BioLogicalNeuron: A Biologically Inspired Neural Network Layer with Homeostatic Regulation and Adaptive Repair Mechanism

BioLogicalNeuron: A Biologically Inspired Neural Network Layer with Homeostatic Regulation and Adaptive Repair Mechanism | 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 BioLogicalNeuron: A Biologically Inspired Neural Network Layer with Homeostatic Regulation and Adaptive Repair Mechanism MD Hakim, Mohammad Alam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6213558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Neural networks face persistent challenges in maintaining stability and robustness during training, particularly in noisy or high-dimensional domains like molecular analysis. Inspired by biological neural systems that leverage homeostasis and self-repair to sustain functionality, this paper proposes BioLogicalNeuron—a novel neural network layer that integrates calcium-driven homeostatic regulation, adaptive repair mechanisms, and dynamic stability monitoring. The layer mimics biological calcium dynamics to maintain neuronal activity within optimal ranges, proactively triggers targeted synaptic repair and adaptive noise injection to counteract degradation, and modulates learning rates via real-time health metrics. Extensive experiments across multiple molecular and chemical datasets show that BioLogicalNeuron achieves state-of-the-art break performance. The layer's performance is particularly strong on molecular datasets, where its biological mechanisms naturally align with molecular structure learning. Through detailed analysis of calcium dynamics and health-stability relationships, this work demonstrate that BioLogicalNeuron achieves a biologically plausible balance between stability and plasticity, offering insights into both artificial and biological neural networks. This results suggest that incorporating biological mechanisms into neural architectures can lead to more robust and effective learning systems, particularly for molecular and chemical analysis tasks. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Biophysics Biological sciences/Biotechnology Neural Networks Homeostatic Regulation Adaptive Repair Biological Inspiration Molecular Datasets Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers invited by journal 27 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Editor invited by journal 21 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 12 Mar, 2025 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-6213558","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427930537,"identity":"ad1e3f51-89fd-461f-aebd-adc711fe83c7","order_by":0,"name":"MD Hakim","email":"data:image/png;base64,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","orcid":"","institution":"Bangladesh Sweden Polytechnic Institute","correspondingAuthor":true,"prefix":"","firstName":"MD","middleName":"","lastName":"Hakim","suffix":""},{"id":427930539,"identity":"5207305d-204e-4e62-a95c-69af9eaf3ff1","order_by":1,"name":"Mohammad Alam","email":"","orcid":"","institution":"Bangladesh Sweden Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Alam","suffix":""}],"badges":[],"createdAt":"2025-03-12 15:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6213558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6213558/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09114-8","type":"published","date":"2025-09-30T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78493244,"identity":"d1c4b6a7-993e-4b1b-80db-4a74027f564c","added_by":"auto","created_at":"2025-03-14 03:16:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4774878,"visible":true,"origin":"","legend":"","description":"","filename":"submission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6213558/v1_covered_2921461e-1cf0-4163-b460-65e6718949a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BioLogicalNeuron: A Biologically Inspired Neural Network Layer with Homeostatic Regulation and Adaptive Repair Mechanism","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neural Networks, Homeostatic Regulation, Adaptive Repair, Biological Inspiration, Molecular Datasets","lastPublishedDoi":"10.21203/rs.3.rs-6213558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6213558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Neural networks face persistent challenges in maintaining stability and robustness during training, particularly in noisy or high-dimensional domains like molecular analysis. Inspired by biological neural systems that leverage homeostasis and self-repair to sustain functionality, this paper proposes BioLogicalNeuron—a novel neural network layer that integrates calcium-driven homeostatic regulation, adaptive repair mechanisms, and dynamic stability monitoring. The layer mimics biological calcium dynamics to maintain neuronal activity within optimal ranges, proactively triggers targeted synaptic repair and adaptive noise injection to counteract degradation, and modulates learning rates via real-time health metrics. Extensive experiments across multiple molecular and chemical datasets show that BioLogicalNeuron achieves state-of-the-art break performance. The layer's performance is particularly strong on molecular datasets, where its biological mechanisms naturally align with molecular structure learning. Through detailed analysis of calcium dynamics and health-stability relationships, this work demonstrate that BioLogicalNeuron achieves a biologically plausible balance between stability and plasticity, offering insights into both artificial and biological neural networks. This results suggest that incorporating biological mechanisms into neural architectures can lead to more robust and effective learning systems, particularly for molecular and chemical analysis tasks.","manuscriptTitle":"BioLogicalNeuron: A Biologically Inspired Neural Network Layer with Homeostatic Regulation and Adaptive Repair Mechanism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 03:08:16","doi":"10.21203/rs.3.rs-6213558/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T11:23:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T07:06:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-07T10:03:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308141721900303824292746698073550330051","date":"2025-03-30T10:14:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46427129784846309746328374666431230110","date":"2025-03-29T14:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-27T12:29:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-27T10:56:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-21T05:12:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T07:53:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-12T15:44:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce8e3bba-b370-4c41-963f-dde78acc2b0d","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45593548,"name":"Biological sciences/Biochemistry"},{"id":45593549,"name":"Biological sciences/Biological techniques"},{"id":45593550,"name":"Biological sciences/Biophysics"},{"id":45593551,"name":"Biological sciences/Biotechnology"}],"tags":[],"updatedAt":"2025-10-06T15:59:25+00:00","versionOfRecord":{"articleIdentity":"rs-6213558","link":"https://doi.org/10.1038/s41598-025-09114-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-30 15:56:59","publishedOnDateReadable":"September 30th, 2025"},"versionCreatedAt":"2025-03-14 03:08:16","video":"","vorDoi":"10.1038/s41598-025-09114-8","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09114-8","workflowStages":[]},"version":"v1","identity":"rs-6213558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6213558","identity":"rs-6213558","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}