Organic Convolution in The Ventral Visual Pathway Can Explain the Variety of Shape Tuning in Area V4

Organic Convolution in The Ventral Visual Pathway Can Explain the Variety of Shape Tuning in Area V4 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Organic Convolution in The Ventral Visual Pathway Can Explain the Variety of Shape Tuning in Area V4 Carl Gold This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6299308/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This modeling study proposes a novel theory to explain the selectivity of V4 neurons to varying degrees of curvature in diverse receptive field arrangements reported in previous recording studies. A simple, unsupervised approach to curvature selectivity can explain a wide variety of past observations: V1 aggregates points into selectivity for lines and edges at a variety of rotations; V2 aggregates orien- tated segments into selectivity for corners at a variety of angles and rotations; V4 aggregates corners into curvature selectivity for a variety of degrees, rotations and positions. The model is implemented in 900,000 integrate and fire units in standard cortical micro-circuits obeying Dale’s law using real- istic biophysical parameters. A novel method is used to simulate integrate and fire units with tensor programming and GPU hardware: By combining convolution with the time course of postsynaptic potentials, computation occurs in a feedforward cascade of loosely synchronized spikes. The model has relatively few parameters which are tuned by a stochastic search with manual fine tuning. A syn- thesis of hierarchical and convolutional models, this study adds novel elements to both: In comparison to previous hierarchical models there is a novel V4 model for selectivity and a biologically realistic computation based on single spikes with tensor convolution as the simulation engine. In comparison to previous convolution models with deep learning there is much more biological realism and a novel unsupervised approach to connection formation that requires orders of magnitude fewer parameters. Spiking Convolution Neural Network V2 V4 Full Text Additional Declarations Competing interest reported. I have submitted patent applications based on some of the concepts demonstrated in this study: U.S. Pat. App. Ser. No. 16/125,818 and provisional application 63337083. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6299308","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445890137,"identity":"09dd7870-3b21-42fa-911a-cdc20bd10cc7","order_by":0,"name":"Carl Gold","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2PIWvDQBTH7yh0JuPceGNr8xVeiKnYh7mYzlRM1q0qU+VsQkU/wEzgIPqgYiZpbcZMZqY26GAiImKXG4WKJFQOej9z73Hvx/89QiyW/wlVR82EiubhD/2OVvBQA40XjYKd0y1KYkJ7FPaUl4rU9Rhflor+zGEkd8/BvkTisivVqkB2j4qG6GO25YPbDPy0+JKgF/PiFW9VkEyJogsM4miGg+sQgrTIk0bh+NahsA99S42P8frzT5FRLqteBXQKGSJn4CD91krClmlvChQ6JQh9Tzgz3BB9S1RcphOO0HkLE1Na7uuxO7zIvPdqfjcSIpevunDZTbtiOHxtHBNsWugeP4ZWJlidNm2xWCxnwy9He1/92pt6ZwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Carl","middleName":"","lastName":"Gold","suffix":""}],"badges":[],"createdAt":"2025-03-25 02:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6299308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6299308/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98624328,"identity":"84b32fc0-baa5-4ace-9097-70d5602de661","added_by":"auto","created_at":"2025-12-19 17:08:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11917635,"visible":true,"origin":"","legend":"","description":"","filename":"cgorganicconvorevision.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6299308/v1_covered_0249f584-d3de-4b90-9db3-11b3db6ef10b.pdf"}],"financialInterests":"Competing interest reported. 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