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 capacity 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.

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Software Engineering [SEN]
Intelligent and autonomous systems

Bohte, S.M, La Poutré, J.A, & Kok, J.N. (2000). Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks. Software Engineering [SEN]. CWI.