Memory and the computational brain : why cognitive science will transform neuroscience
 Author/Creator
 Gallistel, C. R., 1941
 Language
 English.
 Imprint
 Chichester, West Sussex, U.K. ; Malden, MA : WileyBlackwell, 2009.
 Physical description
 xvi, 319 p. : ill. ; 26 cm.
 Series
 Blackwell/Maryland lectures in language and cognition.
Access
Available online

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QP360.5 .G35 2009
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Unknown
QP360.5 .G35 2009

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QP360.5 .G35 2009
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Contributors
 Contributor
 King, Adam Philip.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. [288]298) and index.
 Contents

 Preface. 1. Information. Shannon's Theory of Communication. Measuring Information. Efficient Coding. Information and the Brain. Digital and Analog Signals. Appendix: The Information Content of Rare Versus Common Events and Signals. 2. Bayesian Updating. Bayes' Theorem and Our Intuitions About Evidence. Using Bayes' Rule. Summary. 3. Functions. Functions of One Argument. Composition and Decomposition of Functions. Functions of More than One Argument. The Limits to Functional Decomposition. Functions Can Map to MultiPart Outputs. Mapping to MultipleElement Outputs Does Not Increase Expressive Power. Defining Particular Functions. Summary: Physical/Neurobiological Implications of Facts about Functions. 4. Representations. Some Simple Examples. Notation. The Algebraic Representation of Geometry. 5. Symbols. Physical Properties of Good Symbols. Symbol Taxonomy. Summary. 6. Procedures. Algorithms. Procedures, Computation, and Symbols. Coding and Procedures. Two Senses of Knowing. A Geometric Example. 7. Computation. Formalizing Procedures. The Turing Machine. Turing Machine for the Successor Function. Turing Machines for is even Turing Machines for + Minimal Memory Structure. General Purpose Computer. Summary. 8. Architectures. OneDimensional LookUp Tables (IfThen Implementation). Adding State Memory: FiniteState Machines. Adding Register Memory. Summary. 9. Data Structures. Finding Information in Memory. An Illustrative Example. Procedures and the Coding of Data Structures. The Structure of the ReadOnly Biological Memory. 10. Computing with Neurons. Transducers and Conductors. Synapses and the Logic Gates. The Slowness of It All. The TimeScale Problem. Synaptic Plasticity. Recurrent Loops in Which Activity Reverberates. 11. The Nature of Learning. Learning As Rewiring. Synaptic Plasticity and the Associative Theory of Learning. Why Associations Are Not Symbols. Distributed Coding. Learning As the Extraction and Preservation of Useful Information. Updating an Estimate of One's Location. 12. Learning Time and Space. Computational Accessibility. Learning the Time of Day. Learning Durations. Episodic Memory. 13. The Modularity of Learning. Example 1: Path Integration. Example 2: Learning the Solar Ephemeris. Example 3: "Associative" Learning. Summary. 14. Dead Reckoning in a Neural Network. Reverberating Circuits as Read/Write Memory Mechanisms. Implementing Combinatorial Operations by TableLookUp. The Full Model. The Ontogeny of the Connections? How Realistic is the Model? Lessons to be Drawn. Summary. 15. Neural Models of Interval Timing. Timing an Interval on First Encounter. Dworkin's Paradox. Neurally Inspired Models. The Deeper Problems. 16. The Molecular Basis of Memory. The Need to Separate Theory of Memory from Theory of Learning. The Coding Question. A Cautionary Tale. Why Not Synaptic Conductance? A Molecular or SubMolecular Mechanism? Bringing the Data to the Computational Machinery. Is It Universal? References. Glossary. Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 "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. This book provides a provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain. It proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory. It 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. It is based on lectures in the prestigious BlackwellMaryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2009
 Responsibility
 C.R. Gallistel and Adam Philip King.
 Series
 Blackwell/Maryland lectures in language and cognition
 ISBN
 9781405122870
 1405122870
 9781405122887
 1405122889