Pattern analysis with spiking neurons using delay coding
Spiking neurons, receiving temporally encoded inputs, can compute radial basis functions (RBFs) by storing the relevant information in their delays. These delays can be learned using exclusively locally available information (basically the time difference between the pre- and post-synaptic spike). Our approach gives rise to a biologically plausible algorithm for finding clusters in a high-dimensional input space. Furthermore, we show that our learning mechanism makes it possible that such RBF neurons can perform some kind of feature extraction. Finally we demonstrate that this model allows the recognition of temporal sequences even if they are distorted in various ways.
spiking neurons delay coding clustering temporal sequences