 24 h after the training. The retention tests\nconsisted of placing the subject in the conditioning chamber for 5 minutes (retention period) with no additional aversive stimuli.\nHistology\nTo verify electrode tracks, all subjects were euthanized under isoflurane anesthesia (5%) and perfused transcardially with saline followed by 4% paraformaldehyde (PFA). Brains were removed and fixed in 4% PFA (72 h), followed by cryoprotection in 30% sucrose\n(72 h). Coronal sections (30-50 μm) were obtained using a cryostat (Leica CM1950) and kept in PBS 4â¦C before mounting. Each section was mounted on a glass slide and covered with Fluoroshield-DAPI (Abcam, USA). Sections were examined for cell nuclei (DAPI\n470 nm) and electrodes track (DiI, 550 nm) using a fluorescence microscope (Zeiss AX10 Imager.M2) and the ZenPro software (Zen\n(blue edition) version 3.97, Zeiss Microscopy).\nQUANTIFICATION AND STATISTICAL ANALYSIS\nData and statistical analysis were performed with MATLAB R2019a custom scripts and standard functions (https://doi.org/10.6084/\nm9.figshare.32595861). Figures were generated using Adobe Illustrator (CS6 v16.0.0).\nCPP and CFC behavioral analysis\nTo evaluate memory retention in the CPP task, a CPP score was calculated for each pre-Test and retention period (3 h and 24 h posttraining). The score was calculated by measuring the amount of time spent in the conditioned chamber minus the amount of time\nspent in the non-conditioned chamber, divided by the total amount of time spent in both chambers. To get a sense of the absolute\nchange in the subjectâs context preference (normalized score) and therefore memory robustness, the CPP scores were normalized by\nsubtracting the pre-Test score from the score of the retention period and then dividing by (1 â pre-Test score). CPP scores comparison between taskâs tests (pre-Test, at 3 h and at 24 h) within a group, were analyzed with the non-parametric repeated-measures\nFriedman test and the Wilcoxonâs signed-rank test as a post hoc. The comparison of the normalized score to zero (zero considered as\nno shift in the contextual preference and therefore, no memory) was done by one-sample Wilcoxon test. Normalized scores comparison among groups was analyzed using Kruskal-Wallis and the Mann-Whitney U tests.\nTo evaluate aversive memory retention in the CFC task, freezing behavior was calculated during the pre-conditioning period and\ncompared to both retention periods at 3 h and 24 h post-training. Freezing behavior was defined as continuous immobility for more\nthan 5 s, showing no other behavior such as grooming or exploring behavior (actively sniffing in a particular zone). The proportion of\nthe amount of time that the subject spent immobile (% freezing) was calculated by dividing the amount of the time of immobility by the\ntotal duration of the test (5 min). Comparison of the percentage of freezing behavior between pre-conditioning, and retention periods\n(3 h and 24 h tests), was analyzed with the non-parametric repeated-measures Friedman test and the Wilcoxonâs signed-rank test as\na post hoc. Comparison of the percentage of freezing behavior between experimental groups was analyzed with Kruskal-Wallis and\nthe Mann-Whitney U tests.\nAw
131ake-sleep classification and quantification\nElectrophysiological recordings were analyzed offline. For awake-sleep states analysis each recording video was visually inspected\nto choose periods of 900 s where the subject was clearly awake (that included quiet wakefulness states) and sleeping. For each 900 s\nbouts per state, both EMG and EEG signals were filtered from 10-100 Hz and 0.5-30 Hz, respectively. First, to analyze subjectâs\nmovement per state, EMG power was calculated using the RMS in a 10 s window. Based on the EMG power comparison between\nstates a threshold of the EMG power + 1.5 std deviation was used for awake-sleep states classification for the following analysis of\nthe recording session. Proportion of the total time per state per session was calculated by dividing the amount of time per state by the\ntotal time of the recording session. Proportion of EMG power during sleep was normalized to the EMG power during awake. To classify NREM and REM sleep phases, the EEG signal was filtered between 0.5-4.5Hz corresponding to the Delta frequency band and\nbetween 6-10 Hz corresponding to the Theta frequency band. Then, to calculate Delta and Theta power, each recording session was\nbinned in 5 s windows. Next, the spectrogram calculation per window was normalized by the mean power spectrogram of the entire\nrecording from that session. Delta and Theta power and Delta/Theta ratio were quantified per window and averaged to obtain the\npower for each frequency and ratio per session. Delta/Theta ratios per window were used to classify NREM and REM periods as\nfollows. Periods with Delta/Theta ratios below 1.0 were classified as REM periods, while periods with Delta/Theta ratios higher\nll\ne4 Current Biology 36, 1â14.e1âe6, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nthan 2.0 were classified as NREM periods. To get the proportion of the NREM and REM periods, the amount of time in NREM and\nREM periods was normalized to the total amount of sleep per recording session. Additionally, to quantify the power per session of\nBeta and Sigma oscillations, the EEG signal was filtered between 15-50 Hz corresponding to Beta frequency, and between 11-15Hz\ncorresponding to Sigma frequency. Power for each frequency band was calculated and averaged as described above for Delta and\nTheta frequency bands.\nSleep time, sleep phase fraction, EMG power, and normalized Delta, Beta, and Sigma power were analyzed with non-parametric\nstatistics. Comparison between groups was done with the non-parametric Kruskal-Wallis (KW) test and, if applicable, followed by the\nMann-Whitney U (MW-U) post hoc test.\nPeriBBN stimuli EMG and EEG analysis\nTo further verify the effect of the BBN presentation over the awake-sleep states, averaged EMG power and Delta power of 5 s windows were compared before and after sound stimulation times. Comparison between EMG power or Delta power from Pre-sound\nstimulus windows to Post-sound stimulus windows were done by getting the logarithm of the ratio from EMG power or Delta power\nPre/Post and compared to zero using one sample Wilcoxonâs signed-rank test. In a similar way, averaged Delta/Beta and Delta/\nSigma ratios were calculated in 5 s windows before and after BBN stimuli times. For comparison between groups, post-sound\nover pre-sound ratio was calculated and then compared using a non-parametric Kruskal-Wallis test.\nOnline detected SWRs validation and analysis\nPutative SWRs detected during the recording acquisition were reevaluated offline to verify their identity and remove any potential\nfalse positives. The criteria of SWRs identification were to have a ripple power greater than the mean ripple power + 3 std deviation\nand a duration greater than 30 ms. To do this validation, the hippocampal LFP signal from NREM periods of sleep was band-pass\nfiltered between 150 and 250 Hz. Next, the mean and threshold of the ripple power were calculated. Ripple power from each event\ndetected was evaluated in windows of 100 ms before and after the event-detected time. The events having a ripple power over the\nthreshold were selected. Next, high ripple power events duration was calculated using the ripple power autocorrelation. Ripple duration was estimated from autocorrelation peak to periods where the autocorrelation peaks remained above the autocorrelationâs\nmean ± 2.0 std deviation.125 Events with a duration shorter than 30 ms were discarded. Next, spectrograms (0 - 400 Hz) were calculated for each 200 ms window from verified SWRs (-100 ms, +100 ms, from SWR detection). Then, spectrograms were normalized by\nthe mean power spectrogram of the entire recording from that session. From the normalized spectrograms, individual SWR peak\nfrequency and duration in time domain were quantified. Individual SWRs normalized ripple power was quantified as the sum of values\nfrom 150-250 Hz and from 0 - 100 ms from SWRs detection (absolute power). Finally, spectrograms from individual SWRs were averaged to get the mean spectrogram per recording. Spectrograms were averaged to get a mean spectrogram per experimental condition. Additionally, Individual SWRs spectrograms were averaged in the time-domain (from -100 ms to +100 ms, from SWR detection) to get a mean ripple power per experimental condition. SWRs rate was averaged per subject while rippleâs power, duration and\npeak frequency were averaged grouping SWRs per experimental condition.\nSWRs data (rate, power, duration, and peak frequency) were analyzed with non-parametric statistics. To compare the effect of the\nsound stimulation within a group, data were analyzed with two-tailed Wilcoxonâs signed-rank test (WSR). To compare the effect of\nsound stimulation between two groups, data was normalized by the mean of the data from no sound (NS) condition, where NS data\nmean = 1.0. For statistical comparison, Mann-Whitney U (MW-U) test was used. To compare the effect of sound stimulation among\ngroups, Kruskal-Wallis (KW) analysis was used followed by the post hoc MW-U test. SWRs power over time waves were analyzed\nwith a two-way ANOVA.\nPeri-SWR single-unit analysis\nSilicon probe array recordings (4 shanks, 16 channels each) were sorted offline using PCA and K-means based algorithms (https://\ngithub.com/TOMORI233/mysort/tree/master) followed by manual cluster curation using Open Sorter softw
131are (Tucker-Davis Technologies.126 Additionally, clustering, cell duplication, and unit quality were evaluated using MLIB-Matlab toolbox for analyzing spike\ndata.120 SWR-associated units were defined as units within a ±100 ms window around the online ripple detection times. Raster plot,\nperi-ripple histogram (corrected by the total amount of online ripples per experimental condition), and spike density function (SDF)\nwere calculated for each cell. Cell inclusion criteria for the analysis were based on unit count and SDF-firing rate within a ±50 ms\nwindow around the ripple detection time, for which peri-ripple firing rate had to be above 2.5-fold from baseline. For included cells,\nunitsâ quantification per ripple was done in 50 ms windows. The baseline period (Baseline) was considered from 0 â 50 ms from the\nstart of the 200 ms window, and the SWR period was considered from -15 to 35 ms around ripple detection time. Then, baselineSWR-associated firing correction was calculated by subtracting the number of units in the baseline window from the ripple window\n(SWR-baseline). To determine the net influence of BBN presentation over cell firing, a modulation index was defined as the base-10\nlogarithm of the ratio between the number of spikes in the corresponding time window during BBN presentation divided by the number of spikes in that time window during NS (spiking modulation index). Thus, a spiking modulation index below 0 denotes a reduction\nin cell firing, while above 0 denotes an increase. Next, to evaluate which cells were significantly modulated by BBN stimulation, the\nnumber of units from all ripples during baseline, ripple, and SWR-baseline windows were compared between no sound and sound\npresentation conditions using a two-tailed Mann-Whitney U test. Cells showing a significant difference between no sound condition\nand sound conditions were considered ââmodulated cellsââ. Based on this, the fraction of cells showing a significant firing reduction\n(âDownâ), firing increase (âUpâ), or no significant change (âno changeâ), were calculated. To identify a cell population effect induced by\nBBN presentation, modulation indexes distribution from both total and modulated cells populations were compared to 0 per analysis\nll\nCurrent Biology 36, 1â14.e1âe6, August 3, 2026 e5\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle\nwindow (non-parametric WSR). Lastly, modulation indexes distribution from all cells in On-SWR and Off-SWR were compared by\nusing Mann-Whitney U test.\nOffline spindle detection and analysis\nTo analyze the effect of BBN presentation over spindle activity, we used a criteria for single spindle identification as having spindle\npower greater than the mean spindle power + 3 std deviation and a duration greater than 350 ms.127,128 First, EEG signal from NREM\nperiods of sleep was band-pass filtered between 11 and 15 Hz. Next, Fast Fourier Transform was used to calculate the mean and\nthreshold of the spindle power of the entire recording session. Each session was binned in 5 s windows and evaluated for above\nthreshold events. For high spindle power events greater than 100 ms apart, duration was calculated using the spindle power autocorrelation. Spindle duration was estimated from autocorrelation peak to periods where the autocorrelation peaks remained above\nthe autocorrelationâs mean ± 2.0 std deviation.125 Events with a duration less than 350 ms were discarded. The LFP signal from positive spindles identified was stored as 2 s window (from -1 to +1 s from spindle peak). Next, spectrograms (0 - 400 Hz) were calculated\nfor 2 s window and normalized by the mean power spectrogram of the recording from that session. From the normalized spectrograms, individual normalized spindle power was quantified from 11-15 Hz from â500 ms to 500 ms period around the spindle\npeak. Spindle rate was averaged per subject while individual spindles power were averaged grouping spindles per experimental condition. Spindle power and rate were normalized to the no sound (NS) condition, where NS mean = 1.0 and analyzed with non-parametric statistics. Comparisons of raw and normalized data within a group, NS condition and BBN-presentation condition data were\ndone with two-tailed Wilcoxonâs signed-rank test. To compare the effect of sound stimulation among groups, spindle power and rate\nfrom the second hour were evaluated with the Kruskal-Wallis test followed by the Mann-Whitney U post hoc test.\nSlow oscillations phase analysis\nTo evaluate the phase of the slow oscillations (SO) at which the sound stimuli occurred, EEG signals from NREM periods were filtered\nbetween 0.1-1.0 Hz. Next, the Hilbert transform was used to calculate the instantaneous wrapped phase.84,126,129 For analyses\nrelating BBN timing to cortical slow oscillation phase, phase values were defined based on the onset of each BBN stimulus. The\nSO phase statistical analysis was performed using the Circular Statistics MATLAB Toolbox.121 To evaluate whether SO phase\nand ripple power were correlated, correlation coefficients were calculated per session and per subject. Grouped correlation coefficients from all subjects per experimental condition were compared to 0 using the non-parametric Wilcoxonâs signed-rank test.\nSound to ripple delay time quantification\nTo evaluate if variability in the delay between SWR and the BBN stimulus was correlated with the degree of the ripple power reduction; sound onset times were subtracted from the ripple onset times during an On-SWR protocol (delta time). Sound-ripple delta time\nwas correlated to the corresponding ripple power value. Delta times and ripple power correlations were done per recording per subject. To determine if there was a significant influence as a group, correlation coefficients from all subjects were compared to 0 using\nthe non-
131parametric Wilcoxonâs signed-rank test.\nSound duration-intensity curve\nTo compare the effects of different BBN features over ripple power, a combination of stimulus intensities (15, 35, and 50 dB) and\nstimulus durations (15, 35, and 50 ms) were used. Sleep recordings were performed after a spontaneous behavior session, in which\nthe first sleep hour didnât have sound stimulation. During the second hour of sleep, sound duration-intensity combinations were triggered by online SWR detection (On-SWR modality). For SWRs power statistical comparison within a condition, data was normalized\nby the mean of the data from no sound (NS) condition, where NS data mean = 1.0, and a non-parametric Wilcoxonâs signed-rank test\nwas used. For the comparison of the effect of sound stimulation among all groups, the raw and normalized SWR power during the\nsecond hour was used. Then, evaluated with the Kruskal-Wallis test followed by the Dunn-Sidak post hoc test.\nll\ne6 Current Biology 36, 1â14.e1âe6, August 3, 2026\nPlease cite this article in press as: Salgado-Puga et al., Exposure to broadband noise during non-REM sleep impairs hippocampal sharp-wave ripples\nand memory consolidation, Current Biology (2026), https://doi.org/10.1016/j.cub.2026.06.039\nArticle ","fulltextMode":"markdown","pdfExternalUrl":"","pdfPublic":false,"meta":{},"featured":false,"sortOrder":28,"createdAt":"2026-07-14T16:52:13.986Z","products":["r2drive"]},{"id":"pub_wZIkJdU","slug":"dynamics-of-dentate-gyrus-place-cells-and-dentate-spikes-during-spatial-and-nonspatial-changes-in-environments","status":"published","type":"preprint","bibtexKey":"demetrovich2025","title":"Dynamics of Dentate Gyrus Place Cells and Dentate Spikes During Spatial and Nonspatial Changes in Environments","authors":["Peyton G. Demetrovich","Laura Lee Colgin"],"year":2025,"month":"October","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.10.24.684382","url":"https://www.biorxiv.org/content/10.1101/2025.10.24.684382v2","abstract":"The dentate gyrus (DG) is thought to play a key role in the formation of dissociable memory representations for similar contexts. Neurons in the DG receive highly processed spatial and nonspatial sensory information from the medial and lateral entorhinal cortices, respectively. Here, we tested the extent to which different spatial and nonspatial stimuli modulate place cell firing patterns and dentate spike dynamics, performing extracellular recordings of DG place cells and local field potentials in rats of both sexes. DG place cells exhibited different firing patterns between familiar and novel environments, but not with nonspatial stimuli; dentate spikes associated with lateral entorhinal cortex input increased during exploration of ethologically relevant (especially social) stimuli.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2025.10.24.684382v2.full.pdf","pdfPublic":true,"meta":{"species":"rat","sex":"male and female","behavioralParadigm":"freely moving"},"featured":false,"sortOrder":0,"createdAt":"2026-06-16T07:46:05.106Z","products":["r2drive"]},{"id":"pub_q9OEmsg","slug":"modular-reconfigurable-fiber-based-neural-probe-morf-probe-with-interchangeable-and-tunable-optical-waveguide-microfluidic-channel-and-microelectrodes","status":"published","type":"preprint","bibtexKey":"huang2025morf","title":"Modular, reconfigurable fiber-based neural probe (MoRF probe) with interchangeable and tunable optical waveguide, microfluidic channel, and microelectrodes","authors":["Hengji Huang","Yue Liu","Shuo Yang","Song Hu","Daniel Fine English","Xiaoting Jia"],"year":2025,"month":"July","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.06.29.662251","url":"https://www.biorxiv.org/content/10.1101/2025.06.29.662251v1","abstract":"We developed a low-cost modular and reconfigurable recording and stimulation fiber-based neural probe (MoRF) fabricated via a thermal drawing process and a thermal tapering process. We demonstrated device modularity and reconfigurability, validated electrical/optical/microfluidic drug-delivery performance, and demonstrated in vivo electrophysiological recording and optogenetic stimulation in awake mice.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.bi
131orxiv.org/content/10.1101/2025.06.29.662251v1.full.pdf","pdfPublic":true,"meta":{"species":"mouse","behavioralParadigm":"awake"},"featured":false,"sortOrder":1,"createdAt":"2026-06-16T07:46:05.107Z","products":["r2drive"]},{"id":"pub_GiD8CCE","slug":"density-based-longitudinal-neuron-tracking-in-high-density-electrophysiological-recordings","status":"published","type":"preprint","bibtexKey":"huang2025dant","title":"Density-based longitudinal neuron tracking in high-density electrophysiological recordings","authors":["Yue Huang","Hanbo Wang","Jiaming Cao","Yu Chen","Xuanning Wang","Yujie Zhao","Hengkun Ren","Qiang Zheng","Jianing Yu"],"year":2025,"month":"December","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.64898/2025.12.19.695632","url":"https://www.biorxiv.org/content/10.64898/2025.12.19.695632v1","abstract":"We introduce DANT (Density-based Across-day Neuron Tracking), an unsupervised framework that jointly estimates probe motion and neuron identity for tracking single neurons across days in high-density extracellular recordings. Applied to weeks-long Neuropixels recordings from cortex and striatum in freely moving rats, DANT increases match yield and reduces false negatives while maintaining a low false-positive rate.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.64898/2025.12.19.695632v1.full.pdf","pdfPublic":true,"meta":{"species":"rat","implantDuration":"chronic","experimentDuration":"weeks-long recordings","behavioralParadigm":"freely moving","siliconProbe":"Neuropixels"},"featured":false,"sortOrder":3,"createdAt":"2026-06-16T07:46:05.108Z","products":["r2drive"]},{"id":"pub_cHwzlYw","slug":"ultra-high-density-neuropixels-probes-improve-detection-and-identification-in-neuronal-recordings","status":"published","type":"article","bibtexKey":"ye2025npultra","title":"Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings","authors":["Zhiwen Ye","Andrew M. Shelton","Jordan R. Shaker","Julien Boussard","Jennifer Colonell","Daniel Birman","Sahar Manavi","Susu Chen","Charlie Windolf","Cole Hurwitz","Han Yu","Tomoyuki Namima","Federico Pedraja","Shahaf Weiss","Bogdan C. Raducanu","Torbjørn V. Ness","Xiaoxuan Jia","Giulia Mastroberardino1","L. Federico Rossi","Matteo Carandini","Michael Häusser1","Gaute T. Einevoll1","Gilles Laurent","Nathaniel B. Sawtell","Wyeth Bair","Anitha Pasupathy","Carolina Mora Lopez","Barundeb Dutta","Liam Paninski","Joshua H. Siegle","Christof Koch","Shawn R. Olsen","Timothy D. Harris","Nicholas A. Steinmetz"],"year":2025,"month":"December","journal":"Neuron","volume":"","issue":"","pages":"","publisher":"","doi":"10.1016/j.neuron.2025.08.030","url":"https://www.cell.com/neuron/fulltext/S0896-6273(25)00665-8","abstract":"We developed a silicon probe with much smaller and denser recording sites than previous designs, Neuropixels Ultra (NP Ultra). Neuronal yield in mouse visual cortex recordings increased by more than 2-fold; the spatial extent of extracellular waveforms distinguished axonal from somatic recordings, and genetically identified cortical cell types could be discriminated.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"","pdfPublic":false,"meta":{"species":"lizard","sex":"","age":"","numProbes":"2-4","implantDuration":"acute","behavioralParadigm":"head-fixed","siliconProbe":"Neuropixels Ultra (NP Ultra)","headgear":"custom steel headplates with 3D-printed recording chambers","otherMicrodrives":"Sensapex uMP-4 manipulator"},"featured":false,"sortOrder":4,"createdAt":"2026-06-16T07:46:05.108Z","products":["r2drive"]},{"id":"pub_EHLIqhI","slug":"continuous-contributions-of-the-dorsolateral-striatum-to-movement-initiation-and-execution","status":"published","type":"preprint","bibtexKey":"cao2025dls","title":"Continuous contributions of the dorsolateral striatum to movement initiation and execution","authors":["Jiaming Cao","Yujie Zhao","Jianing Yu"],"year":2025,"month":"November","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.11.10.687756","url":"https://www.bi
131orxiv.org/content/10.1101/2025.11.10.687756v2","abstract":"Using behavior-timed optogenetic inhibition in rats performing a lever-release task, we found that dorsolateral striatum (DLS) inhibition delayed the initiation of forelimb reaching and conditioned lever release and, when delivered during reaching, interrupted execution. These findings suggest that expression of many learned movement patterns depends continuously on DLS activity.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2025.11.10.687756v2.full.pdf","pdfPublic":true,"meta":{"species":"rat","behavioralParadigm":"lever-release task"},"featured":false,"sortOrder":5,"createdAt":"2026-06-16T07:46:05.108Z","products":["r2drive"]},{"id":"pub_-hpw2Jk","slug":"neuronal-recordings-in-head-fixed-and-freely-moving-mole-rats","status":"published","type":"preprint","bibtexKey":"shirdhankar2025molerat","title":"Neuronal recordings in head-fixed and freely-moving mole-rats","authors":["R. N. Shirdhankar","A. Saeedi","G. E. Fenton","L. Moritz","P.-Y. Jacob","S. Begall","E. P. Malkemper"],"year":2025,"month":"December","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.64898/2025.12.15.693140","url":"https://www.biorxiv.org/content/10.64898/2025.12.15.693140v1","abstract":"We present a protocol for in vivo electrophysiological recordings in awake, head-fixed, and freely moving African mole-rats (Fukomys anselli/micklemi). Using tetrodes and Neuropixels probes we recorded single-unit activity and local field potentials across several cortical and subcortical regions for several weeks, observing prominent low-frequency hippocampal theta rhythms and presenting a new stereotaxic brain atlas.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.64898/2025.12.15.693140v1.full.pdf","pdfPublic":true,"meta":{"species":"African mole-rats","siliconProbe":"Neuropixels probes; also tetrodes","experimentDuration":"several weeks"},"featured":false,"sortOrder":6,"createdAt":"2026-06-16T07:46:05.108Z","products":["mouse-cap","copper-mesh"]},{"id":"pub_IjWsUFg","slug":"ultraslow-serotonin-oscillations-in-the-hippocampus-delineate-substates-across-nrem-and-waking","status":"published","type":"article","bibtexKey":"cooper2025serotonin","title":"Ultraslow serotonin oscillations in the hippocampus delineate substates across NREM and waking","authors":["Claire Cooper","Daniel Parthier","Jeremie Sibille","John J Tukker","Nicolas Tritsch","Dietmar Schmitz"],"year":2025,"month":"July","journal":"eLife","volume":"","issue":"","pages":"","publisher":"","doi":"10.7554/eLife.101105","url":"https://elifesciences.org/articles/101105","abstract":"Using simultaneous recordings of extracellular serotonin (GRAB5-HT3.0) and local field potential in hippocampal CA1 of mice, we revealed prominent ultraslow (\u003c0.05 Hz) 5-HT oscillations during both NREM and WAKE states, suggesting 5-HT dynamics delineate substates within larger brain states.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://elifesciences.org/articles/101105.pdf","pdfPublic":true,"meta":{"species":"mouse","sex":"","age":"","siliconProbe":"","experimentDuration":"1.5-3 h sessions","behavioralParadigm":"","headgear":"","otherMicrodrives":""},"featured":false,"sortOrder":7,"createdAt":"2026-06-16T07:46:05.108Z","products":["r2drive"]},{"id":"pub_BMjuqxI","slug":"dentate-spikes-comprise-a-continuum-of-relative-input-strength-from-the-lateral-and-medial-entorhinal-cortex","status":"published","type":"preprint","bibtexKey":"tarcsay2025ds3","title":"Dentate spikes comprise a continuum of relative input strength from the lateral and medial entorhinal cortex","authors":["Gergely Tarcsay","R. Saxena","R. Long","Justin L. Shobe","Bruce L. McNaughton","Laura A. Ewell"],"year":2025,"month":"October","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.10.27.684857","url":"https://www.biorxiv.org/content/10.1101/2025.10.27.684857v1","abstract":"Using silicon-probe recordings with high spatial resolution spanning all layers of the dentate gyrus, we discovered that the contribution of LEC/MEC inputs to dentate spikes follows a continuous distribution, and introduce a third type (DS3) capturing simultaneous current sinks in the outer/middle molecular layer.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.bi
131orxiv.org/content/10.1101/2025.10.27.684857v1.full.pdf","pdfPublic":true,"meta":{"siliconProbe":"silicon probes (high spatial resolution across dentate gyrus layers)"},"featured":false,"sortOrder":8,"createdAt":"2026-06-16T07:46:05.109Z","products":["r2drive"]},{"id":"pub_CgefJbA","slug":"mental-exploration-of-future-choices-during-immobility-theta-oscillations","status":"published","type":"preprint","bibtexKey":"wang2025mental","title":"Mental exploration of future choices during immobility theta oscillations","authors":["M. Wang","L. Yuan","Stefan Leutgeb","Jill K. Leutgeb"],"year":2025,"month":"February","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.02.03.636313","url":"https://www.biorxiv.org/content/10.1101/2025.02.03.636313v1","abstract":"We observed theta-related neural activity during movement in a hippocampus-dependent working memory task and, unexpectedly, theta oscillations and theta sequences during immobility that preferentially represented remote locations â in particular the next choice â suggesting theta during immobility supports mental exploration of future choices.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2025.02.03.636313v1.full.pdf","pdfPublic":true,"meta":{"species":"rat","behavioralParadigm":"spatial working memory task"},"featured":false,"sortOrder":10,"createdAt":"2026-06-16T07:46:05.109Z","products":["r2drive"]},{"id":"pub_XJAi1GU","slug":"erasable-hippocampal-neural-signatures-predict-memory-discrimination","status":"published","type":"article","bibtexKey":"kinsky2025erasable","title":"Erasable hippocampal neural signatures predict memory discrimination","authors":["Nathaniel R. Kinsky","Daniel J. Orlin","Evan A. Ruesch","Brian Kim","Siria Coello","Kamran Diba","Steve Ramirez"],"year":2025,"month":"March","journal":"Cell Reports","volume":"44","issue":"3","pages":"115391","publisher":"","doi":"10.1016/j.celrep.2025.115391","url":"https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00162-7","abstract":"Using calcium imaging in freely moving mice across a 10-day contextual fear conditioning task, we found two neural signatures predicting memory performance: context-specific place-field remapping and coordinated pre-recall activity. Anisomycin-induced amnesia accelerated cell turnover and arrested learning-related remapping. A rat silicon-probe control used the R2Drive for focal μLED inhibition.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://europepmc.org/articles/PMC12517107?pdf=render","pdfPublic":true,"meta":{"species":"rat","sex":"male and female","age":"5-10 months","weight":"240-480 g","numProbes":"2 (NeuroNexus A1x32; NeuroLight μLED)","experimentDuration":"10-day contextual fear conditioning","behavioralParadigm":"contextual fear conditioning; linear-track running","siliconProbe":"NeuroNexus A1x32-5mm-50-177; NeuroLight μLED probe","headgear":""},"featured":false,"sortOrder":11,"createdAt":"2026-06-16T07:46:05.109Z","products":["r2drive"]},{"id":"pub_DDBgn5c","slug":"age-dependent-alterations-in-the-head-direction-signal-in-a-rat-model-of-fragile-x-syndrome","status":"published","type":"preprint","bibtexKey":"moore2025fxs","title":"Age-dependent alterations in the head-direction signal in a rat model of Fragile X Syndrome","authors":["N. Moore","A. J. Duszkiewicz","A. Asiminas","P. A. Dudchenko","A. Peyrache","E. R. Wood"],"year":2025,"month":"January","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.01.09.632139","url":"https://www.biorxiv.org/content/10.1101/2025.01.09.632139v2","abstract":"Using high-density silicon probes, we recorded postsubiculum activity during exploration and sleep in juvenile and adult Fmr1-/y and wild-type rats. Juvenile Fmr1-/y rats exhibited enhanced head-direction tuning, but by adulthood HD tuning became unstable â revealing network-level dysfunctions in Fragile X Syndrome and their developmental trajectories.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2025.01.09.632139v2.full.pdf","pdfPublic":true,"meta":{"species":"rat","age":"juvenile and adult","behavioralParadigm":"open exploration and sleep","siliconProbe":"high-density silicon probes"},"featured":false,"sortOrder":13,"createdAt":"2026-06-16T07:46:05.110Z","products":["r2drive"]},{"id":"pub_t9kxVL4","slug":"flexible-selection-of-working-memory-representations-to-reduce-cognitive-cost","status":"published","type":"preprint","bibtexKey":"li2025workingmemory","title":"Flexible selection of working memory representations to reduce cognitive cost","authors":["J. Li","A. Z. Xu","C. Bao","A. Albesa-Gonzalez","L. Li","C. Clopath","J. C. Erlich"],"year":2025,"month":"November","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2025.11.28.691186","url":"https://www.bi
131orxiv.org/content/10.1101/2025.11.28.691186v2","abstract":"We show that rats flexibly adjust the format of working memory to reduce cognitive cost: an egocentric task is maintained as a motor plan in frontal cortex, while an allocentric reformulation is stored as a sensory trace in auditory cortex. A cost-sensitive model predicts which format is used and the circuit-level reallocation of working memory.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2025.11.28.691186v2.full.pdf","pdfPublic":true,"meta":{"species":"rat","behavioralParadigm":"egocentric and allocentric working-memory tasks"},"featured":false,"sortOrder":14,"createdAt":"2026-06-16T07:46:05.110Z","products":["r2drive"]},{"id":"pub_MBGHlps","slug":"cortico-hippocampal-interactions-in-a-context-discrimination-task","status":"published","type":"phdthesis","bibtexKey":"tarcsay2025thesis","title":"Cortico-hippocampal interactions in a context discrimination task","authors":["Gergely Tarcsay"],"year":2025,"month":"","journal":"PhD thesis, University of California, Irvine","volume":"","issue":"","pages":"","publisher":"University of California, Irvine","doi":"","url":"https://www.proquest.com/openview/7aea112830831c09db5a4a744d096c18/1?pq-origsite=gscholar&cbl=18750&diss=y","abstract":"This thesis investigates the encoding of contextual memories and a candidate neural mechanism of associative learning in the hippocampus. A hippocampal-dependent context discrimination task was developed and CA1 activity recorded in freely moving mice; context was encoded more reliably when required to solve the task.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://escholarship.org/uc/item/6fr9r7bw","pdfPublic":true,"meta":{"species":"mouse","behavioralParadigm":"context discrimination task (freely moving)"},"featured":false,"sortOrder":15,"createdAt":"2026-06-16T07:46:05.110Z","products":["r2drive"]},{"id":"pub_3zja1qQ","slug":"interictal-spikes-during-spatial-working-memory-carry-helpful-or-distracting-representations-of-space-and-have-opposing-impacts-on-performance","status":"published","type":"preprint","bibtexKey":"yi2024interictal","title":"Interictal spikes during spatial working memory carry helpful or distracting representations of space and have opposing impacts on performance","authors":["Justin D Yi","Maryam Pasdarnavab","Laura Kueck","Gergely Tarcsay","
