The Neural Simulation Language: A System for Brain Modeling

460 Pages·2002·4.021 MB·

by  Alfredo Weitzenfeld, Michael A. Arbib, Amanda Alexander

The Neural Simulation Language
A System for Brain Modeling
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The Neural Simulation Language
A System for Brain Modeling
Alfredo Weitzenfeld
Michael Arbib
Amanda Alexander
The MIT Press
Cambridge, Massachusetts
London, England
© 2002 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) with-
out permission in writing from the publisher.
This book was printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Weitzenfeld, Alfredo.
The neural simulation language : a system for brain modeling / Alfredo Weitzenfeld,
Michael Arbib, Amanda Alexander.
p. cm.
Includes bibliographical references and index.
ISBN 0-262-73149-5 (pbk. : alk. Paper)
1. Neural networks (Neurobiology) 2. Neural networks (Computer science)
3. Brain—Computr simulation. I. Arbib, Michael A. II. Alexander, Amanda. III. Title.
QP363.3 . W45 2002
006.3'2–dc21
2001056253
Contents
Preface xiii
Acknowledgments xvii
1 Introduction 1
1.1 Neural Networks 1
Modeling
Simulation
1.2 Modularity, Object-Oriented Programming, and Concurrency 2
Modularity in Neural Networks 2
Object-Oriented Programming 4
Concurrency in Neural Networks 4
1.3 Modeling and Simulation in NSL 5
Modeling 5
Modules 5
Neural Networks 6
Simulation 7
1.4 The NSL System 9
Simulation System 10
Schematic Capture System 11
Model/Module Libraries 11
Basic Hierarchy 12
1.5 Summary 13
2 Simulation in NSL 15
2.1 Selecting a Model 15
2.2 Simulation Interface 17
2.3 Simulating a Model 18
2.4 Maximum Selector 19
Model Description 19
Simulation Interaction 20
2.5 Hopfield 24
Model Description 25
Simulation Interaction 26
2.6 Backpropagation 31
Model Description 32
Simulation Interaction 35
2.7 Summary 38
3 Modeling in NSL 39
3.1 Implementing Model Architectures with NSLM 39
Modules and Models 39
Neural Networks 46
3.2 Visualizing Model Architectures with SCS 50
3.3 Maximum Selector 52
Model Implementation 53
3.4 Hopfield 56
Model Implementation 56
3.5 Backpropagation 60
Model Implementation 60
3.6 Summary 68
4 Schematic Capture System 69
4.1 SCS Tools 69
Schematic Editor (SE) 69
Icon Editor (IE) 69
NSLM Editor (NE) 70
Library Path Editor (LPE) 70
Library Manager (LM) 70
Consistency Checker (CC) 70
NSLM Generator (NG) 70
NSLM Viewer (NV) 70
4.2 An Example Using SCS 70
Create a Library 70
Create Icons 71
Specifying the Ports on the Icon 74
Creating the Schematic 77
Mouse Action Commands 84
Automatic Generation of Code 84
Manual Generation of Leaf Level Code 85
Generating NSLM Code 87
Compiling and Generating the Executable File 88
Reusing Modules and Models 88
Copying Existing Modules and Models 88
4.3 Summary 88
5 User Interface and Graphical Windows 89
5.1 NSL Executive User Interface 89
System Menu 89
Edit Menu 89
Protocol Menu 90
Simulation Menu 90
Train Menu 90
Run Menu 91
Display Menu 92
Help menu 92
5.2 NslOutFrames 92
The NslOutFrame’s Frame Menu 93
The NslOutFrame’s Canvas Menu 96
NSL Output Graph Types 98
5.3 NslInFrames 100
The NslInFrame’s Menu 100
The NslInFrame’s Canvas Menu 101
NSL Input Graph Types 101
5.4 Summary 102
6 The Modeling Language NSLM 103
6.1 Overview 103
General Conventions 103
VI CONTENTS
Types 104
Variables, Attributes, and Methods 104
Attribute Reference Hierarchies 105
Class Reference Hierarchies 106
