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Neural Networks and Computational Learning Theory

 

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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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