Current Trends in Connectionism: Proceedings of the Swedish Conference on Connectionism, 1995

Portada
Lars F. Niklasson, Mikael B. Bodén
Psychology Press, 1995 - 382 páginas
In order to build "intelligent" machines, many researchers have turned to the only naturally occurring intelligent system: the brain. For quite a while now, both the function and architecture of the brain have served as inspiration to philosophers, psychologists, computer scientists, neurobiologists, physicists and others in their quest for solving problems that seem to require intelligence in their own particular domain. The progress in the field of connectionism -- or artificial neural networks -- has had its ups and downs during its maturing years. Advocates of the field pointed out the virtues of connectionist systems, dealing with low-level cognitive tasks such as visual recognition and pattern completion, and inherent properties such as generalization, fault tolerance and parallel processing. However, research in the field virtually came to a halt at the end of the 1960s when Minsky and Papert published their critical analysis of connectionist systems, Perceptrons. In the beginning of the 1980s, the field was reborn with the appearance of new powerful learning methods which overcame many of the computational problems identified by Minsky and Papert.

This volume is characterized by a number of different research directions distinguished by their perspectives on systems comprising interconnected sets of simple processing elements. Scientists who have strong backgrounds in neurobiology concentrate on the issues involved when modelling natural systems. Researchers with philosophical and psychological backgrounds stress other aspects which might not always be intuitively relevant to biology but instead are concerned with the mind and its higher-order cognitive capabilities. On the other hand, many researchers and engineers in industry take advantage of the wide applicability and mathematical properties of connectionist systems in order to solve practical problems, sacrificing even more of the principles underlying the basic idea of mimicking the function and architecture of the brain. None of these directions are right or wrong, but there has perhaps been too little exchange of knowledge and experience between them.

The main purpose for organizing this conference was to bring together researchers with different backgrounds to exchange ideas and visions in the broad field of connectionism -- providing means for new insights that may push this area to another major breakthrough.
 

Índice

Redish A David
1
Physiological Constraints on Models of Behavior
15
Erik Fransén Anders Lansner
33
A BiophysicallyBased Model of the Neostriatum as Dynami
43
Dynamical Approximation by Neural Nets
57
On Parallel Selective Principal Component Analysis
67
Efficient Neural Net Isomorphism Testing
77
The TECO Theory Simulation of Recognition Failure
87
Some Experiments Using Extra Output Learning to Hint
179
Minimization of Quantization Errors in Digital Implementa
191
Modeling Connectionist and Otherwise
217
A Connectionist Exploration of the Computational Implica
237
Behaviorism and Reinforcement Learning
259
Requirements for
283
Are Representations Still Necessary for Understanding Cog
311
From Limitivism
331

Searching Weight Space for Backpropagation Solution Types
103
Using the Conceptual Graph Model as Intermediate Repre
141
Adaptive Generalization in Dynamic Neural Networks
153
Diversity Neural Nets and Safety Critical Applications
165
Learning to Retrieve Information
345
Vidmar Lucky 371
355
An Overview
371
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