On the computational power of noisy spiking neurons
It has remained unknown wether one can in principle carry out reliable digital
computations with networks of biologically realistic models for neurons. This
article presents rigorous constructions for simulating in real-time arbitrary
given boolean circuits and finite automata with arbitrarily high reliability
by networks of noisy spiking neurons. In addition we show that with the help
of "shunting inhibition" even networks of very unreliable spiking neurons can
simulate in real-time any McCulloch-Pitts neuron (or "threshold gate"), and
therefore any multilayer perceptron (or "threshold circuit") in a reliable
manner. These constructions provide a possible explanation for the fact that
biological neural systems can carry out quite complex computations within 100
msec. It turns out that the assumption that these constructions require about
the shape of EPSP's and the behaviour of the noise are surprisingly weak.
Reference: W. Maass.
On the computational power of noisy spiking neurons.
In D. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in
Neural Information Processing Systems, volume 8, pages 211-217. MIT Press