Table Of ContentTHE
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The Computational Brain
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The Computational Brain
25th Anniversary Edition
Patricia S. Churchland and
Terrence J. Sejnowski
The MIT Press
Cambridge, Massachusetts
London, England
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© 2017 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any
electronic or mechanical means (including photocopying, recording, or
information storage and retrieval) without permission in writing from the
publisher.
This book was set in Palatino LT by Toppan Best-set Premedia Limited. Printed
and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Names: Churchland, Patricia Smith, author. | Sejnowski, Terrence J. (Terrence
Joseph) author.
Title: The computational brain / Patricia S. Churchland and Terrence J.
Sejnowski.
Description: 25th Anniversary edition. | Cambridge, MA : The MIT Press,
[2017] | Series: Computational neuroscience series | Includes bibliographical
references and index.
Identifiers: LCCN 2016034531 | ISBN (pbk. : alk. paper)
Subjects: LCSH: Brain--Computer simulation. | Neural networks
(Neurobiology)
Classification: LCC QP356 .C48 2017 | DDC 612.8/2--dc23 LC record available
at https://lccn.loc.gov/2016034531
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Contents
Series Foreword vii
25th Anniversary Preface ix
Preface xvii
1 Introduction 1
2 Neuroscience Overview 17
Introduction 17
Levels in nervous systems 18
Structure at various levels of organization 27
A short list of brain facts 4 8
3 Computational Overview 61
Introduction 61
Looking up the answer 69
Linear associators 77
Constraint satisfaction: Hopfield networks and Boltzmann
machines 82
Learning in neural nets 9 6
Competitive learning 1 02
Curve fitting 105
Feedforward nets: Two examples 107
Recurrent nets 1 15
From toy world to real world 125
What good are optimization procedures to neuroscience? 1 30
Models: Realistic and abstract 1 36
Concluding remarks 137
4 Representing the World 141
Introduction 141
Constructing a visual world 142
Thumbnail sketch of the mammalian visual system 1 48
Representing in the brain: What can we learn from the
visual system? 157
What is so special about distribution? 1 63
World enough and time 1 74
Shape from shading: A neurocomputational study 1 83
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Stereo vision 188
Computational models of stereo vision 1 99
Hyperacuity: From mystery to mechanism 221
Vector averaging 2 33
Concluding remarks 237
5 Plasticity: Cells, Circuits, Brains, and Behavior 239
Introduction 239
Learning and the hippocampus 2 43
Donald Hebb and synaptic plasticity 2 50
Memories are made of this: Mechanisms of neuronal
plasticity 254
Cells and circuits 281
Decreasing synaptic strength 2 89
Back to systems and behavior 295
Being and timing 3 05
Development of nervous systems 307
Modules and networks 316
6 Sensorimotor Integration 331
Introduction 331
LeechNet 341
Computation and the vestibulo-ocular reflex 3 53
Time and time again 3 79
The segmental swimming oscillator 388
Modeling the neuron 399
Concluding remarks 4 11
7 Concluding and Beyond 413
Afterword 425
Appendix Anatomical and Physiological Techniques 427
Permanent lesions 427
Reversible lesions and microlesions 4 30
Imaging techniques 432
Gross electrical and magnetic recording 4 37
Single-unit recording 440
Anatomical tract tracing 4 42
Notes 445
Glossary 457
References 479
Index 525
vi Contents
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Series Foreword
Computational neuroscience is an approach to understanding the
development 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 par-
ticular interest to this book series include computational mecha-
nisms in neurons, analysis of signal processing in neural circuits,
representation of sensory information, systems models of senso-
rimotor integration, computational approaches to biological motor
control, and models of learning and memory. Further topics of
interest include the intersection of computational neuroscience
with engineering, from representation and dynamics, to observa-
tion and control.
Terrence J. Sejnowski
Tomaso Poggio
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25th Anniversary Preface
When The Computational Brain was published 25 years ago, most of
what we knew about the properties of neurons and neural systems
was derived by recording from single neurons, one at a time. This
technique revealed the sensory features that neurons respond to
and neural activity that correlated with behavior from a limited
sample of neurons. As a consequence, conceptual frameworks for
brain function were based on single neurons, as exemplified by the
grandmother cell theory for visual perception, according to which
your perception of your grandmother depends on a single neuron
that selectively responds to your grandmother. In retrospect, this
narrow scope is similar to the problem faced when looking at a
scene through a soda straw, sampling one pixel at a time from
random locations. Without knowing the complex nature of how the
world projects onto the eye, which is encoded in the higher-order
relationships between pixels, it would be difficult to extract the
global properties of objects in the scene and their configuration in
depth. Extracting motion from the sequence of images in a movie
would be even more demanding.
Our goal in writing The Computational Brain was to develop a
different conceptual framework, one based on large populations
of neurons. The insights motivating this goal had their origin in
advances made by learning algorithms for artificial neural network
models in the 1980s. By being trained on examples with a learning
algorithm for updating the connectivity between units, these
models could solve difficult computational problems by develop-
ing distributed representations in hidden layers between the inputs
and outputs. The patterns of activity among the hidden units of a
trained network had properties that resembled those recorded from
populations of neurons recorded one at a time. For some this was
a revelation, but others depressingly concluded that we would
never be able to understand brain function by randomly sampling
neurons from a distributed population, since you would need to
record from all of them to uncover the transformation ( Robinson,
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