2021/12/10 by Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas +11 · 1 citation
Engineering · Materials Science · Neuroscience · Psychology · #Advanced Memory and Neural Computing #Conducting polymers and applications #Advanced Sensor and Energy Harvesting Materials #Neuromorphic engineering #Robotics #Artificial intelligence #Computer science #Path integration #Robot #Human–computer interaction #Electronics #Sensory system #Neuroscience #Artificial neural network #Psychology #Engineering #Electrical engineering
paper · pdf · doi:10.1126/sciadv.abl5068
openalex publication_date 2021/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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 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.