Unsupervised classification of complex clusters in networks of spiking neurons

For unsupervised clustering in a network of spiking neurons we develop a temporal encoding of continuously valued data to obtain arbitrary clustering capacity and precision with an efficient use of neurons. Input variables are encoded independently in a population code by neurons with 1D graded and overlapping sensitivity profiles. Using a temporal Hebbian learning rule, the network architecture yields reliable clustering of high-dimensional multi-modal data. Additionally, multi-scale sensitivity to the input is achieved by using an appropriate choice of local activation functions. We present a multilayer version of the algorithm to perform a form of hierarchical clustering. We show how synchronous spiking of neurons can emerge with a local Hebbian learning rule and can be exploited by subsequent RBF layers employing the same local learning rule. Neuronal synchrony thus naturally enhances the clustering capabilities of artificial spiking neural networks, which has been widely suggested in neurobiology.