A Neuronal Circuit Based on a Second-Order Memristor

A Neuronal Circuit Based on a Second-Order Memristor | 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 A Neuronal Circuit Based on a Second-Order Memristor Fan Shi, Yinghong Cao, Santo Banerjee, Jun Mou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5168542/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Nonlinear Dynamics → Version 1 posted 9 You are reading this latest preprint version Abstract With the increasing advances in computer science, a neuron with information memory is needed to form an artificial neural network. In this paper, a novel non-volatile Second-Order Memristor (SOM) is presented its analog circuit is given, and it is applied to RLC neurons to simulate synaptic connections and electromagnetic radiation, thus constructing a Second-Order Memristor RLC (SOM-RLC) neuron. Firstly, numerical methods were used to analyze the changes in neuronal dynamic behavior caused by internal and external magnetic fields and synapses, including various bifurcation behaviors and extreme multi stability. Considering that changes in magnetic field can cause energy flow in neurons, the energy distribution of neurons was analyzed. Finally, by constructing an analog circuit of SOM-RLC neuron and implementing chaotic attractors on DSP, the data results were verified, proving its engineering feasibility. Analyzed the biomimetic performance of SOMRLC neuron from different perspectives, explained the main characteristics of neurons under internal and external magnetic field interference, and further advancing the exploitation of artificial neurons. Neural network circuit Multistability Memristor DSP implementation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2024 Read the published version in Nonlinear Dynamics → Version 1 posted Editorial decision: Revision requested 10 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviews received at journal 30 Sep, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers agreed at journal 29 Sep, 2024 Reviewers invited by journal 29 Sep, 2024 Editor assigned by journal 29 Sep, 2024 Submission checks completed at journal 28 Sep, 2024 First submitted to journal 28 Sep, 2024 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-5168542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364456998,"identity":"6acd6917-c63f-4f6a-ac97-a1ca9c10f381","order_by":0,"name":"Fan Shi","email":"","orcid":"","institution":"Dalian Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Shi","suffix":""},{"id":364456999,"identity":"2af21ae4-c43f-4092-b11e-a4d6098eb402","order_by":1,"name":"Yinghong Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYLCCBDBiYHzAUwDmGxCthdmAx4BYLRBdDGwSRGnhbz98TOLhjto8fun2axVvDO4kNrA3b5NgqLmDU4vEmbQ0icQzx4sl55wpuznH4FliA8+xMgmGY89wajFgyDGTSGw7lrjhRk7abR6Dw4kNEkARxobDuLXwv0FoKQZrkX9DQIsE2JYaoJb0Y8wQW3jwa5G48SzZIrHtQOLMGTnMkkC/GLfxpBVbJBzDrYW/P/ngzZ9tdYn9EukPP7ypuCPbz354440PNbi1AAGLBAMDSAE4Ug4wsIHEEvBpAEb6BwaGOiDN/gCsZRSMglEwCkYBOgAAYu1bwnrYi2QAAAAASUVORK5CYII=","orcid":"","institution":"Dalian Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Yinghong","middleName":"","lastName":"Cao","suffix":""},{"id":364457000,"identity":"8e006bd3-03c5-4ab2-9db8-0c7074621958","order_by":2,"name":"Santo Banerjee","email":"","orcid":"","institution":"Polytechnic University of Turin","correspondingAuthor":false,"prefix":"","firstName":"Santo","middleName":"","lastName":"Banerjee","suffix":""},{"id":364457003,"identity":"0aad175b-a227-4447-97e1-b4891e04f36d","order_by":3,"name":"Jun Mou","email":"","orcid":"","institution":"Dalian Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Mou","suffix":""}],"badges":[],"createdAt":"2024-09-28 05:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5168542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5168542/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11071-024-10662-3","type":"published","date":"2024-11-28T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70382721,"identity":"e1a399b4-2798-4ee3-bdf5-e83ae846731b","added_by":"auto","created_at":"2024-12-02 16:29:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8723153,"visible":true,"origin":"","legend":"","description":"","filename":"LaTeX.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5168542/v1_covered_73ac3e5d-76db-4038-a41d-7bca2256ab13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Neuronal Circuit Based on a Second-Order Memristor","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neural network circuit, Multistability, Memristor, DSP implementation","lastPublishedDoi":"10.21203/rs.3.rs-5168542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5168542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the increasing advances in computer science, a neuron with information memory is needed to form an artificial neural network. 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