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Hebbian Learning and Negative Feedback Networks

  • Book
  • © 2005

Overview

Authors:
  1. Colin Fyfe

    1. Applied Computational Intelligence Research Unit, The University of Paisley, UK

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  • Concentrates on one specific architecture and learning rule which no other book does
  • State of the art in artificial neural networks which use Hebbian learning
  • A comparative study of a variety of techniques that have been drawn from extensions of one network
  • The close link between statistics and artificial neural networks is made clear
  • No other direct competition

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XVIII
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  2. Introduction

    1. Introduction

      Pages 1-5

  3. Single Stream Networks

    1. Front Matter

      Pages 7-10
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    2. Background

      Pages 11-29

    3. The Negative Feedback Network

      Pages 31-56

    4. Peer-Inhibitory Neurons

      Pages 57-84

    5. Multiple Cause Data

      Pages 85-109

    6. Exploratory Data Analysis

      Pages 111-136

    7. Topology Preserving Maps

      Pages 137-168

    8. Maximum Likelihood Hebbian Learning

      Pages 169-186

  5. Back Matter

    Pages 315-383
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Keywords

About this book

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.

Reviews

From the reviews of the first edition:

"This book is concerned with developing unsupervised learning procedures and building self organizing network modules that can capture regularities of the environment. … the book provides a detailed introduction to Hebbian learning and negative feedback neural networks and is suitable for self-study or instruction in an introductory course." (Nicolae S. Mera, Zentralblatt MATH, Vol. 1069, 2005)

Authors and Affiliations

  • Applied Computational Intelligence Research Unit, The University of Paisley, UK

    Colin Fyfe

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