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1<meta name="next-size-adjust" content=""/><title>Eshed Margalit</title><meta name="description" content="Personal website for Eshed Margalit, PhD"/><link rel="icon" href="data:image/svg+xml,&lt;svg xmlns=&#x27;http://www.w3.org/2000/svg&#x27; viewBox=&#x27;0 0 100 100&#x27;&gt;&lt;text y=&#x27;.9em&#x27; font-size=&#x27;90&#x27;&gt;🧠&lt;/text&gt;&lt;/svg&gt;"/>
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1</head><body class="__variable_2d3e63 "><div hidden=""><!--$--><!--/$--></div><nav class="Navbar_navbar__f4nQc"><div class="Navbar_navbarContainer__oPUbe Navbar_defaultLayout__RIiZo"><div><a class="SubtleLink_subtleLink__bPV4c" href="/">home</a></div><ul><li><a class="SubtleLink_subtleLink__bPV4c" href="/cv">cv</a></li><li><a class="SubtleLink_subtleLink__bPV4c" href="/resume">resume</a></li><li><a class="SubtleLink_subtleLink__bPV4c" href="/art">sci-art</a></li><li><a class="SubtleLink_subtleLink__bPV4c" href="/blog">blog</a></li></ul></div></nav><main class="page"><header class="art_header__KeOf5"><h1>sci-art</h1><p>Plots and figures made while doing research that I thought were pretty.</p></header><section class="art_gallery__ow_aq"><div><div class="GalleryItem_titleRow__Gy6eH"><h2>Billiard Balls</h2><button type="button" class="GalleryItem_toggleButton__mV83W" aria-expanded="false" title="What is this?">?</button></div><div class="GalleryItem_description__sid__ "><p>I was researching ways to present stimuli resembling retinal waves to a convolutional neural network. Each subplot is the result of running a simulation of a propagating wave, then taking max projection over time to create the repeated band-like effect. The stimuli vary in color, orientation of the wave, and wavefront thickness/frequency.
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3It turns out that presenting these stimuli to a developing CNN can induce receptive fields that resemble those in human visual cortex.</p></div><div class="GalleryItem_imageContainer__Ms_B7"><img alt="Subplot grid of gratings in a circular aperture, that appear almost like striped billiard balls" width="800" height="600" decoding="async" data-nimg="1" class="GalleryItem_galleryImage__guPPU" style="color:transparent" srcSet="/_next/image?url=%2Fsci_art%2Fbilliard_balls.png&amp;w=828&amp;q=75 1x, /_next/image?url=%2Fsci_art%2Fbilliard_balls.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=%2Fsci_art%2Fbilliard_balls.png&amp;w=1920&amp;q=75"/></div></div><div><div class="GalleryItem_titleRow__Gy6eH"><h2>OPM</h2><button type="button" class="GalleryItem_toggleButton__mV83W" aria-expanded="false" title="What is this?">?</button></div><div class="GalleryItem_description__sid__ "><p>When building models of primary visual cortex (V1), I often needed to visualize the preferred orientation of the model&#x27;s neurons. This image was generated by spatially smoothing the orientation preferences of model neurons in a DNN layer that resembles V1. Each color represents a different preferred orientation. This turns out to match the statistics of orientation preference maps (OPMs) in primate V1 pretty well.</p></div><div class="GalleryItem_imageContainer__Ms_B7"><img alt="A rainbow orientation-preference map, where the colors represent different preferred orientations of visual stimuli." width="800" height="600" decoding="async" data-nimg="1" class="GalleryItem_galleryImage__guPPU" style="color:transparent" srcSet="/_next/image?url=%2Fsci_art%2Fconv4_sineff_cosyne_smooth_ori.png&amp;w=828&amp;q=75 1x, /_next/image?url=%2Fsci_art%2Fconv4_sineff_cosyne_smooth_ori.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=%2Fsci_art%2Fconv4_sineff_cosyne_smooth_ori.png&amp;w=1920&amp;q=75"/></div></div><div><div class="GalleryItem_titleRow__Gy6eH"><h2>Hyperblob</h2><button type="button" class="GalleryItem_toggleButton__mV83W" aria-expanded="false" title="What is this?">?</button></div><div class="GalleryItem_description__sid__ "><p>Early in my research on DNN-layer models of primary visual cortex (V1), I wanted to group learned image filters (or &#x27;kernels&#x27;) based on the the correlation of the outputs they produce for some set of inputs. Each square is a learned convolutional filter. Nearby filters produce similar outputs for natural image inputs; this spatial arrangement was achieved with a swap-based algorithm for finding spatial arrangements that optimize local output correlations. Some cool things that emerge from this: color-selective filters cluster near the center (like &#x27;blobs&#x27; in primate V1), and higher spatial frequencies end up on the periphery. There are also global orientation gradients such that nearby filters have similar preferred orientations. This is reminiscent of the &#x27;pinwheel&#x27; structure of orientation preference maps in V1.