Polymorphic Butterfly Attractors in a Bi-Magnetized Tabu Learning Neural Network and Its Analog Circuit Implementation

Polymorphic Butterfly Attractors in a Bi-Magnetized Tabu Learning Neural Network and Its Analog Circuit Implementation | 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 Polymorphic Butterfly Attractors in a Bi-Magnetized Tabu Learning Neural Network and Its Analog Circuit Implementation Hairong Lin, Miao Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8081851/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Nonlinear Dynamics → Version 1 posted 11 You are reading this latest preprint version Abstract Understanding complex dynamical behaviors in neural systems is important for further developing neuromorphic computing and brain-inspired information processing technologies. In most cases, conventional Tabu learning neural networks are capable of exhibiting chaos but usually cannot catch the impact of multiple external electromagnetic stimuli. This paper proposes a bi-magnetized Tabu learning neural network with two improved memristor models, aiming at modeling induction currents produced by dual electromagnetic radiations. We build the mathematical model and study its dynamical evolution by numerical tools such as bifurcation diagrams, Lyapunov spectra, Poincaré sections, and the 0-1 test. A variety of polymorphic butterfly attractors, including single-wing, double-wing, and four-wing chaotic and hyperchaotic forms, as well as multistable phenomena in which infinitely many coexisting attractors may appear under different initial states, are revealed in this investigation. Besides, an analogue circuit is designed and experimentally demonstrated in Multisim. The experimental results are consistent with the numerical predictions, which confirm the physical realizability of the model. This work may provide a feasible platform to explore complex neural dynamics and may also have potential applications in secure communications and neuromorphic hardware. Memristor Tabu learning neural network Polymorphic butterfly attractors Multistability Chaotic dynamics Analog circuit implementation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Nonlinear Dynamics → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 10 Nov, 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. 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