Biomimetic olfactory processor with on-chip one-shot incremental learning for odor identification

Biomimetic olfactory processor with on-chip one-shot incremental learning for odor identification | 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 Physical Sciences - Article Biomimetic olfactory processor with on-chip one-shot incremental learning for odor identification Hong Chen, Ziyi Cheng, Dexuan Huo, Jilin Zhang, Kea-Tiong Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7646030/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electronic noses (E-noses) mimic the mammalian olfactory system and have been used to detect gases in public safety, disease diagnosis, food safety and so on. However, existing E-noses lack of adaptive learning ability, making them susceptible to sensor drift caused by environmental changes, leading to a progressive decline in recognition accuracy over time. Moreover, unlike mammalian olfactory systems with robust learning abilities and adaptation, current E-noses cannot continuously and rapidly learn new odors profiles or compensate for environmental perturbations. To address the limitations, we propose a novel biomimetic olfactory processor ANP-OB which achieves rapid learning capabilities by mimicking the mammalian olfactory bulb. The ANP-OB is able to remember a new gas after learning it only once, and does not forget previously learned gases. With one-shot incremental learning, ANP-OB achieves a recognition accuracy of 99.8% in a 10-class gas recognition task under 60% noise. Furthermore, its power consumption is less than 50 μW with event-driven asynchronous circuits, making it the lowest-power olfactory processor to our knowledge. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Biomedical engineering Electronic nose sensor drift asynchronous olfactory processor one-shot incremental learning gas recognition Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Demooneshotlearning.mp4 A demo video of ANP-OB in Stage I: One-shot learning DemoIncrementallearning.mp4 A demo video of ANP-OB in Stage II: Incremental learning Cite Share Download PDF Status: Posted 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. 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-7646030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":538040638,"identity":"e039aedc-62a5-42f8-a6e4-e68635325331","order_by":0,"name":"Hong 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