Hebbian Learning and Negative Feedback Networks

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Springer Science & Business Media, Jun 7, 2007 - Computers - 383 pages
This book is the outcome of a decade’s research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was “Negative Feedback as an Organising Principle for Arti?cial Neural Networks”. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from • Dr. Darryl Charles [24] in Chapter 5. • Dr. Stephen McGlinchey [127] in Chapter 7. • Dr. Donald MacDonald [121] in Chapters 6 and 8. • Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.
 

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Contents

Introduction
1
Background
11
The Negative Feedback Network
31
PeerInhibitory Neurons
57
Multiple Cause Data
85
Exploratory Data Analysis 111
110
Topology Preserving Maps
137
Maximum Likelihood Hebbian Learning
169
Exploratory Correlation Analysis
247
Multicollinearity and Partial Least Squares 275
274
Twinned Principal Curves
291
The Future 309
308
Previous Factor Analysis Models 323
322
Related Models for ICA
341
Previous Dual Stream Approaches
353
Data Sets
363

9
191
Alternative Derivations of CCA Networks
209
Kernel and Nonlinear Correlations
217
References
371
Copyright

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