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Informationen zum Autor 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. Klappentext 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 Zusammenfassung 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. Inhaltsverzeichnis 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 ¿ is _even Turing Machines for ¿+ 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 Biolog...
Sommario
Preface.
1. Information.
2. Bayesian Updating.
3. Functions.
4. Representations.
5. Symbols.
6. Procedures.
7. Computation.
8. Architectures.
9. Data Structures.
10. Computing with Neurons.
11. The Nature of Learning.
12. Learning Time and Space.
13. The Modularity of Learning.
14. Dead Reckoning in a Neural Network.
15. Neural Models of Interval Timing.
16. The Molecular Basis of Memory.
References.
Glossary.
Index.
Relazione
"The book covers wide-ranging ground--indeed, it passes for a computer science or philosophy textbook in places--but it does so in a consistently lucid and engaging fashion." ( CHOICE , December 2009)
"The authors provide a cogent set of ideas regarding a kind of brain functional architecture that could serve as a thought-provoking alternative to that envisioned by current dogma. If one is seriously concerned with understanding and investigating the brain and how it operates, taking the time to absorb the ideas conveyed in this book is likely to be time well spent." ( PsycCRITIQUES , November 2009)
"Along with a light complement of fascinating psychological case studies of representations of space and time, and a heavy set of polemical sideswipes at neuroscientists and their hapless computational fellow travelers, this book has the simple goal of persuading us of the importance of a particular information processing mechanism that it claims does not currently occupy center stage." ( Nature Neuroscience , October 2009)
