Thomas Natschlaeger, Berthold Ruf, Inst for Theoretical Computer Science, T.U. Graz, Klosterwiegasse 32/2, A-8010 Graz, Austria Spiking neurons, receiving temporally encoded inputs, can compute radial basis functions in a biologically realistic way. They store the relevant information in their delays. In this paper we show how these delays can be learned using exclusively locally available information (basically the time difference between the pre and postsynaptic spike). Our approach gives rise to a biologically plausible algorithm for finding clusters in a high dimensional input space with networks of spiking neurons, even if the environment is changing dynamically.