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Memory and the computational brain : (Record no. 3365)

MARC details
000 -LEADER
fixed length control field	04185cam a22002894a 4500
001 - CONTROL NUMBER
control field	15486448
003 - CONTROL NUMBER IDENTIFIER
control field	OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20191004100325.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	081016s2009 enka b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number	2008044683
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	9781405122887 (pbk. : alk. paper)
040 ## - CATALOGING SOURCE
Original cataloging agency	DLC
Transcribing agency	DLC
Modifying agency	DLC
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number	QP360.5
Item number	.G35 2009
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	612.82 GAL K
Edition number	22
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name	Gallistel, C. R.,
Dates associated with a name	1941-
245 10 - TITLE STATEMENT
Title	Memory and the computational brain :
Remainder of title	why cognitive science will transform neuroscience /
Statement of responsibility, etc.	C. R. Gallistel and Adam Philip King.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	Chichester, West Sussex, UK ;
--	Malden, MA :
Name of publisher, distributor, etc.	Wiley-Blackwell,
Date of publication, distribution, etc.	2009.
300 ## - PHYSICAL DESCRIPTION
Extent	xvi, 319 p. :
Other physical details	ill. ;
Dimensions	25 cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note	Includes bibliographical references (p. [288]-298) and index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note	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 Multi-Part Outputs. Mapping to Multiple-Element 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 f is -even Turing Machines for f + Minimal Memory Structure. General Purpose Computer. Summary. 8. Architectures. One-Dimensional Look-Up Tables (If-Then Implementation). Adding State Memory: Finite-State 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 Read-Only Biological Memory. 10. Computing with Neurons. Transducers and Conductors. Synapses and the Logic Gates. The Slowness of It All. The Time-Scale 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 Table-Look-Up. 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 Sub-Molecular Mechanism? Bringing the Data to the Computational Machinery. Is It Universal? References. Glossary. Index.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Cognitive neuroscience.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Cognitive science.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name	King, Adam Philip.
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a	7
b	cbc
c	orignew
d	1
e	ecip
f	20
g	y-gencatlg
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Dewey Decimal Classification
Koha item type	Stack text books

Holdings
Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Not for loan	Collection code	Home library	Current library	Date acquired	Total Checkouts	Full call number	Barcode	Date last seen	Price effective from	Koha item type
		Dewey Decimal Classification			53/2	All India Institute of Speech and Hearing	All India Institute of Speech and Hearing	04/10/2019		612.82 GAL K	17537	04/10/2019	04/10/2019	Stack text books