{"product_id":"memory-and-the-computational-brain-isbn-9781405122887","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 \u003cp\u003ePreface viii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Information 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eShannon’s Theory of Communication 2\u003c\/p\u003e \u003cp\u003eMeasuring Information 7\u003c\/p\u003e \u003cp\u003eEfficient Coding 16\u003c\/p\u003e \u003cp\u003eInformation and the Brain 20\u003c\/p\u003e \u003cp\u003eDigital and Analog Signals 24\u003c\/p\u003e \u003cp\u003eAppendix: The Information Content of Rare Versus Common 25\u003c\/p\u003e \u003cp\u003eEvents and Signals\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Bayesian Updating 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBayes’ Theorem and Our Intuitions about Evidence 30\u003c\/p\u003e \u003cp\u003eUsing Bayes’ Rule 32\u003c\/p\u003e \u003cp\u003eSummary 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Functions 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFunctions of One Argument 43\u003c\/p\u003e \u003cp\u003eComposition and Decomposition of Functions 46\u003c\/p\u003e \u003cp\u003eFunctions of More than One Argument 48\u003c\/p\u003e \u003cp\u003eThe Limits to Functional Decomposition 49\u003c\/p\u003e \u003cp\u003eFunctions Can Map to Multi-Part Outputs 49\u003c\/p\u003e \u003cp\u003eMapping to Multiple-Element Outputs Does Not Increase Expressive Power 50\u003c\/p\u003e \u003cp\u003eDefining Particular Functions 51\u003c\/p\u003e \u003cp\u003eSummary: Physical\/Neurobiological Implications of Facts about Functions 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Representations 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSome Simple Examples 56\u003c\/p\u003e \u003cp\u003eNotation 59\u003c\/p\u003e \u003cp\u003eThe Algebraic Representation of Geometry 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Symbols 72\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePhysical Properties of Good Symbols 72\u003c\/p\u003e \u003cp\u003eSymbol Taxonomy 79\u003c\/p\u003e \u003cp\u003eSummary 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Procedures 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAlgorithms 85\u003c\/p\u003e \u003cp\u003eProcedures, Computation, and Symbols 87\u003c\/p\u003e \u003cp\u003eCoding and Procedures 89\u003c\/p\u003e \u003cp\u003eTwo Senses of Knowing 100\u003c\/p\u003e \u003cp\u003eA Geometric Example 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Computation 104\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFormalizing Procedures 105\u003c\/p\u003e \u003cp\u003eThe Turing Machine 107\u003c\/p\u003e \u003cp\u003eTuring Machine for the Successor Function 110\u003c\/p\u003e \u003cp\u003eTuring Machines for fis even 111\u003c\/p\u003e \u003cp\u003eTuring Machines for f+ 115\u003c\/p\u003e \u003cp\u003eMinimal Memory Structure 121\u003c\/p\u003e \u003cp\u003eGeneral Purpose Computer 122\u003c\/p\u003e \u003cp\u003eSummary 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Architectures 126\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOne-Dimensional Look-Up Tables (If-Then Implementation) 128\u003c\/p\u003e \u003cp\u003eAdding State Memory: Finite-State Machines 131\u003c\/p\u003e \u003cp\u003eAdding Register Memory 137\u003c\/p\u003e \u003cp\u003eSummary 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Data Structures 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinding Information in Memory 151\u003c\/p\u003e \u003cp\u003eAn Illustrative Example 160\u003c\/p\u003e \u003cp\u003eProcedures and the Coding of Data Structures 165\u003c\/p\u003e \u003cp\u003eThe Structure of the Read-Only Biological Memory 167\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Computing with Neurons 170\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTransducers and Conductors 171\u003c\/p\u003e \u003cp\u003eSynapses and the Logic Gates 172\u003c\/p\u003e \u003cp\u003eThe Slowness of It All 173\u003c\/p\u003e \u003cp\u003eThe Time-Scale Problem 174\u003c\/p\u003e \u003cp\u003eSynaptic Plasticity 175\u003c\/p\u003e \u003cp\u003eRecurrent Loops in Which Activity Reverberates 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The Nature of Learning 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLearning As Rewiring 187\u003c\/p\u003e \u003cp\u003eSynaptic Plasticity and the Associative Theory of Learning 189\u003c\/p\u003e \u003cp\u003eWhy Associations Are Not Symbols 191\u003c\/p\u003e \u003cp\u003eDistributed Coding 192\u003c\/p\u003e \u003cp\u003eLearning As the Extraction and Preservation of Useful Information 196\u003c\/p\u003e \u003cp\u003eUpdating an Estimate of One’s Location 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Learning Time and Space 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eComputational Accessibility 207\u003c\/p\u003e \u003cp\u003eLearning the Time of Day 208\u003c\/p\u003e \u003cp\u003eLearning Durations 211\u003c\/p\u003e \u003cp\u003eEpisodic Memory 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 The Modularity of Learning 218\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExample 1: Path Integration 219\u003c\/p\u003e \u003cp\u003eExample 2: Learning the Solar Ephemeris 220\u003c\/p\u003e \u003cp\u003eExample 3: “Associative” Learning 226\u003c\/p\u003e \u003cp\u003eSummary 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Dead Reckoning in a Neural Network 242\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReverberating Circuits as Read\/Write Memory Mechanisms 245\u003c\/p\u003e \u003cp\u003eImplementing Combinatorial Operations by Table-Look-Up 250\u003c\/p\u003e \u003cp\u003eThe Full Model 251\u003c\/p\u003e \u003cp\u003eThe Ontogeny of the Connections? 252\u003c\/p\u003e \u003cp\u003eHow Realistic Is the Model? 254\u003c\/p\u003e \u003cp\u003eLessons to Be Drawn 258\u003c\/p\u003e \u003cp\u003eSummary 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Neural Models of Interval Timing 266\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTiming an Interval on First Encounter 266\u003c\/p\u003e \u003cp\u003eDworkin’s Paradox 268\u003c\/p\u003e \u003cp\u003eNeurally Inspired Models 269\u003c\/p\u003e \u003cp\u003eThe Deeper Problems 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 The Molecular Basis of Memory 278\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Need to Separate Theory of Memory from Theory of Learning 278\u003c\/p\u003e \u003cp\u003eThe Coding Question 279\u003c\/p\u003e \u003cp\u003eA Cautionary Tale 281\u003c\/p\u003e \u003cp\u003eWhy Not Synaptic Conductance? 282\u003c\/p\u003e \u003cp\u003eA Molecular or Sub-Molecular Mechanism? 283\u003c\/p\u003e \u003cp\u003eBringing the Data to the Computational Machinery 283\u003c\/p\u003e \u003cp\u003eIs It Universal? 286\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003eGlossary 299\u003c\/p\u003e \u003cp\u003eIndex 312\u003c\/p\u003e  \"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  \"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)  \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":47989606908133,"sku":"NP9781405122887","price":71.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781405122887.jpg?v=1761784785","url":"https:\/\/k12savings.com\/es\/products\/memory-and-the-computational-brain-isbn-9781405122887","provider":"K12savings","version":"1.0","type":"link"}