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Memory and the Computational Brain offers a provocativeargument that goes to the heart of neuroscience, proposing that thefield can and should benefit from the recent advances of cognitivescience and the development of information theory over the courseof the last several decades. * A provocative argument that impacts across the fields oflinguistics, cognitive science, and neuroscience, suggesting newperspectives on learning mechanisms in the brain * Proposes that the field of neuroscience can and should benefitfrom the recent advances of cognitive science and the developmentof information…mehr

Produktbeschreibung
Memory and the Computational Brain offers a provocativeargument that goes to the heart of neuroscience, proposing that thefield can and should benefit from the recent advances of cognitivescience and the development of information theory over the courseof the last several decades. * A provocative argument that impacts across the fields oflinguistics, cognitive science, and neuroscience, suggesting newperspectives on learning mechanisms in the brain * Proposes that the field of neuroscience can and should benefitfrom the recent advances of cognitive science and the developmentof information theory * Suggests that the architecture of the brain is structuredprecisely for learning and for memory, and integrates the conceptof an addressable read/write memory mechanism into the foundationsof neuroscience * Based on lectures in the prestigious Blackwell-MarylandLectures in Language and Cognition, and now significantly reworkedand expanded to make it ideal for students and faculty

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 336
  • Erscheinungstermin: 30. März 2009
  • Englisch
  • ISBN-13: 9781444310481
  • Artikelnr.: 37355113
Autorenporträt
C. R. Gallistel is Co-Director of the Rutgers Center forCognitive Science. He is one of the foremost psychologists workingon the foundations of cognitive neuroscience. His publicationsinclude The Symbolic Foundations of Conditional Behavior(2002), and The Organization of Learning (1990). Adam Philip King is Assistant Professor ofMathematics at Fairfield University.
Inhaltsangabe
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 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.