A model for how grid cells contribute to complementary concept representations in the hippocampus

A model for how grid cells contribute to complementary concept representations in the hippocampus | 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 A model for how grid cells contribute to complementary concept representations in the hippocampus Troy M. Houser This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7708281/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 The hippocampus encodes relational memory. The content of hippocampal memories can be spatial or nonspatial (e.g., conceptual knowledge). Moreover, complementary learning systems within the hippocampus encodes distinct levels of representational granularity. For spatial information, these systems encode coarse- and fine-grained details of one’s spatial surrounding. For conceptual information, these systems are hypothesized to encode integrated and differentiated category knowledge. While computational models of this system have elucidated noteworthy mechanisms that the hippocampus may use to achieve these feats, most have considered the hippocampus in isolation. Here, the influence of the entorhinal cortex on complementary learning systems within the hippocampus is modeled as well. The proposed model of the entorhinal-hippocampal circuit offers plausible mechanisms by which it achieves a suite of entorhinal and hippocampal functions that are robustly observed including pattern separation, pattern completion, cognitive maps, and hexagonal firing patterns in entorhinal grid cells. This model offers mechanistic and theoretical insights into the contributions that grid cells make to concept representations and nonspatial cognitive maps in the hippocampus. grid cells category learning complementary learning cognitive map autoencoder hippocampus Full Text Additional Declarations No competing interests reported. Supplementary Files suppinfo.docx 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-7708281","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530873558,"identity":"51799f59-a796-4362-af71-9264399ea9d9","order_by":0,"name":"Troy M. 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