A model for fast analog computations with noisy spiking neurons
We show that networks of spiking neurons can simulate arbitrary feedforward
sigmoidal neural nets in a way which has previously not been considered. This
new approach is based on temporal coding by single spikes (respectively by
the timing of synchronous firing in pools of neurons), rather than on the
traditional interpretation of analog variables in terms of firing rates. As a
consequence we can show that networks of noisy spiking neurons are "universal
approximators" in the sense that they can approximate with regard to temporal
coding any given continuous function of several variables.
Reference: W. Maass.
A model for fast analog computations with noisy spiking neurons.
In J. Bower, editor, Computational Neuroscience: Trends in research,
pages 123-127, 1997.