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B344DSL%UTARLG.ARL.UTEXAS.EDU@ricevm1.rice.edu
B344DSL%UTARLG.ARL.UTEXAS.EDU at ricevm1.rice.edu
Sun Dec 10 14:56:00 EST 1989
This message is an announcement of a forthcoming graduate
textbook neural networks by Daniel S. Levine. The title
of the book is Introduction to Neural and Cognitive
Modeling, and the publisher is Lawrence Erlbaum Associates,
Inc. The book should be in production early in 1990, so
should, with luck, be ready by the start of the Fall, 1990
semester at universities. Chapters 2 to 7 will contain
homework exercises. Some of the homework problems will
involve computer simulations of models already in the
literature. Others will involve thought experiments about
whether a particular network can model a particular
cognitive process, or how that network might be modified to
do so.
The table of contents follows. Please contact the author
or publisher for further information.
Author: Daniel S. Levine
Department of Mathematics
University of Texas at Arlington
Arlington, TX 76019-9408
817-273-3598
b344dsl at utarlg.bitnet
Publisher: Lawrence Erlbaum Associates, Inc.
365 Broadway
Hillsdale, NJ 07642
201-666-4110
Table of Contents:
PREFACE
CHAPTER 1: BRAIN AND MACHINE: THE SAME PRINCIPLES?
What are Neural Networks?
What are Some Principles of Neural Network Theory?
Methodological Considerations
i 36
CHAPTER 2: HISTORICAL OUTLINE
2.1 -- Digital Approaches
The Mc Culloch-Pitts network
Early Approaches to Modeling Learning: Hull and Hebb
Rosenblatt's Perceptrons
Some Experiments with Perceptrons
The Divergence of Artificial Intelligence and Neural
Modeling
2.2 -- Continuous and Random-net Approaches
Rashevsky's Work
Early Random Net Models
Reconciling Randomness and Specificity
2.3 -- Definitions and Detailed Rules for Rosenblatt's
Perceptrons
CHAPTER 3: ASSOCIATIVE LEARNING AND SYNAPTIC PLASTICITY
3.1 -- Physiological Bases for Learning
3.2 -- Rules for Associative Learning
Outstars and Other Early Models of Grossberg
Anderson's Connection Matrices
Kohonen's Work
3.3 -- Learning Rules Related to Changes in Node Activities
Klopf's Hedonistic Neurons and the Sutton-Barto
Learning Rule
Error Correction and Back Propagation
The Differential Hebbian Idea
Gated Dipole Theory
3.4 -- Associative Learning of Patterns
Kohonen's Recent Work: Autoassociation and
Heteroassociation
Kosko's Bidirectional Associative Memory
3.5 -- Equations and Some Physiological Details
Neurophysiological Principles
Equations for Grossberg's Outstar
Kohonen's Early Equations (Example: Simulation of Face
Recognition)
Derivation of the Back Propagation Learning Law Due to
Rumelhart, Hinton, and Williams
Equations for Sutton and Barto's Learning Network
Gated Dipole Equations Due to Grossberg
Kosko's Bidirectional Associative Memory (BAM)
Kohonen's Autoassociative Maps
CHAPTER 4: COMPETITION, INHIBITION, SPATIAL CONTRAST
ENHANCEMENT, AND SHORT-TERM MEMORY
4.1 -- Early Studies and General Themes (Contrast
Enhancement, Competition, and Normalization)
Nonrecurrent Versus Recurrent Lateral Inhibition
4.2 -- Lateral Inhibition and Excitation Between
Sensory Representations
Wilson and Cowan's Work
Work of Grossberg and Colleagues
Work of Amari and Colleagues
Energy Functions in the Cohen-Grossberg and Hopfield-
Tank Models
The Implications of Approach to Equilibrium
4.3 -- Competition and Cooperation in Visual Pattern
Recognition Models
Visual Illusions
Boundary Detection Versus Feature Detection
Binocular and Stereoscopic Vision
Comparison of Grossberg's and Marr's Approaches
4.4 -- Uses of Lateral Inhibition in Higher-level
Processing
4.5 -- Equations for Various Competitive and Lateral
Inhibition Models
Equations of Sperling and Sondhi
Equations of Wilson and Cowan
