title: Analog versus discrete neural networks.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Waterloo, Ontario, Canada.
authors.forename: B
authors.initials: B
authors.lastname: DasGupta
authors.forename: G
authors.initials: G
authors.lastname: Schnitger
mesh_terms: D003202:Computers, Analog; D008433:Mathematics; D016571:Neural Networks, Computer; D009474:Neurons