131Laura A Ewell"],"year":2024,"month":"November","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2024.11.13.623481","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11601362/","abstract":"We recorded hippocampal local field potentials from epileptic mice performing a delayed alternation task. Interictal spikes (IS) disrupted performance when spatially non-restricted and occurring during running, but animals performed well when IS clustered at reward locations.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601362/pdf/nihpp-2024.11.13.623481v1.pdf","pdfPublic":true,"meta":{"species":"mouse","sex":"male","age":"3 months","siliconProbe":"NeuroNexus H64LP (64-ch linear)","behavioralParadigm":"delayed alternation on a figure-8 maze","headgear":"3DNeuro R2Drive for probe lowering","otherMicrodrives":"Axona double-bundle tetrode microdrives"},"featured":false,"sortOrder":9,"createdAt":"2026-06-16T07:46:05.109Z","products":["r2drive"]},{"id":"pub_pvgz5Fw","slug":"laminar-organization-of-vocalization-processing-and-attentional-modulation-in-auditory-cortex","status":"published","type":"phdthesis","bibtexKey":"kar2024thesis","title":"Laminar organization of vocalization processing and attentional modulation in auditory cortex","authors":["Manaswini Kar"],"year":2024,"month":"","journal":"PhD thesis, University of Pittsburgh","volume":"","issue":"","pages":"","publisher":"University of Pittsburgh","doi":"","url":"https://d-scholarship.pitt.edu/concern/etds/eb82afdc-3ad7-4579-9a96-473d2c80cfef","abstract":"","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"https://d-scholarship.pitt.edu/47345/7/Manaswini_Kar_Thesis_2024.pdf","pdfPublic":true,"meta":{"species":"guinea pig","behavioralParadigm":"vocalization categorization / auditory attention","sex":"male and female","weight":"500-1000g","siliconProbe":"Neuropixels 1.0"},"featured":false,"sortOrder":16,"createdAt":"2026-06-16T07:46:05.110Z","products":["r2drive"]},{"id":"pub_Iv3NZ1U","slug":"volitional-stopping-is-preceded-by-a-transient-beta-oscillation","status":"published","type":"preprint","bibtexKey":"","title":"Volitional stopping is preceded by a transient beta oscillation","authors":["Joana F. Doutel Figueira","Reetta A. Ojala","Dmitrii Vasilev","Alejandro De Miguel","Lucas Jeay-Bizot","Ryo Iwai","Isabel Raposo","Negar Safaei","Lilian de Sardenberg Schmid","Uri Maoz","Masataka Watanabe","Nelson K. Totah"],"year":2024,"month":"12","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2024.12.28.630599","url":"https://doi.org/10.1101/2024.12.28.630599","abstract":"Human motor cortex EEG beta (15-30 Hz) oscillations undergo transient power modulations (bursts) during volitional control of movements. They are a potential control signal for brain-machine interfaces and are a therapeutic target in Parkinsonâs disease. The prevailing view is that EEG beta bursts increase during stopping and immobility, but do not precede stopping. In contrast to prior work in humans and animals that used a latent and unobservable stopping time in the stop-signal task, we developed a translational animal model to align EEG with overt action stopping. We recorded 32-electrode EEG along with the angular velocity of a treadmill while head-fixed rats stopped in-progress running on a freely-rotating, non-motorized treadmill. Contrasting prior work, motor cortex beta bursts increased before stopping and not during stopping or immobility. Using information theoretic measures, we show that beta power was informative about treadmill velocity 200 msec in the future, but only during planning to stop. By introducing artificial temporal jitter to mimic the estimation of stopping time used in prior work, we show that this predictive brain-action relationship fails with even small jitter. Finally, we use a variety of machine learning methods to show that, despite EEG beta oscillations being a clear neural correlate preceding stopping, it has limited utility for real-time action decoding. Our work suggests a new conceptual model for neural control of action stopping.","fulltext":"Abstract\nHuman motor cortex EEG beta (15-30 Hz) oscillations undergo transient power modulations (bursts) during volitional control of movements. They are a potential control signal for brain-machine interfaces and are a therapeutic target in Parkinsonâs disease. The prevailing view is that EEG beta bursts increase during stopping and immobility, but do not precede stopping. In contrast to prior work in humans and animals that used a latent and unobservable stopping time in the stop-signal task, we developed a translational animal model to align EEG with overt action stopping. We recorded 32-electrode EEG along with the angular velocity of a treadmill while head-fixed rats stopped in-progress running on a freely-rotating, non-motorized treadmill. Contrasting prior work, motor cortex beta bursts increased before stopping and not during stopping or immobility. Using information theoretic measures, we show that beta power was informative about treadmill velocity 200 msec in the future, but only during planning to stop. By introducing artificial temporal jitter to mimic the estimation of stopping time used in prior work, we show that this predictive brain-action relationship fails with even small jitter. Finally, we use a variety of machine learning methods to show that, despite EEG beta oscillations being a clear neural correlate preceding stopping, it has limited utility for real-time action decoding. Our work suggests a new conceptual model for neural control of action stopping.\n\nIntroduction\nCortical EEG beta band (15-30 Hz) oscillation power is modulated during volitional movement. Over 75 years ago, seminal field potential recordings from the cortical surface in awake humans undergoing neurosurgery showed beta power was higher during immobility and decreased prior to and during movement1. Beta oscillations may maintain muscle contraction (e.g., clinching a fist or holding a posture2) and hence promote the âstatus quoâ3. Externally driving beta oscillations using transcranial alternating current stimulation of human pre-supplementary motor area (pre-SMA) counteracts movement by reducing movement velocity and force4,5. Intrinsically generated beta bursts in human subthalamic nucleus (part of the cortico-subcortical network for volitional action control) are associated with reduced velocity of upcoming movements and slowed reaction times6. Both lines of evidence indicate that beta bursts have a modulatory contribution that promotes immobility and counteracts movement. While beta oscillations need to be suppressed before moving, the corollary that beta oscillations need to increase before stopping has not been clearly established. This constrains understanding how beta oscillations are a biomarker in Parkinsonâs Disease and their potential as a control signal for brain-machine interfaces (BMIs).\n\nBeta oscillations and action stopping have been studied using the stop-signal task in humans and animals. The stop-signal task dominates the field with citations approaching 10,000 per year (see Appendix 1 of 7). It is the chosen task for studying response inhibition in international multi-laboratory human cognitive test batteries8. In a study using this task, human motor cortex beta oscillation power did not change before or after stopping although pre-SMA beta oscillation power did change after stopping9. Beta oscillations also increase only after stopping in the subthalamic nucleus in humans performing the stop-signal task10. In contrast, other studies have shown subtle increases in beta power prior to stopping in human pre-SMA and frontal cortex, but not in primary motor cortex11,12. Yet, other human EEG studies have found that sensorimotor cortex beta band oscillations occur at the time of stopping or after stopping13â
13116, while translational EEG recordings from the macaque monkey showed no change in beta band activity prior to or during stopping 17. These mixed results have led some authors to conclude that beta band activity is not âcausally linkedâ to stopping13 and that such signals are not useful as a stop-signal for BMIs17. However, the large number of contradictory studies may be due to the nature of the stop-signal task.\n\nIn the stop-signal task, a sensory signal instructs the subject to cancel a planned movement. There is no overt movement and no observable stopping time. Rather, the time at which the subject stops planning to move (i.e., the stop-signal reaction time, or SSRT) is estimated and thus an unobservable and hidden variable. The SSRT is mathematically modelled for each recording session using the distribution of reaction times across all trials of the recording session18. This task design precludes aligning brain activity with stopping in two ways. First, the model and the way the SSRT is calculated varies across studies7. Moreover, the results of this calculation depend on the delay between the stimulus and the stop-signal, which also varies across studies19. Second and critically, while the time of action stopping (and any preceding neural correlate) surely varies from trial-to-trial, the SSRT is a single time point for the entire recording session. Indeed, using electromyography from response-related muscles to estimate the covert stopping of an in-preparation movement has implied that the SSRT might be overestimated12,20. Testing whether beta band oscillations need to be increased before stopping â and are thus useful as a BMI stop signal â requires an overt behavioral measure of stopping an in-progress action.\n\nHere, we present a behavioral task paradigm that permits precision alignment of beta band power with an overt, measurable stopping time. In this paradigm, rats choose to stop in-progress running and return to immobility. The peak velocity of a non-motorized treadmill reveals the time at which the rat initiates stopping. We recorded cortex-wide, 32-electrode EEG. In contrast with prior studies using the stop-signal task, we observed a single beta cycle burst at a highly consistent time prior to stopping, but not during stopping or immobility. We used information theory-based methods and machine learning to test the degree to which an overt stop time can be decoded from motor cortex EEG beta band power and show that beta oscillations are predictive of stopping on average but do so poorly on a trial-by-trial basis. Lastly, we show that there is a potential upper limit for tolerable SSRT estimation error, after which the relationship between beta band power and subsequent stopping will no longer be apparent.\n\nResults\nWe studied the relationship between EEG beta band (15-30 Hz) oscillations and spontaneous (uninstructed) overt stopping of an in-progress action in rats. We trained rats to perform a Go/NoGo task while head-fixed on a non-motorized, low-friction treadmill. Rats were required to remain immobile for 0.5 to 2.0 sec before onset of a sensory stimulus. One stimulus instructed a Go response, which was to run past a distance threshold (Figure 1A). The other stimulus instructed a NoGo response, which required remaining immobile for the entire duration of the NoGo stimulus (1.5 sec). During some correct rejection (CR) trials, the rat began running but chose to return to immobility before crossing the distance threshold and then sat immobile for the remainder of the stimulus presentation (Figure 1B). These âvolitional stopsâ (VS) are volitional movements that consist of a stimulus-guided action followed by a non-instructed, internal decision to cancel the Go response and return to immobility. The peak velocity of these movements provides an overt, trial-by-trial measure of action stopping. We calculated response velocity from the angular position of the treadmill (32 samples/sec) and registered 55,833 CR trials with volitional stops across 306 recording sessions from 14 male rats. We divided CR trials into three groups (T3, T2, and T1) based on tert
131iles of peak velocity (18,577 large, 18,513 medium and 18,743 small peak velocities). The small velocity group (T1) does not contain a VS. Instead, T1 trials included those in which the rat was balancing on the treadmill producing a small movement and those in which the rat sat immobile. All analyses used the T3 group unless otherwise stated. Figure 1C shows the average trial velocity for volitional stops compared to false alarm trials.\n\nFigure 1.\nDownload figureOpen in new tab\nFigure 1.\nThe velocity profile of volitional stopping and false alarm errors in the Go/NoGo task for the head-fixed rat on a treadmill.\n(A) NoGo stimulus-aligned velocity during false alarm trials. The data are from one session. Each row in the plot represents a trial and the white dots depicted when the response threshold (running distance) was crossed. (B) NoGo stimulus-aligned velocity during all CR trials in one example session. Each row in the plot is a trial. There are no white dots (as in panel B) because the rats did not commit a false alarm (FA) response. Dark blue is immobility. On numerous CR trials, the rat began running but then stopped the in-progress running before crossing the distance threshold. These VSs are visible as lighter blue, green, orange, and yellow in the plot. Variability in the peak velocity across trials is apparent and could be divided into large, medium, and small peak velocities (tertiles). (C) The NoGo stimulus-aligned T3 VS trial velocity (orange line) and FA velocity (black line) are plotted as an average across 14 rats. The shading shows the standard error.\n\nWe assessed how beta power changes around the time of overt stopping by recording 32-electrode EEG bilaterally and across the entire rostrocaudal extent of the rat cortex (Figure 2A). We calculated the power spectrum in the beta band and aligned it to trial-by-trial peak velocities. For each frequency at which we calculated power, we z-score normalized each trial to the power for the entire recording session. We assessed the topographical distribution of beta oscillations in a ± 200 msec window around peak velocity. We observed increased beta power over the left motor/somatomotor cortex (Figure 2B). Beta power was significantly lateralized (Figure 2C). In line with prior work showing that beta oscillation power decreases prior to movement onset1,15,21, we observed left motor/somatomotor cortex beta band power decreased during the 500 msec window prior to stimulus onset (Figure 2D). On the other hand, these motor-related beta oscillations were distinct from those evoked by feedback (reward or error signals), which were localized to frontal electrodes (Figure 2E) consistent with prior work17,22. Therefore, all subsequent analyses used beta band signals from these three electrodes and averaged results across them.\n\nFigure 2.\nDownload figureOpen in new tab\nFigure 2.\nBeta power prior to stopping during VS trials has a unique cortical topography.\n(A) The schematic shows the 32-electrode skull-surface EEG array and the corresponding brain areas for each electrode. (B) The z-scored beta power (average, N = 14 rats) during ±200 msec window around peak velocity in T3 VS trials. (C) The bar plot shows the average lateralization index across 14 rats and the standard error. (D) The z-scored beta power 500 msec prior to stimulus onset on T3 VS trials (N = 14 rats). (E) The z-scored beta power in the 500 msec after reward (left plot) or auditory cue indicating that a false alarm (error) was committed (right plot). The power is averaged across 14 rats.\n\nBeta power transiently increases prior to stopping and positively scales with stopping larger actions\nNumerous studies using the stop-signal task have shown a change in motor cortex beta power occurs too late to be predictive of stopping and instead that beta power increases during stopping (i.e., deceleration of movement) and subsequent immobility9,11,14,15. Figure 3A shows that sensorimotor cortex beta power transiently increased prior to peak velocity. The increase occurred during an epoch in which response velocity was still increasing and prior to the initiation of action stopping (black line, Figure 3A). Recent work has suggested that sensorimotor mu (9 â 11 Hz) oscillations can appear simultaneously with beta oscillations, and that beta oscillations can be a harmonic of the underlying mu band oscillation 23. We performed a simple and intuitive test for harmonics proposed by Schaworonkow (2023). If the beta oscillations are a harmonic of a mu oscillation, then the observed beta band peak frequency for each recording session will be an integer multiple of the mu band peak frequency. As proposed by Schaworonkow (2023), we used a scatter plot with a line showing the putative beta band peak frequency harmonics to visualize this cross-frequency band relationship. The points did not fall along the harmonics line and therefore the beta oscillation is genuine (Figure 3B). Our results indicate that beta power increases prior to overt action stopping.\n\nFigure 3.\nDownload figureOpen in new tab\nFigure 3.\nBeta power preceding VSs are a neuronal correlate of action stopping.\n(A) Averaged beta power (15-30 Hz) aligned to peak velocity on T3 VS trials. The dotted line indicates the time of the VS (i.e., peak velocity). The orange line shows the average velocity (a.u.) of the treadmill. (B) The beta band peak frequency is plotted against the alpha band peak frequency. Each dot is the peak frequency for a single session. The black line shows a putative harmonic relationship between the alpha band and beta band oscillations. (C) The lines show the z-scored ITC values averaged across 14 rats. The shading shows the standard error. The orange line is aligned to peak velocity on VS trials. The purple line is aligned with stimulus onset. (D) The histograms present the average (N = 14 rats) z-scored beta power aligned to peak velocity in two task epochs: VS and spontaneous task-unrelated stopping during the inter-trial interval (ITI). The orange and blue lines, respectively, show the average treadmill velocity (a.u.) for VS and stopping in-progress spontaneous running during the ITI.\n\nHowever, it is a distinct possibility that beta oscillations are simply evoked by movement and phase-locked to the stop initiation event rather than occurring as an intrinsic oscillation that precedes stopping. We computed inter-trial phase coherence (ITC) to test this possibility. As a positive control, we calculated the ITC for task events in which we expected an evoked beta oscillation (i.e., after sensory stimulus onset24â26). Whereas we observed the expected high ITC associated with a stimulus-evoked beta oscillation, we did not observe any change in ITC around peak velocity (Figure 3C). Thus, the beta oscillation is an intrinsic change in brain activity prior to stopping.\n\nIt is possible that pre-stopping beta power only occurs in the context of learned stimulus-response mappings, rather than as a neuronal correlate of general action stopping. In our task, the rat has learned two stimulus-response mappings, initiates an incorrect response on CR trials, and makes a second decision to switch to the correct stimulus-response mapping before crossing the response threshold27. We tested whether beta power was specific to this cognitive context by measuring beta power around spontaneous running and stopping during the inter-trial interval (ITI). Rats were free to repeatedly engage in bouts of running during the ITI (at the expense of delayed stimulus onset), and they did so frequently. We calculated average beta band power aligned to peak velocity events detected during the ITI and found a similar beta power increase prior to this general stopping behavior (Figure 3D).\n\nAfter observing that beta oscillations precede stopping, we aimed to test whether there is a predictive relationship between beta oscillations and stopping. We hypothesized that, if beta power is predictive of stopping, then there would be a relationship between the intensity of the response being stopped and the magnitude of beta power in the 200 msec prior to stopping. Figure 4A illustrates the power spectrum, across a wider range of frequencies (1 â 45 Hz), aligned to the time of peak velocity separately for the 3 velocity tertiles. The plots show that increasing need for stopping is associated with a larger beta band power immediately prior to stopping. We formally tested this relationship by comparing the frequency band-averaged and time-averaged (200 msec pre-peak velocity) beta power across peak velocity magnitudes (Figure 4B). A Bayesian independent samples one-way ANOVA indicated strong evidence for the alternative hypothesis that beta power differed with stopping magnitude (BF10 = 4.89Ã10126) with post-hoc t-tests indicating differences between all groups (T1 vs T2, BF10 = 6.55Ã1042; T1 vs T3, BF10 = 2.87Ã10130; T2 vs T3, BF10 = 4.72Ã1018). Thus, beta oscillations precede and predict subsequent action stopping.\n\nFigure 4.\nDownload figureOpen in new tab\nFigure 4.\nBeta oscillations precede and predict subsequent action stopping.\n(A) The power spectrum across EEG frequencies aligned to the VS. The dotted line shows the VS time (i.e., peak velocity). The orange line shows the average angular velocity (a.u.) of the treadmill in each peak velocity tertile. (B) Frequency band-averaged and time-averaged beta band power (z-scored to session beta band power) in the 200 msec window before the peak velocity for each velocity tertile. (C) An illustration of the steps used to acquire the beta band envelope. The instantaneous beta band envelope was calculated using Hilbert transformed EEG signal and then rectifying the transformed signal. The plot shows an example of raw EEG (45 Hz lowpass filtered), the beta band filtered signal, and the beta band and envelope. (D) Brain-to-treadmill transfer entropy
131in three 400 msec time windows around the peak velocity (each shown by a different line color). The different time windows consisted of distinct aspects of volitional action during CR trials: running (blue line, â0.4 to 0 sec before peak velocity), planning and execution of stopping (orange line, â0.2 to 0.2 sec around peak velocity), and stopping (yellow, 0 to 0.4 sec after peak velocity). The x-axis shows the time lag between the two signals (beta band envelope and treadmill velocity) and the lag value (t) should be read as the information about the treadmill at time, t, after the neural signal. The lines show the average across 14 rats. In the time window including both stop planning and stop initiation (orange line) only, TE increased when the time lag into the past of the beta envelope was shorter than 200 msec.\n\nThe head-fixed rat responding on a treadmill permits a brain-behavior temporal prediction analysis that is not possible when utilizing the stop-signal task. We aimed to predict the future state of treadmill velocity from the past state of beta power. We calculated brain-to-treadmill transfer entropy (TE), which is an information theory-based measure of the temporal causality between two signals28,29. We calculated the instantaneous beta band power envelope using Hilbert transformed EEG signal (Figure 4C). We then computed directional TE from the beta envelope to treadmill velocity at various time lags into the past of the beta envelope. TE increases when the past of the beta envelope yields additional information about the current state of treadmill velocity beyond the information garnered only from past velocity. Such TE increases may be interpreted as a causal influence between the signals28,29, in this case from the brain to the treadmill. We calculated TE in three windows of identical duration (400 msec). These windows primarily consisted of distinct aspects of volitional action during CR trials: running, planning and execution of stopping, and stopping. In the time window including both stop planning and stop initiation, TE increased when the time lag into the past of the beta envelope was shorter than 200 msec (Figure 4D). This indicates that the beta envelope is predictive of the state of the treadmill around 200 msec or less into the future. In contrast, in the time windows when the rats were either accelerating or executing stopping, the predictivity remained stable and lower. By using information theory to analyze the relationship between beta power and response velocity, we found that beta power is predictive of volitional stopping approximately 200 msec in the future.\n\nBeta oscillation bursts transiently increase in a brief window immediately prior to stopping\nIt has also been proposed that beta bursts occur at random times (i.e., lacking temporal regularity across trials) and that the total count of beta bursts predicts stopping12,15. Implicit to this conceptual model is that beta bursts are accumulated to a threshold at which point action stopping is triggered. Given that we observed a transient increase in beta power prior to overt stopping that has not been previously observed in the stop-signal task, we reassessed the beta burst accumulation theory within the context of overt stopping. We first developed a data-driven approach to find an optimal threshold for detecting beta bursts. We hypothesized that beta burst probability increases prior to peak velocity. Accordingly, setting the beta burst detection threshold too low would collect more noise, which would not be expected to change over time. On the other hand, setting the threshold too high would miss bursts and would therefore be less sensitive to change over time. Figure 5A shows the burst probability change over time, prior to stop initiation, for different thresholds ranging from low (the median of the beta envelope) to high (6 times the median of the beta envelope). A threshold of 2 times the median beta envelope power was the optimal criterion. Bursts were 42.4 ± 16.1 msec (SEM) duration (Figure 5B). This burst duration is approximately a single beta oscillation cycle (i.e., 44 msec cycle duration for the 22.5 Hz midpoint of the 15-30 Hz beta band). Approximately 50% of trials contained at least one burst in the 200 msec prior to peak velocity (Figure 5C). (Note that less than 100% is expected on a trial-by-trial basis when sampling a noise-containing signal). In contrast to prior findings based on the stop-signal task, a transient increase in beta bursts occurred in the 200 msec window prior to the initiation of stopping and returned to baseline immediately after stop initiation.\n\nFigure 5.\nDownload figureOpen in new tab\nFigure 5.\nRegularly timed beta burst occurs immediately prior to stopping.\n(A) The plot shows burst probability (calculated in 200 msec bins) normalized to the first 200 msec bin during a 1 sec window prior to peak velocity. Bursts were detected using a gradually increasing threshold (steps of 0.1 times the median of the session-wide beta band power envelope) to the highest threshold tested (six times the median) at the bottom of the y-axis. (B) The box plot shows distribution of burst times. Outliers are marked with red crosses. The red line indicates the mean burst duration. The data set include all detected bursts in the 200 msec before VS initiation from all sessions and rats. (C) The percentage of VS trials with at least one burst occurring in the 200 msec window prior to the peak velocity. The dotted line shows the VS time. The line shows the average across 14 rats and the shading illustrates the standard error across rats. (D) The across-trial Fano Factor mean across 14 rats is plotted with the standard error shown as shading around the line. The dotted line indicates the VS time. A decrease in Fano Factor indicates a relative increase in the regularity of across-trial beta burst timing.\n\nAccording to the beta burst accumulator framework, the timing of beta bursts across trials should remain random to a similar degree over time around stopping an action. We tested whether this is the case by computing the across-trial Fano Factor30. In contrast to this framework, we found that beta burst timing was modulated over time (Figure 5D). The timing of beta bursts rapidly became more regular â not more random â prior to the initiation of stopping. Importantly, beta burst rate quickly returned toward a baseline level of random timing immediately after the initiation of stopping when the rats were decelerating. This result is not in line with a conceptual model that randomly timed beta bursts are accumulated to threshold15,31.\n\nSingle trial decoding of VS time from spontaneously occurring beta band power\nOur results show that a single beta oscillation cycle occurs prior to volitional stopping at a highly consistent time across trials (Figure 5B-D). The trial-averaged power of the beta oscillation scales with the magnitude of the action to be stopped (Figure 4A, 4B). The ongoing, single trial beta oscillation is informative about the future state of the treadmill velocity specifically when preparing a VS (Figure 4D). Therefore, it is feasible that this EEG neuronal event could be used to decode the timing of an upcoming VS. We developed two decoding approaches (regression-based and classification-based) to evaluate the predictability of single trial VS time. We assessed the accuracy and robustness of a diverse range of machine learning models, encompassing both linear and non-linear methods. We pooled trials across sessions for each rat.\n\nWe first aimed to predict the exact timing, in milliseconds, of peak velocity using the beta band power envelope in the prior 400 msec. We used a 200 msec window size for the model and slid the window in increments of 10 msec (approximately one-third of a beta burst event, Figure 5B). To comprehensively evaluate model performance, we explored a diverse set of parametric and non-parametric regression algorithms spanning a range of complexities, including tree-based ensembles, distance-based models, and multi-layered neural networks (Supplementary Table 1 provides a complete list of models). We elected not to include recurrent neural networks (RNNs) in our analysis because of their complexity relative to the simplicity and size of the dataset, which can cause the RNNs to overfit quickly. Instead, we substituted an RNN with a feedforward multi-layered neural network. A baseline model was included for reference (see Methods for detailed description of this model). This baseline predicted the average time-to-event from the training folds as a constant value for the test folds. We performed cross-validation and hyperparameter tuning and maintained consistency in the seeding across models (see methods section for detailed description). The evaluation metric for model selection and comparison was the average cross-validated R² score across all folds. A model predicting the mean of the dependent variable for every data point will mathematically obtain an R² of zero since it does not explain any variability beyond the average value. However, slight deviations in R² can occur when using data splits, due to small differences in the average value of the dependent variable between the training and test sets. Figure 6A presents the R² cross-validated scores of the best-performing model for each rat (results for all models are shown for each rat in Supplementary Table 1). The low R2 values indicate that features of the ongoing beta band power envelope alone are insufficient for precisely decoding the trial-wise VS time. While some models showed marginally better performance than the baselineâand the signal appeared more predictive for certain ratsâthe overall performance was poor. This suggests that the beta band power envelope lacks the necessary information for predicting VS time at the millisecond timescale. Moreover, distance-based models, such as K-Nearest Neighbors and Support Vector Machines, which rely heavily on measuring similarity between observations, struggled with the task and were never the top-performing model for any rat. This poor performance is likely due to the overlapping nature of the sequences generated by the sliding window, resulting in highly similar feature vectors despite 10 msec incremental changes in the regression value at each time step.\n\nFigure 6.\nDownload figureOpen in new tab\nFigure 6.\nDecoding vol
