Memory and the Computational Brain Why

Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty

C. R. Gallistel is Co-Director of the Rutgers Center for Cognitive Science. He is one of the foremost psychologists working on the foundations of cognitive neuroscience. His publications include The Symbolic Foundations of Conditional Behavior (2002), and The Organization of Learning (1990).

Adam Philip King is Assistant Professor of Mathematics at Fairfield University.

Preface	p. viii
Information	p. 1
Shannon's Theory of Communication	p. 2
Measuring Information	p. 7
Efficient Coding	p. 16
Information and the Brain	p. 20
Digital and Analog Signals	p. 24
Appendix: The Information Content of Rare Versus Common Events and Signals	p. 25
Bayesian Updating	p. 27
Bayes' Theorem and Our Intuitions about Evidence	p. 30
Using Bayes' Rule	p. 32
Summary	p. 41
Functions	p. 43
Functions of One Argument	p. 43
Composition and Decomposition of Functions	p. 46
Functions of More than One Argument	p. 48
The Limits to Functional Decomposition	p. 49
Functions Can Map to Multi-Part Outputs	p. 49
Mapping to Multiple-Element Outputs Does Not Increase Expressive Power	p. 50
Defining Particular Functions	p. 51
Summary: Physical/Neurobiological Implications of Facts about Functions	p. 53
Representations	p. 55
Some Simple Examples	p. 56
Notation	p. 59
The Algebraic Representation of Geometry	p. 64
Symbols	p. 72
Physical Properties of Good Symbols	p. 72
Symbol Taxonomy	p. 79
Summary	p. 82
Procedures	p. 85
Algorithms	p. 85
Procedures, Computation, and Symbols	p. 87
Coding and Procedures	p. 89
Two Senses of Knowing	p. 100
A Geometric Example	p. 101
Computation	p. 104
Formalizing Procedures	p. 105
The Turing Machine	p. 107
Turing Machine for the Successor Function	p. 110
Turning Machines for fis_even	p. 111
Turing Machines for f+	p. 115
Minimal Memory Structure	p. 121
General Purpose Computer	p. 122
Summary	p. 124
Architectures	p. 126
One-Dimensional Look-Up Tables (If-Then Implementation)	p. 128
Adding State Memory: Finite-State Machines	p. 131
Adding Register Memory	p. 137
Summary	p. 144
Data Structures	p. 149
Finding Information in Memory	p. 151
An Illustrative Example	p. 160
Procedures and the Coding of Data Structures	p. 165
The Structure of the Read-Only Biological Memory	p. 167
Computing with Neurons	p. 170
Transducers and Conductors	p. 171
Synapses and the Logic Gates	p. 172
The Slowness of It All	p. 173
The Time-Scale Problem	p. 174
Synaptic Plasticity	p. 175
Recurrent Loops in Which Activity Reverberates	p. 183
The Nature of Learning	p. 187
Learning As Rewiring	p. 187
Synaptic Plasticity and the Associative Theory of Learning	p. 189
Why Associations are Not Symbols	p. 191
Distributed Coding	p. 192
Learning As the Extraction and Preservation of Useful Information	p. 196
Updating an Estimate of One's Location	p. 198
Learning Time and Space	p. 207
Computational Accessibility	p. 207
Learning the Time of Day	p. 208
Learning Durations	p. 211
Episodic Memory	p. 213
The Modularity of Learning	p. 218
Path Integration	p. 219
Learning the Solar Ephemeris	p. 220
"Associative" Learning	p. 226
Summary	p. 241
Dead Reckoning in a Neural Network	p. 242
Reverberating Circuits as Read/Write Memory Mechanisms	p. 245
Implementing Combinatorial Operations by Table-Look-Up	p. 250
The Full Model	p. 251
The Ontogeny of the Connections?	p. 252
How Realistic Is the Model?	p. 254
Lessons to Be Drawn	p. 258
Summary	p. 265
Neural Models of Interval Timing	p. 266
Timing an Interval on First Encounter	p. 266
Dworkin's Paradox	p. 268
Neurally Inspired Models	p. 269
The Deeper Problems	p. 276
The Molecular Basis of Memory	p. 278
The Need to Separate Theory of Memory from Theory of Learning	p. 278
The Coding Question	p. 279
A Cautionary Tale	p. 281
Why Not Synaptic Conductance?	p. 282
A Molecular or Sub-Molecular Mechanism?	p. 283
Bringing the Data to the Computational Machinery	p. 283
Is It Universal?	p. 286
References	p. 288
Glossary	p. 299
Index	p. 312
Table of Contents provided by Ingram. All Rights Reserved.

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Memory and the Computational Brain Why Cognitive Science will Transform Neuroscience

Summary

Author Biography

Table of Contents

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