The computational power of formal models for
networks of spiking neurons is compared with
that of other neural network models based on
McCulloch Pitts neurons (i.e. threshold gates)
respectively sigmoidal gates. In particular it
is shown that networks of spiking neurons are
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A neural network is said to be nonoverlapping if there is at most one
edge outgoing from each node. We investigate the number of examples
that a learning algorithm needs when using nonoverlapping neural
networks as hypotheses. We derive bounds for this sample complexity
in terms of the Vapnik-Chervonenkis dimension. ... more >>>

A simple extension of standard neural network models is introduced that
provides a model for neural computations that involve both firing rates and
firing correlations. Such extension appears to be useful since it has been
shown that firing correlations play a significant computational role in
many biological neural systems. Standard ... more >>>