Networks of spiking neurons: the third generation of neural network models

W. Maass

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, 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.