Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Matthias Oster, Shih-Chii Liu
Recurrent networks that perform a winner-take-all computation have been studied extensively. Although some of these studies include spik- ing networks, they consider only analog input rates. We present results of this winner-take-all computation on a network of integrate-and-ﬁre neurons which receives spike trains as inputs. We show how we can con- ﬁgure the connectivity in the network so that the winner is selected after a pre-determined number of input spikes. We discuss spiking inputs with both regular frequencies and Poisson-distributed rates. The robustness of the computation was tested by implementing the winner-take-all network on an analog VLSI array of 64 integrate-and-ﬁre neurons which have an innate variance in their operating parameters.