{"product_id":"memory-and-the-computational-brain-isbn-9781405122870","title":"Memory and the Computational Brain","description":"\u003ci\u003eMemory and the Computational Brain\u003c\/i\u003e 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.   \u003cul type=\"disc\"\u003e \u003cli\u003eA provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain\u003c\/li\u003e \u003cli\u003eProposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory\u003c\/li\u003e \u003cli\u003eSuggests 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\u003c\/li\u003e \u003cli\u003eBased 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\u003c\/li\u003e \u003c\/ul\u003e  Preface.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 1. Information.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Shannon’s Theory of Communication.\u003cbr\u003e \u003cbr\u003e Measuring Information.\u003cbr\u003e \u003cbr\u003e Efficient Coding.\u003cbr\u003e \u003cbr\u003e Information and the Brain.\u003cbr\u003e \u003cbr\u003e Digital and Analog Signals.\u003cbr\u003e \u003cbr\u003e Appendix: The Information Content of Rare Versus Common Events and Signals.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 2. Bayesian Updating.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Bayes’ Theorem and Our Intuitions About Evidence.\u003cbr\u003e \u003cbr\u003e Using Bayes’ Rule.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 3. Functions.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Functions of One Argument.\u003cbr\u003e \u003cbr\u003e Composition and Decomposition of Functions.\u003cbr\u003e \u003cbr\u003e Functions of More than One Argument.\u003cbr\u003e \u003cbr\u003e The Limits to Functional Decomposition.\u003cbr\u003e \u003cbr\u003e Functions Can Map to Multi-Part Outputs.\u003cbr\u003e \u003cbr\u003e Mapping to Multiple-Element Outputs Does Not Increase Expressive Power.\u003cbr\u003e \u003cbr\u003e Defining Particular Functions.\u003cbr\u003e \u003cbr\u003e Summary: Physical\/Neurobiological Implications of Facts about Functions.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 4. Representations.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Some Simple Examples.\u003cbr\u003e \u003cbr\u003e Notation.\u003cbr\u003e \u003cbr\u003e The Algebraic Representation of Geometry.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 5. Symbols.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Physical Properties of Good Symbols.\u003cbr\u003e \u003cbr\u003e Symbol Taxonomy.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 6. Procedures.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Algorithms.\u003cbr\u003e \u003cbr\u003e Procedures, Computation, and Symbols.\u003cbr\u003e \u003cbr\u003e Coding and Procedures.\u003cbr\u003e \u003cbr\u003e Two Senses of Knowing.\u003cbr\u003e \u003cbr\u003e A Geometric Example.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 7. Computation.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Formalizing Procedures.\u003cbr\u003e \u003cbr\u003e The Turing Machine.\u003cbr\u003e \u003cbr\u003e Turing Machine for the Successor Function.\u003cbr\u003e \u003cbr\u003e Turing Machines for ƒ \u003csub\u003eis _even\u003c\/sub\u003e\u003cbr\u003e \u003cbr\u003e Turing Machines for ƒ\u003csub\u003e+\u003c\/sub\u003e\u003cbr\u003e \u003cbr\u003e Minimal Memory Structure.\u003cbr\u003e \u003cbr\u003e General Purpose Computer.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 8. Architectures.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e One-Dimensional Look-Up Tables (If-Then Implementation).\u003cbr\u003e \u003cbr\u003e Adding State Memory: Finite-State Machines.\u003cbr\u003e \u003cbr\u003e Adding Register Memory.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 9. Data Structures.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Finding Information in Memory.\u003cbr\u003e \u003cbr\u003e An Illustrative Example.\u003cbr\u003e \u003cbr\u003e Procedures and the Coding of Data Structures.\u003cbr\u003e \u003cbr\u003e The Structure of the Read-Only Biological Memory.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 10. Computing with Neurons.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Transducers and Conductors.\u003cbr\u003e \u003cbr\u003e Synapses and the Logic Gates.\u003cbr\u003e \u003cbr\u003e The Slowness of It All.\u003cbr\u003e \u003cbr\u003e The Time-Scale Problem.\u003cbr\u003e \u003cbr\u003e Synaptic Plasticity.\u003cbr\u003e \u003cbr\u003e Recurrent Loops in Which Activity Reverberates.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 11. The Nature of Learning.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Learning As Rewiring.\u003cbr\u003e \u003cbr\u003e Synaptic Plasticity and the Associative Theory of Learning.\u003cbr\u003e \u003cbr\u003e Why Associations Are Not Symbols.\u003cbr\u003e \u003cbr\u003e Distributed Coding.\u003cbr\u003e \u003cbr\u003e Learning As the Extraction and Preservation of Useful Information.\u003cbr\u003e \u003cbr\u003e Updating an Estimate of One’s Location.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 12. Learning Time and Space.