Table Of ContentBrain Computation as Hierarchical Abstraction
Computational Neuroscience
Terence J. Sejnowski and Tomaso A. Poggio, editors
For a complete list of books in this series, see the back of the book and http://mit-
press.mit.edu/Computational_Neuroscience
Brain Computation as Hierarchical Abstraction
Dana H. Ballard
The MIT Press
Cambridge, Massachusetts
London, England
© 2015 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any
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This book was set in ITC Stone Serif Std by Toppan Best-set Premedia Limited, Hong
Kong. Printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Ballard, Dana H. (Dana Harry), 1946– author.
Brain computation as hierarchical abstraction / Dana H. Ballard.
p. ; cm. — (Computational neuroscience)
Includes bibliographical references and index.
ISBN 978-0-262-02861-5 (hardcover : alk. paper)
I. Title. II. Series: Computational neuroscience.
[DNLM: 1. Brain— physiology. 2. Mental Processes— physiology. 3. Models,
Neurological. 4. Nerve Net— physiology. 5. Neural Networks (Computer)
6. Neurons— physiology. WL 337]
QP357.5
612.8’23343— dc23
2014021822
10 9 8 7 6 5 4 3 2 1
Contents
Series Foreword ix
Preface xi
Acknowledgments xiii
Part I Setting the Stage 1
1 Brain Computation 3
1.1 Introducing the Brain 7
1.2 Computational Abstraction 13
1.3 Different than Silicon 21
1.4 The Brain’ s Tricks for Fast Computation 25
1.5 More Powerful than a Computer? 30
1.6 Do Humans Have Non-Turing Abilities? 34
1.7 Summary 38
2 Brain Overview 41
2.1 Spinal Cord and Brainstem 44
2.2 The Forebrain: An Overview 54
2.3 Cortex: Long-Term Memory 60
2.4 Basal Ganglia: The Program Sequencer 63
2.5 Thalamus: Input and Output 68
2.6 Hippocampus: Program Modifications 70
2.7 Amygdala: Rating What’ s Important 76
2.8 How the Brain Programs Itself 78
2.9 Summary 80
Part II Neurons, Circuits, and Subsystems 81
3 Neurons and Circuits 83
3.1 Signaling Strategies 85
3.2 Receptive Fields 89
3.3 Modeling Receptive Field Formation 95
vi Contents
3.4 Spike Codes for Cortical Neurons 102
3.5 Reflexive Behaviors 109
3.6 Summary 112
3.7 Appendix: Neuron Basics 113
4 Cortical Memory 127
4.1 Table Lookup Strategies 128
4.2 The Cortical Map Concept 135
4.3 Hierarchies of Maps 139
4.4 What Does the Cortex Represent? 146
4.5 Computational Models 154
4.6 Summary 160
5 Programs via Reinforcement 163
5.1 Evaluating a Program 168
5.2 Reinforcement Learning Algorithms 173
5.3 Learning in the Basal Ganglia 177
5.4 Learning to Set Cortical Synapses 186
5.5 Learning to Play Backgammon 192
5.6 Backgammon as an Abstract Model 199
5.7 Summary 200
Part III Embodiment of Behavior 201
6 Sensory-Motor Routines 203
6.1 Human Vision Is Specialized 204
6.2 Routines 210
6.3 Human Embodiment Overview 214
6.4 Evidence for Visual Routines 219
6.5 Changing the Agenda 230
6.6 Discussion and Summary 232
7 Motor Routines 235
7.1 Motor Computation Basics 238
7.2 Biological Movement Organization 240
7.3 Cortex: Movement Plans 248
7.4 Cerebellum: Checking Expectations 253
7.5 Spinal Cord: Coding the Movement Library 255
7.6 Reading Human Movement Data 263
7.7 Summary 272
8 Operating System 275
8.1 A Hierarchical Cognitive Architecture 279
8.2 Program Execution 283
8.3 Humanoid Avatar Models 289
8.4 Module Multiplexing 293
Contents vii
8.5 Program Arbitration 298
8.6 Alerting 305
8.7 Program Indexing 307
8.8 Credit Assignment 309
8.9 Implications of a Modular Architecture 313
8.10 Summary 316
Part IV Awareness 319
9 Decision Making 321
9.1 The Coding of Decisions 322
9.2 Deciding in Noisy Environments 325
9.3 Social Decision Making 330
9.4 Populations of Game Players 341
9.5 Summary 345
10 Emotions 349
10.1 Triune Phylogeny 351
10.2 Emotions and the Body 354
10.3 Somatic Marker Theory 361
10.4 The Amygdala’ s Special Role 366
10.5 Computational Perspectives 369
10.6 Summary 373
11 Consciousness 377
11.1 Being a Model 378
11.2 Simulation 392
11.3 What Is Consciousness For? 402
11.4 Summary 406
Notes 411
References 413
Index 435
Series Foreword
Computational neuroscience is an approach to understanding the develop-
ment and function of nervous systems at many different structural scales,
including the biophysical, the circuit, and the systems levels. Methods
include theoretical analysis and modeling of neurons, networks, and brain
systems and are complementary to empirical techniques in neuroscience.
Areas and topics of particular interest to this book series include compu-
tational mechanisms in neurons, analysis of signal processing in neural
circuits, representation of sensory information, systems models of senso-
rimotor integration, computational approaches to biological motor con-
trol, and models of learning and memory. Further topics of interest include
the intersection of computational neuroscience with engineering, from
representation and dynamics, to observation and control.
Terrence J. Sejnowski
Tomaso Poggio
Description:The vast differences between the brain's neural circuitry and a computer's silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organizat
