Brain Computation as Hierarchical Abstraction

by Dana H. Ballard

Overview

An argument that the complexities of brain function can be understood hierarchically, in terms of different levels of abstraction, as silicon computing is.

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 organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its computation is factored into different levels of abstraction.

Drawing on several decades of progress in computational neuroscience, together with recent results in Bayesian and reinforcement learning methodologies, Ballard factors the brain's principal computational issues in terms of their natural place in an overall hierarchy. Each of these factors leads to a fresh perspective. A neural level focuses on the basic forebrain functions and shows how processing demands dictate the extensive use of timing-based circuitry and an overall organization of tabular memories. An embodiment level organization works in reverse, making extensive use of multiplexing and on-demand processing to achieve fast parallel computation. An awareness level focuses on the brain's representations of emotion, attention and consciousness, showing that they can operate with great economy in the context of the neural and embodiment substrates.

Product Details

ISBN-13:	9780262534123
Publisher:	MIT Press
Publication date:	02/20/2015
Series:	Computational Neuroscience Series
Pages:	456
Product dimensions:	5.90(w) x 8.90(h) x 1.00(d)
Age Range:	18 Years

About the Author

Dana H. Ballard is Professor in the Department of Computer Sciences at the University of Texas at Austin, where he has appointments in Psychology, the Institute for Neuroscience, and the Center for Perceptual Systems. He is the author of An Introduction to Natural Computation (MIT Press).

Table of Contents

Series Foreword ix

Preface xi

Acknowledgments xiii

Part 1 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 Amygdal: Rating what's Important 76

2.8 How the Brain Programs itself 78

2.9 Summary 80

Part 2 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

3.4 Spike Codes for Cortical Neurons 102

3.5 Reflexive Behaviors 109

3.6 Summary 112

3.7 Appendix: Neuron Behaviors 109

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 3 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

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 4 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

What People are Saying About This

Giorgio Ascoli

Neuroscientists see molecules, spikes, and synapses, yet fail to grasp the computing essence of neural processes; computational scientists are not yet fluent in the language of evolution to graduate from engineering to reverse engineering. Hopping on the giant shoulders of David Marr and Churchland & Sejnowski, Ballard finds a remarkable vantage point on brain computation.

Joel Moses

Levels of abstraction is a key architectural approach in computer science. This approach to hierarchical systems is not sufficiently utilized in other fields. In this important volume Dana Ballard explores how computation in the human brain can be effectively modeled using levels of abstraction.

Wolfram Schultz

This is a straightforward and highly readable formalization of brain function that has been needed for many years. The author synthesizes widely diverse material and concepts and presents a charming text derived from many years of intensive reflections and thoughtful dialogues.

Endorsement

Neuroscientists see molecules, spikes, and synapses, yet fail to grasp the computing essence of neural processes; computational scientists are not yet fluent in the language of evolution to graduate from engineering to reverse engineering. Hopping on the giant shoulders of David Marr and Churchland & Sejnowski, Ballard finds a remarkable vantage point on brain computation.

—Giorgio Ascoli, University Professor, Department of Molecular Neuroscience, George Mason University; author of Trees of the Brain, Roots of the Mind

From the Publisher

Levels of abstraction is a key architectural approach in computer science. This approach to hierarchical systems is not sufficiently utilized in other fields. In this important volume Dana Ballard explores how computation in the human brain can be effectively modeled using levels of abstraction.

— Joel Moses , Institute Professor and former Provost, MIT

This is a straightforward and highly readable formalization of brain function that has been needed for many years. The author synthesizes widely diverse material and concepts and presents a charming text derived from many years of intensive reflections and thoughtful dialogues.

— Wolfram Schultz , University of Cambridge

Neuroscientists see molecules, spikes, and synapses, yet fail to grasp the computing essence of neural processes; computational scientists are not yet fluent in the language of evolution to graduate from engineering to reverse engineering. Hopping on the giant shoulders of David Marr and Churchland & Sejnowski, Ballard finds a remarkable vantage point on brain computation.

— Giorgio Ascoli , University Professor, Department of Molecular Neuroscience, George Mason University; author of Trees of the Brain, Roots of the Mind