Analysis of Program Code: Adaptive AI Model

Analysis of Program Code: Adaptive AI 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Cite Share Download PDF editorial Analysis of Program Code: Adaptive AI Model Andrii Atorin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7933791/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 The model is built upon the theoretical foundation of TSKI-4.1 [1]: original formulas, principles, and algorithms were used. A technical specification [2] was prepared, describing the strict order of tick stages, the mirror synapse model,the rules of plasticity and stabilization according to TSKI-4.1. The key goals of the simulation are to demonstrate the adaptive properties of the model and the stabilization of synaptic plasticity within a group of neurons. During the simulation, the following were confirmed: the model’s adaptivity; its ability to stabilize the number of synapses in a group under synaptic plasticity; and its capacity to maintain the final number of synapses after active restructuring of connections. The roles of feedback loops and the inhibitory neuron were also analyzed. adaptive AI neural network model interval stabilization TSKI-4.1 synaptic plasticity adaptive restructuring feedback loops inhibitory neuron self-organization dynamic equilibrium phase transitions of activity cognitive network simulation synaptogenesis Full Text Additional Declarations No competing interests reported. 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-7933791","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"editorial","associatedPublications":[],"authors":[{"id":536040282,"identity":"e13a1faf-b7f9-4a3d-93ca-2e7d4f5b9619","order_by":0,"name":"Andrii Atorin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACZoYHDBYgBuMDIMHDR1ADG1BLAoMEWK8BSAsbKVrYICQhHfLzmw8bJFRIyOvObn5W+TXHTgZoxsNHN/BoMTjGlpyQcEbCcNudY2a3ZbclAx3GZmycg08LG4/xgcQ2CcZtNxLMbktuYwZq4WGTxqdFvo3/M0iL/bYb6d+KJbfVE9bCcIyHOQGoJXHbjRwzxo/bDhPWYnAszdgA6JfkbXfOFEszbjvOw8ZMwC/yzYcfS3yosLHddrt948ef26rt+dmbHz7G6zA4AEYKMw+IwUyUcqgWxh9Eqx4Fo2AUjIKRBADNU0U427ffcwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Andrii","middleName":"","lastName":"Atorin","suffix":""}],"badges":[],"createdAt":"2025-10-23 15:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7933791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7933791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95529175,"identity":"677f9546-e4f2-4b12-bef3-1ebeedca4ff3","added_by":"auto","created_at":"2025-11-10 10:16:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":252433,"visible":true,"origin":"","legend":"","description":"","filename":"ReviewarticleSimulationofadaptiveAI1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7933791/v1_covered_3d0c3271-c5d1-46d8-a8b9-2e538824a95f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of Program Code: Adaptive AI Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"adaptive AI, neural network model, interval stabilization, TSKI-4.1, synaptic plasticity, adaptive restructuring, feedback loops, inhibitory neuron, self-organization, dynamic equilibrium, phase transitions of activity, cognitive network simulation, synaptogenesis","lastPublishedDoi":"10.21203/rs.3.rs-7933791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7933791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The model is built upon the theoretical foundation of TSKI-4.1 [1]: original formulas, principles, and algorithms were used. 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