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dc.contributor.authorKrauhausen, Imke
dc.contributor.authorGkoupidenis, Paschalis
dc.contributor.authorMelianas, Armantas
dc.contributor.authorKeene, Scott T.
dc.contributor.authorLieberth, Katharina
dc.contributor.authorLedanseur, Hadrien
dc.contributor.authorSheelamanthula, Rajendar
dc.contributor.authorKoutsouras, Dimitrios
dc.contributor.authorTorricelli, Fabrizio
dc.contributor.authorMcCulloch, Iain
dc.contributor.authorBlom, Paul W. M.
dc.contributor.authorSalleo, Alberto
dc.contributor.authorvan de Burgt, Yoeri
dc.contributor.authorGiovannitti , Alexander
dc.date.accessioned2021-09-27T06:59:24Z
dc.date.available2021-09-27T06:59:24Z
dc.date.issued2021-09-13
dc.identifier.citationKrauhausen, I., Gkoupidenis, P., Melianas, A., Keene, S. T., Lieberth, K., Ledanseur, H., … Giovannitti, A. (2021). Local sensorimotor control and learning in robotics with organic neuromorphic electronics. Proceedings of the Neural Interfaces and Artificial Senses. doi:10.29363/nanoge.nias.2021.023
dc.identifier.doi10.29363/nanoge.nias.2021.023
dc.identifier.urihttp://hdl.handle.net/10754/671954
dc.description.abstractArtificial intelligence applications have demonstrated their enormous potential for complex processing over the last decade, however they still lack the efficiency and computing capacity of the brain. In living organisms, data signals are represented by sensory and motor processes that are distributed, locally merged and capable of forming dynamic sensorimotor associations through volatile and non-volatile connections. Using similar computational primitives, neuromorphic circuits offer a new way of intelligent information processing that makes it possible to adaptively oberserve, anaylze, operate and interact in real-world scenarios [1-6]. In this work we present a small-scale, locally-trained organic neuromorphic circuit for sensorimotor control and learning, on a robot navigating inside a maze. By connecting the neuromorphic circuit directly to environmental stimuli through sensor signals, the robot is able to respond adaptively to sensory cues and consequently forms a behavioral association to follow the way to the exit. The on-chip sensorimotor integration with low-voltage organic neuromorphic electronics opens the way towards stand-alone, brain-inspired circuitry in autonomous and intelligent robotics.
dc.publisherFundació Scito
dc.relation.urlhttps://www.nanoge.org/proceedings/NIAS/613b3ce107e59b3f0e4775bb
dc.rightsArchived with thanks to Fundació Scito
dc.titleLocal sensorimotor control and learning in robotics with organic neuromorphic electronics
dc.typeConference Paper
dc.contributor.departmentKAUST Solar Center (KSC)
dc.contributor.departmentChemical Science Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.conference.date2021 September 22nd - 23rd
dc.conference.nameNeural Interfaces and Artificial Senses (NIAS)
dc.conference.locationOnline, Spain
dc.eprint.versionPre-print
dc.contributor.institutionMax Planck Institute for Polymer Research, Mainz
dc.contributor.institutionInstitute of Complex Molecular Systems, Eindhoven University of Technology, 5600 MB, Eindhoven, The Netherlands
dc.contributor.institutionDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, USA;
dc.contributor.institutionUniversity of Cambridge, Department of Engineering, UK
dc.contributor.institutionDept. Of Information Engineering, University of Brescia
dc.contributor.institutionDepartment of Chemistry, University of Oxford, UK
kaust.personSheelamanthula, Rajendar
kaust.personMcCulloch, Iain


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