Memory and the Computational... | Buy

Summary

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

Preface
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
Bayesian Updating
Bayes' Theorem and Our Intuitions About Evidence
Using Bayes' Rule
Summary
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
Representations
Some Simple Examples
Notation
The Algebraic Representation of Geometry
Symbols
Physical Properties of Good Symbols
Symbol Taxonomy
Summary
Procedures
Algorithms
Procedures, Computation, and Symbols
Coding and Procedures
Two Senses of Knowing
A Geometric Example
Computation
Formalizing Procedures
The Turing Machine
Turing Machine for the Successor Function
Turing Machines for â _{is _even}
Turing Machines for â₊
Minimal Memory Structure
General Purpose Computer
Summary
Architectures
One-Dimensional Look-Up Tables (If-Then Implementation)
Adding State Memory: Finite-State Machines
Adding Register Memory
Summary
Data Structures
Finding Information in Memory
An Illustrative Example
Procedures and the Coding of Data Structures
The Structure of the Read-Only Biological Memory
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
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
Learning Time and Space
Computational Accessibility
Learning the Time of Day
Learning Durations
Episodic Memory
The Modularity of Learning
Example 1: Path Integration
Example 2: Learning the Solar Ephemeris
Example 3: "Associative" Learning
Summary
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
Neural Models of Interval Timing
Timing an Interval on First Encounter
Dworkin's Paradox
Neurally Inspired Models
The Deeper Problems
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
Table of Contents provided by Publisher. All Rights Reserved.

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