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Connections between neural networks and boolean functions

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

Topics: QA Mathematics
Publisher: Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science
Year: 2005
OAI identifier: oai:eprints.lse.ac.uk:13924
Provided by: LSE Research Online
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