Recurrent circuitry stabilizes representational geometry across neural circuits

Recurrent circuitry stabilizes representational geometry across neural circuits | 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 Short Report Recurrent circuitry stabilizes representational geometry across neural circuits Prashant C. Raju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9581425/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 Neural populations exhibit representational drift---the gradual reorganization of which neurons encode what---yet behavior remains stable. This stability-drift paradox has focused attention on whether population centroids are preserved over time. We ask a different question: how reliably does the pairwise distance structure among stimuli reproduce across independent trial subsets within a session? We quantify this using geometric stability (Shesha), the Spearman rank correlation between split-half representational dissimilarity matrices. Across 229 area-session observations spanning 68 brain regions in a visual discrimination task (Steinmetz et al. 2019), geometric stability predicts trial-by-trial neural-behavioral coupling (ρ= 0.18, p = 0.005) while centroid drift does not (ρ = 0.002, p = 0.976). The regional hierarchy: striatum most stable (\bar{S} = 0.44), hippocampus least ($\bar{S} = 0.19)---runs roughly opposite to the temporal stability hierarchy, showing that geometric and temporal stability are dissociable properties of population codes. In a sensory hierarchy with a causal manipulation (Bolding & Franks 2018), piriform cortex is more geometrically stable than olfactory bulb (0.57 vs. 0.47, n = 11), and silencing recurrent excitatory connections with tetanus toxin reduces piriform stability toward olfactory bulb levels (0.60 vs. 0.53, p = 0.16, n = 5/7). The latter result is directionally consistent with an attractor network model in which recurrent coupling amplifies split-half RDM consistency (ρ = +0.64, p = 0.010) by completing patterns from sparse feedforward input. Geometric stability is a functionally relevant, circuit- dependent property of neural population codes that is missed by temporal stability measures. Representational geometry Neural population code Geometric stability Representational drift Piriform cortex Recurrent circuitry Split-half reliability Neuropixels Full Text Additional Declarations No competing interests reported. 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-9581425","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":633391979,"identity":"3394b112-01d6-4f88-b661-a95a7980618c","order_by":0,"name":"Prashant C. 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This stability-drift paradox has focused attention on whether population centroids are preserved over time. We ask a different question: how reliably does the pairwise distance structure among stimuli reproduce across independent trial subsets within a session? We quantify this using geometric stability (Shesha), the Spearman rank correlation between split-half representational dissimilarity matrices. Across 229 area-session observations spanning 68 brain regions in a visual discrimination task (Steinmetz et al. 2019), geometric stability predicts trial-by-trial neural-behavioral coupling (ρ= 0.18, p = 0.005) while centroid drift does not (ρ = 0.002, p = 0.976). The regional hierarchy: striatum most stable (\\bar{S} = 0.44), hippocampus least ($\\bar{S} = 0.19)---runs roughly opposite to the temporal stability hierarchy, showing that geometric and temporal stability are dissociable properties of population codes. In a sensory hierarchy with a causal manipulation (Bolding \\\u0026 Franks 2018), piriform cortex is more geometrically stable than olfactory bulb (0.57 vs. 0.47, n = 11), and silencing recurrent excitatory connections with tetanus toxin reduces piriform stability toward olfactory bulb levels (0.60 vs. 0.53, p = 0.16, n = 5/7). The latter result is directionally consistent with an attractor network model in which recurrent coupling amplifies split-half RDM consistency (ρ = +0.64, p = 0.010) by completing patterns from sparse feedforward input. 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