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

Harnessing Dynamics of Van Der Pol Oscillators for Phase Computing with Oscillatory Neural Networks | Research Square 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 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with Oscillatory Neural Networks Filip Sabo, Fancheng Du, Nil Dinç, Aida Todri-Sanial This is a preprint; it has not been peer reviewed by a journal.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 Review 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 Review 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. Also discoverable on Platform About Our Team In Review Editorial Policies 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

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