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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 clusteringdoi:10.1109/72.991428 pmid:18244443 fatcat:aezwugdd2veblcxwjhsktloluu