Behavioral Time Scale Synaptic Plasticity (BTSP) endows Hyperdimensional Computing with brain-like information retrieval flexibility

Behavioral Time Scale Synaptic Plasticity (BTSP) endows Hyperdimensional Computing with brain-like information retrieval flexibility | 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 Behavioral Time Scale Synaptic Plasticity (BTSP) endows Hyperdimensional Computing with brain-like information retrieval flexibility Wolfgang Maass, Chengting Yu, Yujie Wu, Aili Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6742959/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 Hyperdimensional computing (HDC) addresses massively parallel implementations of symbolic computations that are both more transparent than ANNs and LLMs and more suitable for in-memory computing on highly energy-efficient analog hardware. It captures an essential aspects of brain computations: objects, concepts, and their attributes are encoded by very sparse distributed representations. But currently known methods for binding these tokens together entail deficits in flexible information retrieval. We show that a mechanism which the brain employs for binding, Behavioral Time Scale Synaptic Plasticity (BTSP), overcomes these deficiencies by adding attractor features to high-dimensional representations. They drastically improve the capability to recover from composed representations the tokens which have been bound together in them. One arrives in this way at a functionally more powerful HDC paradigm that provides new perspectives both for understanding how brains carry out symbolic computations, and for implementing them in novel energy-efficient and massively parallel neuromorphic hardware. Physical sciences/Mathematics and computing Physical sciences/Nanoscience and technology Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.pdf Supplementary Information 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-6742959","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466267982,"identity":"2521ff17-60aa-4920-a5c2-cf2abd51f2af","order_by":0,"name":"Wolfgang Maass","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYFAC5oYPSDwbYrQwNs5A4qVJwFgSWNRi1XKYsBaD442NjV9q7tg1iB1++PHHn/N1BufXAhkMdnU4tZw52Ngsc+xZcoN0mrE0b9ttCYMbz40lJBiScdpidiOx/bEE2+FkBukEA2nGBpCWYwwSBgzM+LQ0Nkv8A2lJ//zzx59zIC3MPxIY6vFqafzYdtiOQTrHTIKH7YCEwfk2NokDSOGADuxBfmHsO5zAJp1TZs3bliw58wYbm2WDwXHJBhxaJNubDzb++HbYnl86ffPNH3/s+PnOH2O++aOimh+XLSDAzMPAkNgG50okAAkDfBqAkfkD6EAEl/8AfuWjYBSMglEw4gAAlUVdKMcukAUAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Machine Learning and Neural Computation, Graz University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Maass","suffix":""},{"id":466267983,"identity":"8d9a2ba0-830d-4745-bfe7-323826e32c1d","order_by":1,"name":"Chengting Yu","email":"","orcid":"https://orcid.org/0009-0007-7210-879X","institution":"Institute of Machine Learning and Neural Computation, Graz University of Technology. 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