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NeuroCOLT
Technical Report NC-TR-97-042
On
the Effect of Analog Noise in Discrete-Time Analog Computations
Wolfgang
Maass
Technische Universitaet Graz
Austria
Pekka
Orponen
University of Jyväskylä
Finland
Abstract
We introduce a model for analog computation with discrete time
in the presence of analog noise that is flexible enough to cover the
most important concrete cases, such as noisy analog neural nets and
networks of spiking neurons. This model subsumes the classical model
for digital computation in the presence of noise. We show that the
presence of arbitrarily small amounts of analog noise reduces the
power of analog computational models to that of finite automata, and
we also prove a new type of upper bound for the VC-dimension of computational
models with analog noise.
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