131itional stop time from ongoing EEG beta band power envelope cannot achieve high accuracy using a diverse set of parametric and non-parametric algorithms.\n(A) The distribution of cross-validated scores of the best-performing model for each rat. The regression model task was to decode the VS time on each trial. Model performance was evaluated using average R² across all folds, with the highest average R² identifying the best model. (B) The distribution of cross-validated accuracies for each ratâs best-performing model. The classification model task was to distinguish between the baseline and pre-VS event epoch. Overall performance was assessed by averaging accuracy across all folds, with the highest average accuracy determining the best model. For each rat, a star indicates the 95th percentile of chance-level classification accuracy. This chance level was determined by training the top-performing model using its optimized hyperparameters on shuffled training folds and then applying it to the corresponding test folds.\n\nTherefore, we next simplified the problem and removed overlap between sequences by testing whether the beta band power envelope contained any information predictive of the VS event without focusing on precise timing. We divided the beta band power envelope, on each trial, into two time-locked segments: a baseline epoch (â400 to â200 msec before peak velocity) and a pre-VS epoch (â200 msec until peak velocity time). This approach enabled treating the problem as a binary classification task aiming to determine whether there are distinguishable beta band envelope patterns preceding peak velocity. The classification task focused on training models to discriminate between these two periods, maximizing accuracy as a performance metric. We evaluated the same range of machine learning algorithms as in the regression task (though substituting Linear Regression with Logistic Regression, see Supplementary Table 2). Cross-validation and hyperparameter tuning were performed similarly, creating folds accounting for individual trials and, in the case of classification, with the dataset being perfectly balanced between positive (pre-event) and negative (baseline) classes. Thus, accuracy was chosen as the primary evaluation metric since it effectively captures the modelsâ ability to distinguish between the two classes. The baseline model for this task was computed by shuffling class labels (see Methods section for description of this procedure). Figure 6B shows the cross-validated accuracy of the best-performing model for each rat (results for all models are shown for each rat in Supplementary Table 2). Some models achieved statistically significant accuracy levels for a sub-set of rats, indicating that the beta envelope contains some information capable of distinguishing baseline from pre-VS event sequences. However, despite this over-simplification of the problem compared to the previous regression task, these results were often statistically indistinguishable from chance. Furthermore, unlike the time-to-event results, distance-based models were often the top performers in the classification task. This suggests that when comparing distinct baseline and pre-event periods (without the overlapping data from the sliding window used in the regression task), measurable differences exist between these periods that can be captured using techniques that rely on calculating the similarity between multiple trials.\n\nEstimating a latent volitional stopping time can tolerate only a few hundred milliseconds of trial-wise temporal error\nHere, we have shown that the beta oscillation dynamics prior to overt stopping are clearly distinguishable from prior findings that used the stop-signal task9,11â13,15â17,24. Inherent to the stop-signal task is that stopping occurs at a latent, unobservable time that is estimated as the identical time point across all trials in a single experiment. Naturally, this estimated stop time (the SSRT) must vary randomly with respect to the actual (and unobservable) stop time on each trial of the stop-signal task. Therefore, we next sought to introduce artificial jitter into our overt stopping times, such that we could calculate a temporal limit beyond which estimation error would occlude the beta power relationship with stopping. With this analysis, we aim to suggest what level of across-trial estimation error might be acceptable if the stop-signal task is used to seek for a relationship between beta band oscillations and stopping. We first performed 100 shuffles of each trialâs peak velocity time over a uniform interval of ±5 msec. We then calculated the average beta burst rate across these 100 shuffled peak velocity event times. After performing this procedure on each trial, we then obtained the trial averaged beta burst rate characterized by an artificial jitter of ±5 msec in the VS time. Subsequently, we repeated this procedure in 5 msec steps out to a final window of ±700 msec. This procedure assesses how much temporal jitter (from 5 msec to 700 msec) the alignment between beta bursting and VS time can handle before the relationship between the two is destroyed. We estimated the jitter duration at which this occurs by performing a one-sided t-test of the hypothesis that beta burst rate in the jittered data is less than the observed burst rate at the time point at which beta burst rate was maximal in the population (rat) average. Figure 7 shows that randomly jittering an observable stop initiation time by more than 330 msec will likely occlude the relationship between beta burst rate and subsequent initiation of stopping. Our analysis suggests that when using a stop-signal task, if trial-by-trial variability in the SSRT exceeds 330 msec, then it will be impossible to detect beta oscillations increasing prior to stopping.\n\nFigure 7.\nDownload figureOpen in new tab\nFigure 7.\nOver 330 msec error in estimating the SSRT likely occludes the relationship with beta burst and stopping.\nThe stop initiation time was randomly jittered in 5 msec steps (y-axis). Beta burst rate was aligned to the jittered stop initiation times (time 0 on the x-axis, dotted black line). As the temporal shift grows larger (proceeding downward on the y-axis), bursts rate decreases until it is significantly lower (one-sided t-test, p\u003c0.05) than the observed burst rate at 330 msec (red line).\n\nDiscussion\nIn this study, we investigated how beta band power relates to overt stopping of an in-progress action in rats head-fixed on a non-motorized treadmill. We show a lateralized increase in beta power over the left motor/somatomotor cortex prior to stopping. Furthermore, we show that the intensity of the beta power increase is positively correlated with an increasing need to stop. The rate of beta bursts was modulated prior to stopping and this modulation occurred within a highly specific time window (200 msec before stopping). Using information theory to evaluate the predictive temporal relationship between beta power and response velocity, we found that beta power is informative about volitional stopping approximately 200 msec in the future. Information theory-based methods for inferring temporal predictions between signals were used to show that motor cortex beta power is informative about volitional stopping approximately 200 msec in the future, but uninformative about the state of the treadmill during stopping or return to immobility. Thus, a single beta oscillation precedes stopping with high trial-by-trial temporal consistency and holds predictive power over stopping. Lastly, we used synthetic data to show that, in the absence of an observable stop initiation time, estimating stopping time cannot tolerate more than 330 msec of trial-by-trial variability without losing the ability to detect a relationship between beta oscillations and subsequent stopping. This suggests a potential upper limit for tolerable SSRT estimation error, after which the relationship between beta band power and subsequent stopping will no longer be apparent.\n\nWhile some studies have failed to find a relationship between beta oscillations and subsequent stopping9,10,13â17, those that have reported a relationship11,12 support two conceptual models. However, our results do not support either of them. One model suggests that randomly timed beta bursts on each trial are predictive of stopping15. According to this model, sustained increases in beta oscillation power only appear because bursts of one cycle (or a few cycles) of the beta oscillation occur at random times, which results in the appearance of a sustained oscillation when power is averaged across trials31. If randomly timed beta bursts precede stopping, then burst count must be accumulated to a threshold, which fits with long-held concepts from both
131spike rate neurophysiology and computational models of action selection that have suggested that the stochastic accumulation of events to a threshold triggers selection and execution of a learned action18,32. Instead, our data support a model in which a single beta oscillation cycle occurs at a highly consistent and non-random time immediately prior to stopping. Another model proposes that increased beta activity is associated with an inability to change motor programs 24,33. The volitional movements that we have studied may be thought of as a change from one action (running) to another action (stopping). We observed a transient increase in beta power immediately prior to stopping.\n\nTherefore, our results suggest that beta may do the opposite of this model: beta oscillations promote change. We observed beta oscillations before stopping an in-progress run in two contexts: a change between two learned stimulus-response rules27, as well as volitional stopping during the inter-trial interval. In sum, prior models do not account for our results.\n\nOur results not matching the current conceptual models is due to two aspects of our experiments that are unique. First, we measured an overt stopping time, which permitted precision alignment between brain activity and stopping initiation time on a trial-by-trial basis. In contrast, the SSRT is hidden from observation, and a singular across-trial estimate7,18. Second, we continuously measured response trajectory which allowed calculation of transfer entropy, a brain-behavior directional analysis. In the stop-signal task, response trajectory is not measured because the subject is canceling a planned movement without ever having overtly moved4,9,11â17,20. Presumably, the same neural correlate of stopping observed before overt stopping could be detectable in the stop-signal task, if that task had precise alignment to the latent stopping time. We tested this idea by assessing how much we could shift the stopping time before the relationship between beta oscillations and stopping is no longer detectable. This analysis resulted in an estimate of tolerable trial-by-trial error (330 msec) beyond which it would be impossible to detect the relationship. We presume that the reason prior studies using stop-signal task offer inconsistent and contradictory reports is that they may be unable to estimate the SSRT within this tolerance window.\n\nWhen we aligned beta oscillations to overt stopping, our analysis of cortical topography revealed an unexpected finding. We observed lateralization of the beta power increase, which was on the same side at the population level. This might be due to engaging stopping using a preferential paw. Paw preference has been observed at an individual level in rats and there is evidence of population level preference to the right paw in a reaching task34,35. However, a recent meta-analysis found no population level paw preference36. That study proposed that, in some cases, a population level paw preference could be due to random samples of small size. On the other hand, the meta-analysis examined behavioral paw preference in (skilled) forearm reaching rather than stopping in-progress running. Moreover, in most of the studies the rat strains used were Long Evans, Sprague-Dawley or Wistar, whereas we used Lister-Hooded, which was used in only one of the studies considered in the meta-analysis. Future work can determine whether in our sample (n = 14 rats) there was a paw preference to right side, which manifested as stronger beta power in the contralateral motor cortex.\n\nBeta band oscillations are a therapeutic target and potential biomarker in Parkinsonâs disease37â41. Better understanding of the relation of increased beta power and stopping can be used for diagnostics and developing therapeutics. Prior work has attempted to use machine learning to decode the SSRT from ongoing EEG beta band power in humans12. That work reports low R2 values (0 to 0.03 on average) showing little-to-no ability to decode stop time from ongoing beta oscillations. However, prior attempts to decode stopping time from brain activity may have failed due to the covert nature of the SSRT. Here, we used overt stopping to test the degree to which stop time could be decoded from preceding motor cortex beta oscillation power. We used a wide variety of machine learning and pattern recognition algorithmsâlinear and non-linear, parametric and non-parametricâto predict time-to-stopping in milliseconds and to classify a baseline epoch from an epoch preceding imminent stopping. In contrast to prior work in which beta bursts after participants are cued to stop, we tested a more challenging scenario of predicting volitional stopping in a continuous, end-to-e
131nd fashion (the model reads the beta envelope and outputs the time-to-event using a sliding window). Despite observing a clear relationship between beta power and subsequent stopping, we found that none of a variety of models could decode trial-by-trial stop time with high accuracy. We conclude that beta oscillations are a neural correlate of stopping but are not informative enough to be utilized in a brain-machine interface. It is possible that EEG, while attractive from a translational standpoint due to non-invasive access to these signals, is not suitable for decoding due to its distance from the transmembrane currents underlying the field potential. This may be compounded by the potential for beta oscillations associated with the transmembrane currents to occur among different subsets of neurons on each instance of stopping. Single unit and local field potential recordings during the overt volitional stopping paradigm will yield an answer to this question.\n\nHere, we have shown that a single beta oscillation cycle occurs immediately prior to stopping and that the power scales with a greater need to stop. We used an overt volitional stopping paradigm to overcome the challenges caused by the estimated SSRT and thus resolve contradictory evidence in the prior literature. We show that the two predominant models for the role of beta in volitional action control cannot account for our data. Our results suggest a novel and distinct model. Beyond a better understanding of the neural correlates of vol
131itional actions, our results have practical implications for biomarkers, therapeutic targets, and adaptive deep brain stimulation in individuals with Parkinsonâs disease37â41.\n\nMaterial and Methods\nSubjects\n14 male Lister-Hooded rats (140-190 g body weight when implanted with electrode array and cranial chamber). Rats were pair-housed for 7 days before implantation and were thereafter single housed. Behavioral task training and experiments occurred during the ratsâ active phase. The rats were housed in a reversed light-dark (07:00 lights off, 19:00 lights on) cycle. All procedures were carried out after approval by local authorities and in compliance with the German Law for the Protection of Animals in experimental research (Tierschutzversuchstierverordnung) and the European Community Guidelines for the Care and Use of Laboratory Animals (EU Directive 2010/63/EU).\n\nSurgery\nThe animal was anesthetized with isoflurane (â¼1.0 â 2.0%). Heart rate was monitored throughout surgery. Buprenorphine (0.06 m/kg, s.c.), meloxicam (2.0 mg/kg, s.c.), and enrofloxacin (10.0 mg/kg, s.c.) were administered. An incision was made once the rat was no longer responsive to paw pinch. Skin and connective tissue were removed to expose the skull from the frontal bone to the neck muscle and from left to right temporal muscles. The wound margin was cauterized. The exposed bone was wiped dry and cleaned with 5% hydrogen peroxide. The bone surface was then scratched with a bone curette in a grid pattern to facilitate adhesion of the adhesive for the UV light polymerizing cement used to affix the head-fixation implant to the skull. Two component UV-curing adhesive (OptiBond, Kerr) was applied to the skull and UV cured for 30 sec at full intensity (Superlite 1300, M+W Dental). A 32-electrode polyimide electrode array (rat functional EEG, Neuronexus) was implanted aligned to bregma using an alignment mark on the array. The edges of the array were fixed to the skull using two-component dental cement (Paladur). The dental cement was used in small quantity to avoid flowing under the array between the electrodes and the skull surface. After fixation of the electrode array to the skull, a custom-made head-fixation implant was attached to the skull using UV-curing cement (Tetric EvoFlow, Ivoclar). The dental cement was bonded to the adhesive by UV curing for 60 sec at full intensity. The head-fixation implant consists of a chamber approximately the diameter of dorsal surface of the rat skull and a head-fixation post on the posterior chamber wall. A craniotomy was made over the left cerebellum, and a reference electrode (flattened 99.9% pure silver wire) was inserted through this craniotomy and laid on the surface of the dura. The craniotomy was filled with viscous, electrically conductive agar. The chamber was filled with 2-component dental cement (Paladur, Kulzer). The skin was glued to the sides of the implant using tissue glue (Histoacryl, B. Braun).\n\nRats recovered for 5 days after surgery. Buprenorphine (0.06 m/kg, s.c.) was administered every 12 hours for 3 days in some rats and other rats were injected with meloxicam (2.0 mg/kg, s.c.) every 24 hours for 3 days. Rehydrating and easily consumable food was provided (DietGel Recovery, ClearH2O).\n\nHandling and water restriction\nFor five days prior to surgery, the rats were handled twice a day, once in the morning and in the evening. Each session lasted at least five minutes. After five days of post-surgical recovery, access to water was restricted. During training and experiments, the rats were given 8-12 mL total water per day. Most of the water was consumed as reward during the behavioral task. The remainder of the total water volume was supplied to the rats in the cage after training. The total volume of water available daily was restricted to this level for between 5 and 14 days, while rats learned and performed stimulus discrimination experiments. After an epoch of restricted water availability, rats were provided ad libitum access to water for 24 hours.\n\nHead-fixation and behavioral apparatus\nThe rat was head-fixed on a cylindrical, non-motorized fibreglass treadmill that rotated forward or backward freely on low-friction ball bearings. The treadmill and head-fixation a
131pparatus were inside a large Faraday cage (approximately 2 m x 2 m x 2 m) with sound proofing material. A TTL pulse-controlled pump was used to deliver 10% sucrose water via a reward port that was placed at the mouth of the rat. A computer screen (behind glass with electromagnetic shielding designed to not cause a Moire effect) in front of the rat was used to display visual stimuli covering the entire visual field of the rat. Treadmill angular position was recorded via an analog signal output from a rotary encoder (MA3-A10-125-B, US Digital) attached to the rotational axis of the cylindrical treadmill. The signal output varied between 0 V and +5 V, which mapped linearly to the rotational angle of the treadmill. The signal was sampled at 32 kHz, digitized (Neuralynx signal acquisition system), and velocity was calculated offline (in MATLAB).\n\nHabituation to head-fixation and behavioral task training\nHabituation to head-fixation consisted of a single 20-minute session. After habituation, rats were trained to commit an instrumental response for reward. Approximately 5 uL of reward solution (10% sucrose in water) was delivered for small âshakingâ or body movements on the treadmill. The threshold for triggered reward was gradually increased to train the rat to make larger body movements and eventually steps. Threshold crossings were marked with a bridging stimulus (0.1 sec duration, 500 Hz auditory tone) to aid in learning the link between movement and reward. Eventually, rats would continuously walk and receive reward. This stage required from 3 to 10 sessions (one per day). Once an animal was running and licking simultaneously (which yielded approximately 7 mL of reward solution in a session lasting 20-30 minutes), we trained the rat to make instrumental (Go) responses contingent upon the presentation of a visual stimulus.\n\nInitially, we presented a 15 sec duration visual stimulus. The stimulus was a full field, black and white drifting grating (2.4 cycles/sec, 0.005 cycles/pixel spatial frequency, 75 deg orientation). Rats were trained to respond to the stimulus by continuously delivering reward for running during stimulus presentation. Reward delivery was triggered by crossing a threshold (a.u.) that was the same for all rats and all sessions and set at a level that was associated with bilateral locomotion. The stimulus was followed by an inter-trial interval (ITI). The ITI duration was drawn randomly from a distribution ranging from 2 to 3 sec (0.05 sec bins size).\n\nAfter 2 sessions, the rats were trained to not respond prior to stimulus onset. The ITI was reduced to 1 to 2 sec, and any running that crossed a velocity threshold (manually set to capture running, same for all rats and sessions) resulted in a 0.5 sec time-out from the task and a resetting of the ITI. After one or two sessions, the rats started to suppress running during the ITI. Once this was achieved, we reduced stimulus duration in small steps (10 sec, 5 sec, 2.5 sec) over a few sessions. When stimulus duration is 2.5 sec, rats exhibit a vigorous and low-latency response upon stimulus onset. At this point in training, we reduce the ITI to 0.5 to 1 sec, and after a few sessions we reduce stimulus duration to 1.5 sec (i.e., a speeded reaction time task). Rats were given 600 trials per session. This typically yielded approximately 6 mL of sucrose solution during the task. Behavior was considered stable when omission rate was below 10%.\n\nThe Go/NoGo paradigm was introduced with the addition of a NoGo stimulus. The NoGo stimulus was at least 70 degrees different from the Go stimulus. Go and NoGo stimulus trials were delivered in pseudo-random order and in equal proportion. At most, two trials of the same stimulus type could occur consecutively. A Go response required crossing a distance threshold, which roughly corresponded to taking one step. A response offset window (0 to 0.75 sec after stimulus onset) was introduced to compensate for the pre-potent drive to respond. During this period, running did not count towards the distance threshold. This allowed low latency movements but forced the rat to appraise the stimulus and decide whether to respond. After the offset window, crossing the distance threshold caused the stimulus to disappear. Hits were rewarded (three 7 uL pulses). Responses to the NoGo stimulus led to an auditory error signal (0.5 sec duration, brown noise, 60 dB) and a time-out of 6 seconds prior to the next ITI. Training was complete when performance was above 85% and omission rate was less than 10%.\n\nNeurophysiological recordings\nWideband (0.1 Hz to 6 kHz) signals were recorded at 32 kHz (Digital Lynx SX, Neuralynx). Both neurophysiological signals and treadmill velocity data were down sampled to 200 Hz (unless stated otherwise).\n\nVolitional stops peak velocity rationale and division\nTo obtain the time of volitional stopping initiation on correct rejection trials (N = 55,833), which corresponds to the maximum velocity they achieved before stopping (peak velocity), we searched for the maximum velocity from stimulus onset time until 1.75 sec after. For every rat, the peak velocities were divided into three groups based on their tertiles: large peak velocity (T3, N = 18,577 trials), medium peak velocity (T2, N = 18,513 trials), and small peak velocity (T1, N = 18,743 trials). The small velocity/T1 group contained some trials in which the rat sat immobile without a volitional stop. All the further analysis is focused on the T3 group, unless stated otherwise.\n\nPower\nThe continuous wavelet transform (CWT) was calculated with the MATLAB function, cwt, in a 4 sec window around the volitional stop time to avoid window edge artifacts. The wavelet used was an Analytic Morlet Wavelet, with 48 voices per octave and frequency limits from 1 to 45Hz. Once CWT was computed, each trial was cut to a time window of â1 to +1 sec around the volitional stop time. Eac
131h time window was z-scored, within each frequency bin, using the mean and standard deviation of the entire session.\n\nBeta burst detection\nWe calculated the beta envelope for each session by bandpass filtering the EEG signal for the beta band (15 - 30 Hz) using a 2nd order Butterworth filter, then performing a Hilbert transformation followed by rectification. We detected beta bursts using a data-driven thresholding approach (described in the results section). We calculated the probability of a burst occurring during the time window around the VS time in a â1 to +1 sec window around the VS. We calculated this probability in 200 msec bins for all trials. The bursting probability of the first bin (1 to 0.8 sec before VS) was subtracted from all the bins to assess how the bursting probability changed over time around the VS time.\n\nInter-trial coherence (ITC)\nWe calculated ITC to measure phase-synchronization of beta oscillations across VS trials. We performed this analysis within sessions on the 3 sensorimotor electrodes with the largest beta power. Because trial number affects the ITC, we excluded sessions that had a total of VS trials less than the median of all sessions (median = 56). From the Hilbert transformed beta band oscillation in each session, we obtained the instantaneous phase angle of the oscillation. ITC was calculated as:\nEmbedded Image\n\nwhere Embedded Image and θ is the phase angle. We compared the calculated ITC values against values that would be expected by chance by cutting the EEG signal at a random time point and flipping the two segments and then repeating ITC calculation. This was done 100 times to obtain a surrogate set of ITC values for each session. The mean and standard deviation of the surrogate dataset ITC values were used to calculate a z-scored ITC on each session. We also performed an identical analysis aligned to stimulus onset as a positive control in which high stimulus-evoked ITC was expected\n\nFano Factor\nTo determine whether beta bursts occur at a consistent time or a random time across trials we calculated the Fano factor. We calculated the Fano factor for each session by multiplying the standard deviation across trials by 2 and dividing that by the mean across trials. This was done for each time point (5 msec bin size) in the time window around the VS time.\n\nTransfer entropy (TE)\nWe downsampled the two continuous signals, EEG and treadmill velocity, to 320 Hz. TE was calculated as in prior work42. We calculated TE in bins of 6 msec with a lag of 500 msec.\n\nModeling\nMachine learning algorithms were run in Python using scikit-learn. The regression-based analysis for decoding the peak velocity time used a 200 msec window, slid in 10 msec steps. Each sliding window sequence was represented as the beta envelope over time bins of N = 5 msec, resulting in 40 features per sequence. The target variable Y in this task ranged from 200 msec (for the first sequence) to 0 msec (the leading edge of the final sequence), effectively encoding the time-to-event for each window. For each rat, the sequence data was divided into five folds for cross-validation and hyperparameter tuning. To prevent data leakage from the sliding window approach, folds were created by sampling entire trials, each containing an identical number of sequences due to the time-lock applied during preprocessing. Furthermore, to maintain consistency across models, a fixed random seed was used, ensuring all models were trained on the same dataset splits. For each model, we defined a grid of hyperparameters (see code in \u003cLINK> for details) and conducted a search for the best-performing configuration across all five folds (training on the k-1 folds and testing the model on the left-out fold, to avoid double dipping). The evaluation metric for model selection and comparison was the average cross-validated R² score across all folds. Additionally, a baseline model was included for reference. This baseline used the average time-to-event from the training folds as a constant value that we then applied for prediction over the test folds. A model predicting the mean of the dependent variable for every data point will, by definition (or the R²) obtain an R² of zero, since it does not explain any variability beyond the average value. However, slight deviations in R² can occur when using data splits, due to small differences in the average value of the dependent variable between the training and test sets.\n\nThe classification-based analysis discriminated between a baseline and a pre-VS period. Each trial yielded two labeled observations: one from the baseline peri
131od and one from the pre-event period. As in the regression task, each 200 msec sequence was divided into time bins of size N = 5 msec, resulting in 40 features that capture the beta band envelope dynamics. We evaluated the same range of machine learning algorithms as in the regression task (though substituting Linear Regression with Logistic Regression). Cross-validation and hyperparameter tuning were performed as for the regression-based analysis. However, the baseline model was calculated differently for the classification-based analysis. Chance-level performance for each model was estimated by shuffling class labels 200 times for each cross-validation train fold and running hyperparameter tuning on each shuffled dataset, effectively obtaining 200 shuffles * 5 folds = 1000 chance-level accuracies per model for the best hyperparameter configuration. This procedure removed any patterns in the data, allowing us to establish a random accuracy distribution for each algorithm. The significance of the observed performance was determined by comparing the modelâs average cross-validated accuracy against the 95th percentile of the random accuracy distribution. If the observed accuracy exceeded this threshold, it indicated that the modelâs performance was unlikely due to chance and suggested that the beta envelope contained information distinguishing baseline from pre-event periods.\n\nSupplementary Figure 1.\nDownload figureOpen in new tab\nSupplementary Figure 1.\nAverage cross-validation R² score for time-to-event regression task.\nSupplementary Figure 2.\nDownload figureOpen in new tab\nSupplementary Figure 2.\nAverage cross-validation accuracy score for pre-event sequence classification task.\nAcknowledgements\nThe authors thank Prof. Aaron Schurger for discussions about machine learning analyses and the decoding analyses performed in this paper.\n\nReferences","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2024.12.28.630599v1.full.pdf+html","pdfPublic":true,"meta":{"species":"rat"},"featured":false,"sortOrder":20,"createdAt":"2026-06-25T12:00:11.850Z","products":["remy-system","remy-implants","remy-surgery-holder","remy-chamber","remy-holder","remy-chambers","remy-poles"]},{"id":"pub_87dMANs","slug":"mousegoggles-an-immersive-virtual-reality-headset-for-mouse-neuroscience-and-behavior","status":"published","type":"article","bibtexKey":"","title":"MouseGoggles: an immersive virtual reality headset for mouse neuroscience and behavior","authors":["Matthew Isaacson","Hongyu Chang","Laura Berkowitz","Rick Zirkel","Yusol Park","Danyu Hu","Ian Ellwood","Chris B. Schaffer "],"year":2024,"month":"12","journal":"Nature Methods","volume":"22","issue":"","pages":"380-385","publisher":"","doi":"10.1038/s41592-024-02540-y","url":"https://doi.org/10.1038/s41592-024-02540-y","abstract":"Small-animal virtual reality (VR) systems have become invaluable tools in neuroscience for studying complex behavior during head-fixed neural recording, but they lag behind commercial human VR systems in terms of miniaturization, immersivity and advanced features such as eye tracking. Here we present MouseGoggles, a miniature VR headset for head-fixed mice that delivers independent, binocular visual stimulation over a wide field of view while enabling eye tracking and pupillometry in VR. Neural recordings in the visual cortex validate the quality of image presentation, while hippocampal recordings, associative reward learning and innate fear responses to virtual looming stimuli demonstrate an immersive VR experience. Our open-source systemâs simplicity and compact size will enable the broader adoption of VR methods in neuroscience.","fulltext":"MouseGoggles: an immersive virtual reality headset for mouse neuroscience and behavior\nMatthew Isaacson, Hongyu Chang, Laura Berkowitz, Rick Zirkel, Yusol Park, Danyu Hu, Ian Ellwood & Chris B. Schaffer \nNature Methods volume 22, pages380â385 (2025) Cite this article\n\nSave article\n39k Accesses\n\n9 Citations\n\n381 Altmetric\n\nMetricsdetails\n\nAbstract\nSmall-animal virtual reality (VR) systems have become invaluable tools in neuroscience for studying complex behavior during head-fixed neural recording, but they lag behind commercial human VR systems in terms of miniaturization, immersivity and advanced features such as eye tracking. Here we present MouseGoggles, a miniature VR headset for head-fixed mice that delivers independent, binocular visual stimulation over a wide field of view while enabling eye tracking and pupillometry in VR. Neural recordings in the visual cortex validate the quality of image presentation, while hippocampal recordings, associative reward learning and innate fear responses to virtual looming stimuli demonstrate an immersive VR experience. Our open-source systemâs simplicity and compact size will enable the broader adoption of VR methods in neuroscience.\n\nSimilar content being viewed by others\n\nMoculus: an immersive virtual reality system for mice incorporating stereo vision\nArticle Open access\n12 December 2024\n\nA miniaturized mesoscope for the large-scale single-neuron-resolved
131imaging of neuronal activity in freely behaving mice\nArticle 20 June 2024\n\nVirtual reality: a powerful technology to provide novel insight into treatment mechanisms of addiction\nArticle Open access\n06 December 2021\nMain\nVirtual reality (VR) systems for laboratory animals have enabled fundamental neuroscience research, supporting the study of neural processes underlying complex cognitive tasks using neural recording strategies that require head fixation1,2,3,4. VR gives the experimenter full control over the subjectâs visual experience and allows experimental manipulations infeasible with real-world experiments, including teleportation and visuomotor mismatch paradigms4. VR with head-fixed mice has traditionally relied on panoramic displays composed of projector screens1,3 or arrays of light-emitting diode (LED) displays2,4 positioned 10â30âcm away from the eyes to remain within the mouseâs depth of field. This necessitates displays that are orders of magnitude larger than the mouse, resulting in complex, costly and light-polluting systems that can be challenging to integrate into many neural recording setups. In addition, fixed experimental equipment (for example, cameras, lick ports and microscope objectives) can obstruct the mouseâs visual field, potentially reducing immersion in the virtual environment. Inspired by modern VR solutions for humans, we set out to design a headset-based VR system for mice to overcome the constraints of panoramic VR.\n\nResults\nMiniature VR headset design\nUsing small circular displays and short-focal length Fresnel lenses, we designed eyepieces suited to mouse eye physiology (Fig. 1a). Spherical distortion of the display by the lens results in a near-constant angular resolution of 1.57 pixels per degree and Nyquist frequency of 0.78 cycles per degree (c.p.d.)âjust above the 0.5 c.p.d. spatial acuity of mouse vision5âand a field of view (FOV) coverage spanning up to 140° (Fig. 1b,c) per mouse eye. The optical design positions the display near infinity focus (Fig. 1d), estimated as the optimal focal length for mouse vision, making the clarity of image presentation robust to small deviations in eye position, as validated by imaging gratings projected on the back of an enucleated mouse eye (Extended Data Fig. 1). Using two eyepieces separated to accommodate a typical mouse intereye distance (Extended Data Fig. 2), we achieve 230° horizontal FOV coverage with ~25° of binocular overlap, and 140° vertical FOV coverage spanning â55° to 85° elevation at a headset pitch of 15°. This configuration covers a large fraction of the mouseâs visual field, which we approximate from a previous study6 to span 180° in azimuth and 140° in elevation per eye, with the optical axis centered on 70° azimuth and 10° elevation (Fig. 1e). Unique features of this headset-based system are the independent control over each eyeâs display (allowing stereo correction7) and the ability to adjust headset pitch to enable greater overhead stimulationâan understudied area of vision in head-fixed VR contexts probably important for prey animals.\n\nFig. 1: Headset-based VR design.\nFig. 1: Headset-based VR design.\nFull size image\na, Components and orientation of headset eyepieces, each containing a 2.76-cm-diameter circular LED display and 1.27-cm-diameter Fresnel lens housed in a 3D-printed enclosure. b, Optical modeling of display and Fresnel lens for infinity focus, with viewing angles of 0â70° (one half of the 140° total FOV coverage) linearly mapped onto the circular display. c,d, Optical model estimate for the apparent resolution (c) and focal distance (d) as a function of viewing angle. e, A Winkel tripel projection of the mouseâs estimated visual field overlaid with the headset display visual field coverage. f, Communication diagrams of the MouseGoggles Mono monocular display system (left) and MouseGoggles Duo binocular display system (right), with SPI-based display control and additional input/output communication schemes. g, The Godot video game engine-generated 3D environment with split-screen viewports and spherical shaders to map the scene onto the dual-display headset.\n\nTo generate images and video for the eyepiece displays, we designed two types of control systems. The first connects a single display to a high-speed microcontroller, ideal for simple monocular visual stimulation experiments commonly used in vision neuroscience (Fig. 1f). The second connects two dis
