Spatial computing enables flexible cognitive control in neural networks

Spatial computing enables flexible cognitive control in neural networks | 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 Article Spatial computing enables flexible cognitive control in neural networks Mikael Lundqvist, Abhirup Bandyopadhyay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7083743/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 Flexible and generalizable control over sensory representations is needed to achieve human-level cognition. Here we implement a novel principle, Spatial computing, in a cortical neural network model. By explicitly utilizing the cortical topography as an additional coding dimension, it is possible to flexibly update the cognitive status of sensory representations. We first demonstrate that Spatial computing requires distance-dependent like-to-like connections and local winner-takes-all-dynamics resulting from non-specific feedback inhibition to be stable. Both motifs are consistent with cortical connectivity. By making use of topography, it is trivial to perform several cognitive tasks such as prioritizing, deleting or re-ordering the rank of items held in working memory, as needed during mental arithmetic. Importantly, spatial computing dissociates the substrate of sensory representations (cortical connectivity) from that of cognitive control (topographical dynamics), thereby allowing learned operations to automatically generalize across representations. Our results suggest that biology is likely to utilize the physical dimensions of the cortex to perform computations and that taking space into account in artificial networks may significantly improve their computational capabilities. Biological sciences/Neuroscience/Computational neuroscience/Network models Biological sciences/Neuroscience/Cognitive neuroscience/Cognitive control Biological sciences/Neuroscience/Learning and memory/Working memory Full Text Additional Declarations There is NO Competing Interest. 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. 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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-7083743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483438515,"identity":"ff57a7c8-6031-49cc-8075-4a9b745d7961","order_by":0,"name":"Mikael Lundqvist","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACZjYgUcAgAyQNwIifh7GBCC0GDDwwLRKSPYS0MKBoYWCQMDhDQINuO1vihw9ALQa3mzfe5imoqzM+c7iB8UcFbi1mh9kOS84AablzrNiax+CwhNnZxgZmHjxWmR1mb5DmAWm5kWMG1HtAwuw8YwMzYxteLc2//yC01EkY9zM2MP78h9dhx6QZoFokPhgwSxjwNjYw8Dbg1ZJm2WMgwSN5I63Y4oMB0GNnDjYc5jmGR8v5Y8Y3flTYyPHdSN54I+FPHT9/T/rDhz9qcGuBAgkkEggOENSAqnEUjIJRMApGARoAAKkeSzmInYMpAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1011-5234","institution":"Karolinska Institutet","correspondingAuthor":true,"prefix":"","firstName":"Mikael","middleName":"","lastName":"Lundqvist","suffix":""},{"id":483438516,"identity":"f66637df-3211-4536-a815-73d7a56e1c8b","order_by":1,"name":"Abhirup Bandyopadhyay","email":"","orcid":"","institution":"Karolinska Institutet","correspondingAuthor":false,"prefix":"","firstName":"Abhirup","middleName":"","lastName":"Bandyopadhyay","suffix":""}],"badges":[],"createdAt":"2025-07-09 12:11:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7083743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7083743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88846056,"identity":"59eb82d1-e3b9-472f-a560-49d04b6799e5","added_by":"auto","created_at":"2025-08-12 04:03:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1464732,"visible":true,"origin":"","legend":"Article File","description":"","filename":"SubmittedMS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7083743/v1_covered_bda80c6e-edde-4f87-8c26-7fd9af1b1fc5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Spatial computing enables flexible cognitive control in neural networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7083743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7083743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Flexible and generalizable control over sensory representations is needed to achieve human-level cognition. 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