SPATIAL AND TEMPORAL PATTERN-ANALYSIS VIA SPIKING NEURONS

Spiking neurons, receiving temporally encoded inputs, can compute radi al basis functions (RBFs) by storing the relevant information in their delays. In this paper we show how these delays can be learned using e xclusively locally available information (basically the time differenc e between the pre- and postsynaptic spikes). Our approach gives rise t o a biologically plausible algorithm for finding clusters in a high-di mensional input space with networks of spiking neurons, even if the en vironment is changing dynamically. Furthermore. we show that our learn ing mechanism makes it possible that such RBF neurons can perform some kind of feature extraction where they recognize that only certain inp ut coordinates carry relevant information. Finally we demonstrate that this model allows the recognition of temporal sequences even if they are distorted in various ways.