131plays to a Raspberry Pi 4 using a split-screen display driver (Fig. 1f). We used the user-friendly video game engine Godot to quickly build three-dimensional (3D) environments, program experimental paradigms and perform low-latency input/output communication to external equipment with frame-by-frame synchronization. With a two-eye viewport and custom shaders to map the 3D environments onto the eyepieces (Fig. 1g), MouseGoggles generates high-performance VR scenes at 80âfps and \u003c130âms input-to-display latency during full-screen updates. The entire monocular and binocular display systems can be housed in a single enclosure of 3D-printed parts or in smaller headset form factors by separating the Raspberry Pi from the eyepieces (Extended Data Fig. 3 and Supplementary Video 1). This compact design enables more mobile and rotatable VR systems (Supplementary Video 2) but results in partial occlusion of the mouseâs whiskers, with slightly more or less occlusion depending on the headset pitch angle (Extended Data Fig. 4).\n\nValidation of image presentation\nTo validate the function of our eyepiece design, we delivered visual stimulation with the monocular display, named MouseGoggles Mono, to anesthetized, head-fixed mice during two-photon calcium imaging of the visual cortex (Fig. 2a). When using blue stimuli to excite V1 neurons, thereby reducing spectral overlap with green GCaMP6s fluorescence8, we found that the monocular display produced 99.3% less stray light contamination into the fluorescence imaging channels than a traditional unshielded LED monitor (Fig. 2b). Total stray light from the eyepiece display was equivalent to that produced by a carefully shielded monitor, and we did not detect any additional light reaching the light detection systems by entering the pupil and scattering through the brain (Extended Data Fig. 5).\n\nFig. 2: Neural recording in headset VR.\nFig. 2: Neural recording in headset VR.\nFull size image\na, The experimental setup for MouseGoggles Mono visual stimulation with two-photon imaging of mouse V1 layer 2/3 neurons expressing GCaMP6s. b, Light contamination measurements from five repetitions of a maximum-brightness blue flicker stimulus into blue (468â488ânm) and green (488â550ânm) imaging channels, using either a flat LED monitor or the monocular display eyepiece. Raw intensity values were normalized to the maximum intensity from the monitor. c, Direction- and orientation-selective fluorescence change (ÎF) responses from 6 example neurons from 12 directions of drifting grating stimuli (meanâ±âs.d. of 6 repetitions). d, RF maps for two example cells. Left: inferred spike rate heatmap based on stimulus location. Right: 2D Gaussian fit to the heatmap, with the average half width at half maximum (hwhm) shown. e, A histogram of calculated RF size for all cells well fit by a 2D Gaussian (nâ=â341 cells). f, The SF tuning of normalized activity for all cells well fit by a log-Gaussian function (nâ=â124 cells). g, A histogram of preferred SF. h, The contrast frequency tuning of normalized activity for all cells well fit by a NakaâRushton function (nâ=â202). i, A histogram of semisaturation contrast. j, The experimental setup for hippocampal electrophysiological recordings during simulated walking on a spherical treadmill with MouseGoggles Duo. k, Rendered view (top) and side view (middle) of the virtual linear track, with headset views at three different positions (bottom). l, An example place cell across the entire virtual linear track session, showing the raster plot of neural activity (top) and tuning curve (bottom; FR, firing rate). m, A position-ordered heatmap of all detected place cells (nâ=â54 cells), showing binned firing rate (FR, z-scored) over position. n, Place cell characteristics over all recorded sessions: fraction of cells with place selectivity (nâ=â9 sessions, top), place field width (nâ=â39 cells within 10â80 virtual cm, middle) and information rate (nâ=â54 cells, bottom). The box plot displays median and 25th and 75th quartiles, with the whiskers representing the most extreme nonoutliers.\n\nPresenting drifting bars and gratings elicited orientation- and direction-selective responses (Fig. 2c) from which we calculated stimulus tuning properties of primary visual cortex layer 2/3 (V1 L2/3) neurons nearly identical to those previously obtained with traditional displays, such as a median receptive field (RF) radiu
131s of 6.2° (versus 5â7° with a monitor9) (Fig. 2d,e), maximal neural response at a spatial frequency (SF) of 0.042âc.p.d. (versus 0.04âc.p.d. (ref. 10)) (Fig. 2f,g), and a median semisaturation contrast of 31.2% (versus 34% (ref. 11)) (Fig. 2h,i), demonstrating that the display produces in focus, high-contrast images for the mouse visual system.\n\nTo validate the efficacy of the binocular MouseGoggles system, named MouseGoggles Duo, for simulating virtual environments, we displayed a linear track to awake, head-fixed mice positioned on a spherical treadmill while simultaneously performing electrophysiological recording of hippocampal cornu ammonis (CA1) neurons (Fig. 2j,k). Place fields developed over the course of a single session of virtual continuous-loop linear track traversal (Fig. 2l), with place cells (19% of all cells versus 15â20% with projector VR12) found to tile the entire virtual track over multiple recording sessions (Fig. 2m). Many place cells with high spatial information encoded field widths as small as 10â40 virtual cm, or 7â27% of the total track length, but we also observed larger field widths of 50â80 virtual cm, which more closely match the size of visually distinct zones of the track (Fig. 2n). Taken together, these data demonstrate that MouseGoggles effectively conveys virtual visual and spatial information to head-fixed mice.\n\nLearned and innate behaviors in immersive VR\nTo assess our ability to condition mouse behaviors in headset VR, we trained mice on a 5-day continuous-loop linear track place learning protocol in which mice were given liquid rewards for licking at a specific virtual location using MouseGoggles Duo (Fig. 3a and Extended Data Fig. 6). After 4â5âdays of training in the linear track, mice exhibited increased anticipatory licking (licking inside the reward zone just before a reward) and reduced exploratory licking in an unrewarded control zone (Fig. 3b). We trained two mouse cohorts with different reward locations in the same virtual linear track and found a statistically significant increase in lick preference in the reward zones during unrewarded day 4â5 probe trials (two-tailed MannâWhitney U test, Pâ=â0.02; Fig. 3c,d), demonstrating spatial learning in VR similar to previous results with a projector-based system13.\n\nFig. 3: Conditioned and innate behaviors in headset VR.\nFig. 3: Conditioned and innate behaviors in headset VR.\nFull size image\na, Mouse licking behavior during days 1 and 5 of a 5-day virtual linear track place learning protocol using MouseGoggles Duo, separated into an exploratory licking (defined as licks not initiated by a liquid reward delivery) and post-reward licking (mean of all trialsâ±âs.e.m., nâ=â5 mice). b, The proportion of exploratory licks in reward versus control zone, across days (mean of all trialsâ±âs.e.m., nâ=â5 mice). c, The exploratory lick rate during unrewarded probe trials on days 4 and 5 as a function of position in the virtual linear track for mice trained for a reward in zone A (left) and zone B (right) (mean lick rate of probe trialsâ±âs.e.m., nâ=â5 mice each for reward zone). d, The proportion of licking in reward versus control zone on probe trials in which no reward was delivered, pooling mice conditioned to associate reward with zones A and B. The box plots display median and 25th and 75th quartiles, with the whiskers representing the most extreme nonoutliers (nâ=â10 mice; median 46.3% for reward versus 28.6% for control; Pâ=â0.02, two-tailed MannâWhitney U test). e, Looming stimuli consisting of a dark circular object approaching at constant velocity before reaching the closest distance at tâ=â0âs. f, An example âstartleâ response from a head-fixed mouse from the looming stimulus, characterized by a jump up and arching of the back (see also Supplementary Video 3). g, The proportion of mice that displayed a startle response after presentation of a looming stimulus (as determined from manual behavior scoring) as a function of stimulus repetition and comparing headset or projector-based VR (nâ=â6â7 mice for all repetitions with headset, nâ=â4â5 mice with projector). An exponential decay curve is fit to the headset-based VR startle responses.\n\nA potential benefit of MouseGoggles over panoramic displays is a greater degree of immersion in the virtual environment, as the headset effectively blocks irrelevant and conflicting visual stimuli. To determine whether innate behavioral responses can be elicited by more immersive head-fixed VR, we presented looming visual stimuli to naive mice that had no prior experience with head-fixed displays (Fig. 3e). On the first presentation of a looming stimulus using MouseGoggles Duo, nearly all mice displayed a head-fixed startle response
131(a rapid jump or kick, with an arched back and tucked tail, manually scored; Fig. 3f and Supplementary Video 3), while a nearly identical experiment on a traditional projector-based VR system (Extended Data Fig. 7) produced no immediate startles. Startle responses were found to rapidly extinguish with repeated looming stimuli (Fig. 3g), an adaptation previously observed with defensive responses to looming in freely walking mice14.\n\nVR with integrated eye and pupil tracking\nTo enable monitoring of eye and pupil dynamics while mice experience the VR environment, we developed a binocular headset, named MouseGoggles EyeTrack, with infrared (IR)-sensitive cameras embedded in the eyepieces (Fig. 4a). We used this headset with head-fixed mice walking on a small-footprint linear treadmill15 (Fig. 4b). Each eye is seen through reflection on an angled hot mirror and spectrally separated from the display by software-based removal of red light from the VR scene (Fig. 4c). We calibrated this system to account for visual distortion from the Fresnel lens and tilted camera view (Fig. 4d), and used Deeplabcut16 for offline tracking of points on the eyes and pupils to enable absolute measurements of pupil diameter and position (Fig. 4e). Assuming typical values for eye distance from the lens and eye size, we estimated the eyeâs optical axis relative to the virtual visual field (Fig. 4f). Presenting looming stimuli with MouseGoggles EyeTrack (Fig. 4f and Supplementary Video 4) led to a sharp slowdown or reversal of forward walking (Fig. 4g,h) and vertical shifts in gaze position during the overhead loom (Fig. 4i,j). Since eye movements during head fixation are known to be associated with attempted head movements16, these gaze shifts following the looming stimulus may in part be related to attempted escape behaviors. Pupil diameter was also found to increase after the loom (Fig. 4k,l), but unlike the walking slowdown and gaze-shift behaviors that persisted across repeated loom stimuli, pupil dilation responses were found to diminish with additional repetitions of the stimulus (Cuzickâs trend test, Pâ=â0.007), similar to the behavioral adaptation of the startle response (Fig. 3g).\n\nFig. 4: Eye and pupil tracking during VR looming stimuli.\nFig. 4: Eye and pupil tracking during VR looming stimuli.\nFull size image\na, The design of combined VR display and eye-tracking camera eyepiece enclosure (left), with an exploded view showing the layout of eyepiece components (right). b, The experimental setup of MouseGoggles EyeTrack for head-fixed mice walking on a linear treadmill (treadmill model adapted from ref. 15). c, A raw IR image of the eye-tracking camera during VR. d, An eye-tracking camera image of a grid with calibrated gridlines (millimeter spacing), to correct distortions. e, Eye-tracking camera frames of left and right eyes with Deeplabcut-labeled points on the border of the eyelid and pupil. f, Example frames of a centered looming stimulus, with left and right eye optical axes mapped onto the VR visual field, demonstrating increased eye pitch after the loom (bottom). g, The average treadmill velocity during looming stimuli onset across all 15 repetitions of the stimulus (meanâ±âs.d. of 5 mice in shaded region). h, A box plot of mean walking velocity change during 0â2âs after looming stimulus onset (relative to baseline), averaged across each set of three looming stimuli (left, right and centered loom), with no significant trend over repeat repetitions (nâ=â5 mice, one-sided Cuzickâs trend test, Pâ=â0.37). The box plot displays median and 25th and 75th quartiles, with whiskers representing the most extreme nonoutliers. i, The average change in eye pitch angle (relative to baseline) during looming stimuli across all 15 repetitions of the stimulus (meanâ±âs.d. of 5 mice in shaded region). j, A box plot of the change in eye pitch angle during 0â2âs after looming stimulus onset, averaged across each set of three looming stimuli, with no significant trend over repeat repetitions (nâ=â5 mice, one-sided Cuzickâs trend test, Pâ=â0.24). The box and whiskers are defined as in h. k, The average change in pupil diameter during looming stimuli onset across the first set of three repetitions of the looming stimulus (meanâ±âs.d. of five mice in shaded region). l, A box plot of the average change in pupil diameter during 0â3âs after looming stimulus onset, averaged across each set of three looming stimuli, with stars denoting a statistically significant trend (nâ=â5 mice, one-sided Cuzickâs trend test, Pâ=â0.007). The box and whiskers are defined as in h.\n\nDiscussion\nOur headset-based VR system demonstrates substantial improvements over panoramic display systems, enabling traditional mouse neuroscience and behavioral experiments as well as opening the door to more experiments of innate behaviors during head fixation. Since the original submission of this study, two other headset-based VR systems have been described, Moculus17,18 and iMRSIV19. Collectively, these systems reinforce the benefits of headset VR, including reduced cost and form factor, the ability to present stereoscopic 3D VR scenes and greater immersivity of the VR environment for mice, demonstrated by rapid visual learning in a pattern discrimination task and cliff avoidance in a virtual elevated maze test with Moculus17, and fear responses to looming visual stimuli with iMRSIV19 and MouseGoggles.\
131n\nAlthough the benefits of headset-based VR are substantial, our MouseGoggles system has some limitations. Partial occlusion of the whiskers (depending on headset pitch) may confound the sensory experience of virtual navigation. Input-to-display latency may be too slow to support closed-loop experiments with fast behaviors (for example, eye movements) or neural events, although latency can be substantially reduced with future display driver optimizations. The simple optical design of MouseGoggles eyepieces, while making clear image presentation robust to precise eye position, limits the FOV coverage to 140°, whereas the more complex optical designs of Moculus and iMRSIV can support increased FOV (although at the expense of requiring more precise alignment of the display to the eye)17,19. Finally, the low-resolution displays used here, while suited for mice, may be insufficient for animals with increased visual acuity (for example, rats or tree shrews) and would need to be replaced with higher-resolution displays. By contrast, the simple low-cost design of MouseGoggles enables high-performance mouse VR using a single Raspberry Pi computer, with no external graphics processing unit required to run the feature-rich Godot 3D game engine. This enables greater scalability and increased throughput for VR training and experiments, with a complete VR setup (using a linear treadmill) fitting within a 14âÃâ14âcm footprint. Finally, our integration of IR cameras facilitates video-oculography-based eye and pupil tracking during VR. MouseGoggles establishes a flexible platform to further improve and expand mouse VR technologies, such as in multisensory VR applications (for example, with whisker stimulation20), in rotatable VR setups21 to engage or manipulate the vestibular system during VR or even in free-walking VR with further miniaturization of the headset. With our MouseGoggles system, we prioritized a design that is easy for new users to assemble and install to facilitate replication, modification and future development.\n\nMethods\nOptical design\nOptical modeling of the VR eyepiece was performed using OpticStudio in sequential mode, with custom scripts written with Matlab (version 2022b) used for analysis and plotting. The Fresnel lens model was supplied by the manufacturer (FRP0510, Thorlabs). The display was positioned at the focal length of the lens (10âmm) for infinity focus. We estimate this to be near the center of a mouseâs depth of field on the basis of previous research testing the impact of various focal length lenses on free-walking mice in a rotational optomotor assay, where it was found that either no lens or a +7âD lens with the display at a distance of ~30 (20â40) cm resulted in the strongest behavioral reactions, whereas lenses outside of this range negatively affected optomotor responses22. These data suggest that infinity focus (equivalent to a +3.33âD lens with the display at 30âcm) is near the center of the mouseâs depth of field. Using the optical model set to infinity focus, the apparent display resolution and focal distance was estimated by first casting parallel rays from the eye position (that is, rays that appear at infinity focal depth) for multiple viewing angles (0â70°, in 10° increments). For each viewing angle, the mean position of the rays as they intersect with the display (in pixels from the display center) was calculated, and the true focal point of the rays was calculated from the position with minimum variance in ray spread. The resolution by viewing angle was calculated from the slope of the line of viewing angle as a function of pixel position. The focal depth by viewing angle was calculated from the distance between each viewing angleâs focal point and the display; the inverse of this distance (in cm) quantifies the focal distance in diopters away from infinity focus. Two-dimensional (2D) projections of the visual field coverage of the display, as seen through the lens, were estimated assuming a constant display resolution of 1.57 pixels per degree: pixels were mapped onto a sphere with the center of the display pointing straight ahead, then rotated to match the final position in a typical headset orientation (45° azimuth, 15° elevation). The mouseâs FOV (shown in Fig. 1 and Extended Data Figs. 4 and 7) was approximated on the basis of prior measurements of V1 retinotopic organization6 that found RF centers roughly spanning from 0° to 140° in azimuth and â40° to 60° in elevation. Extending these RF centers by a radius of 20°, we approximate the mouse FOV as a 180°âÃâ140° ellipse centered at 70° azimuth and 10° elevation (probably overestimating the FOV in the lower periphery where RF centers were not found).\n\nDisplay hardware\nFor the MouseGoggles Mono display used for V1 imaging, a circular, 16-bit color TFT LED display (TT108RGN10A, Shenzhen Toppop Electronic) w
131as connected to a Teensy 4.0 microcontroller (Teensy40, PJRC) using a custom printed circuit board, with a short focal length Fresnel lens (FRP0510, Thorlabs). For the MouseGoggles Duo headset, two of the same displays were connected to a Raspberry Pi 4 (Pi 4B-2GB, Adafruit); both displays were connected to the serial peripheral interface (SPI) 0 port with different chip select pins (display 0 on CE0, display 1 on CE1) to allow independent display control. For the MouseGoggles EyeTrack headset, a slightly larger circular display was used (1.28âinch liquid crystal display module, model 19192, Waveshare). Plastic enclosures used to house the components (Teensy/Raspberry Pi, printed circuit board, displays, Fresnel lenses) were printed using a 4K resin 3D printer (Photon Mono X, Anycubic).\n\nDisplay software\nTo present visual stimuli on MouseGoggles Mono, a custom Arduino script was written for the Teensy 4.0 microcontroller and uploaded via the Arduino IDE (version 1.8.15) using the Teensyduino add-on (version 1.57). Pattern drawing commands are read over serial communication from a host personal computer (PC) and utilize the Adafruit graphics functions library (https://github.com/adafruit/Adafruit-GFX-Library) to create simple visual stimuli such as drifting gratings, edges and flickers. To control the display from a host PC, custom scripts written with Matlab and Python (version 2.8.8) were used. To render 3D scenes for MouseGoggles Duo, the Linux-compatible game engine Godot (version 3.2.3.stable.flathub) was installed on the Raspberry Pi OS (based on 32-bit Debian Bullseye) using the Flathub Linux-based app distribution system. Unlike the Unity game engine commonly used for neuroscience VR applications, Godot was selected for MouseGoggles because it is an open-source engine with reduced computational requirements that can operate at high framerates on simple hardware (for example, a Raspberry Pi 4), despite its feature-rich and powerful 3D rendering capabilities. The 3D environment was mapped onto the circular displays using custom Godot shaders to warp the default rendered view (which linearly maps a flat plane in the virtual scene onto the flat plane of the display) to the spherical view created by the headset (which linearly maps viewing angles onto the flat plane of the display). To deliver these rendered views to the circular displays, a custom display driver was modified from an existing open-source Raspberry Pi display driver for SPI-based displays (https://github.com/juj/fbcp-ili9341), which functions by copying a subset of the default high-definition multimedia interface (HDMI) framebuffer (the size of the frame subset determined by the resolution of the SPI display) and streams the frame data over the SPI channel. Our customized driver modifies the original to enable control of two SPI displays simultaneously; this is achieved by copying a subset of the framebuffer that is twice the height of a single SPI display, with the top half defining the frame for display 0 and the bottom half for display 1. For every program loop, where the loop frequency is determined by the desired framerate, the program streams the top half of the framebuffer with chip select 0 (connected to display 0) enabled, followed by streaming the bottom half of the framebuffer with chip select 1 (connected to display 1) enabled. During each refresh cycle, the entire frame of each display is updated. Latency could be reduced in the future using the âadaptive display stream updatesâ mode of the display driver (not yet implemented with MouseGoggles), where only pixels that changed from the previous frame are streamed to the display.\n\nAcute whole retina imaging during visual stimulation\nSurgical procedure for imaging the displayâs projection onto the back of the enucleated mouse eye was modified from a previous method23. Immediately after a previously scheduled euthanasia using CO2 and secondary euthanasia by cervical dislocation, curved jewelerâs forceps were used to enucleate the eyeball and remove the optic nerve and connective tissue. The dissected eyeball was immediately moved into room-temperature phosphate-buffered saline. Under a stereomicroscope, the sclera, choroid and retinal pigment epithelium were removed from the eye, and the intact retina was verified visually. The eye was then placed on a 3D-printed holder with a central hole matching the typical eye diameter, with the eyeâs optical axis facing upward. A mini camera (OV5647, Arducam) was mounted below the eye facing upward and manually focused onto the back of the eye. Either a traditional flat monitor was mounted above the eye at a 10âcm distance, or a MouseGoggles Mono eyepiece was mounted on an adjustable optical post, with the display facing downward toward the eye. Vertically and horizontally drifting grating stimuli were then presented to the eye during by either a monitor (at a 10âcm distance) or MouseGoggles Mono display during the acquisition of retinal-plane images.\n\nEye-tracking hardware and software\nTo perform eye and pupil tracking with the MouseGoggles EyeTrack headset, each eyepiece included additional slots for a mini IR camera module (OV5647, Arducam), an IR hot mirror (FM01, Thorlabs) placed on a 15° angle between the Fresnel lens and display, and a custom circuit board
131with two surface-mount IR LEDs (VSMB2943GX01, Vishay) positioned on either side of the camera module. Each camera was positioned along the side of the angled eyepiece enclosure, facing the display, with an IR view of the mouse eye on the opposite side of the Fresnel lens based on reflection off of the hot mirror. Each camera was independently controlled using a Raspberry Pi 3 through the libcamera library (libcamera.org), acquiring 30 framesâsâ1 at 800âÃâ800 pixel resolution. Videos were preprocessed to extract the red imaging channel (which excludes most of the blue/green VR display) of a 500âÃâ500âpixel region of interest centered on the eye. Offline eye and pupil tracking of this preprocessed video was performed using the Deeplabcut16 toolbox for tracking the pixel coordinates of the top, bottom, left and right points of the pupil and eyelids. The pixel coordinates of the pupil center were calculated by averaging the left- and right-side points of the pupil, and the pupil diameter was calculated by the distance between these two points; the top and bottom points of the pupil were ignored due to their often-unreliable tracking as the mouse partially closes its eyelids. Lateral eye movements due to face movement behaviors were corrected by subtracting the movements of the eye center (defined as the average between the eyelidâs left and right side point coordinates) from the pupil center, similar to previous methods24. Lateral movements of the pupil center in pixels were then converted to movements in millimeters using a calibrated transformation estimating the radial distortion produced by the Fresnel lens and the cameraâs tilted point of view. The following equations were used to relate positions (in millimeters) in the eye position plane to coordinates (in pixels) in the acquired images:\n\nwhere ex and ey are the Cartesian coordinates of the eye plane, a and b are fitting parameters accounting for the radial lens distortion, exâ² and eyâ² are the Cartesian coordinates after lens distortion, c and d are fitting parameters accounting for the cameras tilted view, and px and py are the camera pixel x and y coordinates. Parameters a, b, c and d were manually calibrated for each eyepiece to convert a millimeter-spaced grid placed at the mouse eye position into pixel coordinates (Fig. 4d). After pupil movements (in millimeters) relative to the eye center were calculated, eye rotations could then be estimated. Since IR glare and reflections off the Fresnel lens partially obstructed the view of the pupil, estimating eye rotations on the basis of elliptical pupil distortions as has been previously demonstrated24 was unreliable. Instead, we estimated eye rotations on the basis of the lateral movement of the pupil center and the approximate distance between the pupil and the mouse eye center, as measured previously25 (3.2â3.4âmm axial length and 0.35â0.4âmm anterior chamber depth, yielding ~1.3âmm from the pupil to the eye center). Pupil movements were converted to eye rotations by the following equations:\n\nwhere eyaw and epitch are the eye yaw and pitch angles, px and py are the pupil x and y coordinates relative to the eye center (rotated so the x coordinate follows the horizon) and pr is the radial distance of the pupil from the eyeball center. Eye yaw and pitch angles were mapped onto the visual field relative to their approximate optical centers located at ±70° azimuth and 10° elevation.\n\nHeadset rotation feedback\nTo perform closed-loop feedback so headset rotation leads to rotation in the VR environment, an integrated sensor (6 degree-of-freedom gyroscope and accelerometer, LSM6DSOX, Adafruit) or magnetometer (LIS3MDL, Adafruit) was attached to a rotating mount supporting a MouseGoggles Duo headset. Accelerometer or magnetometer readings were measured by a Teensy 4.0 microcontroller (Teensy40, PJRC) running Arduino code to convert sensor readings into absolute headset orientation relative to straight ahead and relay these values via a Universal Serial Bus (USB) connection to the Raspberry Pi to control virtual movement.\n\nAnimals\nAll animal procedures complied with relevant ethical regulations and were performed after approval by the Institutional Animal Care and Use C
131ommittee of Cornell University (protocol number 2015-0029). All mice were housed in a climate-controlled facility kept at 22°âC and 40â50% humidity, under a 12âh lightâdark cycle with ad libitum access to food and water. All behavioral experiments were performed during the night phase. For mouse intereye distance measurement (Extended Data Fig. 2), a variety of mouse genotypes and ages were used: C57BL/6 (three females, five males; 4â16âmonths old), APPnl-g-f heterozygotes26 (three males, four females; 2â3âmonths old), TH::Cre heterozygotes (line Fl12, www.gensat.org) (one male; 16âmonths old) and Drd2::Cre heterozygotes (line ER44, www.gensat.org) (one male, two females; 4âmonths old). For two-photon calcium imaging and hippocampal electrophysiology experiments (Fig. 2), 6â9-month-old C57BL/6J male mice were used (three mice for imaging and two for electrophysiology). For virtual linear track behavioral conditioning experiments (Fig. 3aâd), ten male 2â4-month-old C57BL/6 mice were used. For looming visual stimulus behavioral experiments measuring startle reactions (Fig. 3e,f), eight male 2â7-month-old C57BL/6 mice were used. For looming visual stimulus experiments during eye and pupil tracking (Fig. 4), five male 4â5-month-old C57BL/6 mice were used.\n\nSurgical preparation for head-fixed behavior\nMice were anesthetized with isoflurane (5% for induction, 1% for maintenance) and placed on a feedback-controlled heating pad. Surgeries were performed on a stereotaxic apparatus where the heads of mice were fixed with two ear bars. Ointment (Puralube, Dechra) was applied to both eyes for protection. Injection of Buprenex (dose 0.05âmgâkgâ1) was given for analgesia. Lidocaine (2.5âmgâkgâ1) was administered to the scalp after being disinfected by 75% ethanol and povidoneâiodine. A small incision (~12â15âmm) along the sagittal line of the skull was made to expose a section of the skull sufficiently large to place a custom-designed titanium head plate. The head was rotated so that the bregma and lambda features of the skull were level. The surface of the skull was gently scratched by a scalpel to remove the periosteum. After the skull was completely dry, a thick layer of Metabond (Parkell) was applied to cover the skull surface. A titanium head plate was mounted on top of the Metabond and aligned with the surface of the skull and position of the eyes. The head plate was further secured by an additional layer of Metabond. Postoperative ketoprofen and dexamethasone were administered subcutaneously, and the mouse was returned to its home cage on a heating pad for recovery. All behavior tests were performed at least 1âweek after surgery.\n\nSurgical preparation for calcium imaging\nMice underwent surgical procedures for head-fixed behavior with modifications to accommodate a viral injection. First, a 3âmm craniotomy was made above V1 (anterior-posterior 3âmm, medial-lateral 2.5âmm from Bregma, centerline) on the right hemisphere. A 50ânl bolus of AAV9-Syn-GCaMP6s (Addgene) diluted to 1012âvgâmlâ1 was injected to the target V1 layer 2/3 (dorsal-ventral â0.2âmm from the brain surface). A 3âmm glass window then replaced the hole in the skull, and the titanium head plate was secured to the skull with Metabond. Postoperative ketoprofen and dexamethasone were administered subcutaneously, and the mouse was allowed to fully recover in a cage on a heating pad. Four weeks were allowed for viral expression before imaging.\n\nSurgical preparation for electrophysiology\nMice underwent surgical procedures for head-fixed behavior with modifications to accommodate a chronic electrode implant. First, a craniotomy was made above the dorsal CA1 (anterior-posterior 1.95âmm, medial-lateral 1.5âmm from Bregma, centerline), and a burr hole was made in the contralateral occipital plate for the placement of a ground screw. A stainless-steel wire was soldered to the ground screw and threaded through the head plate, which was then secured to the skull with Metabond. A 64-channel single-shank silicon probe (NeuroNexus) was adhered to a metal moveable micro drive (R2Drive, 3Dneuro) to allow vertical movement of the probe after implantation. The probe was implanted above the dorsal CA1 (dorsal-ventral â1.1âmm from brain surface), and the craniotomy was sealed with a silicone elastomer (DOWSIL 3â4680, Dow Chemical). Copper mesh was fixed to the Metabond that surrounded the micro drive and formed a cap. Ground and reference wires were soldered to the copper mesh to reduce environmental electrical noise. While the mouse was in the home cage, the copper mesh was covered with an elastic wrap to prevent debris from entering the cap.\n\nTwo-photon microscopy\nTwo-photon calcium imaging was performed using a Ti:Sapphire laser (Coherent Vision S Chameleon; 80âMHz repetition rate, 75âfs pulse duration) at 920ânm to excite the GCaMP6s calcium indicator, with ~35âmW power at the sample. Imaging signals were acquired using ScanImage27 software (SI2022) into separate blue and green color channels (separated by a 488ânm long-pass dichroic; channel 1 using a 510/84 (center wavelength/bandwidth, both in nanometers) bandpass filter and channel 2 using 517/65). A transistor-transistor logic (TTL) signal from the monocular display was acquired into an additional unused imaging channel for synchronizing imaging with visual stimuli. The 256âÃâ256âpixel image frames were acquired at 3.41âHz.\n\n
