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.
KAUST Repository Item: Exported on 2021-12-13
Acknowledgements: We acknowledge F. Keller, A. Steinmetz, and A. Becker from the Max Planck Institute for Polymer Research (MPIP) for significant contribution in the design and realization of the experimental setup (maze, 3D-printed parts, and video recording) and electronics (customization of the robot and additional hardware for conditioning). We also acknowledge H.-J. Guttmann and C. Bauer for assistance in the clean room facilities of MPIP. We also acknowledge G. Malliaras for relevant preliminary discussions and B. Meijer for support