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.