131Monocular visual stimulation\nVisual stimulation experiments were performed with anesthetized, head-fixed mice by positioning a MouseGoggles Mono display at the mouseâs left eye for contralateral two-photon imaging in the right hemisphere. The display was oriented to 45° azimuth and 0° elevation respective to the long axis of the mouse. To measure display light contamination, a maximum-brightness blue square (covering a 66°-wide region of the visual field) was flickered at 0.5âHz for five repetitions, first by the monocular display, then by a flat LED monitor (ROADOM 10.1â² Raspberry Pi Screen, Amazon) positioned 10âcm from the mouse eye and oriented at 70° azimuth and 0° elevation, with the cranial window either unblocked or blocked with a circular cut piece of black masking tape (T743-2.0, Thorlabs) (Extended Data Fig. 5). To measure V1 neuron visual stimulus encoding, neurons with RFs in the monocular display center were located by presenting a drifting grating stimulus in a small square region in the center of the display (24-pixel/15.3°-wide square and 24âpixel/15.3° SF) once every 6âs, cycling through four directions (right, down, left and up) until V1 L2/3 neurons excited by the stimulus were found through the live view of the fluorescence microscope. In this position, a single visual stimulation protocol was performed with three mice. All stimuli were blue square-wave gratings shown at 100% contrast, 1âHz temporal frequency (TF) and 20 pixel/12.7° SF unless otherwise noted. First, RF mapping was performed by presenting a four-direction bar sweep stimulus (right, down, left and up; 0.5âs per direction, 2âs total) in one location at a time in a 5âÃâ5 grid at the center of the display. Each segment of the grid was a 12-pixel/7.6°-wide square, where 6âÃâ12âpixel/3.8âÃâ7.6° bright bar sweeps were shown. Stimuli were presented in the 25 segments one at a time in a random order with 6âs between stimuli, for a total of five repetitions at each location. Next, orientation and direction tuning were measured by presenting drifting gratings at 12 angles (0â330°, in 30° increments) in a random order, at a 40âpixel/25.5° SF in an 80-pixel/51°-wide square that rotated on the basis of the angle of the grating. Each stimulus was 1âs in duration with 6âs between stimuli, for five repetitions. Finally, SF, TF and contrast tuning were measured using unidirectional (rightward) drifting gratings in an 80-pixel/51°-wide square region; only a single direction was used to reduce the number of stimuli and the duration of the experiment. The stimulus set consisted of the default grating stimulus (100% contrast, 1âHz TF, 12.7° SF) varied across five SFs (4, 10, 20 40 and 80 pixels; 2.5°, 6.4°, 12.7°, 25.5° and 51°, respectively), six TFs (0.5, 1, 2, 4, 8, and 12âHz) and six contrast values (5-bit bright/dark bar values of 15/15, 18/12, 21/9, 24/6, 27/3 and 30/0âfor contrast values of 0%, 20%, 40%, 60%, 80% and 100%, respectively), for a total of 15 unique stimuli. Each stimulus was 2âs in duration with 6âs between stimuli, for five repetitions.\n\nCalcium imaging analysis\nScanimage Tiff files were processed through suite2p28 (v0.10.3) for motion stabilization, active region of interest segmentation and spike inference, followed by custom Matlab scripts for analysis and plotting. Segmented cells were manually screened for accurate classification, resulting in 410 cells pooled from 3 mice (142, 112 and 156 cells from mouse 1, 2 and 3, respectively). For each cell, time-series vectors of the extracted fluorescence and inferred spikes were aligned for each stimulus repetition to calculate the average stimulus response. Activity was quantified at baseline and during each stimulus from the mean of all inferred spikes during baseline frames (2âs preceding each stimulus) and during the stimulus presentation (1 or 2âs in duration depending on the stimulus). RF size was estimated similarly to previous methods9. In brief, the mean response to the four-direction stimuli presented in a 5âÃâ5 grid was fit with a 2D Gaussian function using lsqcurvefit(@D2GaussFunctionRot) in Matlab, estimating an ellipse with two independent widths for the major and minor axes. The R
131F size was calculated by averaging the half width at half maximum of the major and minor axes. Only cells well fit to the 2D Gaussian were included (resnorms \u003c0.25; 341 cells). Normalized SF tuning curves were estimated similarly to previous methods10 by fitting each cellâs responses with a log-Gaussian function:\n\nwhere SF is the spatial frequency, SFpref is the preferred spatial frequency and Ï is a fitting parameter describing the width of the curve. Only cells well fit to the function were included (adjusted R2â>â0.8; 124 cells). Normalized contrast tuning curves were estimated similarly to previous methods11 by fitting each cellâs responses with a NakaâRushton function:\n\nwhere c is the contrast, c50 is the semisaturation contrast and n is a fitting exponent describing the sharpness of the curve. Only cells well fit to the function were included (adjusted R2â>â0.8, 202 cells).\n\nSpherical treadmill and lick port\nThe spherical treadmill system was built on the basis of an existing design1. A 20-cm-diameter Styrofoam ball was suspended by compressed air. Locomotion of mice was tracked through ball movement by two optical flow sensors (ADNS-3080 Optical Flow Sensor APM2.6) mounted on the bottom and side of the ball. The optical sensors sent the ball motion to an Arduino Due via SPI which was processed by a custom script (https://github.com/Lauszus/ADNS3080) and relayed via a USB connection to a Raspberry Pi or PC. The roll, pitch and yaw movements of the ball were transformed to drive the corresponding animal movements in the VR environment. Velocity gain was calibrated to ensure a one-to-one correspondence between the distances traveled in the virtual environment and on the surface of the ball. A 1.83-mm-diameter stainless-steel lick port along with a customized capacitive sensor29 was used to deliver water rewards and measure licking behavior. Water was delivered through a solenoid valve (SSZ02040672P0010, American Science Surplus) operated by a Teensy 4.0 microcontroller (Teensy40, PJRC) and relay (4409, Adafruit). The microcontroller received valve open commands from and transmitted lick detections to the Raspberry Pi over USB using Xinput (https://github.com/dmadison/ArduinoXInput).\n\nLinear treadmill\nThe linear treadmill was modified from an existing design (linear treadmill with encoder, Labmaker). Custom 3D-printed wall plates were used to accommodate a larger head mount, and custom code was uploaded to the treadmill microcontroller to convert treadmill motion into emulated computer mouse y movements, relayed via a USB connection to the Raspberry Pi to control virtual movement. Similarly to the spherical treadmill, velocity gain was calibrated to ensure a one-to-one correspondence between the distances traveled in the virtual environment and on the surface of the treadmill.\n\nHead-fixed behavioral tests\nBefore the start of behavioral tests, all mice were habituated in the room where the experiment would be performed for at least 1âday, followed by at least 5âdays of habituation on the spherical or linear treadmill (without a VR system attached). On each treadmill training day, mice were head-fixed on a custom-designed holder via the mounted head plate, and the head was positioned on the center of the spherical treadmill or positioned on the linear treadmill so that the body was contained by the treadmill walls. After room and treadmill habituation, mice were then habituated to the MouseGoggles Duo or MouseGoggles Eyetrack headset by positioning the headset to the mouse eyes using sliding optical posts so that both eyes were approximately positioned at the center of the eyepieces, typically 0.5â1âmm from the lens surface. A gray image was then shown on the VR display for 10âmin before VR experiments were started.\n\nHippocampal electrophysiology and analysis\nRecordings were conducted using the Intan RHD2000 interface board or Intan Recording Controller, sampled at 30âkHz. Amplification and digitization were done on the head stage. Data were visualized with Neurosuite software (Neuroscope). Mice concurrently underwent neural activity screening and head-fixed behavior habituation. For screening, activity from each amplifier channel was monitored while a mouse foraged for sugar pellets in an open field (30âcmâÃâ30âcmâÃâ12âcm), and the electrode was lowered (\u003c125âµm per day) until area CA1 layers were visible, identified by physiological features of increased unit activity and local field potential ripples30. The mouseâs virtual position on the ball was synchronized with electrophysiological data using a TTL pulse from the Teensy microcontroller connected to the Raspberry Pi.\n\nSpike sorting, unit identification and encoding of virtual position\nElectrophysiology data were analyzed with custom Python code (https://github.com/lolaBerkowitz/SNLab_ephys) using the Nelpy python package (https://github.com/nelpy/nelpy). Spike sorting was performed semiautomatically with KiloSort (https://github.com/cortex-lab/KiloSort), followed by manual curation using the softw
131are Phy (github.com/kwikteam/phy) and custom-designed plugins (https://github.com/petersenpeter/phy-plugins). Identified units were assessed by manual inspection of auto-correlograms, waveforms, waveform distribution in space and PCA metrics. Units with high contamination in the first 2âms of the auto-correlogram or with visible noise clusters were discarded. Spatial tuning curves were created by binning spike data and the mouseâs virtual position into 3âcm bins. Raw spike and occupancy maps were smoothed using a Gaussian kernel (3âcm s.d.). Only spike data from when the animalâs velocity was greater than 5âcmâsâ1 was used. A spatial information content score31 was calculated for each cell by the following definition:\n\nwhere the virtual environment is divided into N spatial bins, Pi is the occupancy of bin i, λi is the mean firing rate for bin i and λ is the overall mean firing rate of the cell. A surrogate set of information content scores was created by shuffling the position coordinates 500 times and computing the spatial information content score for the resulting tuning curves at each shuffle. A cell was defined as a place cell if the observed information content score was greater than the 95th percentile of shuffled scores and if the cellâs peak rate was at least 1âHz. For cells that met these requirements for spatial information content, field detection was performed using the find_fields function in neuro_py (https://github.com/ryanharvey1/neuro_py). In brief, a field was defined as an area that encompassed at least 30% of a local peak of the ratemap. Place fields were at minimum 10 virtual cm and at most 80 virtual cm, or 7â53% of the virtual linear track (150 virtual cm length).\n\nLinear track place learning\nA virtual linear track was designed for the MouseGoggles Duo headset using the Godot video game engine (https://godotengine.org/). The track was 1.5âm long and 6âcm wide, with 5-cm-high walls that were divided into three equal-length, visually distinct wall sections: (1) black and green vertical stripes, (2) black and white spots and (3) black and green horizontal stripes. In addition, a tall black tower was located at 0.9âm (track start 0âm, track end 1.5âm) to provide a more distal cue of location. The virtual location of the mouse began at 0.04âm and oriented at 0°, looking straight down the track; mice were constrained to locations within the track that were at least 4âcm from the nearest wall to prevent camera views clipping through the walls, limiting the total habitable length of the track to 1.42âm. The spherical treadmill pitch controlled forward/backward walking, while mouse heading was maintained at 0° to keep mice traversing down the track. Liquid rewards were given through the lick port at a specific location along the track to condition licking behavior at that location over time; rewards were given at 0.5âm for mice 1â5 (cohort A) and at 1.0âm for mice 6â10 (cohort B). Three days before training, mice were provided with 1.2âml of water daily and their body weight was continuously monitored, with additional water supplementation administered to maintain their body weight above 85% of their pretraining weight. After habituation for head-fixed behavior, all mice underwent a 5-day linear track place-learning protocol with the following parameters:\n\nDays 1â2: liquid reward is automatically delivered when the mouse reaches the reward location.\n\nDay 3: for the first three trials, liquid reward is automatically given. For trials 4+, the mouse must first lick in the reward zone (no farther than 0.25âm away from the reward location) before a reward is delivered at the mouseâs location of licking.\n\nDays 4â5: similar to day 3 (trials 1â3 guarantee reward; trials 4+ require licks), with a random 20% of trials unrewarded (probe trials).\n\nMice performed one session of track traversals per day, where each session consisted of 40 laps down the track. Once mice reached the end of the track (located at 1.46âm), the traversal finished, and the mice were teleported back to the beginning to start a new lap. If mice did not reach the track end within 60âs, the trial data were discarded but still counted toward the 40-la
131p session limit. Licks were detected by the rising edge of the lick sensor and were recorded alongside mouse position during each traversal. All licking data was binned by location into 5-cm-wide bins, while the first and last bin were excluded due the mouseâs constrained position away from the walls. Lick rates were calculated by dividing the number of licks in each binned position by the time spent at that position. âPost-rewardâ licks were defined as a series of licks that quickly followed a reward delivery (starting within 3âs of a delivered reward) and continued until the lick rate dropped below 1 lickâsâ1). All licks occurring at other times than after a reward delivery were defined as âexploratory licksâ. Reward and control zones were defined as regions spanning ±0.25âm (ten total position bins) from the rewarded location. The fraction of exploratory licks in the reward and control zones were calculated by dividing the total number of licks in each zone by the total licks in all 28 habitable bins. Chance-level zone licking was calculated by dividing the size of the zones in bins by the total habitable zone of the track (10/28â=â35.71%). During days 4â5, data were subdivided into rewarded versus unrewarded âprobeâ trials, where probe trials contain no post-reward licks. Statistically significant differences in the proportion of licks in reward versus control zones during probe trials was calculated using the two-tailed MannâWhitney U test (ranksum function in Matlab), comparing the mean lick proportions of each mouse with trials pooled from days 4 and 5.\n\nProjector-based VR system\nTo compare the VR headset with a traditional panoramic display, a VR environment was generated by the Unity video game engine and projected onto a custom-built conical rear-projection screen (Stewart FilmScreen 150) surrounding the mouse using two projectors (Optoma HD141X Full 3D 1080p 3000 Lumen DLP). The projection screen covered 260° in azimuth of the mouseâs visual field and spanned an elevation of 92° (â28° to 64°) with a circular hole at the top to accommodate a microscope objective (Extended Data Fig. 7).\n\nLoomâstartle experiment and analysis\nHead-fixed mice walking on the spherical treadmill were recorded using an HD webcam (NexiGo N980P, 1080p 60âfps). Mice were shown looming visual stimuli with the MouseGoggles Duo headset, appearing as a dark circular object in the sky 45° in elevation and 45° to either the left or right from straight ahead, beginning 20âm away and approaching at 25âmâsâ1 until disappearing at 0.6âm away. Left or right looms were displayed pseudorandomly, separated by 10âs, for ten repetitions (five left and five right). Video clips of mice during each looming stimulus presentation (from 5âs before the loom to 5âs after) were created and evaluated by two independent scorers who were blind to the goals of the experiment, although they could not be completely blinded to the experimental condition (headset versus projector) owing to the nature of the recorded videos. Both scorers were given the following instructions for determining startle responses (and other behaviors) from clips:\n\nâIn each 10âs clip, look for a behavioral reaction to the loom stimulus. The loom is a dark spot that appears on the screen, grows exponentially in size, then disappears, the full process taking ~0.8âs. The loom may be easy or hard to see based on the display used and the starting position of the loom, but will always be visible when it reaches its max size and disappears.\n\n[For each clip], write down your confidence level (0â3) in seeing that reaction in each clip. Except for grooming, these reactions should be something the mouse was not doing before the loom, but started doing during or immediately at the end of the loom. For the grooming reaction, write down whether the mouse was grooming during the stimulus, even if it was grooming before it as well.\n\nPossible reactions:\n\nStartle: burst of movement, jump or kick\n\nTense up: back arches, tailbone or tail curling under\n\nStop: stops, from a moving state\n\nRun: starts running, from a stopped or slowly walking state\n\nTurn: rear end of its body swings to the side\n\nGrooming: uses its paws to wipe at mouth/whiskers/eyes\n\nConfidence scores:\n\n0: reaction did not happen\n\n1: reaction possible happened\n\n2: reaction probably happened\n\n3: reaction definitely happenedâ\n\nDue to the startle and tense up reactions being difficult for the scorers to differentiate, the individual confidence scores for these two behaviors were combined into a single reaction score, where the larger of the two became the new score. To classify responses for each mouse, repetition and experimental condition, the average of the two scores (one from each scorer) was taken, where average scores of 1.5 or greater were classified as a startle response to the looming stimulus. The proportion of startle responses was calculated by dividing the number of startle responses by the number of observations at each repetition and was fit with an exponential decay function with offset:\n\nwhere r is the repetition number, R1 is the startle response proportion at râ=â1, λ is the decay rate constant and b is the offset. The experiment was initially performed with two mice where startle responses were first observed in the VR headset, after which a second cohort of mice was tested with both the VR headset and the projector-based system. For mice that began with the headset VR (4/6 mice), 10âdays elapsed before testing with the projector to attempt to restore the novelty of the looming stimulus. For mice that began with the projector VR, only 1âday elapsed before testing with the headset. Neither of the two âprojector-firstâ mice was startled in projector VR, but both were startled in headset VR.\n\nLoomâeye-tracking experiment and analysis\nHead-fixed mice walking on the linear treadmill were presented with looming visual stimuli similar to the loomâstartle experiment, using the MouseGoggles EyeTrack headset. Fifteen repetitions of the looming stimulus were presented in five sets of three conditions: a looming object approaching from 45° right, 45° left or center. The loom was 45° in elevation for all c
131onditions, and the visual scene was blacked out above 64° elevation to match the vertical extent of the projector VR system. Treadmill velocity and the timing of looming stimulus presentation was logged by the game engine. An eye-tracking video for each eye was acquired independently and synchronized to the looming stimuli afterward using the blue channel of the eye-tracking video, which acquires a partial view of the VR display through the hot mirror so that looming stimuli can be observed (alongside eye tracking in the red channel). Post-loom walking speed change was calculated from the walking speed after the loom (average velocity during 2âs from loom onset), relative to the baseline walking speed (average velocity during 2âs before stimulus onset). The post-loom eye pitch angle change and pupil diameter change were calculated similarly, except that the average pupil diameter change was calculated during the 3âs after the loom onset owing to the relatively slower pupil response we observed. Post-loom walking speed change, eye pitch angle change and pupil diameter change were then averaged across each set of three looming conditions (left, right and center) for each mouse individually. Statistically significant trends of these three measurements by repetition number of the looming stimuli were determined by Cuzickâs trend test in Matlab (https://github.com/dnafinder/cuzick). An average time-series response of walking speed and eye pitch change was calculated by averaging the response across all looming repetitions for each mouse individually, followed by calculating the average and s.d. of the response across mice. Since the pupil diameter response habituated with increased loom repetition, an average time-series response of pupil diameter change was calculated using only the first set of three looming stimuli.\n\nReporting summary\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\n\nData availability","fulltextMode":"markdown","pdfExternalUrl":"https://www.nature.com/articles/s41592-024-02540-y.pdf","pdfPublic":true,"meta":{},"featured":false,"sortOrder":23,"createdAt":"2026-06-30T19:14:13.699Z","products":["r2drive"]},{"id":"pub_FYyXbfQ","slug":"the-dream-implant-a-lightweight-modular-and-cost-effective-implant-system-for-chronic-electrophysiology-in-head-fixed-and-freely-behaving-mice","status":"published","type":"article","bibtexKey":"","title":"The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice","authors":["Tim Schröder","Robert Taylor","Muad Abd El Hay","Abdellatif Nemri","Arthur França","Francesco Battaglia","Paul Tiesinga","Marieke L. Schölvinck","Martha N. Havenith"],"year":2024,"month":"07","journal":"JoVE","volume":"","issue":"","pages":"","publisher":"","doi":"10.3791/66867","url":"https://www.jove.com/t/66867/the-dream-implant-lightweight-modular-cost-effective-implant-system#erratum","abstract":"Chronic electrophysiological recordings in rodents have significantly improved our understanding of neuronal dynamics and their behavioral relevance. However, current methods for chronically implanting probes present steep trade-offs between cost, ease of use, size, adaptability, and long-term stability.\n\nThis protocol introduces a novel chronic probe implant system for mice called the DREAM (Dynamic, Recoverable, Economical, Adaptable, and Modular), designed to overcome the trade-offs associated with currently available options. The system provides a lightweight, modular and cost-effective solution with standardized hardware elements that can be combined and implanted in straightforward steps and explanted safely for recovery and multiple reuse of probes, significantly reducing experimental costs.\n\nThe DREAM implant system integrates three hardware modules: (1) a microdrive that can carry all standard silicon probes, allowing experimenters to adjust recording depth across a travel distance of up to 7 mm; (2) a three-dimensional (3D)-printable, open-source design for a
131wearable Faraday cage covered in copper mesh for electrical shielding, impact protection, and connector placement, and (3) a miniaturized head-fixation system for improved animal welfare and ease of use. The corresponding surgery protocol was optimized for speed (total duration: 2 h), probe safety, and animal welfare.\n\nThe implants had minimal impact on animals' behavioral repertoire, were easily applicable in freely moving and head-fixed contexts, and delivered clearly identifiable spike waveforms and healthy neuronal responses for weeks of post-implant data collection. Infections and other surgery complications were extremely rare.\n\nAs such, the DREAM implant system is a versatile, cost-effective solution for chronic electrophysiology in mice, enhancing animal well-being, and enabling more ethologically sound experiments. Its design simplifies experimental procedures across various research needs, increasing accessibility of chronic electrophysiology in rodents to a wide range of research labs.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"","pdfPublic":false,"meta":{"species":"mouse","headgear":"DREAM implant"},"featured":false,"sortOrder":24,"createdAt":"2026-07-01T04:32:01.623Z","products":["r2drive"]},{"id":"pub_r8tNSD4","slug":"the-td-drive-a-parametric-open-source-implant-for-multi-area-electrophysiological-recordings-in-behaving-and-sleeping-rats","status":"published","type":"article","bibtexKey":"","title":"The TD Drive: A Parametric, Open-Source Implant for Multi-Area Electrophysiological Recordings in Behaving and Sleeping Rats","authors":["Tim Schröder","Jacqueline van der Meij","Paul van Heumen","Anumita Samanta","Lisa Genzel"],"year":2024,"month":"04","journal":"Jove","volume":"","issue":"","pages":"","publisher":"","doi":"10.3791/66457","url":"https://www.jove.com/t/66457/the-td-drive-parametric-open-source-implant-for-multi-area","abstract":"Intricate interactions between multiple brain areas underlie most functions attributed to the brain. The process of learning, as well as the formation and consolidation of memories, are two examples that rely heavily on functional connectivity across the brain. In addition, investigating hemispheric similarities and/or differences goes hand in hand with these multi-area interactions. Electrophysiological studies trying to further elucidate these complex processes thus depend on recording brain activity at multiple locations simultaneously and often in a bilateral fashion. Presented here is a 3D-printable implant for rats, named TD Drive, capable of symmetric, bilateral wire electrode recordings, currently in up to ten distributed brain areas simultaneously. The open-source design was created employing parametric design principles, allowing prospective users to easily adapt the drive design to their needs by simply adjusting high-level parameters, such as anterior-posterior and mediolateral coordinates of the recording electrode locations. The implant design was validated in n = 20 Lister Hooded rats that performed different tasks. The implant was compatible with tethered sleep recordings and open field recordings (Object Exploration) as well as wireless recording in a large maze using two different commercial recording systems and headstages. Thus, presented here is the adaptable design and assembly of a new electrophysiological implant, facilitating fast preparation and implantation.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"","pdfPublic":false,"meta":{"species":"rat"},"featured":false,"sortOrder":25,"createdAt":"2026-07-01T04:33:48.479Z","products":["td-drive"]},{"id":"pub_jiSHr-w","slug":"simultaneous-electrophysiology-and-optogenetic-perturbation-of-the-same-neurons-in-chronically-implanted-animals-using-led-silicon-probes","status":"published","type":"article","bibtexKey":"kinsky2023uled","title":"Simultaneous electrophysiology and optogenetic perturbation of the same neurons in chronically implanted animals using μLED silicon probes","authors":["Nathaniel R. Kinsky","Mihály Vöröslakos","Jose Roberto Lopez Ruiz","Laurel Watkins de Jong","Nathan Slager","Sam McKenzie","Euisik Yoon","Kamran Diba"],"year":2023,"month":"December","journal":"STAR Protocols","volume":"4","issue":"4","pages":"102570","publisher":"","doi":"10.1016/j.xpro.2023.102570","url":"https://www.cell.com/star-protocols/fulltext/S2666-1667(23)00537-3","abstract":"A protocol for performing causal and reproducible neural circuit manipulations in chronically implanted, freely moving animals using μLED silicon probes. We describe steps for introducing optogenetic constructs, preparing and implanting a μLED probe, performing simultaneous in vivo electrophysiology with focal optogenetic perturbation, and recovering a probe following termination of an experiment.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"","pdfPublic":false,"meta":{"species":"rat, mouse","implantDuration":"chronic","behavioralParadigm":"freely moving","siliconProbe":"μLED silicon probe (Neurolight)","numReuses":"recovered and reused"},"featured":false,"sortOrder":2,"createdAt":"2026-06-16T07:46:05.107Z","products":["r2drive"]},{"id":"pub_fBwYdoM","slug":"brightness-illusions-evoke-pupil-constriction-preceded-by-a-primary-visual-cortex-response-in-rats","status":"published","type":"article","bibtexKey":"","title":"Brightness illusions evoke pupil constriction preceded by a primary visual cortex response in rats ","authors":["Dmitrii Vasilev","Isabel Raposo","Nelson K Totah"],"year":2023,"month":"03","journal":"Cerebral Cortex","volume":"33","issue":"12","pages":"7952-7959","publisher":"","doi":"10.1093/cercor/bhad090","url":"https://academic.oup.com/cercor/article/33/12/7952/7084649?login=false","abstract":"The mind affects the body via central nervous system (CNS) control of the autonomic nervous system (ANS). In humans, one striking illustration of the âmindâbodyâ connection is that illusions, subjectively perceived as bright, drive pupil constriction. The CNS network driving this pupil response is unknown and requires an animal model for investigation. However, the pupil response to this illusion has long been thought to occur only in humans. Here, we report that the same brightness illusion that evokes pupil constriction in humans also does so in rats. We surveyed the role of most of rat cortex in this âmindâbodyâ connection by recording cortex-wide EEG. These recordings revealed that, compared to a luminance-matched control stimulus, the illusion of brightness for a specific stimulus color and size, evoked a larger response in primary visual cortex (V1) and not in secondary visual, parietal, or frontal cortex. The response preceded pupil constriction suggesting a potential causal role of V1 on the pupil. Our results provide evidence that this âmindâbodyâ connection is not confined to humans and that V1 may be part of a mammalian CNS network for bodily reactions to illusions.","fulltext":"Introduction\nMental processes mediated by the central nervous system (CNS) can affect the body via the autonomic nervous system (ANS). The âmindâbodyâ connection is apparent from the effects of psychological stress on immune and gastrointestinal function (Glaser and Kiecolt-Glaser 2005; Mawdsley and Rampton 2005; Poller et al. 2022), and placebo effects on pain driven by a personâs beliefs (Geuter et al. 2016). One fascinating âmindâbodyâ connection is that subjective illusions of brightness cause the eyeâs pupil to constrict (Laeng and Endestad 2012) due to the CNS driving the parasympathetic arm of the ANS which constricts the pupil (McDougal and Gamlin 2017). The brain regions, cell types, and synaptic projections that mediate this âmindâbodyâ interaction remain completely unexplored because there is no animal model permitting intracerebral (invasive) investigation.\n\n
131In this study, we presented a brightness illusion (which humans subjectively perceive as bright (Laeng and Endestad 2012)) to head-fixed rats while performing pupillometry and simultaneous brain-wide 32-electrode EEG. We used the Asahi stimulus (Fig. 1), which was created by Prof. Akiyoshi Kitaoka (Department of Psychology, Ritsumeikan University, Osaka, Japan) based on luminance-gradient stimuli in earlier work (Zavagno 1997). We show that the Asahi stimulus drives a pupil constriction in rats. On the other hand, a luminance-matched control stimulus, which does not evoke illusory brightness in humans, did not cause a pupil constriction in rats. The Asahi stimulus also drove a larger cortex EEG event-related potential that was confined to primary visual cortex and preceded pupil constriction. Our results show that the rat is a viable animal model for studying how neural processing of illusions can affect autonomic control of the body.\n\nMaterials and methods\nSubjects\nMale, Lister-Hooded rats (140â190 g) were obtained from Charles River. After a 7-day acclimation period, rats were implanted with a chamber and head-post and, in some cases, an EEG array. After implantation, rats were single housed. Experiments were carried out during the ratsâ active phase (housing illumination from 7 P.M. to 7 A.M.). All procedures were carried out with the prior approval of local authorities and in compliance with the European Community Guidelines for the Care and Use of Laboratory Animals.\n\nSurgical procedures\nThe surgical procedure was identical to prior work (Vasilev et al. 2022). Briefly, the rat was anesthetized using isoflurane and head-fixed using ear bars. We administered buprenorphine (0.06 mg/kg, s.c.), meloxicam (2.0 mg/kg, s.c.), enrofloxacin (10.0 mg/kg, s.c.), and lidocaine (0.5%, s.c. over skull) and waited 10â15 min (and for lack of response to paw pinch) before beginning surgical procedures. An EEG array (Neuronexus, CM32) was laid onto the cleaned and dried skull and fixed in place using dental cement (two-stage, powder/liquid Paladur). A custom-made skull implant was used for head-fixation (machine shop, Max Planck Institute for Biological Cybernetics). The implant was fixed onto the skull using UV-curing primer and dental cement (Tetric Evoflow, Dental Bauer). A craniotomy was made on the left occipital bone for a ground wire (99.9% pure silver). One end was flattened using an industrial press into anâ~â1â2 mm wide rectangle, which then was twisted into a roll to fit the craniotomy and inserted into the space between bone and dura. A rolled shape was used to increase the potential surface area in contact with CSF. The craniotomy was filled with viscous agar, which stabilized the wire and provided a conductive medium between the ground wire and the CSF. The other end of the ground wire was soldered to the ground wire of the electrode interface board of the EEG array. The wires and array were buried under dental cement (Paladur). The skin around the implant was glued to the implant using tissue glue (Histoacryl, B. Braun). Post surgical recovery lasted five days. During the first three days (surgery itself was counted as day 1), the rat was injected either every 12 h with buprenorphine or every 24 h with meloxicam (same dosages as pre-operative). During the first 5 days, enrofloxacin was injected every 24 h (same dosage as pre-operative). A rehydrating, nutritious, easily consumed, and palatable food was provided during recovery (DietGel Recovery, Clear H2O).\n\nIllustrations of the Asahi stimulus and the luminance-matched control stimulus. The Asahi stimulus is typically perceived by humans to have a brighter-than-white glare in the center. Rearranging the shape (control stimulus) abolishes the perception of brightness. The screen shots show that the actual luminance at the center of each stimulus is identical, despite appearing to be brighter in the center of the Asahi stimulus.\nFig. 1Illustrations of the Asahi stimulus and the luminance-matched control stimulus. The Asahi stimulus is typically perceived by humans to have a brighter-than-white glare in the center. Rearranging the shape (control stimulus) abolishes the perception of brightness. The screen shots show that the actual luminance at the center of each stimulus is identical, despite appearing to be brighter in the center of the Asahi stimulus.\
