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Eric L. Schwartz - Computational neuroscience

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The thirty original contributions in this book provide a working definition of computational neuroscience as the area in which problems lie simultaneously within computer science and neuroscience. They review this emerging field in historical and philosophical overviews and in stimulating summaries of recent results. Leading researchers address the structure of the brain and the computational problems associated with describing and understanding this structure at the synaptic, neural, map, and system levels. The overview chapters discuss the early days of the field, provide a philosophical analysis of the problems associated with confusion between brain metaphor and brain theory, and take up the scope and structure of computational neuroscience. Synaptic-level structure is addressed in chapters that relate the properties of dendritic branches, spines, and synapses to the biophysics of computation and provide a connection between real neuron architectures and neural network simulations. The network-level chapters take up the preattentive perception of 3-D forms, oscillation in neural networks, the neurobiological significance of new learning models, and the analysis of neural assemblies and local learning rides. Map-level structure is explored in chapters on the bat echolocation system, cat orientation maps, primate stereo vision cortical cognitive maps, dynamic remapping in primate visual cortex, and computer-aided reconstruction of topographic and columnar maps in primates. The system-level chapters focus on the oculomotor system VLSI models of early vision, schemas for high-level vision, goal-directed movements, modular learning, effects of applied electric current fields on cortical neural activity neuropsychological studies of brain and mind, and an information-theoretic view of analog representation in striate cortex. Eric L. Schwartz is Professor of Brain Research and Research Professor of Computer Science, Courant Institute of Mathematical Sciences, New York University Medical Center. Computational Neuroscience is included in the System Development Foundation Benchmark Series.

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title Computational Neuroscience System Development Foundation Benchmark - photo 1

title:Computational Neuroscience System Development Foundation Benchmark Series
author:Schwartz, Eric L.
publisher:MIT Press
isbn10 | asin:0262192918
print isbn13:9780262192910
ebook isbn13:9780585358949
language:English
subjectNeural networks (Neurobiology)--Congresses, Neural computers--Congresses, Nervous system--Mathematical models--Congresses, Computational neuroscience--Congresses, Computer Simulation--congresses, Models, Neurological--congresses, Nervous System--physiolog
publication date:1990
lcc:QP363.3.C64 1990eb
ddc:591.1/88
subject:Neural networks (Neurobiology)--Congresses, Neural computers--Congresses, Nervous system--Mathematical models--Congresses, Computational neuroscience--Congresses, Computer Simulation--congresses, Models, Neurological--congresses, Nervous System--physiolog
Page i
Computational Neuroscience
Page ii
System Development Foundation Benchmark Series
Max Brady, editor, Robotics Science, 1989
Max V. Mathews and John R. Pierce, editors, Current Directions in Computer Music Research, 1989
Philip R. Cohen, Jerry Morgan, and Martha E. Pollack, editors, Intentions in Communication, 1990
Eric L. Schwartz, editor, Computational Neuroscience, 1990
Page iii
Computational Neuroscience
Edited by Eric L. Schwartz
Page iv 1990 Massachusetts Institute of Technology All rights reserved - photo 2
Page iv
1990 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form or 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 by Asco Trade Typesetting Ltd. in Hong Kong and printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Computational neuroscience / edited by Eric L. Schwartz.
p. cm.(System Development Foundation benchmark series)
Collection of papers presented at a 1987 symposium.
"A Bradford book."
Includes bibliographies and index.
ISBN 0-262-19291-8
1. Neural circuitryComputer simulationCongresses. 2. Neural
computersCongresses. 3. Nervous systemMathematical models
Congresses. 1. Schwartz, Eric L. II. Series.
[DNLM: 1. Computer Simulationcongresses. 2. Models,
Neurologicalcongresses. 3. Nervous Systemphysiology
congresses. 4. Neurosciencescongresses. WL 26.5 C738 1987]
QP363.3.C64 1990
591.1'88dc20
DNLM/DLC
for Library of Congress 89-12441
CIP
Page v
CONTENTS
Introduction
ix
I
Overviews
1
Some Historical Notes
Wilfrid Rall
3
2
Brain Metaphor and Brain Theory
John G. Daugman
9
3
Strategies for Analyzing Complex Organization in the Nervous System: I. Lesion Experiments
Paul Grobstein
19
4
Computational Neuroscience: Scope and Structure
Donald H. Perkel
38
5
What Is Computational Neuroscience?
Patricia S. Churchland, Christof Koch, and Terrence J. Sejnowski
46
6
Content-Addressable Memory Storage by Neural Networks: A General Model and Global Liapunov Method
Stephen Grossberg
56

Page vi
II
The Synaptic Level
7
Dendritic Branches, Spines, Synapses, and Excitable Spine Clusters
Wilfrid Rall and Idan Segev
69
8
The Significance of Real Neuron Architectures for Neural Network Simulations
Gordon M. Shepherd
82
9
The Biophysics of Computation: Toward the Mechanisms Underlying Information Processing in Single Neurons
Christof Koch
97
III
The Network Level
10
A Model Cortical Architecture for the Preattentive Perception of 3-D Form
Stephen Grossberg
117
11
Modular Learning in Hierarchical Neural Networks
Dana H. Ballard
139
12
Local Rules for Synaptic Modification in Neural Networks
G. Palm
154
13
Computational Constraints on Higher Neural Representations
Jerome A. Feldman
163
14
Neural Assemblies As Building Blocks of Cortical Computation
George L. Gerstein and Mark R. Turner
179
15
The Neurobiological Significance of the New Learning Models
David Zipser and David E. Rumelhart
192
16
Structure and Function in an Oscillating Neural Network
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