</p></div><div class="GalleryItem_imageContainer__Ms_B7"><img alt="Visualization of image filters arranged in a sunflower-like pattern." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="GalleryItem_galleryImage__guPPU" style="color:transparent" srcSet="/_next/image?url=%2Fsci_art%2Ffilter_pinwheel.png&amp;w=828&amp;q=75 1x, /_next/image?url=%2Fsci_art%2Ffilter_pinwheel.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=%2Fsci_art%2Ffilter_pinwheel.png&amp;w=1920&amp;q=75"/></div></div><div><div class="GalleryItem_titleRow__Gy6eH"><h2>Orientation Pinwheel</h2><button type="button" class="GalleryItem_toggleButton__mV83W" aria-expanded="false" title="What is this?">?</button></div><div class="GalleryItem_description__sid__ "><p>This results follows from the previous entry &#x27;Hyperblob&#x27;. It is a visualization of the orientation preferences of the filters in a DNN layer that resembles V1. Each dot represents a filter, and the color indicates its preferred orientation. The arrangement is similar to the &#x27;pinwheel&#x27; structure found in primate V1, where nearby neurons have similar preferred orientations. The size of the dot here is proportional to the filter&#x27;s orientation selectivity, such that smaller points have broader orientation tuning.</p></div><div class="GalleryItem_imageContainer__Ms_B7"><img alt="Rainbow colored dots arranged in a sunflower-like pattern." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="GalleryItem_galleryImage__guPPU" style="color:transparent" srcSet="/_next/image?url=%2Fsci_art%2Fori_pinwheel.png&amp;w=828&amp;q=75 1x, /_next/image?url=%2Fsci_art%2Fori_pinwheel.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=%2Fsci_art%2Fori_pinwheel.png&amp;w=1920&amp;q=75"/></div></div><div><div class="GalleryItem_titleRow__Gy6eH"><h2>Orientation Grass</h2><button type="button" class="GalleryItem_toggleButton__mV83W" aria-expanded="false" title="What is this?">?</button></div><div class="GalleryItem_description__sid__ "><p>A visualization of many thousands of artificial neurons in a DNN layer that resembles primary visual cortex (V1). We trained this model with an additional spatial constraint that encourages nearby artificial neurons to have correlated outputs. Visualizing the orientation preference of these neurons with the orientation and color of small line segments highlights a global organization pattern.</p></div><div class="GalleryItem_imageContainer__Ms_B7"><img alt="A grid of thousands of line segments, each with a color corresponding to its orientation." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="GalleryItem_galleryImage__guPPU" style="color:transparent" srcSet="/_next/image?url=%2Fsci_art%2Forientation_grass.png&amp;w=828&amp;q=75 1x, /_next/image?url=%2Fsci_art%2Forientation_grass.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=%2Fsci_art%2Forientation_grass.png&amp;w=1920&amp;q=75"/></div></div><div><div class="GalleryItem_titleRow__Gy6eH"><h2>Stimulus Optimization</h2><button type="button" class="GalleryItem_toggleButton__mV83W" aria-expanded="false" title="What is this?">?</button></div><div class="GalleryItem_description__sid__ "><p>Each column shows six examples of images that were synthesized to maximally activate the neurons in a given channel of a given DNN layer. Columns further to the right correspond to deeper layers in the network. Notice how common eye-like structures become in later layers!</p></div><div class="GalleryItem_imageContainer__Ms_B7"><img alt="A grid of images synthesized to maximally activate artificial neurons in a neural network." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="GalleryItem_galleryImage__guPPU" style="color:transparent" srcSet="/_next/image?url=%2Fsci_art%2Fstimopt.png&amp;w=828&amp;q=75 1x, /_next/image?url=%2Fsci_art%2Fstimopt.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=%2Fsci_art%2Fstimopt.png&amp;w=1920&amp;q=75"/></div></div></section></main><!--$--><!--/$--><!--$!--><template data-dgst="BAILOUT_TO_CLIENT_SIDE_RENDERING"></template><!--/$--><footer class="Footer_footer__rZB2Q"><div class="Footer_content__Reui2"><div class="Footer_socialSection__BOiDx"><div class="LinkSection_grid__XeiWO"><a href="https://www.linkedin.com/in/eshed-margalit-437222a7/" target="_blank" rel="noopener noreferrer" class="LinkSection_iconButton___qLho" 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