131nOpen in new tabDownload slide\nHandling and habituation\nRats were handled daily for at least 5 min per day from the day of arrival in housing until the day of surgery, which was 7 days. Animals were neither food nor water restricted. Habituation consisted of one session (~25 min) of head-fixation on a freely-rotating treadmill in front of a computer screen. Rats were free to run or remain immobile and a mixture of locomotor activity was observed.\n\nVisual stimuli and stimulus presentation\nStimuli consisted of the Asahi stimulus, the control stimulus, and a gray screen used during the inter-stimulus interval. All stimuli were equiluminant (15â16 lux measured at the head-post). Stimuli were presented 50 cm from the ratâs head. The resolution was 1280 à 720 pixels. Stimuli were created in Adobe Illustrator with a canvas set to match the screen resolution. These files were exported to JPEG at 72 ppi. The stimuli were presented using Psychtoolbox implemented in MATLAB. The stimuli were presented at four visual angles (10°, 20°, 40°, and 60°) and in two colors (yellow, 580 nm, HEX: #ffff00 and green, 508 nm, HEX: #00ff28). As many stimuli were placed on the screen as possible, thus covering the entire visual field or most of it. Stimuli were presented for a duration of 4 s. The inter-stimulus intervals were drawn from a distribution with a flat hazard rate that spanned 4â8 s with a 0.5-s resolution. The flat hazard rate was used to reduce expectation of stimulus onset.\n\nPupillometry data acquisition and processing\nVideos were recorded at 45 frames per second from the ratâs right eye with near-infrared illumination (Thor Labs LED, M850L3 and Thor Labs Collimation optics, COP4-B). Frames were acquired using a near-infrared camera (Allied Vision, G-046B) and variable zoom lens, fixed 3.3à zoom lens, and 0.25à zoom lens attachment (Polytec, 1â60,135, 1â62,831, 6044). Acquisition occurred over a GigE connection (MATLAB image processing toolbox). The camera provided a TTL pulse with each video frame. These TTL pulses were recorded directly into the neurophysiology system (Neuralynx).\n\nWe used an in-house custom algorithm and computer code to extract pupil size from the recorded video frames. The procedure is reviewed in detail in prior work (Vasilev et al. 2022). Briefly, images were Gaussian blurred, converted into a binary image, and then subjected to edge detection, closed contour detection, and fitting of ellipses to the closed contours. In cases where the algorithm was not able to find an ellipse of an area bigger than predefined minimal allowed area (or smaller than maximal, respectively), then the value of the pupil in this frame was left blank. This was also applied to the frames which captured the animal blinking. Blank frames were linearly interpolated. The pupil detection algorithm was implemented using the OpenCV package in Python 3.7.\n\nThe pupil size was normalized to a pre-stimulus baseline (1 sec duration) by calculating a z-score. The z-score was calculated on each trial by subtracting the baseline mean from each pupil size data point and then dividing this array by the standard deviation of the baseline data points. The latency for pupil constriction was calculated as in prior work (Bergamin and Kardon 2003). We first smoothed the pupil size with an 11-point, second-order SavitzkyâGolay filter (sgolayfilt in MATLAB). The signal was differentiated to obtain velocity and the velocity was lowpass filtered at 6 Hz with a second-order Butterworth filter (filtfilt in MATLAB). This signal was then differentiated to obtain acceleration and the latency to constrict was defined as the time point with the largest negative acceleration.\n\nEâEG signal acquisition and analysis\nEEG signals were recorded using a flexible polyimide array with 32 platinum electrodes (Neuronexus, H32). Signals were recorded against animal ground, pre-amplified at the ratâs head (Neuralynx, HS-36), and then amplified and digitized at 32 kHz (Neuralynx, Digital Lynx SX). The analysis focused on bilateral electrodes placed over frontal cortex (locations relative to Bregma: 1.5 mm anterior, ±1.2 mm lateral; 3.6 mm anterior, ±1.2 mm lateral) and visual cortex (locations relative to Bregma: 5.0 mm posterior, ±1.5 mm lateral; 5.0 mm posterior, ±3.0 mm lateral; 5.0 mm posterior, ±4.4 mm lateral; 7.0 mm posterior, ±1.5 mm lateral; 7.0 mm posterior, ±3.0 mm lateral; 7.0 mm posterior, ±4.4 mm lateral). EEG signals were first low pass filtered at 5 Hz and then downsampled to 320 Hz. The entire signal was mean subtracted. EEG topographical plots were produced using the MATLAB command, scatteredInterpolant with natural neighbor interpolation. The contour plots (contourf function in MATLAB) used 50 levels.\n\nStatistics\nWe used estimation statistics to report effect sizes and the confidence intervals for effect sizes (DABEST toolbox in MATLAB (Ho et al. 2019; Calin-Jageman and Cumming 2019a)). Bayesian statistics were used for assessing evidence (or lack thereof) for the null hypothesis and for the alternative hypothesis (Keysers et al. 2020). Bayesian statistics were calculated in JASP software. The hypothesis tests performed assessed whether the evidence favored H1 (alternative hypothesis) over H0 (null hypothesis), which is BF10 in the nomenclature of Keysers et al. (2020).\n\nCode accessibility\nData and code will be shared upon request.\n\nResults\nIn sum, 14 male, lister-hooded rats were head-fixed on a non-motorized treadmill and passively exposed to visual stimuli. Rats were free to walk or sit immobile during the experiment; however, in the absence of rewards or a goal-directed task, they remained immobile and passively viewed the stimuli. Pupillometry was performed in a closed faraday cage in total darkness (except for the computer screen) such that environmental illumination was held constant throughout the experiment and across subjects. All sensory stimuli as well as the gray screen during the inter-trial interval, were equiluminant. We presented the Asahi stimulus (Fig. 1), which humans perceive to have a bright glare in the center (Laeng and Endestad 2012). We also presented a luminance-matched control stimulus (Fig. 1) that due to the rearrangement of the Asahi stimulus into a new structure, does not evoke a brightness percept in humans (Laeng and Endestad 2012). We stimulated most or all the entire visual field by tiling the computer screen with as many stimuli as possible (Supplementary Figs. 1â3). We tested the hypothesis that the Asahi stimulus would cause a pupil constriction relative to the luminance-matched control stimulus.\
131n\nGiven the lack of prior work in animals, as well as differences between the human and rodent visual system (e.g. spectral sensitivity and visual acuity), we screened a range of stimulus sizes (10°, 20°, 40°, and 60°) and two colors. Although yellow stimuli (~580 nm wavelength) were used in the human study (Laeng and Endestad 2012), the spectral sensitivity of the rat retina is diminished around 580 nm and lacking sensitivity over 590 nm, whereas it is very sensitive below 530 nm (Peirson et al. 2018). Therefore, we presented 580 nm (yellow) and 508 nm (green) stimuli.\n\nWe presented the Asahi and control stimuli (4 s duration) 50 times each, in random order, and at unexpected times to avoid confounds of rhythmically entraining the pupil or brain activity. However, the surprising onsets of stimuli drive a large pupil dilation (Beatty 1982; Murphy et al. 2011; Breton-Provencher and Sur 2019) that can easily out-compete constriction driven by brightness perception. Any influence of illusory brightness on parasympathetic nervous system activity controlling the sphincter pupillae muscle (constriction) must compete against the antagonistic dilator pupillae muscle controlled by sympathetic nervous system activity. Thus, it is possible that strong surprise-evoked sympathetic activation will out-weight any influence that sensing the brightness illusion has on parasympathetic activation.\n\nThe Asahi illusion evokes a pupil constriction in rats\nDespite the apparently large dilation evoked by both the Asahi and the control stimuli (Fig. 2A and B), we observed a strong and robust constriction only after the Asahi stimulus. The surprise-evoked dilation makes the constriction merely appear small due to the range of the y-axis in Fig. 2A and B; therefore, we also plot the pupil size in the 300â700 ms window after stimulus onset in the figure insets. We compared the average pupil size (500â700 ms after stimulus onset) between the Asahi and the control stimulus. The Asahi stimulus was associated with a lower pupil size relative to the control stimulus for six of the eight screened stimulus conditions (Table 1). We report effect sizes quantifying how much the baseline z-scored pupil size differed between Asahi and control stimulus in Fig. 2C. We judged which stimulus colors and sizes were optimal drivers of pupil constriction using the 95% confidence intervals around the effect size (shown in Fig. 2C, right panel). The confidence intervals estimate the probable effect sizes that would be observed in large population experiments (Calin-Jageman and Cumming 2019a, 2019b). For instance, the yellow 20° Asahi stimulus evoked a significantly smaller z-scored pupil size than the control stimulus (Table 1) with an effect size of â0.525 (Fig. 2C, yellow circle), yet the confidence intervals around this effect size tempered our beliefs of a strong difference between the Asahi and control stimuli. In this case, the estimated effect sizes ranged from the z-scored pupil size after the Asahi stimuli being as much as 1.230 lower, but potentially 0.187 higher than its control stimulus counterpart. As a contrasting case, consider instead the green 20° stimuli. The 95% confidence intervals estimated that the weakest likely difference in z-scored pupil size would still be 0.232 lower for the Asahi stimulus compared with the control stimulus.\n\nThe Asahi stimulus evokes a pupil constriction in rats. (A, B) The SEM of pupil size is plotted from 1 sec before stimulus onset until 1.75 s later. Pupil size was normalized relative to the pre-stimulus pupil size using a z-score. The gray line is the pupil size around the control stimulus and the colored line is relative to the Asahi stimulus. The insets show the pupil size in a 300â700 ms window after stimulus onset. The y- and x-axes are identical across the insets. The dotted line shows the baseline pupil size. Constriction is negative and dilation is positive. (A) plots the pupil response to yellow stimuli, whereas (B) plots the response to green stimuli. (C) The mean pupil size (z-scored to 4-s pre-stimulus baseline) from 500 to 700 ms after stimulus onset is plotted for each stimulus. Dots are individual rats. In the yellow 60°, 40°, and 20° conditions and the 10° green condition only 13 rats are plotted due to corruption of the file containing stimulus onset time markers. The right panel shows the effect size between the Asahi stimulus and the control stimulus.\
131nFig. 2The Asahi stimulus evokes a pupil constriction in rats. (A, B) The SEM of pupil size is plotted from 1 sec before stimulus onset until 1.75 s later. Pupil size was normalized relative to the pre-stimulus pupil size using a z-score. The gray line is the pupil size around the control stimulus and the colored line is relative to the Asahi stimulus. The insets show the pupil size in a 300â700 ms window after stimulus onset. The y- and x-axes are identical across the insets. The dotted line shows the baseline pupil size. Constriction is negative and dilation is positive. (A) plots the pupil response to yellow stimuli, whereas (B) plots the response to green stimuli. (C) The mean pupil size (z-scored to 4-s pre-stimulus baseline) from 500 to 700 ms after stimulus onset is plotted for each stimulus. Dots are individual rats. In the yellow 60°, 40°, and 20° conditions and the 10° green condition only 13 rats are plotted due to corruption of the file containing stimulus onset time markers. The right panel shows the effect size between the Asahi stimulus and the control stimulus.\nOpen in new tabDownload slide\nTable 1Open in new tabResults of Bayesian one-tailed paired t-test of the alternative hypothesis that the average pupil size was lower after the Asahi stimulus compared to the control stimulus.\nStimulus condition\tBayes factor\tEvidence favoring or against alternative hypothesis\nGreen, 10°\tBF10â=â28.515\tStrong evidence favoring\nGreen, 20°\tBF10â=â102.058\tStrong evidence favoring\nGreen, 40°\tBF10â=â0.280\tModerate evidence against\nGreen, 60°\tBF10â=â4.196\tModerate evidence favoring\nYellow, 10°\tBF10â=â200.140\tStrong evidence favoring\nYellow, 20°\tBF10â=â18.734\tStrong evidence favoring\nYellow, 40°\tBF10â=â13.001\tStrong evidence favoring\nYellow, 60°\tBF10â=â0.298\tModerate evidence against\nIn summary, our results demonstrate that, in six of the eight stimulus conditions tested, the Asahi stimulus evoked a significantly smaller pupil size to some degree (circles in Fig. 2C are below 0). Examination of which confidence intervals are below zero indicates that the green 20° and 10° Asahi stimuli are the most optimal drivers of pupil constriction within the stimulus conditions tested in this study. Therefore, the effect of illusory brightness on pupil size may be restricted to specific color- and size-dependent conditions.\n\nWe next quantified the magnitude of constriction to better understand how large of an effect the two optimal Asahi stimuli had on pupil size relative to the pre-stimulus baseline. Constriction magnitude was defined as the mean pupil size from when the pupil began constriction (specific to each rat) until 700 ms after stimulus onset. We found that the optimal stimuli for evoking pupil constriction (green 20° and green 10° Asahi stimuli) evoked baseline z-scored constrictions of â0.528 (a 299-fold reduction) andâââ0.303 (a 154-fold reduction). In this same time window of constriction to the Asahi stimulus, the control stimulus instead evoked a purely dilatory response. Pupil size increased by z-scores of +0.304 (an 820-fold increase) andâ+â0.267 (a 193-fold increase) for the green 20° and 10° stimuli, respectively. The pupil dilations are large but not outside the natural physiological range of the pupil, which increases to z-scores of nearly 6.0 after the stimulus (Fig. 2A, B). Thus, constriction was specific to the brightness illusion, whereas after the control stimulus there was no constriction and, in fact, the opposite occurred. Importantly, the constriction evoked by the Asahi stimulus was large and robust across subjects, despite the antagonistic competition from surprise-evoked sympathetic activation of the dilator pupillae muscle. We focus the remaining analysis on these two most effective stimuli.\n\nIt is unlikely that fixation on local changes in contrast could explain the observed pupil constriction. Stimuli tiled nearly all, or all the visual field. Fixation on local contrast changes cannot explain the pupil constriction given that rats do not have foveal vision (Euler and Wässle 1995) and the visual acuity of rats is poorer than the sharp changes in local contrast of the 10° and 20° stimuli (Artal et al. 1998; Prusky et al. 2002). Moreover, the same black-to-white contrast changes occur in the yellow and green stimuli, yet only the green Asahi stimuli were associated with a pupil constriction. Nevertheless, we compared eye position between the Asahi and control stimuli during the first 600 ms after stimulus onset found that they did not differ. Bayesian paired t-tests comparing the location of the center of the pupil between the green Asahi stimulus and green luminance-matched control stimulus supported the null hypothesis that location did not differ (x-position for 20° and 10° stimuli were BFâ=â0.34 and 0.34, respectively; y-position for 20° and 10° stimuli were BFâ=â0.29 and 0.34, respectively). Moreover, saccades were exceptionally rare (81% of trials had not a single eye movement, SEM, n =â14 rats). Given the low proportion of trials with eye movements (19%) and the visual acuity and lack of fovea in rats, it is unlikely that local contrast changes or eye movements explain the observed constriction.\n\nWe also assessed whether ongoing running could affect our findings given that pupil size changes during running in rodents (Erisken et al. 2014). We observed the rats to remain inactive and passively view the stimuli (Supplementary Fig. 4). We compared the treadmill velocity during these experiments with the velocities recorded during other experiments in which rats were trained to respond to a Go stimulus by running on the treadmill. In the goal-directed behavioral experiments, the velocities were pooled from 14 rats, 306 sessions, and 74,671 hit trials. In comparison to active behavior, the stimuli in this experiment were passively viewed without locomotion.\n\nThe visual cortex responds to the Asahi stimulus in rats\nWe next assessed whether the Asahi stimulus differentially engaged any cortical regions in comparison to the luminance-matched control stimulus. In 10 of the 14 rats, we recorded EEG from anterior frontal cortex to visual cortex using a 32-electrode array implanted directly onto the skull and aligned to bregma. We obtained event-related potentials (ERPs) for each electrode in a window starting 750 ms before stimulus onset and lasting until 750 ms after stimulus onset. A topographical plot revealed that the response was confined to posterior electrodes laying over visual cortex. The Asahi and the control stimuli evoked a visual cortex response for both yellow and green stimuli of all sizes (Supplementary Fig. 5A, B). A Bayesian one-sided t-test of the hypothesis that the ERP peak was larger for the âbrighterâ stimulus (i.e. the Asahi stimulus) in comparison to the control stimulus was supported in the case of the 10° green Asahi stimulus (BF10â=â5.67, Fig. 3A, B). Notably, the 10° green Asahi stimulus also evoked a pupil constriction. Intriguingly, the 20° green Asahi stimulus was also associated with a large pupil constriction but not a larger ERP. One possible explanation for this is that neuronal response to the 10° stimulus is stronger and registered more efficiently at the EEG electrode.\n\nAn Asahi stimulus that evoked pupil constriction also evoked a larger event-related potential in visual cortex. (A) The SEM of the visual cortex EEG is plotted from 750 ms before and until 750 ms after stimulus onset. The gray line is the ERP around the control stimulus and the colored line is relative to the green 10° Asahi stimulus. (B) The maximal potential in the ERP is plotted for each stimulus. Dots are individual rats. The inset shows the effect size between the Asahi stimulus and the control stimulus in mV. The asterisk indicates support for the alternative hypothesis (BFâ>â3). (C) The average ERP peak magnitude across 10 rats is shown on a scale of 0 (blue) to 0.06 mV (red). Electrode locations are shown relative to bregma at the origin.\nFig. 3An Asahi stimulus that evoked pupil constriction also evoked a larger event-related potential in visual cortex. (A) The SEM of the visual cortex EEG is plotted from 750 ms before and until 750 ms after stimulus onset. The gray line is the ERP around the control stimulus and the colored line is relative to the green 10° Asahi stimulus. (B) The maximal potential in the ERP is plotted for each stimulus. Dots are individual rats. The inset shows the effect size between the Asahi stimulus and the control stimulus in mV. The asterisk indicates support for the alternative hypothesis (BFâ>â3). (C) The average ERP peak magnitude across 10 rats is shown on a scale of 0 (blue) to 0.06 mV (red). Electrode locations are shown relative to bregma at the origin.\nOpen in new tabDownload slide\nA topographical plot of the ERP peak magnitude for the 10° green Asahi stimulus shows that it was confined, not to all visual regions, but specifically to primary visual cortex (V1) for the Asahi and the control stimuli (Fig. 3C). These electrodes were located at 5.0 and 7.0 mm posterior to bregma with three electrodes (per hemisphere) at each posterior location situated laterally from bregma at 1.5 (overlaying V2MM), 3.0 (overlaying V2ML at the anterior electrode and V1M at the posterior electrode), and 4.4 mm (overlaying V1 at the anterior electrode and V1B at the posterior electrode). The ERP was largest over V1.\n\nFinally, we sought to determine whether the V1 field potential response preceded pupil constriction, which could suggest a potential causal role of V1 on the pupil. The SEM of the ERP peak latency after stimulus onset was 244.1â±â8.0 ms, whereas the SEM of the constriction latency was 341.0â±â24.1 ms for the green 10° Asahi stimulus. The V1 response preceded the pupil response by ~â100 ms.\n\nDiscussion\nThe effect of brightness illusions on autonomic control of the pupil are a powerful tool for studying how parts of the CNS involved in higher mental functions can affect the body (Laeng and Endestad 2012). The neuronal correlates of this âmindâbodyâ connection are unknown, and their discovery requires an animal model that permits probing cell types and specific neuronal projections using single cell recordings and optogenetic tagging of neurons by their projection target.\n\nHere, using a novel combination of head-fixation, pupillometry and brain-wide, global EEG recordings in rats, we show that the pupil constricts after the same brightness illusion that causes pupil constriction in humans. Thus, our results establish the rat as an animal model for studying how sensing a brightness illusion drives a physiological reaction in the body. We note two differences between the dynamics of the pupil response in our rat model and the prior work in humans (Laeng and Endestad 2012). First, in the human study, a dilation began prior to stimulus onset, which we did not observe in rats. We suspect the dilation in human work may have occurred due to the use of a fixed 0.5 s inter-stimulus interval, which would produce an expectation of a rhythmically appearing stimulus that may have led to preparatory changes in arousal and visual system activity. Indeed, sensory signal oscillations can entrain brain activity and affect expectancy (Lakatos et al. 2008; Cravo et al. 2013). In contrast, we presented stimuli at random intervals from a distribution with a flat hazard rate to dim
131inish expectation. Second, in the human study, the Asahi stimulus evoked a long lasting constriction for ~â400 ms, whereas we observed only a brief constriction interrupted by a large dilation. In our experiments, the stimulus presentations were unexpected and surprising, which drives stimulus-evoked dilation (Beatty 1982; Murphy et al. 2011; Preuschoff et al. 2011; Liao et al. 2016; Breton-Provencher and Sur 2019). These dilations may have prevented the constriction from continuing for a longer duration in our experiments. In contrast, the study in human subjects used rhythmically presented stimuli. Prior work has shown that such periodic stimuli either do not evoke pupil dilations or evoke smaller pupil dilations that diminish over time-on-task (Beatty 1982; Liao et al. 2016). Therefore, in the absence of a surprise-evoked pupil dilation, the constriction evoked by the Asahi stimulus would be allowed to continue uninterrupted in human subjects.\n\nOne current limitation of our animal model is that the optimal conditions for evoking pupil constriction may not be fully characterized and will require psychophysical studies. We screened a variety of stimulus sizes in two colors and found that, in most cases, the brightness illusion evoked pupil constriction. Although the spectral sensitivity of the rat retina is diminished around 580 nm (yellow) light, there is still some sensitivity which would permit yellow Asahi stimuli to have an effect. However, the constriction was particularly strong for green (530 nm) stimuli of small size (10° or 20°). Lower wavelengths and smaller size stimuli were the most optimal stimuli for driving a pupil response to illusory brightness in rats. Constructing Asahi stimuli from even lower wavelengths, such as 480 nm (blue), may prove more optimal in rats. In humans, brightness illusions evoked by blue stimuli are subjectively perceived as brighter and evoke a stronger pupil constriction than higher wavelengths (including yellow, red, and magenta stimuli) (Suzuki et al. 2019).\n\nAlthough our model does not include a behavioral report of perceived brightness by the rats, our paradigm allows connecting changes in the body with a high-level mental process (Dum et al. 2019). In this case, the mental process is the processing of the sensory information present in the Asahi stimulus. The processing of sensory information has been termed âsensingâ and is distinct from âperceptionâ, which can be thought of as imbuing an interpretation on sensory information, or awareness of sensory information (Charbonneau et al. 2022). Our findings link the âmental processâ of sensing illusory brightness to a change in the body state.\n\nIn support of this mental process interceding in the very fast reflexive and automatic ANS control of the iris sphincter muscle (Clarke and Ikeda 1985; Young and Lund 1994), we found that the pupil constriction to illusory brightness was delayed in comparison to the pupillary light reflex (PLR). In humans, the PLR requires approximately 250 ms after stimulus onset for high intensity light and as much as 400 ms for very low intensity light (Ellis 1981; Bergamin and Kardon 2003; Fotiou et al. 2007). A direct comparison of these latencies in rats is not possible because prior work defining PLR latency in rats used the time constant of an exponential fit (Liu et al. 2017), whereas human studies and our study defined latency as the time of maximal negative acceleration. The brightness illusion (e.g. the 10° green Asahi stimulus) required 341 ms to evoke constriction similar to very low intensity light in humans. The delayed constriction may be due to the time (additional forebrain and hindbrain synapses) required for a âmental processâ to physiologically intervene in the brainstem-ANS neuronal activity controlling the PLR.\n\nV1 may be a key node in a shared mammalian neural network for bodily reactions to illusions\nWe used an array of 32 electrodes to cover cortex bilaterally from frontal back to visual and identify which cortical regions respond and which cortical regions do not. The brightness illusion evoked a larger ERP than the control stimulus in only one of the two conditions that optimally drove pupil constriction (i.e. the green 10° stimulus). Surprisingly, the green 20° Asahi stimulus drove a similar pupil constriction without evoking a larger ERP relative to the control stimulus. It is possible that neurons are better tuned to the 10° stimulus and, therefore, a stronger ERP is measured at the EEG electrode. However, the ability to speculate why the larger ERP is observed after only one of the two stimuli that evoked pupil constriction is limited by the spatial resolution of EEG signals. Psychophysical studies can be used to identify the optimal color and size of stimuli for driving the pupil response in rats, and then use those optimized stimuli to drive a more effective V1 neural response. Our animal model creates the possibility to perform invasive recordings targeting single neurons that are tuned to the color and size of these optimal stimuli. These experiments will establish a tighter link between V1 neuronal activity and the pupil response to illusory brightness.\n\nNo other cortical regions responded to the brightness illusion as though it were physically brighter than the control stimulus. Visual cortex was likely to respond because illusions require the processing of the gestalt of a visual scene (Purves et al. 2004) and such high-level visual processing relies on visual cortex in non-human primates and in mice (Rossi et al. 1996; Roe et al. 2005; Pak et al. 2019; Saeedi et al. 2022). Surprisingly, however, other cortical regions did not respond more strongly to the brightness illusion. For instance, although frontal regions have been implicated in the awareness and interpretation of stimuli (Panagiotaropoulos et al. 2012) and can modulate both parasympathetic and sympathetic control of the pupil via monosynaptic input to brainstem noradrenergic neurons (Luppi et al. 1995; Joshi et al. 2016; Liu et al. 2017; Breton-Provencher and Sur 2019; Totah et al. 2021), frontal cortex was not particularly responsive to the brightness illusion. However, it is also possible that the activity of small populations of neurons in frontal cortex (or other cortical regions) is involved in the processing of the brightness illusion, but that their activity is not apparent in the EEG signal. An additional possibility is that this mindâbody connection is mediated entirely within a brainstem-V1-brainstem loop.\n\nOur data are consistent with the idea that V1 may be a potential cause of pupil constriction, given that the V1 response preceded pupil constriction. The latency of the maximal ERP after the 10° green Asahi stimulus was 244 ms, whereas the pupil did not constrict until 341 ms after this stimulus. V1 may control the ANS via either an unknown direct projection or via a poly-synaptic set of subcortical synapses. These forebrain circuits must eventually modulate one or tw
131o brainstem areas involved in this basic reflex (Clarke and Ikeda 1985; Young and Lund 1994). The midbrain olivary pretectal nucleus (OPN) is one target for forebrain neurons. The other is the post-synaptic target of the OPN: the preganglionic neurons of the Edinger-Westphal nucleus that project to the ciliary ganglion of the parasympathetic nervous system. The forebrain cannot directly influence the ciliary ganglion (located in the posterior orbital socket) or the iris sphincter muscle. A prudent interpretation of our data is that the role of V1 neuronal activity in pupillary constriction to illusory brightness is inconclusive given that the larger ERP only occurred for one of two optimal stimuli evoking pupil constriction. Resolving this question will require testing the role of V1 using optogenetic inhibition, as well as recording activity in projection-target defined V1 neurons (using opto-tagging) and in potentially involved sub-cortical structures. These experiments are impossible in humans but are now permissible using this rat model.\n\nA new animal model of importance for studying âmindâbodyâ interaction\nOur finding establishes the first animal model for studying how the CNS response involved in sensing a brightness illusion drives a pupil response. By demonstrating that the same brightness illusion that drives pupil constriction in humans also does so in rats, we show that this type of âmindâbodyâ connection is present at an earlier stage of evolution than previously thought. Illusory brightness-evoked pupillary constriction in rats and humans makes it possible that an elemental nervous system architecture, which supports perceptual influences on the pupil, is shared by early mammals and those that evolved more complex perceptual and cognitive processes (e.g. apes). Subjective perception and its effect on body physiology in primates may have grown from a seed that evolved in early mammals or developed independently in a case of convergent evolution. Our rat model can be used to uncover the physiological basis of this âmindâbodyâ interaction in rodents and potentially primates.\n\nAcknowledgments\nWe thank Dr. Claudius Kratochwil and Dr. Henry Evrard for comments on the manuscript. We wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources.\n\nAuthor contributions\nConceptualizationâNT; Data acquisition and curationâDV, IR; Formal analysisâDV, NT; MethodologyâDV, NT; Project administrationâNT; SupervisionâNT; VisualizationâNT; WritingâNT.\n\nCRediT authors statement\nDmitrii Vasilev (Data curation, Formal analysis, Investigation, Methodology), Isabel Raposo (Data curation, Investigation), Nelson Totah (Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writingâoriginal draft).\n\nFunding\nThis work was funded by the Max Planck Society and the University of Helsinki (Helsinki Institute of Life Science).\n\nConflict of interest statement: None declared.","fulltextMode":"markdown","pdfExternalUrl":"https://academic.oup.com/cercor/article-pdf/33/12/7952/50538228/bhad090.pdf","pdfPublic":true,"meta":{"species":"rat","sex":"male","weight":"140g-190g"},"featured":false,"sortOrder":18,"createdAt":"2026-06-25T11:48:56.894Z","products":["remy-system","remy-implants","remy-surgery-holder","remy-chamber","remy-poles","remy-holder","remy-chambers"]},{"id":"pub__adlV4k","slug":"focusing-perceptual-attention-in-the-past-constrains-outcome-based-learning-in-the-future-by-adjusting-cortico-cortical-interactions","status":"published","type":"preprint","bibtexKey":"","title":"Focusing perceptual attention in the past constrains outcome-based learning in the future by adjusting cortico-cortical interactions","authors":["Dmitrii Vasilev","Negar Safaei","Ryo Iwai","Hessam Bahmani","Ioannis S. Zouridis","Masataka Watanabe","Nikos K. Logothetis","Nelson K. Totah"],"year":2023,"month":"05","journal":"bioRxiv","volume":"","issue":"","pages":"","publisher":"","doi":"10.1101/2022.01.22.477334","url":"https://www.bi
131orxiv.org/content/10.1101/2022.01.22.477334v2","abstract":"Contemporary neuroscience and psychiatry suggest that attention to decision outcomes guides rule learning by adjusting stimulus-outcome associations. Separately, sensory neurophysiology conceptualizes attention as a âfilterâ that improves perception. Here, we show that the contemporary view is incomplete by demonstrating an unconventional and novel effect of perceptual attention on subsequent outcome-based rule learning. Moreover, we show for the first time in rodents that, like in primates, this attentional process involves tuning of modality specific cortico-cortical interactions. We designed a novel head-fixed rat-on-a-treadmill apparatus and used it to train rats to discriminate auditory-visual stimuli using one modality and then reduced stimulus discriminability in that modality. We observed perceptual learning suggesting engagement of perceptual attention. Moreover, engaging visual perceptual attention resulted in more saccades and increased frontal-visual cortex EEG Granger causality relative to engaging auditory perceptual attention. We then presented novel and easily discriminable stimuli in both modalities and measured outcome-driven learning in the other modality. Learning was slower after engaging perceptual attention. Our work suggests that a more complete description of learning requires integrating these previously siloed concepts of attention. Moreover, treating impaired set-shifting as a trans-diagnostic symptom may require targeting different neural circuits for perceptual attention or outcome-based attention depending on which type of attention is impaired in each neuro-psychiatric disorder.","fulltext":"Abstract\nContemporary neuroscience and psychiatry suggest that attention to decision outcomes guides rule learning by adjusting stimulus-outcome associations. Separately, sensory neurophysiology conceptualizes attention as a âfilterâ that improves perception. Here, we show that the contemporary view is incomplete by demonstrating an unconventional and n
