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dc.contributor.authorKrauhausen, Imke
dc.contributor.authorKoutsouras, Dimitrios A
dc.contributor.authorMelianas, Armantas
dc.contributor.authorKeene, Scott
dc.contributor.authorLieberth, Katharina
dc.contributor.authorLedanseur, Hadrien
dc.contributor.authorSheelamanthula, Rajendar
dc.contributor.authorGiovannitti, Alexander
dc.contributor.authorTorricelli, Fabrizio
dc.contributor.authorMcculloch, Iain
dc.contributor.authorBlom, Paul WM
dc.contributor.authorSalleo, Alberto
dc.contributor.authorvan de Burgt, Yoeri
dc.contributor.authorGkoupidenis, Paschalis
dc.date.accessioned2022-01-28T14:46:51Z
dc.date.available2022-01-28T14:46:51Z
dc.date.issued2021-12-10
dc.identifier.issn2375-2548
dc.identifier.other34890232
dc.identifier.otherPMC8664264
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333108
dc.description.abstractIn living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.
dc.languageeng
dc.publisherAmerican Association for the Advancement of Science (AAAS)
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.sourcenlmid: 101653440
dc.sourceessn: 2375-2548
dc.titleOrganic neuromorphic electronics for sensorimotor integration and learning in robotics.
dc.typeArticle
dc.date.updated2022-01-28T14:46:51Z
prism.issueIdentifier50
prism.publicationNameSci Adv
prism.volume7
dc.identifier.doi10.17863/CAM.80532
rioxxterms.versionofrecord10.1126/sciadv.abl5068
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.contributor.orcidKrauhausen, Imke [0000-0001-5633-389X]
dc.contributor.orcidKoutsouras, Dimitrios A [0000-0002-9456-9630]
dc.contributor.orcidMelianas, Armantas [0000-0002-3443-0987]
dc.contributor.orcidKeene, Scott [0000-0002-6635-670X]
dc.contributor.orcidLieberth, Katharina [0000-0001-6160-7162]
dc.contributor.orcidSheelamanthula, Rajendar [0000-0001-5223-9580]
dc.contributor.orcidGiovannitti, Alexander [0000-0003-4778-3615]
dc.contributor.orcidTorricelli, Fabrizio [0000-0002-7932-0677]
dc.contributor.orcidMcculloch, Iain [0000-0002-6340-7217]
dc.contributor.orcidBlom, Paul WM [0000-0002-6474-9497]
dc.contributor.orcidSalleo, Alberto [0000-0002-7448-9123]
dc.contributor.orcidvan de Burgt, Yoeri [0000-0003-3472-0148]
dc.contributor.orcidGkoupidenis, Paschalis [0000-0002-0139-0851]
dc.identifier.eissn2375-2548


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Attribution-NonCommercial 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International