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Abstract:
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.
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