Harnessing Dynamics of Van Der Pol Oscillators for Phase Computing with Oscillatory Neural Networks

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This study explores van der Pol oscillators in oscillatory neural networks for associative memory tasks, demonstrating enhanced synaptic resolution, speed, coupling range, and noise robustness compared to sine wave oscillators.

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The paper studies oscillatory neural networks that use van der Pol (vdP) oscillators instead of the commonly used sinusoidal Kuramoto (sine-wave) oscillators, with an emphasis on associative memory performance. Using analytical work and simulations, the authors derive a Phase Transition Function (PTF) needed for ONN phase-based computing and present a circuit design and circuit-level simulation approach for a vdP-ONN architecture. The results report advantages of vdP oscillators including increased synaptic resolution, faster time-to-settle, larger coupling range, and improved noise robustness compared with sine-wave oscillators. A major limitation explicitly stated is that the manuscript is a preprint and has not been peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract An oscillatory neural network (ONN) is a novel physics-based computing architecture that leverages the collective dynamics of coupled oscillators with sinusoidal Kuramoto oscillators being typically chosen. However, van der Pol (vdP) oscillators introduce a higher nonlinearity, which we explore for computations. This work presents the first in-depth study of ONNs with vdP oscillators, focusing on their capabilities in regard to associative memory tasks. We derive the Phase Transition Function (PTF), a crucial feature for ONN computing. Furthermore, we detail the circuit design and simulation results for implementing a vdP-ONN architecture. Analytical and circuit-level simulations demonstrate the benefits of vdP oscillators such as increased synaptic resolution, faster time-to-settle, larger coupling range, and superior noise robustness in comparison to standard sine wave oscillators.
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Harnessing Dynamics of Van Der Pol Oscillators for Phase Computing with Oscillatory Neural Networks | 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 Harnessing Dynamics of Van Der Pol Oscillators for Phase Computing with Oscillatory Neural Networks Filip Sabo, Fancheng Du, Nil Dinç, Aida Todri-Sanial This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8907993/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract An oscillatory neural network (ONN) is a novel physics-based computing architecture that leverages the collective dynamics of coupled oscillators with sinusoidal Kuramoto oscillators being typically chosen. However, van der Pol (vdP) oscillators introduce a higher nonlinearity, which we explore for computations. This work presents the first in-depth study of ONNs with vdP oscillators, focusing on their capabilities in regard to associative memory tasks. We derive the Phase Transition Function (PTF), a crucial feature for ONN computing. Furthermore, we detail the circuit design and simulation results for implementing a vdP-ONN architecture. Analytical and circuit-level simulations demonstrate the benefits of vdP oscillators such as increased synaptic resolution, faster time-to-settle, larger coupling range, and superior noise robustness in comparison to standard sine wave oscillators. Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Neuroscience Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 May, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 18 Feb, 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. 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