Precise calcium-to-spike inference using biophysical generative models

Precise calcium-to-spike inference using biophysical generative models | 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 Precise calcium-to-spike inference using biophysical generative models Gerard Broussard, Giovanni Diana, Francisco Urra Quiroz, B. Semihcan Sermet, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6017950/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The intramolecular dynamics of fluorescent indicators of neural activity can distort the accurate estimate of action potential (“spike”) times. In order to develop a more accurate spike inference algorithm we characterized the kinetic responses to calcium of three popular indicator proteins, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow and brain slice recordings. jGCaMP8f showed a use-dependent slowing of fluorescence responses that caused existing inference methods to generate numerous false positives. From these data we developed a multistate model of GCaMP and used it to create Bayesian Sequential Monte Carlo (Biophys SMC ) and machine learning (Biophys ML ) inference methods that reduced false positives substantially. This biophysical method dramatically improved spike time accuracy, detecting individual spikes with a median uncertainty of 4 milliseconds, a performance level that reached the theoretical limit and is twice as accurate as any previous method. Our framework thus highlights advantages of physical model-based approaches over model-free algorithms. Biological sciences/Neuroscience Biological sciences/Neuroscience/Molecular neuroscience Full Text Additional Declarations There is NO Competing Interest. Animal experiments were performed in accordance with the guidelines of Institut Pasteur, France, and all protocols were approved by the Ethics Committee #89 of Institut Pasteur (CETEA; approval #DHA180006). Cite Share Download PDF Status: Under Review 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-6017950","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":445856984,"identity":"8deb4b23-d5e6-4d08-90cf-13fc8e9cb6a0","order_by":0,"name":"Gerard Broussard","email":"","orcid":"","institution":"Princeton Neuroscience Instistitute","correspondingAuthor":false,"prefix":"","firstName":"Gerard","middleName":"","lastName":"Broussard","suffix":""},{"id":445856985,"identity":"c392ff2e-3c7c-409e-91ea-03bb42f5c27b","order_by":1,"name":"Giovanni Diana","email":"","orcid":"","institution":"Institut Pasteur","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Diana","suffix":""},{"id":445856986,"identity":"85e77e10-5aa1-42e3-9202-aafde661305f","order_by":2,"name":"Francisco Urra Quiroz","email":"","orcid":"","institution":"Institut Pasteur","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"Urra","lastName":"Quiroz","suffix":""},{"id":445856987,"identity":"ec4b38f3-8258-4d9c-9d06-7d6557b4acec","order_by":3,"name":"B. 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In order to develop a more accurate spike inference algorithm we characterized the kinetic responses to calcium of three popular indicator proteins, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow and brain slice recordings. jGCaMP8f showed a use-dependent slowing of fluorescence responses that caused existing inference methods to generate numerous false positives. From these data we developed a multistate model of GCaMP and used it to create Bayesian Sequential Monte Carlo (Biophys\u003csub\u003eSMC\u003c/sub\u003e) and machine learning (Biophys\u003csub\u003eML\u003c/sub\u003e) inference methods that reduced false positives substantially. This biophysical method dramatically improved spike time accuracy, detecting individual spikes with a median uncertainty of 4 milliseconds, a performance level that reached the theoretical limit and is twice as accurate as any previous method. 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