Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas, S. T. Keene, Katharina Lieberth, Hadrien Ledanseur, Rajendar Sheelamanthula, Alexander Giovannitti, Fabrizio Torricelli, Iain McCulloch, Paul W. M. Blom, Alberto Salleo, Yoeri van de Burgt, Paschalis Gkoupidenis

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


In 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 sensorimo-tor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environ-mental 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 decen-tralized sensorimotor integration.
Original languageEnglish (US)
JournalScience advances
Issue number50
StatePublished - Dec 10 2021


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