131ovel effect of perceptual attention on subsequent outcome-based rule learning. Moreover, we show for the first time in rodents that, like in primates, this attentional process involves tuning of modality specific cortico-cortical interactions. We designed a novel head-fixed rat-on-a-treadmill apparatus and used it to train rats to discriminate auditory-visual stimuli using one modality and then reduced stimulus discriminability in that modality. We observed perceptual learning suggesting engagement of perceptual attention. Moreover, engaging visual perceptual attention resulted in more saccades and increased frontal-visual cortex EEG Granger causality relative to engaging auditory perceptual attention. We then presented novel and easily discriminable stimuli in both modalities and measured outcome-driven learning in the other modality. Learning was slower after engaging perceptual attention. Our work suggests that a more complete description of learning requires integrating these previously siloed concepts of attention. Moreover, treating impaired set-shifting as a trans-diagnostic symptom may require targeting different neural circuits for perceptual attention or outcome-based attention depending on which type of attention is impaired in each neuro-psychiatric disorder.\n\nIntroduction\nAttention is critical for learning outcomes predicted by stimuli in a changing environment 1,2. Outcome-related attention has been studied using the attentional set-shifting paradigm, in which the rewarded dimension (e.g., color) of multi-dimensional stimuli (e.g., various colored shapes) must be learned through trial-and-error. When presented with novel stimuli, learning to respond to a previously unrewarded stimulus dimension (shape) is slow 3â7, presumably because attention is âstuckâ on the previously rewarded dimension (color) and must be shifted to the newly rewarded dimension (shape) 3,8.\n\nAttention has also been studied as a sensory filter that improves the ability to discriminate similar stimuli by altering sensory neuron representations 9â11. This other conceptualization of attention leads to the intriguing question of whether focusing perceptual attention onto one dimension of the environment could influence shifting attention during subsequent outcome-based learning. Answering this question not only characterizes more precisely the role of attention in learning, but also clarifies which forms of attention could underlie attentional set-shifting impairment in individuals diagnosed with attention deficit hyperactivity disorder, autism, obsessive-compulsive disorder, schizophrenia, or substance use disorder 12â16.\n\nWe tested the role of perceptual attention and concomitant changes in neuronal representations on subsequent learning in a novel attentional set-shifting paradigm. We trained head-fixed rats in Go/NoGo auditory-visual attentional set-shifting task. Prior to assessing the ability to learn responding to novel stimuli using the previously unrewarded modality, perceptual attention was manipulated by presenting either difficult or easy discriminations in the currently rewarded modality. We observed perceptual learning (i.e., the ability to tell apart similar stimuli improves with task experience) during difficult discriminations, which suggests that perceptual attention was engaged in that task condition 17â22. During difficult discriminations, we also observed increased EEG Granger causality magnitude between frontal cortex and sensory cortex neurons tuned to the currently rewarded modality. Critically, we observed that learning to respond to novel and easily discriminable stimuli in the other modality was slower after subjecting rats to difficult discriminations in the previously rewarded modality.\n\nResults\nWe studied how engaging perceptual attention onto one sensory modality affects subsequent outcome-based learning in another modality using an auditory-visual attentional set-shifting task for head-fixed rats. The task presented compound auditory-visual stimuli constructed from pure frequency tones and visual drifting gratings (Figure 1A). These stimuli were chosen because the relevant feature in each sensory modality (i.e., grating orientation or tone frequency) could be parametrically varied to manipulate stimulus discriminability and engage perceptual attention. Rats were initially trained to associate specific responses (Go response â treadmill running; NoGo response â immobility) with stimuli in one modality (Figure 1B). The two stimuli in the irrelevant modality were each presented an equal number of times with the Go stimulus (300 trials/session) and the NoGo stimulus (300 trials/session) and in randomized order. We trained 6 rats to perform auditory discrimination and 9 rats to perform visual discrimination (N = 15 rats). Stimuli in both modalities were easily discriminable based on prior visual and auditory psychophysics experiments in rodents 23â27.\n\nFigure 1.\nDownload figureOpen in new tab\nFigure 1.\nThe auditory-visual attentional set-shifting task and the method for defining similar levels of stimulus discriminability across rats.\
131n(A) An example stimulus set (Go: 4.7 kHz tone; NoGo: 8.2 kHz tone randomly coupled with a 50° or 340° visual drifting grating). (B) Rats were trained to remain immobile for 0.5 sec prior to sensory stimulus onset. After stimulus presentation, rats could choose to run or remain immobile. Heatmaps show angular velocity across trials during one session (rotary encoder voltage change/sec scaled by 103). Positive velocity (yellow) indicates forward movement and negative velocity (blue) indicates backwards movement. Near-zero velocity indicates immobility. Backward movement was rare. The magenta dots indicate the time of response threshold crossing on hit trials. The white line indicates stimulus onset. (C) The learning curve from an IDS of an example rat. The % correct responses (50 trial bins) was fit with a Sigmoid function. The trial bin at which the fitted line reached 80% is the learning timepoint.\n\nThe experiment tested the hypothesis that engaging perceptual attention onto one modality would affect learning the responses associated with novel stimuli in the other modality. The experiment had three steps repeated once. First, we obtained a learning baseline during an intra-dimensional shift (IDS). In the IDS, the ability to learn the responses associated with novel stimuli was assessed when the relevant modality was unchanged. Stimuli in both modalities were easily discriminable. A new session (600 trials) was completed each day until learning finished (i.e., the mean performance for an entire session was >85%). The baseline learning timepoint was defined as the trial at which 80% correct was reached on a Sigmoid function fit to the binned % correct performance calculated in 50 trial bins (Figure 1C).\n\nAfter measuring baseline learning during an IDS, we manipulated perceptual attention onto the relevant modality of the learned stimuli. During two consecutive attention manipulation sessions, perceptual attention was manipulated by requiring either easy or difficult discriminations in the currently relevant modality. Seven rats were presented with the easily discriminable stimuli already learned during the IDS. Eight rats were presented difficult to discriminate stimuli in the relevant modality by changing the NoGo stimulus feature to have greater similarity with the Go stimulus, without modifying the Go stimulus or the stimuli in the irrelevant modality. In other words, stimuli identical to those learned during the IDS were presented, with the exception that the NoGo stimulus was less discriminable from the Go stimulus. Discrimination difficulty was set to a common level across rats using a psychophysics staircase procedure designed to estimate the NoGo stimulus orientation (when vision was relevant) or frequency (when audition was relevant) that would generate â¼71% correct performance for each rat. By targeting 71% correct performance, rats were not guessing, but were well below the performance achieved after learning the IDS (median ± SE: 92.3 ± 2.4%, N = 9 rats in visual modality; 89.3 ± 1.5%, N = 6 rats in auditory modality). Discrimination ability varied across individual rats (Figure S1) but the staircase procedure estimated the NoGo stimulus needed to produce a similar degree of discrimination difficulty (71% correct) across rats. In the last 600 trials of the staircase procedure, the percent correct responses were close to the target of 71% (median ± SE: 71.5 ± 1.7% in visual modality and 73.0 ± 1.0% in auditory modality). A Bayesian Wilcoxon test suggested that these data moderately support the null hypothesis that observed performance did not differ from 71% (BF10 = 0.210). After obtaining a baseline learning timepoint during an IDS and then requiring either easy or difficult discriminations in the relevant modality for two sessions, in the final step of the experiment we compared the number of trials required to learn an extra-dimensional shift (EDS) against the baseline learning timepoint. In the E
131DS, novel and easily discriminable stimuli were presented in both modalities and the rats learned to discriminate stimuli in the previously irrelevant modality.\n\nFinally, the three steps of the experiment (i.e., IDS, attention manipulation, EDS) were repeated once and subjecting each rat to the other discrimination difficultly level. This design enabled a within-subjects comparison of EDS learning after easy versus difficult discriminations in the previously relevant modality. One potential confound of this design is that the second EDS could be faster than the first EDS because both modalities had been previously relevant during the second EDS; however, this is unlikely due to the use of novel stimuli in each shift. Moreover, effects of EDS stage were mitigated by counterbalancing (8 rats performed difficult discriminations prior to first EDS and 7 rats performed easy discriminations prior to first EDS). Our data also demonstrate that learning timepoint did not differ between the first and second EDS (median ± SE: 1898 ± 1028 versus 3403 ± 1400 trials, N = 15 rats). A Bayesian within-subjects Wilcoxon test (BF10 = 0.557) suggested that the evidence provided weak support for the null hypothesis. Overall, high performance was achieved in the final session of each EDS (median ± SE: 88.9 ± 1.1% in the visual modality and 91.5 ± 0.6% in the auditory modality).\n\nDifficult discriminations evoked perceptual learning and was associated with increased modality-specific corticocortical neuronal population interactions\nWe first assessed whether requiring difficult discriminations engaged perceptual attention to a greater extent, relative to easy discriminations, prior to the E
131DS. Figure 2A plots dâ from the last 600 trials of the staircase procedure, through the two attentional manipulation sessions, and in the 200 trials before the introduction of novel stimuli during the EDS. During the staircase procedure, the near-zero effect size demonstrates that the level of stimulus discriminability (easy or difficult) was challenged to similar levels in all rats (Figure 2B). However, during subsequent sessions, dâ was higher when the rats were presented with the easily discriminable Go and NoGo stimuli learned during the prior IDS and dâ was lower when the NoGo stimulus was made less distinguishable from the Go stimulus (negative effect sizes, Figure 2B).\n\nFigure 2.\nDownload figureOpen in new tab\nFigure 2.\nDiscrimination ability improved, frontal-sensory cortex EEG Granger causality magnitude increased, and saccade rate increased during repeated sessions of difficult discriminations.\n(A) dâ is plotted in the final 600 trials of the staircase procedure, across two attention manipulation sessions and during the 200 trials before the introduction of novel stimuli in the EDS. The âxâ marks the mean performance with standard error in each condition. The dots are rats (N = 15 rats, each rat exposed to both attention conditions; exceptionally, one rat was tested in only one session in the easy condition and another rat was tested in only one session in the difficult condition). Asterisks mark the result of a Bayesian Wilcoxon tests indicating that the alternative hypothesis (i.e., dâ differs between sessions in the difficult discrimination condition) is strongly (*** BF10 > 10), moderately (** BF10 > 3), or weakly (* BF10 > 2) supported by the data. (B) Effect sizes between the easy and difficult discrimination conditions (with 95% confidence intervals) are plotted for each stage of the experiment. (C) Granger causality magnitude was calculated for all electrode-pairs and collapsed across sessions and rats. The âxâ and error bars indicate the mean and standard error in each condition. Each data point is an electrode-pair. The number of electrode-pairs differs due to removal of electrodes with noise contamination (visual relevant â easy: N = 144 electrode-pairs, difficult: N = 432 elec-trode-pairs; auditory relevant â easy: N = 288 electrode-pairs, difficult: N = 144 electrode-pairs. Asterisks mark the result of a Bayesian Wilcoxon tests indicating that the alternative hypothesis (i.e., that magnitude differs between the easy and difficult discrimination conditions) is strongly (*** BF10 > 10), moderately (** BF10 > 3), or weakly (* BF10 > 2) supported by the data. (D) The effect sizes (±95% confidence intervals) comparing Granger causality magnitude in the easy and difficult discrimination conditions. (E) The saccade rate is plotted for individual rats. These data were not recorded in the entire sample (auditory relevant â easy: N = 21 sessions; visual relevant â easy: N = 5 sessions; auditory relevant â difficult: N = 7 sessions; visual relevant â difficult: N = 16 sessions). (F) The effect sizes (±95% confidence intervals) are plotted using same conventions as the other panels in the figure.\n\nWe observed a gradual improvement in discriminability across difficult discrimination sessions. A Bayesian two-way ANOVA strongly suggested that performance improved across sessions in the difficult discrimination condition (interaction between session number and discriminability of the stimuli, BF10 = 56.802). Post-hoc Bayesian Wilcoxon tests suggested that these data provide weak support of the alternative hypothesis that discriminability improved between the staircase procedure and the second attention manipulation session (BF10 = 2.716) but strongly support the alternative hypothesis for an improvement between the staircase procedure and the first 200 trials of the EDS session (BF10 = 70.880). There was also strong support for improved discriminability between the first attentional manipulation session and the first 200 trials of the EDS session (BF10 = 399.407). Improved discrimination with experience (i.e., perceptual learning) during repeated sessions of difficult discriminations suggests that perceptual attention was more engaged in that condition relative to when easy discriminations were required.\n\nPrior work in non-human primates has shown that, when cued to attend to a
131stimulus within a receptive field relative to attending outside the receptive field, there is an increase in Granger causality between frontal cortex and visual cortex field potentials recorded in that receptive field r28. Thus, increased frontal-visual Granger causality magnitude in indicative of perceptual attention being engaged to improve stimulus discriminability. Therefore, we tested the hypothesis that difficult discriminations were associated with higher frontal-visual cortex Granger causality compared to easy discriminations, but only when the visual modality was relevant. In 4 of the 15 rats, EEG signals were recorded bilaterally across the entire cortex using a flexible 32 electrode array chronically implanted directly onto the skull. Twelve electrodes covered visual cortex bilaterally and four covered frontal cortex bilaterally. Granger causality was measured between all electrode-pairs. We assessed differences at the electrode-pair level; a subject-level analysis was not possible because easy and difficult discrimination conditions were in different modalities for each subject (visual relevant â easy: N = 1 rat, difficult: N = 3 rats; auditory relevant â easy: N = 2 rats, difficult: N = 1 rat). Electrode-pair Granger causality magnitudes were pooled across rats and across the two attention manipulation sessions and the 200 trials prior to the introduction of novel stimuli prior to the E
131DS.\n\nThere were distinct differences in frontal-visual Granger causality magnitude during difficult discriminations relative to easy discriminations depending on which modality was being discriminated (Figure 2C, 2D). A Bayesian two-way ANOVA suggested that these data provide strong support for the alternative hypothesis of an interaction between task difficulty and relevant modality (BF10 = 8.349E4). Post-hoc Bayesian Wilcoxon tests indicated evidence strongly supporting the alternative hypothesis that bottom-up (visual-to-frontal) Granger causality magnitude was higher during difficult discriminations relative to the easy discriminations when the visual modality was relevant (BF10 = 298.155), whereas the null hypothesis was strongly supported in the top-down (frontal-to-visual) direction (BF10 = 0.089). On the other hand, when the auditory modality was relevant, the directionality was reversed in that there was strong support for the alternative hypothesis in the top-down direction (albeit, weaker that when the visual modality was relevant, BF10 = 16.287) while the null hypothesis was moderately supported in the bottom-up direction (BF10 = 0.037). These data suggest that requiring difficult discriminations in one modality alters interactions between frontal cortex and the sensory cortex associated with the modality being discriminated.\n\nFinally, we used saccade rate as a behavioral measure of attentive behavior. In the difficult discrimination condition, rats performed more saccades per second especially when the visual modality was relevant (Figure 2E, 2F). There was moderate support for the alternative hypothesis that difficult discriminations were associated with increased saccade rate (BF10 = 3.084).\n\nEngaging perceptual attention onto one modality slows extra-dimensional set-shifting\nWe predicted that engaging perceptual attention onto one modality would affect future outcome-based learning. Specifically, we tested the hypothesis that difficult discriminations would increase the shift cost (i.e., make learning responses to novel stimuli using the previously unrewarded modality during an EDS more difficult relative to baseline learning of responses to novel stimuli using the previously rewarded modality during an IDS). We found that the prior experience performing difficult discriminations in one modality was associated with an increased shift cost for learning about novel stimuli in the other modality (Figure 3). The effect size was large: on average 1,726 more trials were required for learning about novel stimuli after performing difficult discriminations in the other modality. The 95% confidence intervals of this effect were a minimum of 577 additional trials required for learning and up to 2,874 additional trials. A Bayesian within-subjects Wilcoxon test indicated strong support for the alternative hypothesis (BF10 = 24.345). This finding was robust against changes in the bin size using for calculating % correct performance and the definition of the learning timepoint. A Bayesian within-subjects Wilcoxon test indicated the data provide strong support for the alternative hypothesis when 10, 20, 30, and 100 trial bin sizes were used with the 80% correct learning timepoint (BF10 = 20.145, 25.070, 11.348, and 21.207, respectively). Similarly, when considering instead 75% correct performance as the learning timepoint, data across all trial bin sizes (10, 20, 30, 50, and 100 trials) strongly supported the alternative hypothesis (BF10 = 16.737, 25.039, 13.790, 18.155, and 22.633, respectively). Although there was a clear effect of discrimination difficulty in the previously relevant modality on subsequent learning of the EDS, the degree to which perceptual learning occurred in the previously relevant modality (change in dâ) was not correlated with the shift cost (Pearsonâs R = -0.334; Bayesian Correlation BF10 = 0.169 which indicated moderate-to-strong support for the null hypothesis). Our data suggest that difficult discriminations in one modality subsequently slows learning the responses associated with novel and easily discriminable stimuli in another modality.\n\nFigure 3.\nDownload figureOpen in new tab\nFigure 3.\nSlower EDS learning after difficult discriminations in the previously relevant modality.\nThe plot shows the shift cost when the EDS occurs after requiring easy or difficult discriminations in the previously relevant modality. The dots are individual rats (same rats in both conditions). The inset shows the effect size (±95% confidence intervals). One rat was removed because it was an outlier in the difficult discrimination condition (2.6 standard deviations from the median) since it performed in an opposing trend to the other rats. However, including the outlier, the alternative hypothesis was still weakly supported, whereas the null hypothesis was not (BF10 = 2.038). Asterisks mark the result of a Bayesian Wilcoxon test indicating that the alternative hypothesis is strongly (*** BF10 > 10), moderately (** BF10 > 3), or weakly (* BF10 > 2) supported by the data.\n\nNumber of rewards, arousal, and response criterion were not affected during the manipulation of perceptual attention\nWe propose that learning was affected by the historical focus of perceptual attention, but other non-attentional factors may differ between task conditions requiring easy or difficult discriminations and those other factors could influence subsequent learning of the EDS. For instance, rats could obtain fewer rewards during difficult discriminations by committing more response omissions. However, we found that a similar number of rewards were obtained in both discrimination conditions (Figure 4A). A Bayesian Wilcoxon test indicated either a lack of evidence supporting either the null or alternative hypotheses during the first attentional manipulation session (BF10 = 1.939), weak support for the null hypothesis during the second attentional manipulation session (BF10 = 0.574) and modulate support for the null hypothesis during the 200 trials prior to introduction of novel stimuli during the EDS (BF10 = 0.385). Moreover, the effect sizes were small (Figure 4B). Rats received only on average 7.2% fewer, 9.5% fewer, and 3.2% fewer rewards during difficult discriminations in those task epochs.\n\nFigure 4.\nDownload figureOpen in new tab\nFigure 4.\nNon-attentional factors did not differ between easy and difficult discrimination conditions.\n(A) The number of rewards obtained in different stages of the task. Each dot is a rat (N = 15 rats tested in both conditions). (B) The effect sizes (with 95% confidence intervals) compare the number of rewards re
131ceived in the difficult discrimination versus easy discrimination condition at each task stage. Differences are small magnitude. (C) The median pupil size is plotted for each rat and each session, comparing the difficult discrimination and easy discrimination conditions. The units are arbitrary and scaled by 10â3. Pupil size was measured in a subset of rats. Each dot indicates the session median pupil size for one rat. (D) The effect sizes (with 95% confidence intervals) compare the difference in median pupil size between easy and difficult discrimination conditions at each task stage. (E, F) The post-reward pupil dilation magnitude is plotted. Plotting conventions are identical to those in C and D. (G) The plots show treadmill velocity traces aligned to stimulus onset in the different task stages. The units are rotary encoder voltage change/sec scaled by 103. The lines are the mean across rats and the shading is the standard error of the mean. Solid lines represent velocity on hit trials. Dotted lines represent error (false alarm) trial running. Most groups contain 11 rats except for 10 rats in the difficult discrimination condition for the second attention manipulation session and 12 rats in the difficult discrimination condition for the 200 trials preceding the introduction of novel stimuli before the EDS.\n\nAnother factor that could be altered during the manipulation of perceptual attention is arousal. We used pupil size as an index of arousal (Figure S2A). Luminance was held constant across subjects and task sessions by placing the experimental set-up inside of a walk-in, sealed faraday cage and using a constant head-fixation location. Rats were transported in light-blocking cages and tested under red (>590 nm) light. The median pupil size did not differ between conditions (Figure 4C, 4D). A Bayesian Wilcoxon test indicated weak support for the null hypothesis (first attention manipulation session, BF10 = 0.470; second attention manipulation session, BF10 = 0.439; during the 200 trials prior to introduction of new stimuli during the EDS, BF10 = 0.505). The effect sizes were small and in opposing directions across task epochs (means: 5.0% smaller, 5.7% larger, and 14.2% smaller) indicating that arousal was neither consistently higher nor lower when difficult discriminations were required. We also measured arousal related to reward consumption using the peak of the trial-averaged pupil dilation aligned to reward delivery (Figure S2B). The reward associated pupil dilation also did not differ between discrimination conditions (Figure 4E, 4F). A Bayesian Wilcoxon test indicated weak support for the null hypothesis (first attention manipulation session, BF10 = 0.465; second attention manipulation session, BF10 = 0.447; during the 200 trials prior to introduction of new stimuli during the EDS session, BF10 = 0.462). The effect sizes were small and not in a consistent direction across task epochs (means: 6.8% smaller, 1.9% larger, and 11.9% smaller across task epochs). Overall, these data suggest that arousal did not differ between sessions requiring easy discriminations compared to difficult discriminations before the EDS.\n\nFinally, it is possible that greater error commissions during difficult discriminations was associated with a shift in response criterion and this could influence subsequent learning during the EDS. We predicted that during difficult discriminations, a shifted response criterion would be indicated by a reduced Go response latency (running onset) and an increased Go response magnitude (peak velocity) and that these changes would be in the same direction on hit and false alarm trials. We examined these two aspects of the response by directly inspecting Go response movement dynamics (Figure 4G). There were no apparent differences in the latency to initiate responses. In all 3 sessions, during difficult discriminations, false alarm responses were of a higher magnitude. On the other hand, hit responses were lower magnitude during the first attention manipulation session, whereas in the second attention manipulation session and the 200 trials prior to the introduction of novel stimuli before the EDS, the hit responses were slightly higher magnitude. This increased magnitude was not as large as the increase on false alarm trials. In sum, changes in response dynamics during difficult discriminations were inconsistent across task epochs and trial types.\n\nDiscussion\nStudies on the role of attention in learning have focused on how manipulating associations between stimuli and rewards can cause attention to shift between stimuli dimensions during learning 1,2. However, attention has also been conceptualized as a filter that improves sensory perception 9â11. It is unknown whether focusing perceptual attention onto one stimulus dimension could subsequently influence how outcome-based attention is controlled during learning to shift dimensions in the context of novel stimuli. Although engaging perceptual attention with a perceptual learning paradigm has been shown to slow switching from one stimulus dimension to another 29, such task switching does not assess how perceptual attention modulates outcome-based learning since it required switching between two known rules using familiar stimuli. Thus, it remains unknown whether engaging perceptual attention interacts with outcome-based attention during learning.\n\nHere, we demonstrate that perceptual attention directly affects learning rate and therefore uncover a new role of attention in learning.\n\nWe manipulated modality-specific perceptual attention by requiring either easy or difficult discriminations in the relevant modality prior to testing the ability to learn an EDS to the other modality with novel stimuli in both modalities. We show that, during repeated sessions of difficult discriminations, perceptual learning occurred. This was accompanied by altered modality-specific frontal-visual EEG Granger causality magnitude during difficult discriminations. This result is consistent with prior findings in non-human primates showing that frontal-visual Granger causality increases during the engagement of perceptual attention 28. It is also consistent with prior work showing that perceptual learning may engage higher level cortex in the usage of sensory information to control behavior 30. We interpret the occurrence of perceptual learning and the accompanied modality-specific cortico-cortical Granger causality increase as indirect signs that modality-specific perceptual attention was engaged during difficult discriminations. There has been some debate over whether observing perceptual learning indicates such engagement 31. For instance, perceptual learning occurs in conditions that do not engage top-down perceptual attention, such as presentation of subliminal stimuli or instructing subjects to not attend to stimuli 32,33. On the other hand, numerous studies have suggested that perceptual learning requires the engagement of top-down perceptual attention 17â22. It is likely that in goal-directed tasks, such as the one used here, perceptual learning is due to attentive practice discriminating the stimuli 34. Furthermore, we used saccade rate as a potential measure of attentional engagement. The frequency of this behavior increased during difficult discriminations, which provides additional support for the engagement of attention during difficult discriminations. Overall, our behavioral and electrophysiological results suggest greater engagement of perceptual attention during difficult relative to easy discriminations.\n\nAfter differentially engaging perceptual attention with either easy or difficult discriminations for two sessions, we presented novel and easily discriminable stimuli in both modalities and assessed learning responses to stimuli in the modality that was previously irrelevant (i.e., an EDS). Importantly, the use of easily discriminable stimuli during the EDS confined manipulation of perceptual attention to pre-learning time points, rather than during the learning process itself. Thus, the historical focus of perceptual attention was manipulated. We found that engaging perceptual attention onto one modality slowed learning about novel stimuli in the other modality relative to a baseline number of trials needed to learn discriminating novel stimuli without changing the relevant modality (i.e., an IDS). Targeting more difficult discriminations or increasing the number of sessions to drive additional perceptual learning could increase the effect on shift cost.\n\nIncreased shift cost is evidence for the mental formation of an âattentional setâ, a rule that classifies complex stimuli according to a single feature 5,35. Engaging perceptual attention by requiring difficult discriminations may have increased the formation of an attentional set. In prior work, attentional set formation has been evoked by maintaining the same rewarded stimulus dimension across repeated novel discriminations or by repeating reversals of stimulus-outcome relationships 5,7,35,36. These manipulations overtrain the rewarded stimulus dimension. Notably, the two attention manipulation sessions requiring easy or difficult discriminations had an identical number of trials so that one condition did not involve overtraining relative to the other. Thus, in contrast to prior work, our result suggests a new method to generate an attentional set that does not require overtraining stimulus-outcome associations.\n\nIt is likely that perceptual attention is one of many cognitive processes that affect attentional set-shifting task performance. For instance, outcome-related attention has a clear role in the task 1,2. Given that multiple factors can influence task performance, it is not surprising that the amount of perceptual learning did n
