Lag-1 Sparing as a Signature of Cortico-Thalamic Conduction Latency: A Biophysical Spiking Model

Lag-1 Sparing as a Signature of Cortico-Thalamic Conduction Latency: A Biophysical Spiking Model | 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 Lag-1 Sparing as a Signature of Cortico-Thalamic Conduction Latency: A Biophysical Spiking Model Rakesh Sengupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9385203/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The Attentional Blink (AB) reveals fundamental temporal limitations in visual awareness, yet the phenomenon of Lag-1 sparing—where the second of two targets is accurately identified if it appears immediately after the first—remains a critical constraint for theoretical models. Existing accounts often attribute sparing to “sluggish” attentional engagement or resource depletion, but frequently conflate transport delays (axonal latency) with synaptic integration (membrane smoothing). We present a biophysical spiking neural network model implemented in the Neural Engineering Framework (Nengo) that explicitly dissociates these two mechanisms through six simulation experiments. We make four contributions. First, a 280 ms round-trip latency—decomposed into feedforward axonal (45 ms), PFC integration (195 ms), and feedback axonal (40 ms) components, each independently constrained by separate empirical sources— creates a “silent window” that produces Lag-1 sparing (Γ(1)/Γ(3) = 1.74, p = 3.24 × 10−4, Bonferroni-corrected). Second, a parametric sweep over δ shows that sparing emerges only within a bounded window (δ ≈ 150–325 ms), so the anatomical value is non-trivially validated rather than chosen by construction. Third, an “Axonal-Only” control (δ = 85 ms) yields a strongly inverted lag curve (Γ(1)/Γ(3) = 0.45, p < 0.001), demonstrating the non-monotonic relationship between delay and performance. Fourth, and most directly, an “Instantaneous Synapses” control (δ = 280 ms, τGABA → 0) preserves sparing at Γ(1)/Γ(3) = 2.04—higher than the standard model—proving that slow synaptic filtering (τGABAB = 150 ms) does not create the sparing window; only the transport delay δ does. The 195 ms integration component is acknowledged as an empirically informed prior from the P3a ERP literature rather than a purely derived constraint; its contribution is directly testable via targeted dlPFC rTMS. These results ground Lag-1 sparing in the physical reality of cortico-thalamic conduction latency. Attentional Blink Lag-1 Sparing Spiking Neural Networks Adaptive LIF Thalamic Reticular Nucleus Nengo Cortico-Thalamic Loop Transport Delay GABAB Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviewers invited by journal 14 May, 2026 Editor assigned by journal 18 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 11 Apr, 2026 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-9385203","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":640091795,"identity":"c65344fd-7915-441c-afe2-138981ed6944","order_by":0,"name":"Rakesh Sengupta","email":"data:image/png;base64,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","orcid":"","institution":"Krea University","correspondingAuthor":true,"prefix":"","firstName":"Rakesh","middleName":"","lastName":"Sengupta","suffix":""}],"badges":[],"createdAt":"2026-04-11 06:38:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9385203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9385203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109405605,"identity":"a57e6923-9d1f-483b-ab07-dc16b5cd87a8","added_by":"auto","created_at":"2026-05-17 13:19:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2763803,"visible":true,"origin":"","legend":"","description":"","filename":"Attentionalblinkpapers.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9385203/v1_covered_5e1b2e4e-d02c-4857-a5ee-4a222985f0cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lag-1 Sparing as a Signature of Cortico-Thalamic Conduction Latency:\n A Biophysical Spiking Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"biological-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bicy","sideBox":"Learn more about [Biological Cybernetics](http://link.springer.com/journal/422)","snPcode":"422","submissionUrl":"https://submission.nature.com/new-submission/422/3","title":"Biological Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Attentional Blink, Lag-1 Sparing, Spiking Neural Networks, Adaptive LIF, Thalamic Reticular Nucleus, Nengo, Cortico-Thalamic Loop, Transport Delay, GABAB","lastPublishedDoi":"10.21203/rs.3.rs-9385203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9385203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Attentional Blink (AB) reveals fundamental temporal limitations in visual awareness, yet the phenomenon of Lag-1 sparing—where the second of two targets is accurately identified if it appears immediately after the first—remains a critical constraint for theoretical models. Existing accounts often attribute sparing to “sluggish” attentional engagement or resource depletion, but frequently conflate transport delays (axonal latency) with synaptic integration (membrane smoothing). We present a biophysical spiking neural network model implemented in the Neural Engineering Framework (Nengo) that explicitly dissociates these two mechanisms through six simulation experiments. We make four contributions. First, a 280 ms round-trip latency—decomposed into feedforward axonal (45 ms), PFC integration (195 ms), and feedback axonal (40 ms) components, each independently constrained by separate empirical sources— creates a “silent window” that produces Lag-1 sparing (Γ(1)/Γ(3) = 1.74, p = 3.24 × 10−4, Bonferroni-corrected). Second, a parametric sweep over δ shows that sparing emerges only within a bounded window (δ ≈ 150–325 ms), so the anatomical value is non-trivially validated rather than chosen by construction. 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