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.

Other Architecture Styles (acm C.1.3), Models of Computation (acm F.1.1), Learning (acm I.2.6), Models (acm I.5.1)
Neural nets (msc 82C32), Learning and adaptive systems (msc 68T05), Pattern recognition, speech recognition (msc 68T10), Knowledge representation (msc 68T30), Neural networks, artificial life and related topics (msc 92B20)
Software (theme 1), Logistics (theme 3), Energy (theme 4)
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.