A model for fast analog computations with noisy spiking neurons

W. Maass

Abstract:

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.