summer school proceedings: contents and ordering info

[Dave.Touretzky@DST.BOLTZ.CS.CMU.EDU]

CONNECTIONIST MODELS: Proceedings of the 1990 Summer School

Edited by
     David S. Touretzky (Carnegie Mellon University),
     Jeffrey L. Elman (University of California, San Diego),
     Terrence J. Sejnowski (The Salk Institute, UC San Diego), and
     Geoffrey E. Hinton (University of Toronto)

ISBN 1-55860-156-2        $29.95          404 pages

     (For bibliographic purposes, the complete table of contents
     and contact numbers for additional information or for use in
     obtaining copies of this book follow the announcement.)


TABLE OF CONTENTS

PART I  MEAN FIELD, BOLTZMANN, AND HOPFIELD NETWORKS

Deterministic Boltzmann Learning in Networks with 
Asymmetric Connectivity                                          3
     C.C. Galland and G.E. Hinton

Contrastive Hebbian Learning in the Continuous Hopfield Model    10
     J.R. Movellan

Mean Field Networks that Learn to Discriminate
Temporally Distorted Strings                                     18
     C.K.I. Williams and G.E. Hinton

Energy Minimization and the Satisfiability
of Propositional Logic                                           23
     G. Pinkas


PART II  REINFORCEMENT LEARNING

On the Computational Economics of Reinforcement Learning         35
     A.G. Barto and P.M. Todd

Reinforcement Comparison                                         45
     P. Dayan

Learning Algorithms for Networks with
Internal and External Feedback                                   52
     J. Schmidhuber


PART III  GENETIC LEARNING
Exploring Adaptive Agency I:  Theory and Methods for
Simulating the Evolution of Learning                             65
     G.F. Miller and P.M. Todd

The Evolution of Learning:  An Experiment in Genetic
Connectionism                                                    81
     D.J. Chalmers

Evolving Controls for Unstable Systems                           91
     A.P. Wieland


PART IV  TEMPORAL PROCESSING

Back-Propagation, Weight Elimination and Time
Series Prediction                                           105
     A.S. Weigend, D.E. Rumelhart, and B.A. Huberman

Predicting the Mackey-Glass Timeseries
with Cascade-Correlation Learning                           117
     R.S. Crowder, III

Learning in Recurrent Finite Difference Networks            124
     F.S. Tsung

Temporal Backpropagation:  An Efficient Algorithm
for Finite Impulse Response Neural Networks                 131
     E.A. Wan


PART V  THEORY AND ANALYSIS 

Optimal Dimensionality Reduction Using Hebbian Learning     141
     A. Levin

Basis-Function Trees for Approximation
in High-Dimensional Spaces                                  145
     T.D. Sanger

Effects of Circuit Parameters on Convergence of
Trinary Update Back-Propagation                             152
     R.L. Shimabukuro, P.A. Shoemaker, C.C. Guest, and M.J. Carlin

Equivalence Proofs for Multi-Layer Perceptron Classifiers and the
Bayesian Discriminant Function                              159
     J.B. Hampshire, II and B. Pearlmutter

A Local Approach to Optimal Queries                         173
     D. Cohn


PART VI  MODULARITY
A Modularization Scheme for Feedforward Networks            183
     A. Ossen

A Compositional Connectionist Architecture                  188
     J.R. Chen


PART VII COGNITIVE MODELING AND SYMBOL PROCESSING