Predefined Reference Variables 106
Importing Libraries 106
Verbatim 107
6.2 Primitive Types 107
Defined Types 107
Declarations 108
Expressions 109
Control Statements 110
Conversions, Casting, and Promotions 111
6.3 Object Types 112
Defined Types 112
Declarations and Instantiations 114
Expressions 117
Control Statements 121
Conversions, Casting, and Promotions 121
6.4 Creation of New Object Types 121
Template 121
Header 122
Inheritance 122
Attributes 123
Methods 123
Static Modifier 124
6.5 Creation of New Module Types 124
Template 125
Header 125
Inheritance 125
Attributes 126
Methods 126
Differential Equations 129
Scheduling 130
Buffering 132
6.6 Creation of New Model Types 133
6.7 Summary 134
7 The Scripting Language NSLS 135
7.1 Overview 136
General Conventions 136
Help 137
Exit 137
7.2 TCL Primitives Types 137
Variables 137
Arrays 138
Expressions and Control Statements 138
Procedures 139
System Commands 140
7.3 NSL Objects, Modules, and Model Types 140
Access 140
CONTENTS VII
Reference Tree for Model Variables 140
Expressions 141
Simulation Methods 144
Simulation Parameters 147
7.4 Input Output 149
Script Files 149
Data Files 149
7.5 Graphics Displays 151
Reference Tree for Canvases 151
Create and Configure 151
Print 155
7.6 Summary 156
8 Adaptive Resonance Theory 157
8.1 Introduction 157
8.2 Model Description 157
Recognition 159
Comparison 160
Search 160
Learning 161
Theorems 162
8.3 Model Implementation 163
Art Module 163
Comparison Module 164
Recognition Module 164
8.4 Simulation and Results 166
Execution 167
Outpu 167t
8.5 Summary 168
9 Depth Perception 171
9.1 Introduction 171
9.2 Model Description: Disparity 173
9.3 Model Implementation: Disparity 174
Dev 175
DepthModel 175
9.4 Simulation and Results: Disparity 176
9.5 Model Description: Disparity and Accommodation 178
9.6 Model Implementation: Disparity and Accommodation 181
Dev2 181
Retina 181
Stereo 182
Visin 182
DepthModel 183
9.7 Simulation and Results: Disparity and Accommodation 183
9.8 Summary 186
10 Retina 189
10.1 Introduction 189
10.2 Model Description 189
Stimulus Shape and Size Dependency 192
10.3 Model Implementation 193
VIII CONTENTS
Visin 194
Receptor 194
Horizontal Cells 195
Bipolars 195
Amacrines 195
Ganglion Cell R2 196
Ganglion Cell R3 197
Ganglion Cell R4 198
10.4 Simulation and Results 198
Simulation Parameters 199
Model Parameters 199
Input Stimulus 200
10.5 Summary 203
Stimulus Size Dependence of R3 cells 204
Predictions Based on the modified model behavior 204
Future Refinements of the Retina Model 205
Providing a Flexible Framework for Modeling anuran retina 205
11 Receptive Fields 207
11.1 Introduction 207
11.2 Model Description 207
11.3 Model Architecture 209
LayerA Module 210
ConnectW Module 211
LayerB Module 213
ConnectQ Module 213
11.4 Simulation and Results 215
11.5 Summary 217
12 The Associative Search Network: Landmark Learning and Hill Climbing 219
12.1 Introduction 219
12.2 Model Description 219
12.3 Model Implementation 221
12.4 Simulation and Results 222
12.5 Summary 223
13 A Model of Primate Visual-Motor Conditional Learning 225
13.1 Introduction 225
13.2 Model Description 228
Network Dynamics 228
Learning Dynamics 231
13.3 Model Implementation 234
Model 234
Train Module 235
CondLearn Module 235
Feature Module 237
Noise Module 240
Motor Module 243
WTA Module 245
13.4 Simulation and Results 246
Simulation 246
Parameters 246
CONTENTS IX