book announcement

[Michael C. Mozer]

In case you don't already have enough to read, the following volume is now
available:

     Mozer, M., Smolensky, P., Touretzky, D., Elman, J., & Weigend, A. (Eds.).
     (1994).  _Proceedings of the 1993 Connectionist Models Summer School_.
     Hillsdale, NJ:  Erlbaum Associates.

The table of contents is listed below.

For prepaid orders by check or credit card, the price is $49.95 US.
Orders may be made by e-mail to "orders at leanhq.mhs.compuserve.com", by
fax to (201) 666 2394, or by calling 1 (800) 926 6579.
Include your credit card number, type, expiration date, and refer to
"ISBN 1590-2".

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Proceedings of the 1993 Connectionist Models Summer School

Table of Contents
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NEUROSCIENCE

Sigma-pi properties of spiking neurons / Thomas Rebotier and Jacques Droulez

Towards a computational theory of rat navigation /
Hank S. Wan, David S. Touretzky, and A. David Redish

Evaluating connectionist models in psychology and neuroscience / H. Tad Blair


VISION

Self-organizing feature maps with lateral connections: Modeling ocular 
dominance / Joseph Sirosh and Risto Miikkulainen

Joint solution of low, intermediate, and high level vision tasks by global 
optimization: Application to computer vision at low SNR /
Anoop K. Bhattacharjya and Badrinath Roysam


COGNITIVE MODELING

Learning global spatial structures from local associations /
Thea B. Ghiselli-Crippa and Paul W. Munro

A connectionist model of auditory Morse code perception / David Ascher

A competitive neural network model for the process of recurrent choice /
Valentin Dragoi and J. E. R. Staddon

A neural network simulation of numerical verbal-to-arabic transcoding /
A. Margrethe Lindemann

Combining models of single-digit arithmetic and magnitude comparison /
Thomas Lund

Neural network models as tools for understanding high-level cognition: 
Developing paradigms for cognitive interpretation of neural network models /
Itiel E. Dror


LANGUAGE

Modeling language as sensorimotor coordination
F. James Eisenhart

Structure and content in word production: Why it's hard to say dlorm
Anita Govindjee and Gary Dell

Investigating phonological representations: A modeling agenda
Prahlad Gupta

Part-of-speech tagging using a variable context Markov model
Hinrich Schutze and Yoram Singer

Quantitative predictions from a constraint-based theory of syntactic ambiguity 
resolution
Michael Spivey-Knowlton

Optimality semantics
Bruce B. Tesar


SYMBOLIC COMPUTATION AND RULES

What's in a rule? The past tense by some other name might be called 
a connectionist net
Kim G. Daugherty and Mary Hare

On the proper treatment of symbolism--A lesson from linguistics
Amit Almor and Michael Rindner

Structure sensitivity in connectionist models
Lars F. Niklasson

Looking for structured representations in recurrent networks
Mihail Crucianu

Back propagation with understandable results
Irina Tchoumatchenko

Understanding neural networks via rule extraction and pruning
Mark W. Craven and Jude W. Shavlik

Rule learning and extraction with self-organizing neural networks
Ah-Hwee Tan


RECURRENT NETWORKS AND TEMPORAL PATTERN PROCESSING

Recurrent networks: State machines or iterated function systems?
John F. Kolen

On the treatment of time in recurrent neural networks
Fred Cummins and Robert F. Port

Finding metrical structure in time
J. Devin McAuley

Representations of tonal music: A case study in the development of temporal 
relationships
Catherine Stevens and Janet Wiles

Applications of radial basis function fitting to the analysis of 
dynamical systems
Michael A. S. Potts, D. S. Broomhead, and J. P. Huke

Event prediction: Faster learning in a layered Hebbian network with memory
Michael E. Young and Todd M. Bailey


CONTROL

Issues in using function approximation for reinforcement learning
Sebastian Thrun and Anton Schwartz

Approximating Q-values with basis function representations
Philip Sabes

Efficient learning of multiple degree-of-freedom control problems with 
quasi-independent Q-agents
Kevin L. Markey

Neural adaptive control of systems with drifting parameters
Anya L. Tascillo and Victor A. Skormin


LEARNING ALGORITHMS AND ARCHITECTURES

Temporally local unsupervised learning: The MaxIn algorithm for maximizing 
input information
Randall C. O'Reilly

Minimizing disagreement for self-supervised classification
Virginia R. de Sa

Comparison of two unsupervised neural network models for redundancy reduction
Stefanie Natascha Lindstaedt

Solving inverse problems using an EM approach to density estimation
Zoubin Ghahramani

Estimating a-posteriori probabilities using stochastic network models
Michael Finke and Klaus-Robert Muller


LEARNING THEORY

On overfitting and the effective number of hidden units
Andreas S. Weigend

Increase of apparent complexity is due to decrease of training set error
Robert Dodier

Momentum and optimal stochastic search
Genevieve B. Orr and Todd K. Leen

Scheme to improve the generalization error
Rodrigo Garces

General averaging results for convex optimization
Michael P. Perrone

Multitask connectionist learning
Richard A. Caruana

Estimating learning performance using hints
Zehra Cataltepe and Yaser S. Abu-Mostafa


SIMULATION TOOLS

A simulator for asynchronous Hopfield models
Arun Jagota

An object-oriented dataflow approach for better designs of neural 
net architectures
Alexander Linden