Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition

Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition | 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 Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition Michele Mastella, Tesse Tiemens, Elisabetta Chicca This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4224027/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 Neural spikes can encode a rich set of information, ranging from the perceived intensity of light sources to the likelihood associated with decisions made in the cortex. Among these capabilities, previous studies demonstrated that spikes can also encode in their activity multiple frequencies at the same time, such as those generated by skin vibrations during textures scanning. However, the mechanism responsible for decoding spikes containing multiple frequencies is yet to be uncovered. In this paper, we introduce a novel spiking neural network model tailored for frequency decomposition of spike trains. Our model mimics neural microcircuits hypothesized in the somatosensory cortex, making it a biologically plausible candidate for decoding spike trains observed in tactile peripheral nerves. We showcase the ability of simple neurons and synapses to replicate the functionality of a phase-locked loop (PLL) and delve into the emergent properties when multiple spiking phase-locked loops (sPLLs) interact with diverse inputs. Furthermore, we demonstrate how these sPLLs can decode textures by leveraging the spectral features of spike trains generated in peripheral nerves. By harnessing our model's frequency decomposition capabilities, we achieve significant performance enhancements over state-of-the-art approaches on a Multifrequency Spike Train (MST) dataset. Our findings underscore the potential of sPLLs in elucidating the mechanisms behind texture decoding in the brain, while also showcasing their potential to outperform conventional SNNs in handling spike trains with multiple frequencies. We believe this study sheds light into the neuronal mechanisms behind texture decoding, while presenting a practical framework for augmenting the capabilities of artificial neural networks in intricate pattern recognition tasks. Physical sciences/Physics/Information theory and computation Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Physics/Statistical physics, thermodynamics and nonlinear dynamics/Complex networks Spiking Neural Network Texture Recognition Somatosensory Touch Neuromorphic Full Text Additional Declarations There is NO Competing Interest. 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. 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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-4224027","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":293964485,"identity":"485afbcc-67c2-4b7a-ace3-fccdd3367e56","order_by":0,"name":"Michele Mastella","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBADGT4G5gNgFh9xGhIYeNgY2BLAbDYStPAYEKdFt4E78ePPHzY8bOxnvn3m+cWQR1CL2QHezdI8CWk8bDy5m2fz9jEUE6NlgzRDwmGgw3I3M/P2MCS2EWPLzx8J/3nY+N88JlrLNgmehAM8bBI5zMw8P4jRcph3mzVPWjJQyzNjxrkNEkRoOd67+eYPGzs5fv7kxwxv/tgk9hPSwsCMzGFskyCoAR38IVnHKBgFo2AUjAAAAKBMNSj/WQKhAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2850-8079","institution":"University of Groningen","correspondingAuthor":true,"prefix":"","firstName":"Michele","middleName":"","lastName":"Mastella","suffix":""},{"id":293964486,"identity":"39306bc3-a9b6-4d89-9ed7-8271d9b04155","order_by":1,"name":"Tesse Tiemens","email":"","orcid":"https://orcid.org/0009-0000-7621-3166","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Tesse","middleName":"","lastName":"Tiemens","suffix":""},{"id":293964487,"identity":"2ed686c1-11b7-416c-a697-724b61208999","order_by":2,"name":"Elisabetta Chicca","email":"","orcid":"https://orcid.org/0000-0002-5518-8990","institution":"University of Groningen","correspondingAuthor":false,"prefix":"","firstName":"Elisabetta","middleName":"","lastName":"Chicca","suffix":""}],"badges":[],"createdAt":"2024-04-05 16:15:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4224027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4224027/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55126968,"identity":"24ec104c-94f5-4e9b-9cd3-06722ecd418f","added_by":"auto","created_at":"2024-04-23 03:09:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3411361,"visible":true,"origin":"","legend":"","description":"","filename":"MastellaetalsPLL240405.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4224027/v1_covered_1117a090-c9cb-4654-bbc9-3e9e4456173d.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Texture Recognition Using a Biologically Plausible Spiking Phase-Locked Loop Model for Spike Train Frequency Decomposition","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Spiking Neural Network, Texture Recognition, Somatosensory, Touch, Neuromorphic","lastPublishedDoi":"10.21203/rs.3.rs-4224027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4224027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Neural spikes can encode a rich set of information, ranging from the perceived intensity of light sources to the likelihood associated with decisions made in the cortex. 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