Anthony, Martin (2003) Boolean functions and artificial neural networks. CDAM research report series, LSE-CDAM-2003-01. Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science, London, UK.Full text not available from this repository.
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.
|Item Type:||Monograph (Report)|
|Additional Information:||© 2003 the author|
|Library of Congress subject classification:||Q Science > QA Mathematics|
|Sets:||Departments > Mathematics
Research centres and groups > Computational, Discrete and Applicable Mathematics@LSE (CDAM)
Actions (login required)
|Record administration - authorised staff only|