2022/02/14 by A. Linares-Barranco, E. Piñero-Fuentes, Linares-Barranco, A. +15 · 2 citations
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #Neural Networks and Reservoir Computing #Neural dynamics and brain function #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2202.07064
openalex publication_date 2022/02/14 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics. Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used at their last steps to produce rhythmic patterns for limbs movement. Different patterns and gaits typically compete through winner-take-all (WTA) circuits to produce the right movements. In this work we present a WTA circuit implemented in a Spiking-Neural-Network (SNN) processor to produce such patterns for controlling a robotic arm in real-time. The robot uses spike-based proportional-integrativederivative (SPID) controllers to keep a commanded joint position from the winner population of neurons of the WTA circuit. Experiments demonstrate the feasibility of robotic control with spiking circuits following brain-inspiration.