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What connectionist models learn: Learning and representation in connectionist networks

Published online by Cambridge University Press:  19 May 2011

Stephen José Hanson  and
David J. Burr
Stephen José Hanson
Affiliation:
Learning and Knowledge Acquisition Group, Siemens Research Center, Princeton, NJ 08540, Electronic mail: [email protected]
David J. Burr
Affiliation:
Artificial Intelligence and Communications Research Group, Bellcore, Morristown, NJ 07960. Electronic mail: [email protected]
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Abstract

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities (including “distributed representations”) or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks.

Keywords

AIcomputational modelingconnectionismlearningneural networkpattern recognitionrules representationsymbols
Type
Target Article
Information
Behavioral and Brain Sciences , Volume 13 , Issue 3 , September 1990 , pp. 471 - 489
Copyright
Copyright © Cambridge University Press 1990

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