131ot correlate with the subsequent shift cost at the individual subject level. However, we were able to exclude several non-attentional factors that could differ between task conditions requiring either easy or difficult discriminations and, thus, affect subsequent learning of the EDS. One such factor, arousal, was assessed using pupillometry and was similar in both discrimination conditions. Reinforcement is another non-attentional factor driving perceptual learning 37 but we found that reward consumption did not differ across the task conditions. Finally, we assessed whether response criterion differed by directly measuring the dynamics of the stimulus-guided behavioral response. Although difficult discriminations were associated with changes in response magnitude, these were inconsistent across task epochs and trial types. Additionally, latency changes were not observed. Collectively, the analysis of response dynamics suggests that criterion changes were not a major factor modulating subsequent EDS learning.\n\nOur data suggest that there are two interacting types of attention that affect rule learning: first, âhow muchâ attention has been historically focused on perceptual features of stimuli 17, and second, attentional capture by decision outcomes. The involvement of two types of attention in rule learning may help explain why psychiatric disorders share impaired attentional set-shifting as a trans-diagnostic symptom. For instance, a diagnosis of autism is associated with deficits in EDS learning 12. Conspicuously, these individuals also present deficits in perceptual learning and perceptual attention 27. EDS learning is also impaired for individuals diagnosed with schizophrenia 13, but without the deficits in perceptual learning 38,39. Schizophrenia is, however, associated with impaired reinforcement learning because of insensitivity to receipt of rewards 40,41. These findings from the psychiatric literature, considered in the context of our findings, suggest a potential explanation for why impaired attentional set-shifting is a trans-diagnostic symptom of different psychiatric disorders. We propose that individuals with schizophrenia may have impaired outcome-based attention, which leads to impaired EDS learning. In contrast, individuals diagnosed with autism may have impaired perceptual attention, providing a different route to impaired EDS learning. If outcome-based and perceptual attention are predominantly weighted toward separate neural circuits, then the treatment of impaired attentional set-shifting may require targeting different neural circuits in autism versus schizophrenia.\n\nUnderstanding the neurobiology of behavior requires first defining which cognitive functions are in use during a behavioral task 42,43. During the attentional set-shifting task, outcome-related attention constrains how values are calculated and updated 1. Based on our finding that focusing perceptual attention onto one modality impairs subsequent learning in another modality, we propose that, in addition to outcome-related attention, the historical focus of perceptual attention places an additional constraint on learning in the attentional set-shifting task. Prior work has shown that engaging perceptual attention has an associated cost, in that it can cause subjects to imagine stimuli that are not physically present 44. Here, we show yet another cost of perceptual attention, which is on subsequent learning about novel stimuli. Although it may be beneficial for survival when attention sharpens perception of one sensory modality, in the context of learning this may occur at the expense of another modality and contribute to inflexible behavior. This double-edged sword of attention illustrates a cognitive constraint placed on learning.\n\nAuthor contributions\nConceptualization â NT; Formal analysis â DV, NT; Funding acquisition â NKL, NT; Investigation â DV, NS, RI, HB, ISZ, Methodology â DV, RI, MW, NT; Project administration â NT; Resources â NKL; Supervision â NT; Visualization â DV, NT; Writing â DV, NT.\n\nMaterials and Methods\nSubjects\nMale rats (Lister-Hooded) were obtained from Charles River at a weight of 140 grams to 190 grams. Rats were housed in pairs for a 7-day acclimation period prior to implantation with a chamber and head-post and, in some cases, an EEG array. After implantation, rats were single housed. All behavioral testing was carried out during the ratsâ active phase and housing illumination was between the hours of 7PM and 7AM. All procedures were carried out with the prior approval of local authorities and in compliance with the European Community Guidelines for the Care and Use of Laboratory Animals.\n\nSurgical procedures\nThe rat was anesthetized using isoflurane (induction chamber for 4% for 3 min and 2.5% for 5 min followed by 2.5% or less via the nose cone). Anesthetic concentration was adjusted throughout the procedure to maintain a heart rate of â¼300 to 350 beats per minute. The rat was head-fixed using ear bars. After fixation, the rat was with buprenorphine (0.06 mg/kg, s.c.), meloxicam (2.0 mg/kg, s.c.), and enrofloxacin (10.0 mg/kg). We injected lidocaine (0.5%, s.c.) under the scalp. Surgical procedures began as soon as the rat was not responsive to the paw pinch (usually 10 to 15 minutes after the injection of painkillers). Skin and underlying connective tissue were removed to expose most of the frontal bone and laterally and posteriorly to the surrounding musculature. Soft tissue bleeding was stopped using cauterization. Skin and underlying connective tissue were removed to expose most of the skull from the frontal bone continuing posteriorly to the neck muscle and also laterally to the left and right temporal muscles. The bone was wiped dry and cleaned with 5% H2O2 applied using a cotton swap. The H2O2 was immediately removed by washing with 40 mL of saline. At this stage, for a sub-set of rats, an EEG array (Neuronexus, CM32) was laid onto the skull and fixed in place using dental cement (2-stage, powder/liquid Paladur). The surrounding exposed bone was scratched using bone curette in a grid pattern to increase the adhesion of the subsequently applied UV-curing primer that serves as a base for fixation of the chamber and head-post onto the skull. If no EEG array was implanted, then the entire skull was scratched. Primer was applied using a brush (Omnibrush, DentalBauer) and UV cured for 30 sec at full intensity (Superlite 1300, M+W Dental). A custom-made skull implant was used for head-fixation (machine shop, Max Planck Institute for Biological Cybernetics). The implant was fixed onto the skull using UV-curing dental cement (Tetric Evoflow, Dental Bauer) that bonds to the underlying layer of primer on the skull. A craniotomy was made on the left occipital bone for a ground wire. The ground wire was 99.9% pure silver. One end was flattened using an industrial press into a â¼1-2 mm wide rectangle, which then was twisted into a roll to fit the craniotomy and inserted into the space between bone and dura. A rolled shape was used to increase the potential surface area in contact with CSF. The craniotomy was filled with viscous agar, which stabilized the wire and is also conductive. The other end of the ground wire was soldered to the ground wire of the electrode interface board of the EEG array. The wires and array were buried under the thick layer of dental cement (Paladur). The skin around the implant was glued to the implant using tissue glue (Histoacryl, B. Braun). Post surg
131ical recovery lasted five days. During the first three days (surgery itself was counted as day one), the animal was injected either every 12 hours with buprenorphine or every 24 hours with meloxicam (same dosages as pre-operative). During the first five days, enrofloxacin was injected every 24 hours (same dosage as pre-operative). A rehydrating, nutritious, easily consumed, and palatable food was provided (DietGel Recovery, Clear H2O).\n\nHandling and habituation\nRats were handled daily for at least five minutes per day from the day of arrival in housing until the day of surgery, which was 7 days. After surgery, the rats were water restricted for at least one full day prior to the first head fixation. Water restriction procedures are explained in detail in the next section. The training procedure can be divided into habituation, training of instrumental response (head-fixed treadmill running), training of stopping response (suppression of premature responding), and training of a NoGo response and discrimination of sensory stimuli using a Go/NoGo response paradigm.\n\nHabituation consisted of one day, which included head fixation on the treadmill with a reward port aligned to ratâs mouth and visual confirmation that it could be easily licked. Extremely tiny movements on the treadmill, such as actions that maintained balance, were rewarded with a 5 uL drop of 10% sucrose solution. The first session lasted 20 min regardless of the amount of water consumed.\n\nStarting from the second day, we trained the instrumental response. The next 1 to 3 sessions were identical to the habituation day, except that every reward was accompanied with a 0.1 sec duration tone (500 Hz). The tone served as a bridging stimulus to help the rats learn the association between movements and rewards. Each reward delivery was 5 microliters. We gradually increased the velocity threshold to encourage stronger movements, first consisting of large swinging motions of the entire body and eventually stepping. Each session lasted 30 min or until they consumed 8 mL of sucrose water, whichever occurred first. When the rat was running and licking simultaneously over continuous periods lasting â¼5 to 10 seconds, they typically received 7 mL of sucrose water during the behavioral session. At this stage, the training protocol was advanced.\n\nIn the next training stage, the Go stimulus was introduced (either auditory or visual, see results section for details). Initially, the stimulus duration was 15 sec with an inter-trial interval (ITI) (selected randomly from a 2 to 3 sec distribution). Rats were trained to run during the stimulus. Any threshold crossing movement that occurred during the stimulus was rewarded and repeated threshold crossings during the same stimulus presentation provided repeated rewards. Threshold crossing movements were defined by a distance threshold which was the same for all rats and corresponded to a burst of running (â¼4 strides). Normally, rats required one session (â¼30 minutes long) to both run during the stimulus and avoid running during the ITI. This indicated that the rat had learned to associate the stimulus with the previously trained action-outcome association.\n\nThe next phases of training focused on training the stopping response. We first introduced a timeout (0.5 sec duration) after a premature response, which was defined as running over a velocity threshold during the ITI. Premature responses also resulted in restarting the ITI after the timeout ended. The velocity threshold was identical for all rats and corresponded to a single step on a treadmill. The premature timeout and restarting of the ITI remained in the task design throughout all subsequent stages of the experiment. After one to two sessions, the rats learned to suppress premature running. At this point, stimulus duration was reduced to 5 seconds and the ITI was reduced to 1 to 2 seconds. Typically, after five sessions, rats were responding to nearly every stimulus and had adjusted their responding to soon after the onset of the stimulus due to the limited window to obtain reward. At this stage, the stimulus duration was further reduced to 2.5 seconds and the ITI was reduced to 1 to 1.5 seconds. Reward drop size was increased to â¼8 uL. We trained rats for 2 or 3 sessions at this stage to adapt them to the shorter stimulus, but there was no qualitative change to their behavior between the 5 sec and 2.5 sec stimulus. After 2 or 3 sessions, the stimulus duration was reduced to our target duration of 1.5 seconds and the ITI was changed to 0.5 to 1.0 sec. Reward drop size was increased to 10 uL. Given the brief stimulus duration, the task became a speeded response task. Stable behavior at this stage was defined as an omission rate of less than 10% and water consumption of at least 6 mL per session.\n\nAs soon as behavior was stable at this stage, the Go/NoGo paradigm was trained. A NoGo stimulus was introduced. Due to the pre-potent drive to respond, we introduced a response offset window (0.75 sec after stimulus onset), during which running did n
131ot count toward the distance threshold. This allowed low latency movements, but forced the rat to perceptually appraise the stimulus and then make a decision. Responding to the NoGo stimulus (i.e., crossing the distance threshold) resulted in a timeout of 6 seconds. Upon response threshold crossing, the stimulus was extinguished and the timeout began. A correct response to the Go stimulus also resulted in extinguishment of the Go stimulus. Reward was delivered in the form of three drops (10 uL) each separated by 0.5 sec. While the duration of prior training sessions previously was based on subjective judgement about the satiety of the rat, the Go/NoGo paradigm training always lasted 400 trials. Performance (number of hits and correct rejections out of total number of trials) reached 80% after 2 to 6 sessions. After reaching this criterion levels of performance, the irrelevant stimulus modality was introduced. Training was continued with the compound auditory-visual stimuli until performance was >85%. Typically, this required between 2 and 10 sessions of training.\n\nAn equal number of Go and NoGo stimuli were presented in each session. The two irrelevant modality stimuli were each presented an equal number of times with the Go and NoGo stimuli. We also introduced a requirement that no more than three NoGo trials could occur in succession due to difficulty suppressing responses. Three NoGo responses would require sitting immobile for a 0.5 to 1.0 sec ITI, then a 1.5 sec NoGo stimulus and repeating this two more times. Although it was possible for rats to do this, we determined that it was an unreasonable level of difficulty.\n\nUpon reaching this criterion levels of performance, training was completed and rats started the experiment consisting of two baseline sessions followed by an intra-dimensional shift. Details of the experimental paradigm are provided in the results section.\n\nWater restriction\nDuring the handling procedure, the availability of water was restricted. During training, rats were provided 8 to 12 mL of water per day (including the volume consumed during the behavioral task) for 5 days followed by 48 hours of ad libitum water availability. We initially provided 10 mL of daily water. In rare cases where body weight loss was approaching 15% drop from the last weight during ad libitum water availability, then 12 mL of daily water was provided. The omission rate of some rats was high unless only 8 mL of water was provided per day. After training in the basic Go/NoGo task, rats were switched to a continuous schedule of water restriction, without intermittent 48-hour windows of ad libitum water availability to prevent fluctuations in motivation caused by intermittent changes in water availability, as well as a stress response due to intermittent water availability (Vasilev et al., 2021), which could affect task performance.\n\nAttentional set-shifting task\nAfter training in the Go/NoGo task, rats performed a series of intradimensional and extra-dimensional set-shifts in which completely novel stimuli were presented and the rats needed to learn the new discrimination through trial-and-error. All rats performed an IDS followed by an EDS and then the procedure was repeated, each time with novel stimuli.\n\nEach IDS was preceded by two baseline sessions to ensure stability of learning the prior discrimination. After learning an IDS, it was followed by a psychophysics experiment that aimed to determine the attribute of the NoGo stimulus that would generate 71% correct performance (see next section for description of the psychophysics experiment).\n\nEach EDS was preceded by two attention manipulation sessions. If the manipulation was to maintain a normal level of attention, then these two sessions were identical to baseline sessions, in that rats needed to demonstrate their ability to perform the previously learned discrimination from the IDS. If the manipulation aimed to hyper-focus attention, then during these two sessions the rats were presented with the Go stimulus from the previously learned IDS and the individually tailored NoGo stimulus designed to generate 71% correct discrimination performance. In both cases, the same stimuli from the irrelevant modality, which were presented during the IDS, were presented during attentional manipulation.\n\nDuring the IDS or EDS, the new stimuli were introduced at trial number 200, such that the first 100 trials of the session served to confirm performance of the previously learned rule. Rats were allowed as many behavioral sessions as needed to finish learning. Each session lasted for 600 trials. Completed learning was defined >85% correct performance for one session.\n\nSensory stimuli\nStimuli in the visual modality were black and white drifting gratings (spatial frequency = 0.005 cycles/pixel). The visual Go and NoGo stimuli were 70 deg apart. Visual stimuli were presented 16cm from the ratâs head. Auditory stimuli ranged from 0.5 kHz to 64.339 kHz (sampling rate = 192 kHz) and the Go and NoGo stimuli were three-quarters of an octave apart. Auditory stimuli were presented using a Lynx E44 sound card (192 kHz sampling rate), an amplifier with a 0.1 kHz to 100 kHz range (Yamaha, AX-397), and stereo speakers with a 0.004 kHz to 100 kHz range (Sony, MDR-Z7) mounted on either side of the ratâs head. A microphone (Bruel & Kjaer, 4959-A) with a range of 0.004 kHz to 70 kHz was used to confirm sound delivery and tune stimulus amplitudes so that the sounds were all â¼65 dB.\n\nStaircase procedure\nThe psychophysics task used a 2-down / 1-up procedure, whereby each correct response resulted in the NoGo stimulus changing toward the Go stimulus by 2 steps and each false alarm resulted in moving the NoGo stimulus 1 step away from the Go stimulus. The step size for auditory stimuli was defined as 100 steps in logarithmic space between the Go and NoGo stimulus and for visual stimuli the step size was 0.5 degrees. Rats were run on the psychophysics task until they could no longer improve their performance for most of a behavioral session (600 trials). This typically required 2-3 sessions.\n\nPupillometry data acquisition and processing\nVideos were recorded at 45 frames per second from the ratâs right eye with near-infrared illumination (Thor Labs LED, M850L3 and Thor Labs Collimation optics, COP4-B). Frames were acquired using a near-infrared camera (Allied Vision, G-046B) and variable zoom lens, fixed 3.3x zoom lens, and 0.25x zoom lens attachment (Polytec, 1-60135, 1-62831, 6044). Acquisition occurred over a GigE connection (MATLAB image processing toolbox). The camera provided a TTL pulse with each video frame. These TTL pulses were recorded directly into the neurophysiology and behavior control system (Neuralynx).\n\nWe used an in-house custom algorithm and computer code, implemented in Python, to extract pupil size from the recorded video frames. Before processing the sequence of frames, one of them was randomly selected to manually crop the ratâs eye to specify the region of interest for the algorithm. At this stage, several meta parameters for each of the following steps were simultaneously selected using manual inspection in a GUI (Figure S3). This was left intentionally to an operatorâs judgement due to natural noise in the image of the ratâs eye (e.g., the eyelid, lashes, or fur could cast a shadow on the pupil). First, each cropped image was blurred using Gaussian blur. The image was then converted into a binary black and white image. The edges on this image were defined using the Cannyâs algorithm (Canny, 1986). Then, closed contours were selected using an algorithm described by Suzuki and colleagues (Suzuki and Abe, 1985). Finally, ellipses were fitted into closed contours using the method of Fitzgibbon and colleagues (Fitzgibbon and Fisher, 1995). If there happened to be several elliptical contours, the ellipse with minimal eccentricity was selected. In cases where the algorithm was not able to find an ellipse of an area bigger than predefined minimal allowed area (or smaller than maximal, respectively), then the value of the pupil in this frame was left blank. This was also applied to the frames which captured the animal blinking. Blank frames were linearly interpolated. The pupil detection algorithm was implemented using the OpenCV package in Python 3.7.\n\nEEG signal acquisition\nEEG signals were recorded using a flexible polyimide array with 32 platinum electrodes (Neuronexus, H32). Signals were recorded against animal ground, pre-amplified at the ratâs head (Neuralynx, HS-36), and then amplified and digitized at 32 kHz (Neuralynx, Digital Lynx SX). The analysis focused on bilateral electrodes placed over frontal cortex (locations relative to Bregma: 1.5 mm anterior, ±1.2 mm lateral; 3.6 mm anterior, ±1.2 mm lateral) and visual cortex (locations relative to Bregma: 5.0 mm posterior, ±1.5 mm lateral; 5.0 mm posterior, ±3.0 mm lateral; 5.0 mm posterior, ±4.4 mm lateral; 7.0 mm posterior, ±1.5 mm lateral; 7.0 mm posterior, ±3.0 mm lateral; 7.0 mm posterior, ±4.4 mm lateral).\n\nTreadmill velocity signal acquisition\nThe treadmill was a freely rotating fiberglass cylinder that c
131ould rotate freely either forward or backward on a metal axis inserted into low friction ball bearings. Treadmill velocity was calculated from an analog signal corresponding to the rotation of the axis, which was recorded using a rotary encoder (US Digital, MA3-A10-125-B). The analog voltage output of the rotary encoder varied between 0V and +5V mapped onto 0 to 360 degrees. The signal was recorded as an analog input in the Neuralynx amplifier. The rotational angle signal was sampled at 32 kHz. It was unwrapped in MATLAB to grow continuously by differentiating the signal and detecting the 0V-to-5V (0-to-360 degrees) and the 5V-to-0V (360-to-0 degrees) resets using the MATLAB function, peakdetect. After unwrapping the rotational angle over time, the signal was lowpass filtered (5 Hz), then downsampled to 100 Hz, and finally differentiated to obtain the angular velocity.\n\nData analysis\nWe defined the shift cost as the number of trials required to reach the learning point in the EDS minus the number of trials required to reach the learning point in the preceding IDS. The learning point was defined as the trial when the performance reached 80%. We defined performance by fitting a âperformance curveâ to the proportion of correct responses in windows of 50 trials calculated by sliding the window in steps of one trial. The performance curve was obtained by fitting a sigmoid function to the proportion of correct responses using the psignifit package with the sigmoid option set to cumulative Gaussian and experiment type set to 2AFC (see https://github.com/wichmann-lab/psignifit/wiki for details).\n\nGranger causality was calculated using the MVGC toolbox for MATLAB (Barnett and Seth, 2014). Standard parameters were used. Before calculating Granger causality, the EEG signals were downsampled to 100 Hz. Calculations were made on the signal from the entire behavioral session without respect to the trial structure of the task. Each signal-pair was conditioned upon a third electrode, moving through all 16 other electrodes on the array that were not located over frontal or visual cortex. We reported the Granger causality magnitude for a frontal-visual electrode-pair as the average magnitude across all 16 conditioning channels.\n\nStatistics\nWe used estimation statistics and report effect sizes and the confidence intervals for effect sizes (DABEST toolbox in MATLAB (Calin-Jageman and Cumming, 2019; Ho et al., 2019)). Bayesian statistics were used for assessing evidence (or lack thereof) for the null hypothesis and for the alternative hypothesis (Keysers et al., 2020). Bayesian statistics were calculated in JASP software. The choice of Bayesian test was selected based on whether the data were normally distributed, which was assessed using a Kolmogorov-Smirnov goodness-of-fit hypothesis test (kstest, MATLAB).\n\n\nFigure S1:\nDownload figureOpen in new tab\nFigure S1:\nThe parameter difference between Go and NoGo stimuli during the staircase procedure.\nData are shown for 6 example rats in the auditory (kHz) or visual (deg) modality.\n\nFigure S2:\nDownload figureOpen in new tab\nFigure S2:\nThe pupil size is shown across an entire example session and in a brief time window aligned to reward delivery.\n(A) Pupil size is plotted over the example session from a single rat. Pupil size was larger at the start of the task across sessions and rats, even when a task is not performed and in stable conditions of darkness. The median is marked by a red line. The inset shows a single frame of the video with the pupil size indicated by the magenta elipse. Pupil size was extracted from video of the eye recorded at 45 frames per second. (B) The tri
131al-averaged peri-reward pupil size aligned to reward delivery is plotted for one example session. The shading shows the error across trials. Post-reward dilation was the peak of the trial averaged pupil dilation during the 4 sec window after reward delivery.\n\nFigure S3:\nDownload figureOpen in new tab\nFigure S3:\nAn image showing the GUI used to perform pupillometry.\nThis GUI was used to manually set parameters for pupil detection on a single, randomly selected video frame. Those parameters were then applied to the entire recording in an automated procedure. (A) The parameter values. (B) The original image subjected to a Gaussian blur. (C) The binary image. (D) Closed contours were selected. (E) An ellipse was fit to the contour corresponding to the pupil.\n\nAcknowledgements\nWe thank Prof. Lewis Chuang and Prof. Joshua Gold for discussions on psychophysics and perceptual learning. We appreciate guidance from Prof. Laura Busse and Dr. Steffen Katzner on pupillometry measurements in rodents. We are grateful for comments on the manuscript from Prof. Peter Dayan, Prof. Luigi Acerbi, Prof. Anna Levina, Prof. Marie Carlén, and Dr. Aron Koszeghy. We thank Prof. Geoffrey Schoenbaum and Dr. Marios Panayi for insightful discussions regarding learning theory. We wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. This work was funded by the Max Planck Society and the University of Helsinki (Helsinki Institute of Life Science). This paper was typeset with the bioRxiv word template by @Chrelli:www.github.com/chrelli/bioRxiv-word-template","fulltextMode":"markdown","pdfExternalUrl":"https://www.biorxiv.org/content/10.1101/2022.01.22.477334v2.full.pdf+html","pdfPublic":true,"meta":{"species":"rat","sex":"male","weight":"140g-190g"},"featured":false,"sortOrder":19,"createdAt":"2026-06-25T11:58:12.694Z","products":["remy-system","remy-implants","remy-surgery-holder","remy-chamber","remy-chambers","remy-holder","remy-poles"]},{"id":"pub_svjlfps","slug":"dentate-gyrus-single-cell-neuronal-activity-in-rats-during-fixed-interval-task-and-subsequent-sleep","status":"published","type":"preprint","bibtexKey":"lipponen2020dentate","title":"Dentate gyrus single cell neuronal activity in rats during fixed interval task and subsequent sleep","authors":["Arto Lipponen","Joonas Sahramäki","Markku Penttonen","Miriam Nokia"],"year":2020,"month":"","journal":"Research Square","volume":"","issue":"","pages":"","publisher":"","doi":"10.21203/rs.3.rs-123425/v1","url":"https://www.researchsquare.com/article/rs-123425/v1","abstract":"We recorded the activity of dentate gyrus principal cells and interneurons while the animal was immobile during a fixed interval task and a subsequent rest period including NREM sleep. Mossy and granule cells preferentially fired during immobility; all cell types fired during NREM sleep, especially during dentate spikes.","fulltext":"","fulltextMode":"markdown","pdfExternalUrl":"","pdfPublic":false,"meta":{"species":"rat","behavioralParadigm":"fixed interval task and subsequent sleep","siliconProbe":"silicon probe"},"featured":false,"sortOrder":12,"createdAt":"2026-06-16T07:46:05.110Z","products":[]}],"testimonials":[{"name":"Laura Berkowitz","role":"Postdoctoral Fellow, Schaffer-Nishimura lab, Cornell University","image":"/uploads/d12ec5faeb68cb3b.webp","quote":"\u003cp>My first introduction to probe implants was using a drive that was completely incased in dental cement. The probe was not recoverable, and the drive was three times as expensive. R2Drives completely changed things for me. The \u003cb>R2Drives make probe implants and explants easy. \u003c/b>Iâm now able to \u003cb>reuse probes several times over\u003c/b> and the R2Drive remains reliable throughout reuse.\u003c/p>","quoteMode":"html","facts":[],"more":"","moreMode":"","publication":"","publications":[{"title":"MouseGoggles: an immersive virtual reality headset for mouse neuroscience and behavior","citation":"Isaacson et al. · Nature Methods · 2024","url":"https://doi.org/10.1038/s41592-024-02540-y"}
131],"_order":0,"featured":true},{"name":"Adrian Duszkiewicz","role":"Postdoctoral Fellow, Paul Dudchenkoâs laboratory, University of Stirling","image":"/uploads/2348dbbb2391e266.webp","quote":"\u003cp>3Dneuro drives are \u003cb>amazing\u003c/b>, it now takes us \u003cb>mere minutes to recove\u003c/b> previously implanted probes and \u003cb>shanks almost never break\u003c/b>. It allows us to keep \u003cb>re-implanting the same probe until the recording sites wear off\u003c/b>, itâs an absolute game changer and also a \u003cb>considerable money saver\u003c/b>.\u003c/p>","quoteMode":"html","facts":[{"label":"Model system","value":"Freely moving rats"},{"label":"Number of R2Drives per implant","value":"up to two"},{"label":"Duration of experiments","value":"Up to 1 week"},{"label":"Probe recovery success","value":"Pretty much 100% of the time"}],"more":"\u003ch2>In a nutshell\u003c/h2>\u003cp>Our group is interested in how genetic anomalies that cause autism spectrum disorder/ intellectual disability (ASD/ID) in humans affect neural representations of space. We focus our investigation on rat models of ASD/ID, as part of the SFARI Autism Rat Models Consortium\u003c/p>\u003cp>We use 3Dneuro drives to implant Neuronexus and Cambridge Neurotech probes in freely moving rats (both adult and juvenile). We then test the rats on a variety of open field paradigms probing the mechanisms of spatial orientation.\u003c/p>\u003ch2>The longer story\u003c/h2>\u003cp>Our brains constantly process information coming from our senses and use it to form a mental image, or (as we call it) a mental representation of the outside world. This process is largely subconscious, but familiar to everyone who found themselves in their home at night during a sudden power outage - navigating around in the dark using exactly such a mental representation. My main research interest is in investigating how this mental process is affected in neurodevelopmental conditions such as Fragile X Syndrome and other forms of autism spectrum disorder/intellectual disability (ASD/ID).\u003c/p>\u003cp>We know that mental representations are formed by coordinated activity of millions of brain cells. While measurements of brain activity are often done in humans, such methods often lack the resolution to pinpoint exactly how individual brain cells communicate with each other. However, using miniaturized brain activity sensors developed specifically for rats, we can look at activity of hundreds of individual brain cells, giving us a small window into the inner workings of the rat brain.\u003c/p>\u003cp>Now, autism is a uniquely human condition, but we can model it in rats by altering their genes to induce mutations seen in some inherited forms of autism. We can then quantify how these mutations affect the way rat brain cells represent the outside world.\u003c/p>\u003cp>But even a rat brain is incredibly complex, made of millions of cells forming billions of connections, and cracking its code knowing the activity of only a fraction of those brain cells is a challenging task. However, there is a notable exception to this rule, specialized brain cells that represent the animalâs current direction in space, not unlike the brainâs version of a GPS.\u003c/p>\u003cp>By monitoring the activity of those âGPSâ brain cells, we can determine the direction the rat is facing at any point just by looking at brain activity alone - we can in essence understand all the information represented in that small part of the rat brain. We can then ask questions about how the brain orients itself in healthy rats as well as in rats with mutations that cause ASD/ID in humans. What is more, we can test how their brains rely on different types of information by confusing them a little, for example, by suddenly moving a prominent landmark or turning the lights off altogether, kind of like a power outage.\u003c/p>\u003cp>Overall, such experiments offer us a small but unique glimpse into how the brain represents the outside world and hopefully they can tell us a lot about how individuals with ASD/ID may experience the world a little differently,\u003c/p>","moreMode":"html","publication":"","publications":[{"title":"Age-dependent alterations in the head-direction signal in a rat model of Fragile X Syndrome","citation":"Moore et al. · bioRxiv · 2025","url":"https://doi.org/10.1101/2025.01.09.632139"}],"_order":0,"featured":false},{"name":"Jose Roberto Lopez Ruiz","role":"Research Investigator, Yoon Lab, University of Michigan","image":"/uploads/59e79e471205f23e.webp","quote":"\u003cp>We utilize the \u003cb>R2Drives in pretty much every chronic implan\u003c/b> that involves our silicon probes, it is extremely important for us to have a \u003cb>reliable\u003c/b>and\u003cb>accurate \u003c/b>way to implant our optoelectrodes. \u003cb>Recoverability \u003c/b>
131is also a key feature that allows us to extend the life of a device over multiple implantations. Everyone who has worked with silicon probes knows how stressful it can be to handle them, having an easy-to-use platform is of great help. I always recommend them to our workshop participants.\u003c/p>","quoteMode":"html","facts":[{"label":"Model system","value":"Rats and mice, freely moving and headfixed"},{"label":"Number of R2Drives per implant","value":"Usually just one, but we have plans of implanting up to 4 on a rat"},{"label":"Probe recovery success","value":"Every time, I havenât had any issues with probe recovery."}],"more":"The Yoon lab at the University of Michigan uses MEMS and microsystems technology to create advanced neural probes. Combining optical stimulation, recording arrays, drug delivery, biocompatible and flexible materials and innovative packaging and interface solutions, they try pushing the boundaries of what tools for mapping the brain and peripheral circuits can do.\n\nI perform the in-vivo validation of the different platforms under development in the laboratory. I explore new neuroscience applications that exploit the capabilities of our devices, and coordinate the dissemination of the available neurotechnologies, providing technical support and training to the neuroscience community.","moreMode":"","publication":"","publications":[{"title":"Simultaneous electrophysiology and optogenetic perturbation of the same neurons in chronically implanted animals using μLED silicon probes","citation":"Kinsky et al. · STAR Protocols · 2023","url":"https://doi.org/10.1016/j.xpro.2023.102570"}],"_order":2,"featured":false},{"name":"Lynn Yap","role":"Postdoctoral Research Scientist, Richard Axelâs laboratory, Zuckerman Mind Brain Behavior Institute at Columbia University","image":"/uploads/940944d704bd79e5.webp","quote":"\u003cp>We are using the \u003cb>R2drives with Neuropixels 2.0 probes\u003c/b> to ask how past experiences influence olfactory perception in mice using a 2-alternative forced choice task. Recently, we started using the new \u003cb>R2rail and are really liking it!\u003c/b>\u003c/p>","quoteMode":"html","facts":[{"label":"Model system","value":"Mice, freely-moving and headfixed"},{"label":"Number of R2Drives per implant","value":"one"},{"label":"Duration of experiments","value":"3 months"},{"label":"Probe recovery success","value":"I always recover the probes, and so far have reused them ~2 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