Bipolar physical reservoir computing for intrinsic processing of signed temporal signals

Bipolar physical reservoir computing for intrinsic processing of signed temporal signals | 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 Bipolar physical reservoir computing for intrinsic processing of signed temporal signals Peng Huang, Ruiqi Chen, Ao Shi, Siyuan Chen, Jiaqi Li, Yading Yi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8716652/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Physical reservoir computing (RC) offers a highly energy-efficient paradigm for processing spatiotemporal signals. However, most existing physical RC implementations rely on electronic devices with inherently unipolar responses, fundamentally constraining their ability to encode and process bipolar signals that are ubiquitous in real-world data, such as financial volatility, directional mechanical motion, and event streams from dynamic vision sensors (DVS). Here, we report a bipolar physical RC system enabled by ZnO-interlayered metal-oxide thin-film transistors that generate intrinsic bipolar responses within a single device. By explicitly incorporating full sign information, the information encoding efficiency is enhanced by a factor of 1.58, accompanied by a substantial reduction in signal ambiguity. Leveraging this capability, the bipolar RC boosts experimental accuracy from 89.9% to 96.9% in DVS-based event recognition compared with unipolar counterparts. Moreover, it exhibits superior performance and energy efficiency in challenging human action classification benchmarks. These results underscore the critical importance of polarity integration and establish a viable pathway toward fully exploiting the computational potential of physical reservoir computing. Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computational science Full Text Additional Declarations There is NO Competing Interest. Supplementary Files BipolarSI.docx Supplementary information - Bipolar physical reservoir computing for intrinsic processing of signed temporal signals Cite Share Download PDF Status: Under Review 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. 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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-8716652","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584839360,"identity":"827e229d-f520-4430-931d-d4ca7661df45","order_by":0,"name":"Peng 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