Resolving Non-identifiability Mitigates Systematic Errors in Simultaneous Models of Neural Tuning and Functional Coupling

Resolving Non-identifiability Mitigates Systematic Errors in Simultaneous Models of Neural Tuning and Functional Coupling | 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 Resolving Non-identifiability Mitigates Systematic Errors in Simultaneous Models of Neural Tuning and Functional Coupling Pratik Sachdeva, Ji Hyun Bak, Jesse Livezey, Christoph Kirst, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7705758/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 A key component to understanding the brain is determining the influence of groups of neurons on each other relative to other influences. In the brain, all neurons are driven by the activity of other neurons, some of which may be simul- taneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through the inclusion of model terms for observed ex- ternal variables (e.g., tuning to stimuli), observed internal variables (e.g., coupling to recorded neural activities), as well as terms for latent sources of variability. De- spite broad utilization, however, evaluation of systematic errors during inference is rarely performed, and sources of systematic error are poorly understood. Through extensive numerical study and analytic calculation, we show that common infer- ence procedures and static and dynamic models typically have systematic errors. Counter to common intuition, we found that model non-identifiability contributes to systematic errors in parameter estimation, not variance inflation, making it a particularly insidious form of statistical error. We demonstrate that accurate pa- rameter selection before estimation resolves model non-identifiability and mitigates the associated systematic errors. In diverse neurophysiology data sets (multiple single unit recordings in primary visual cortex and hippocampus, ECoG from pri- mary auditory cortex), we found that common methods typically overestimate the contributions of interactions between neurons, while the influence of exogenous variables is underestimated. We explain heterogeneity in observed systematic er- rors across neurophysiology data sets in terms of data statistics and experimental design. Together, our results identify the causes of statistical errors in structural equation models of simultaneous systems with endogenous, exogenous, and latent variables, provide inference procedures to mitigate those errors, and reveal and explain the impact of those errors in diverse neural data sets. functional coupling neural tuning simultaneous equations model non- identifiability systematic errors sparsity 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. 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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-7705758","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529047441,"identity":"114bad91-fb27-4252-babb-513269472cc1","order_by":0,"name":"Pratik Sachdeva","email":"","orcid":"","institution":"University of California, Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Pratik","middleName":"","lastName":"Sachdeva","suffix":""},{"id":529047442,"identity":"78e6f9bc-43e5-436c-aa76-f306275ad024","order_by":1,"name":"Ji Hyun Bak","email":"","orcid":"","institution":"Kavli Institute for Fundamental Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Hyun","lastName":"Bak","suffix":""},{"id":529047445,"identity":"62076ad7-08ab-4543-8aa5-6c49dcf7c0d1","order_by":2,"name":"Jesse Livezey","email":"","orcid":"","institution":"Lawrence Berkeley National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Jesse","middleName":"","lastName":"Livezey","suffix":""},{"id":529047447,"identity":"e62764a4-36db-46c5-9b08-15b12acc55ca","order_by":3,"name":"Christoph Kirst","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Kirst","suffix":""},{"id":529047451,"identity":"eb061e6b-98c3-4250-9f63-09d8c408a684","order_by":4,"name":"Loren Frank","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Loren","middleName":"","lastName":"Frank","suffix":""},{"id":529047455,"identity":"d13701a6-1f75-4509-bf1d-32b826e87a2c","order_by":5,"name":"Sharmodeep Bhattacharyya","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"prefix":"","firstName":"Sharmodeep","middleName":"","lastName":"Bhattacharyya","suffix":""},{"id":529047459,"identity":"b5645546-b721-415a-968b-18194bb62cd3","order_by":6,"name":"Kristofer E. 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In the brain, all neurons are driven by the activity of other neurons, some of which may be simul- taneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through the inclusion of model terms for observed ex- ternal variables (e.g., tuning to stimuli), observed internal variables (e.g., coupling to recorded neural activities), as well as terms for latent sources of variability. De- spite broad utilization, however, evaluation of systematic errors during inference is rarely performed, and sources of systematic error are poorly understood. Through extensive numerical study and analytic calculation, we show that common infer- ence procedures and static and dynamic models typically have systematic errors. 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