Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics | 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 Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics Yoeri van de Burgt, Imke Krauhausen, Sophie Griggs, Iain McCulloch, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3878146/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics. Physical sciences/Materials science/Materials for devices/Electronic devices Physical sciences/Materials science/Soft materials/Polymers Physical sciences/Materials science/Materials for devices/Information storage Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementary.docx movieS1.mp4 MovieS1 MovieS2.mp4 MovieS2 movieS3.mp4 MovieS3 movieS4.mp4 MovieS4 movieS5.mp4 MovieS5 movieS6.mp4 MovieS6 Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2024 Read the published version in Nature Communications → 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. 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