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Neural Computation, Vol 8, 805-818, Copyright © 1996 by The MIT Press

ARTICLES

Analog versus discrete neural networks

B DasGupta and G Schnitger
Department of Computer Science, University of Waterloo, Ontario, Canada.

We show that neural networks with three-times continuously differentiable activation functions are capable of computing a certain family of n-bit boolean functions with two gates, whereas networks composed of binary threshold functions require at least omega(log n) gates. Thus, for a large class of activation functions, analog neural networks can be more powerful than discrete neural networks, even when computing Boolean functions.


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Copyright © 1996 by The MIT Press.