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NeuroCOLT
Technical Report NC-TR-97-027
Hebbian
Learning in Networks of Spiking Neurons Using Temporal Coding
Berthold
Ruf, Michael Schmitt
Technische Universitaet Graz
Austria
Abstract
Computational tasks in biological systems that require short
response times can be implemented in a straightforward way by networks
of spiking neurons that encode analogue values in temporal coding.
We investigate the question how spiking neurons can learn on the basis
of differences between firing times. In particular, we provide learning
rules of the Hebbian type in terms of single spiking events of the
pre- and postsynaptic neuron and show that the weights approach some
value given by the difference between pre- and postsynaptic firing
times with arbitrary high precision. Our learning rules give rise
to a straightforward possibility for realizing very fast pattern analysis
tasks with spiking neurons.
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