title: Versatile networks of simulated spiking neurons displaying winner-take-all behavior.
abstract: We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA) behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS). In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid brain-based-device (BBD) under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.
authors.affiliation: The Neurosciences Institute San Diego, CA, USA.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Chen
authors.forename: Jeffrey L
authors.initials: JL
authors.lastname: McKinstry
authors.forename: Gerald M
authors.initials: GM
authors.lastname: Edelman
keywords: brain-based computational model; large-scale spiking neural networks; motor control and learning/plasticity; neurorobotics; sensorimotor control; spike-timing-dependent plasticity; spiking neuronal networks; winner-take-all