Pattern analysis with spiking neurons using delay coding

Spiking neurons, receiving temporally encoded inputs, can compute radial ba sis functions (RBFs) by storing the relevant information in their delays. T hese delays can be learned using exclusively locally available information (basically the time difference between the pre- and post-synaptic spike). O ur approach gives rise to a biologically plausible algorithm for finding cl usters in a high-dimensional input space. Furthermore, we show that our lea rning mechanism makes it possible that such RBF neurons can perform some ki nd of feature extraction. Finally we demonstrate that this model allows the recognition of temporal sequences even if they are distorted in various wa ys. (C) 1999 Elsevier Science B.V. All rights reserved.