Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks

S.M. Bohte, H. La Poutre, J.N. Kok
2002 IEEE Transactions on Neural Networks  
We demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multi-layer network induces hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering
more » ... y and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.
doi:10.1109/72.991428 pmid:18244443 fatcat:aezwugdd2veblcxwjhsktloluu