Clustering with spiking neurons

A neural method for data clustering using temporal segmentation of spiking neurons, which relies only on distances between data points, and is demonstrated on a biological data set.

Abstract

We present a neural method for data clustering using temporal segmentation of spiking neurons. Our clustering algorithm relies only on distances between data points. Each point is associated with a neuron, and the distances are used to determine the synaptic weights between those neurons. The dynamical development of this system leads to synchronous firing of neurons that belong to the same cluster, while different clusters fire at different times. Such dynamic behavior is called temporal segmentation. It is achieved via two mechanisms-intra cluster synchrony, induced by excitatory connections within each cluster, and desynchronization between clusters induced by inhibitory competition. We test our clustering method on the iris data set. For problems that do not have a unique clustering solution, we construct a pair-correlation matrix on the basis of multiple clustering solutions. By performing a second clustering algorithm, this time on the pair-correlation matrix, we are able to define second order clusters of the original distance matrix. This method is demonstrated on a biological data set.