Information Theory and the Brain

doi:10.1017/CBO9780511665516

Information Theory and the Brain deals with an expanding area of neuroscience which provides a framework for understanding neuronal processing. It is derived from a conference held in Newquay, UK, where a select group of scientists from around the world met to discuss the topic. This book begins with an introduction to the basic concepts of information theory and then illustrates these concepts with examples from research over 40 years. Throughout the book, the contributors highlight current research from four different areas: 1) biological networks, 2) information theory and artificial networks, 3) information theory and psychology, 4) formal analysis. Each section includes an introduction and glossary covering basic concepts. This book will appeal to graduate students and researchers in neuroscience as well as computer scientists and cognitive scientists. Neuroscientists interested in any aspect of neural networks or information processing will find this a very useful addition to the current literature in this rapidly growing field.

"...the skeptic as well as the aficionado of information theory will profit from reading this new book." The Quarterly Review of Biology

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Contents

Frontmatter
pp i-iv
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Contents
pp v-x
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List of Contributors
pp xi-xii
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Preface
pp xiii-xiv
- By Roland Baddeley
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1 - Introductory Information Theory and the Brain
pp 1-20
- By Roland Baddeley
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Part One - Biological Networks
pp 21-24
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2 - Problems and Solutions in Early Visual Processing
pp 25-40
- By Brian G. Burton
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3 - Coding Efficiency and the Metabolic Cost of Sensory and Neural Information
pp 41-61
- By Simon B. Laughlin, John C. Anderson, David O'Carroll, Rob De Ruyter Van Steveninck
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4 - Coding Third-Order Image Structure
pp 62-78
- By Mitchell Thompson
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Part Two - Information Theory and Artificial Networks
pp 79-83
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5 - Experiments with Low-Entropy Neural Networks
pp 84-100
- By George Harpur, Richard Prager
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6 - The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons
pp 101-121
- By Stephen P. Luttrell
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7 - Dynamic Changes in Receptive Fields Induced by Cortical Reorganization
pp 122-138
- By Germán Mato, Néstor Parga
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8 - Time to Learn About Objects
pp 139-163
- By Guy Wallis
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9 - Principles of Cortical Processing Applied to and Motivated by Artificial Object Recognition
pp 164-179
- By Norbert Krüger, Michael Pötzsch, Gabriele Peters
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10 - Performance Measurement Based on Usable Information
pp 180-200
- By Martin Elliffe
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Part Three - Information Theory and Psychology
pp 201-203
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11 - Modelling Clarity Change in Spontaneous Speech
pp 204-220
- By Matthew Aylett
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12 - Free Gifts from Connectionist Modelling
pp 221-240
- By John A. Bullinaria
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13 - Information and Resource Allocation
pp 241-254
- By Janne Sinkkonen
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Part Four - Formal Analysis
pp 255-256
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14 - Quantitative Analysis of a Schaffer Collateral Model
pp 257-272
- By Simon Schultz, Stefano Panzeri, Edmund Rolls, Alessandro Treves
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15 - A Quantitative Model of Information Processing in CA1
pp 273-289
- By Carlo Fulvi Mari, Stefano Panzeri, Edmund Rolls, Alessandro Treves
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16 - Stochastic Resonance and Bursting in a Binary-Threshold Neuron with Intrinsic Noise
pp 290-304
- By Paul C. Bressloff, Peter Roper
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17 - Information Density and Cortical Magnification Factors
pp 305-317
- By M. D. Plumbley
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Bibliography
pp 318-340
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Index
pp 341-344
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Information Theory and the Brain

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Contents

Frontmatter
pp i-iv

Contents
pp v-x

List of Contributors
pp xi-xii

Preface
pp xiii-xiv

1 - Introductory Information Theory and the Brain
pp 1-20

Part One - Biological Networks
pp 21-24

2 - Problems and Solutions in Early Visual Processing
pp 25-40

3 - Coding Efficiency and the Metabolic Cost of Sensory and Neural Information
pp 41-61

4 - Coding Third-Order Image Structure
pp 62-78

Part Two - Information Theory and Artificial Networks
pp 79-83

5 - Experiments with Low-Entropy Neural Networks
pp 84-100

6 - The Emergence of Dominance Stripes and Orientation Maps in a Network of Firing Neurons
pp 101-121

7 - Dynamic Changes in Receptive Fields Induced by Cortical Reorganization
pp 122-138

8 - Time to Learn About Objects
pp 139-163

9 - Principles of Cortical Processing Applied to and Motivated by Artificial Object Recognition
pp 164-179

10 - Performance Measurement Based on Usable Information
pp 180-200

Part Three - Information Theory and Psychology
pp 201-203

11 - Modelling Clarity Change in Spontaneous Speech
pp 204-220

12 - Free Gifts from Connectionist Modelling
pp 221-240

13 - Information and Resource Allocation
pp 241-254

Part Four - Formal Analysis
pp 255-256

14 - Quantitative Analysis of a Schaffer Collateral Model
pp 257-272

15 - A Quantitative Model of Information Processing in CA1
pp 273-289

16 - Stochastic Resonance and Bursting in a Binary-Threshold Neuron with Intrinsic Noise
pp 290-304

17 - Information Density and Cortical Magnification Factors
pp 305-317

Bibliography
pp 318-340

Index
pp 341-344

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