Equations of Grossberg and his Co-workers: Analytical
Results
Equations of Hopfield and Tank
Equations of Amari and Arbib
CHAPTER 5: CONDITIONING, ATTENTION, REINFORCEMENT, AND
COMPLEX ASSOCIATIVE LEARNING
5.1 -- Network Models of Classical Conditioning
Early Work: Uttley and Brindley
Rescorla and Wagner's Psychological Model
Grossberg: Drive Representations and Synchronization
Aversive Conditioning and Extinction
Differential Hebbian Theory Versus Gated Dipole Theory
5.2 -- Attention and Short Term Memory in the Context of
Conditioning
Grossberg's Approach to Attention
Sutton and Barto's Approach to Blocking
Some Contrasts Between the Above Two Approaches
Further Connections with Invertebrate Neurophysiology
Gated Dipoles and Aversive Conditioning
5.3 -- Equations for Some Conditioning and Associative
Learning Models
Klopf's Drive-reinforcement Model
Some Later Variations of the Sutton-Barto model:
Temporal Difference
The READ Circuit of Grossberg, Schmajuk, and Levine
The Aplysia Model of Gingrich and Byrne
CHAPTER 6: CODING AND CATEGORIZATION
6.1 -- Interactions Between Short and Long Term Memory in
Code Development: Examples from Vision
Malsburg's Model with Synaptic Conservation
Grossberg's Model with Pattern Normalization
Mathematical Results of Grossberg and Amari
Feature Detection Models with Stochastic Elements
From Feature Coding to Categorization
6.2 -- Supervised Classification Models
The Back Propagation Network and its Variants
Some Models from the Anderson-Cooper School
6.3 -- Unsupervised Classification Models
The Rumelhart-Zipser Competitive Learning Algorithm
Adaptive Resonance Theory
Edelman and Neural Darwinism
6.4 -- Translation and Scale Invariance
6.5 -- Equations for Various Coding and Categorization
Models
Malsburg's and Grossberg's Development of Feature
Detectors
Some Implementation Issues for Back Propagation
Equations
Brain-state-in-a-box Equations
Rumelhart and Zipser's Competitive Learning Equations
Adaptive Resonance Equations
CHAPTER 7: OPTIMIZATION, CONTROL, DECISION MAKING, AND
KNOWLEDGE REPRESENTATION
7.1 -- Optimization and Control
Hopfield, Tank, and the Traveling-Salesman Problem
Simulated Annealing and Boltzmann Machines
Motor Control: the Example of Eye Movements
Motor Control: Arm Movements
Speech Recognition and Synthesis
Robotic Control
7.2 -- Decision Making and Knowledge Representation
What, if Anything, do Biological Organisms Optimize?
Affect, Habit, and Novelty in Neural Network Theories
Neural Control Circuits, Neurochemical Modulation, and
Mental Illness
Some Comments on Models of Specific Brain Areas
Knowledge Representation: Letters and Words
Knowledge Representation: Concepts and Inference
7.3 -- Equations for a Few Neural Networks Performing
Complex Tasks
Hopfield and Tank's "Traveling Salesman" Network
The Boltzmann Machine
Grossberg and Kuperstein's Eye Movement Network
VITE and Passive Update of Position (PUP) for Arm
Movement Control
Affective Balance and Decision Making Under Risk
CHAPTER 8: A FEW RECENT ADVANCES IN NEUROCOMPUTING AND
NEUROBIOLOGY
8.1 -- Some "Toy" and Real World Computing Applications
8.2 -- Some Biological Discoveries
APPENDIX 1: BASIC FACTS OF NEUROBIOLOGY
The Neuron
Synapses, Transmitters, Messengers, and Modulators
Invertebrate and Vertebrate Nervous Systems
Functions of Subcortical Regions
Functions of the Mammalian Cerebral Cortex
APPENDIX 2: DIFFERENCE AND DIFFERENTIAL EQUATIONS IN NEURAL
NETWORKS
Example: the Sutton-Barto Difference Equations
Differential Versus Difference Equations
Outstar Equations: Network Interpretation and Numerical
Implementation
The Chain Rule and Back Propagation
Dynamical Systems: Steady States, Limit Cycles, and
Chaos
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