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Computational Accessibility.\u003cbr\u003e \u003cbr\u003e Learning the Time of Day.\u003cbr\u003e \u003cbr\u003e Learning Durations.\u003cbr\u003e \u003cbr\u003e Episodic Memory.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 13. The Modularity of Learning.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Example 1: Path Integration.\u003cbr\u003e \u003cbr\u003e Example 2: Learning the Solar Ephemeris.\u003cbr\u003e \u003cbr\u003e Example 3: “Associative” Learning.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 14. Dead Reckoning in a Neural Network.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Reverberating Circuits as Read\/Write Memory Mechanisms.\u003cbr\u003e \u003cbr\u003e Implementing Combinatorial Operations by Table-Look-Up.\u003cbr\u003e \u003cbr\u003e The Full Model.\u003cbr\u003e \u003cbr\u003e The Ontogeny of the Connections?\u003cbr\u003e \u003cbr\u003e How Realistic is the Model?\u003cbr\u003e \u003cbr\u003e Lessons to be Drawn.\u003cbr\u003e \u003cbr\u003e Summary.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 15. Neural Models of Interval Timing.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e Timing an Interval on First Encounter.\u003cbr\u003e \u003cbr\u003e Dworkin’s Paradox.\u003cbr\u003e \u003cbr\u003e Neurally Inspired Models.\u003cbr\u003e \u003cbr\u003e The Deeper Problems.\u003cbr\u003e \u003cb\u003e\u003cbr\u003e 16. The Molecular Basis of Memory.\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e The Need to Separate Theory of Memory from Theory of Learning.\u003cbr\u003e \u003cbr\u003e The Coding Question.\u003cbr\u003e \u003cbr\u003e A Cautionary Tale.\u003cbr\u003e \u003cbr\u003e Why Not Synaptic Conductance?\u003cbr\u003e \u003cbr\u003e A Molecular or Sub-Molecular Mechanism?\u003cbr\u003e \u003cbr\u003e Bringing the Data to the Computational Machinery.\u003cbr\u003e \u003cbr\u003e Is It Universal?\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Glossary.\u003cbr\u003e \u003cbr\u003e Index.  \"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.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, December 2009)\u003cbr\u003e \u003cbr\u003e   \u003cp\u003e\"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.\" (\u003ci\u003ePsycCRITIQUES\u003c\/i\u003e, November 2009)\u003c\/p\u003e \u003cp\u003e\"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.\" (\u003ci\u003eNature Neuroscience\u003c\/i\u003e, October 2009)\u003c\/p\u003e  \u003cb\u003eC. R. Gallistel\u003c\/b\u003e 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 \u003ci\u003eThe Symbolic Foundations of Conditional Behavior\u003c\/i\u003e (2002), and \u003ci\u003eThe Organization of Learning\u003c\/i\u003e (1990). \u003cbr\u003e   \u003cp\u003e\u003cb\u003eAdam Philip King\u003c\/b\u003e is Assistant Professor of Mathematics at Fairfield University.\u003c\/p\u003e  \u003ci\u003eMemory and the Computational Brain\u003c\/i\u003e spans the fields of cognitive science, linguistics, psychology, neuroscience, and education, to suggest new perspectives on the way we consider learning mechanisms in the brain.  \u003cp\u003eGallistel and King propose that the architecture of the brain is structured precisely for learning and for memory, and that the concept of an addressable read\/write memory mechanism should be integrated into the foundations of neuroscience. They argue that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory over the recent decades. Based on three lectures given by Randy Gallistel in the prestigious Blackwell-Maryland Lectures in Language and Cognition, the text has been significantly revised and expanded with numerous interdisciplinary examples and models and reflects recent research to make it essential reading for both students and those working in the field.\u003c\/p\u003e  \"Any scientist seriously interested in how the brain does its work will find Gallistel and King's new book indispensable.  It challenges modern dogma and does so in a clear and compelling manner.\"\u003cbr\u003e –Michael Gazzaniga, University of California, Santa Barbara  \u003cp\u003e\"Gallistel and King present a provocative challenge to our current \"standard model\" of information processing in the brain. This book's ideas should be read and digested by both cognitive scientists and neuroscientists - anyone seriously interested in the biological or computational underpinnings of learning.\"\u003cbr\u003e –Joshua B. Tenenbaum, Massachusetts Institute of Technology\u003c\/p\u003e \u003cp\u003e\"A lucid and convincing argument for a particular architecture for encoding information in the brain, based on some key notions of computational cognitive science, a significant contribution to neuroscience.\"\u003cbr\u003e \u003ci\u003e–\u003c\/i\u003eAravind K. Joshi, University of Pennsylvania\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47989606809829,"sku":"NP9781405122870","price":149.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781405122870.jpg?v=1761784785","url":"https:\/\/k12savings.com\/es\/products\/memory-and-the-computational-brain-isbn-9781405122870","provider":"K12savings","version":"1.0","type":"link"}