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A powerful research tool that unites the related work on both modeling control and processing in distributed networks.
Traditional publications in the field of network models have focussed [sic] on specific areas, whereas this collection successfully intersects many related fields. These include:
- control processes,
- modelling features and operations of biological neural networks and neurons,
- simulation of biological experimentation,
- representation of artificial neural networks (ANNs)
Advances covering a broad area in control, learning and neurobiological models - using mathematics and simulation - are reported through the international perspectives of the authors. Their work represents the most recent developments in this fast-moving field and directly engages with its central concerns.
Contents
Editor's Preface iii
1 Learning in Time Varying Environments p.1
Anthony Kuh and Thomas Petsche
2 Cortical Inhibition as Explained by the Competitive Distribution Hypothesis p.31
James A. Reggia, Granger G. Sutton III, C. Lynne D'Autrechy, Sungzoon Choo and Steve L. Armentrout
3 Implementation of Hartline Pools and Neural-Type Cells by VLSI Circuits p.63
Suan-Wei Tsay and Robert W. Newcomb
4 Self-Organizing Parallel Distributed Neural Network Models p.83
A. Garliauskas and A. Malickas
5 Behavioural Simulation of Neural Networks: An Approach Based upon SPICE P.109
Dario D'Amore and Vincenzo Piuri
6 Induction and Polynomial Networks p.143
John F. Elder IV and Donald E. Brown