Population cerebellar neurons compute rewards with single-trial precision and engineerability

Population cerebellar neurons compute rewards with single-trial precision and engineerability | 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 Population cerebellar neurons compute rewards with single-trial precision and engineerability Ming-Kai Pan, Liang-Yin Lu, Peng Chen, Ting-Yu Liang, Liang-Ying Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7357483/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 Precise behavioral control of the brain and related prosthetic design require neuronal algorithms that have single-event precision and temporal accuracy in real time, no ambiguity or trial average allowed. In reward-based learning, the brain must detect each reward timely to support adaptive behavior in dynamic environments, yet the underlying neuronal computations remain elusive. Here, we identify a cerebellar mechanism encoding rewards with single-trial precision. Utilizing cleared-tissue tractography, electrophysiology, and two-photon imaging in a mouse foraging task, we describe an Instantaneous Silencing of Population Neurons (ISPN) in the deep cerebellar nucleus (DCN), triggered by synchronized Purkinje cells (PCs) activity. This transient silencing emerges 100 ms after reward delivery and induces rebound phasic firings of ventral tegmental area (VTA) dopamine neurons for one reward event. Such single-trial precision and temporal accuracy enabled precise reward control, targeting either PC-to-DCN or DCN-to-VTA projections. A single 5-ms optogenetic pulse, leaving 99% of behavioral time untouched, is sufficient to elicit one ISPN for reward, shifting behavior with single-trial precision. Our findings underscore the cerebellum's critical role in real-time reward coding and illustrate how our brains utilize population coding to achieve both temporal and single-event precision, the necessary features in real-world behavioral encoding. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Physiology/Neurophysiology Full Text Additional Declarations Yes there is potential Competing Interest. M.-K.P. is a consultant for Praxis Precision Medicines, Cambridge, Massachusetts, USA; BioPro Scientific, Taiwan; and Sumitomo Pharma Co., Japan. M.-K.P. confirmed that this research is unrelated to the consulting roles. The other authors declare that they have no competing interests. Supplementary Files VideoS1SUandBehaviorNaturev3.mp4 Video S1 VideoS2PC2photonv8trial15Naturev1.mp4 Video S2 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-7357483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":510205885,"identity":"2edba70f-89d0-43bf-98b9-cef89562b879","order_by":0,"name":"Ming-Kai 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