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Boolean functions and artificial neural networks

By Martin Anthony

Abstract

This report surveys some connections between Boolean functions and artificial neural networks. The focus is on cases in which the individual neurons are linear threshold neurons, sigmoid neurons, polynomial threshold neurons, or spiking neurons. We explore the relationships between types of artificial neural network and classes of Boolean function. In particular, we investigate the type of Boolean functions a given type of network can compute, and how extensive or expressive the set of functions so computable is. A version of this is to appear as a chapter in a book on Boolean functions, but the report itself is relatively self-contained

Topics: QA Mathematics
Publisher: Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science
Year: 2003
OAI identifier: oai:eprints.lse.ac.uk:13570
Provided by: LSE Research Online
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