  In this work, a spiking neural network (SNN) is proposed for approximating
differential sensorimotor maps of robotic systems. The computed model is used
as a local Jacobian-like projection that relates changes in sensor space to
changes in motor space. The SNN consists of an input (sensory) layer and an
output (motor) layer connected through plastic synapses, with inter-inhibitory
connections at the output layer. Spiking neurons are modeled as Izhikevich
neurons with a synaptic learning rule based on spike-timing-dependent
plasticity. Feedback data from proprioceptive and exteroceptive sensors are
encoded and fed into the input layer through a motor babbling process. As the
main challenge to building an efficient SNN is to tune its parameters, we
present an intuitive tuning method that considerably reduces the number of
neurons and the amount of data required for training. Our proposed architecture
represents a biologically plausible neural controller that is capable of
handling noisy sensor readings to guide robot movements in real-time.
Experimental results are presented to validate the control methodology with a
vision-guided robot.
