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NeuroCOLT
Technical Report NC-TR-96-045
Networks of Spiking
Neurons:
The Third Generation of Neural Network Models
Wolfgang
Maass
Technische Universitaet Graz
Austria
Abstract
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 computationally
more powerful than these other neural network models. A concrete biologically
relevant function is exhibited which can be computed by a single spiking
neuron (for biologically reasonable values of its parameters), but
which requires hundreds of hidden units on a sigmoidal neural net.
This article does not assume prior knowledge about spiking neurons,
and it contains an extensive list of references to the currently available
literature on computations in networks of spiking neurons and relevant
results from neurobiology.
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Postscript
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