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NeuroCOLT
Technical Report NC-TR-96-031
An
Efficient Implementation of Sigmoidal Neural Nets in Temporal Coding
with Noisy Spiking Neurons
Wolfgang
Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz
Austria
Abstract
We show that networks of rather realistic models for biological neurons
can in principle 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. The resulting new simulation is substantially faster and apparently
more consistent with experimental results about fast information processing
in cortical neural systems. 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.
Our new proposal for the possible organization of computations in
biological neural systems has some interesting consequences for the
type of learning rules that would be needed to explain the self-organization
of such neural circuits. Finally, our fast and noise-robust
implementation of sigmoidal neural nets via temporal coding points
to possible new ways of implementing sigmoidal neural nets with pulse
stream VLSI.
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