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One of the most prominent features of biological neural systems is
that individual neurons communicate via short electrical pulses, the
so-called action potentials or spikes. In this chapter we investigate
possible mechanisms of unsupervised learning and self-organization in
networks of spiking neurons. After giving a brief introduction to
spiking neuron networks we describe a biologically plausible algorithm
for these networks to find clusters in a high dimensional input space
or a subspace of it. The algorithm is shown to work even in a
dynamically changing environment. Furthermore, we study
self-organizing maps of spiking neurons showing that networks of
spiking neurons using temporal coding can achieve a topology
preserving behavior quite similar to that of Kohonen's self-organizing
map. For these networks a mechanism of competitive computation is
proposed that is based on action potential timing. Thus, the winner
in a population of competing neurons can be determined locally and in
generally faster than in approaches which use rate coding. The models
and algorithms presented in this chapter establish further steps
toward more realistic descriptions of unsupervised learning in
biological neural systems.
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