Spikes as Perturbations of Resonant Neural Circuits: From Membrane Biophysics to a Cognitive Framework

Spikes as Perturbations of Resonant Neural Circuits: From Membrane Biophysics to a Cognitive Framework | 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 Spikes as Perturbations of Resonant Neural Circuits: From Membrane Biophysics to a Cognitive Framework Jeremy Sender This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9349842/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 Computational neuron models commonly reduce subthreshold membrane dynamics to a leaky RC integrator, confining computation to the spike and treating the inter-spike membrane trajectory as passive decay. Yet direct impedance measurements and modern channel-specific analyses show that many excitable membranes exhibit band-pass, inductance-like impedance profiles with a tuneable resonant peak—dynamics that a first-order RC model cannot represent. This paper develops a spike-as-perturbation framework in which spikes function as perturbative control events that launch regime-dependent transient trajectories in an equivalent parallel RLC membrane. We present a representational comparison showing that post-perturbation RLC ringdowns carry structured circuit-identity and perturbation-timing state variables absent from RC exponential decays, and demonstrate a concrete computational primitive—phase-based temporal discrimination—that is not present as an intrinsic membrane-level transient feature in matched first-order RC models (independent of what more elaborate network-level RC circuits might achieve through delays or recurrence). A simulation analysis—using representative parameters (Q = 2, τ_RC = 25 ms, f₀ = 10 Hz) and an analytic sensitivity metric—shows that temporal sensitivity persists 2.5× longer under these assumptions than in matched RC decays, with model-derived sensitivity ratios exceeding 17× at 100 ms readout latency for Q = 2 (ratios scale with Q; see Fig. 5c). We derive four falsifiable predictions spanning cellular, network, decoding, and cognitive levels, and discuss implications for neuromorphic architectures incorporating resonant elements and adaptive coupling. The argument is presented in three tiers of decreasing evidential support: a well-supported biophysical substrate, a plausible spike-as-perturbation interpretation, and candidate cognitive-level hypotheses offered for future investigation. Computational Neuroscience Neuronal impedance subthreshold resonance phase-resonance RLC circuit model membrane inductance neural computation spike-as-perturbation Ih neuromorphic computing resonate-and-fire Full Text Additional Declarations The authors declare no competing interests. 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. 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-9349842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619238637,"identity":"3e8cb741-7533-4ed2-b7d2-d27f6f377ab9","order_by":0,"name":"Jeremy Sender","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACxgY4M4GB4QOQYmMnRQvjDJAWZuItTGBg5gHRhLQwt/c+fHSzrU7OnD352GebX9vk+ZgZGD98zMHjsJ7jxsa5bYeNLXueJc/O7btt2MbMwCw5cxseLTPS2KRz2w4kbriRY8yc23ObEaiFjZkXn5b5z9h/57bV1W+4kf+Z2bLntj1hLTPY2Jhz25gTDG7kMDMz/LidSFhLTxqzdM65w4YbzjwzZuxtuJ3cxszYjNcvhu3HGD/nlNXJGxxPfszw489t2/ntzQc/fMSnpQFkFRvMzjYw2YBbPRDIg8k/MO4fnApHwSgYBaNgBAMAH39QffIljosAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0006-1919-0491","institution":"La Trobe University Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Jeremy","middleName":"","lastName":"Sender","suffix":""}],"badges":[],"createdAt":"2026-04-07 23:57:54","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9349842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9349842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106503346,"identity":"37bee16e-25a8-4c24-ab81-fa6b5539b741","added_by":"auto","created_at":"2026-04-09 09:29:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":411325,"visible":true,"origin":"","legend":"","description":"","filename":"SenderRLC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9349842/v1_covered_55d1d18f-50f8-4c2f-849e-5e39c2dd20b2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSpikes as Perturbations of Resonant Neural Circuits: From Membrane Biophysics to a Cognitive Framework\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"La Trobe University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neuronal impedance, subthreshold resonance, phase-resonance, RLC circuit model, membrane inductance, neural computation, spike-as-perturbation, Ih, neuromorphic computing, resonate-and-fire","lastPublishedDoi":"10.21203/rs.3.rs-9349842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9349842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComputational neuron models commonly reduce subthreshold membrane dynamics to a leaky RC integrator, confining computation to the spike and treating the inter-spike membrane trajectory as passive decay. Yet direct impedance measurements and modern channel-specific analyses show that many excitable membranes exhibit band-pass, inductance-like impedance profiles with a tuneable resonant peak—dynamics that a first-order RC model cannot represent. This paper develops a spike-as-perturbation framework in which spikes function as perturbative control events that launch regime-dependent transient trajectories in an equivalent parallel RLC membrane. We present a representational comparison showing that post-perturbation RLC ringdowns carry structured circuit-identity and perturbation-timing state variables absent from RC exponential decays, and demonstrate a concrete computational primitive—phase-based temporal discrimination—that is not present as an intrinsic membrane-level transient feature in matched first-order RC models (independent of what more elaborate network-level RC circuits might achieve through delays or recurrence). A simulation analysis—using representative parameters (Q = 2, τ_RC = 25 ms, f₀ = 10 Hz) and an analytic sensitivity metric—shows that temporal sensitivity persists 2.5× longer under these assumptions than in matched RC decays, with model-derived sensitivity ratios exceeding 17× at 100 ms readout latency for Q = 2 (ratios scale with Q; see Fig. 5c). We derive four falsifiable predictions spanning cellular, network, decoding, and cognitive levels, and discuss implications for neuromorphic architectures incorporating resonant elements and adaptive coupling. The argument is presented in three tiers of decreasing evidential support: a well-supported biophysical substrate, a plausible spike-as-perturbation interpretation, and candidate cognitive-level hypotheses offered for future investigation.\u003c/p\u003e","manuscriptTitle":"Spikes as Perturbations of Resonant Neural Circuits: From Membrane Biophysics to a Cognitive Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 09:28:36","doi":"10.21203/rs.3.rs-9349842/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9774a028-75e0-4b6b-a628-3577f46fd11f","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65893642,"name":"Computational Neuroscience"}],"tags":[],"updatedAt":"2026-04-09T09:28:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 09:28:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9349842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9349842","identity":"rs-9349842","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}