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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Michael Schmitt

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. ...
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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 ...
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