  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.
