Networks of spiking neurons: the third generation of neural network
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, with regard to the number
of the neurons that are needed, 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 neuronal net. On the other hand it is known that any
function that can be computed by a small sigmoidal neural net can also be
computed by a small network of spiking neurons. 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.
Reference: W. Maass.
Networks of spiking neurons: the third generation of neural network models.
In Proc. of the 7th Australian Conference on Neural Networks 1996 in
Canberra, Australia, pages 1-10, 1996.