Robust implementation of a winner-takes-all mechanism in networks of spiking neurons

Abstract

Event Abstract Back to Event Robust implementation of a winner-takes-all mechanism in networks of spiking neurons Stefano Cardanobile1* and Stefan Rotter1, 2 1 Bernstein Center for Computational Neuroscience, Germany 2 Albert-Ludwigs-University, Faculty of Biology, Germany Neural networks implementing winner-takes-all mechanisms are assumed to play an important role in neural information processing [1]. These networks are usually constructed by reciprocally connecting populations of inhibitory neurons in such a manner that the population receiving the most input can suppress the concurrent population. In [2] a winner-takes-all network of rate-based neurons is constructed and a stability analysis for the system of rate equations is carried out. Based on the framework developed in [3], we construct a network consisting of spiking neurons with exponential transfer functions such that the accompanied system of differential equations governing the expected rates coincides with the system developed in [2]. We show that the same winner-takes-all mechanism is realised by the spiking dynamics, although it is prone to classification errors due to its probabilistic nature. Finally, based on simulations, we study the performance of these networks and show that they are efficient for a broad range of system parameters.