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5 - Connectionism and neural networks

Published online by Cambridge University Press:  05 July 2014

By
Ron Sun
Edited by
Keith Frankish  and
William M. Ramsey
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute
Keith Frankish
Affiliation:
The Open University, Milton Keynes
William M. Ramsey
Affiliation:
University of Nevada, Las Vegas
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Summary

Connectionism and neural networks have become a mainstay of artificial intelligence and cognitive science. Nowadays, conferences on neural networks from the perspective of artificial intelligence (or computational intelligence, as some would put it) are held regularly and are usually fairly well attended (such as International Joint Conferences on Neural Networks). At major cognitive science conferences, work based on connectionist models usually occupies a major place. In many engineering conferences and journals, work utilizing neural network models is commonplace. Their popularity and appeal have reached a stable state in a sense. In other words, they have become an integral part of the study and the exploration of intelligence and cognition.

In this chapter, I will first review briefly the history of connectionist models, identifying major ideas and major areas of applications, and then move on to address the issue of symbolic processing in connectionist models; finally, I will expand the discussion to hybrid connectionist models, which incorporate both connectionist and symbolic processing methods.

Keywords

neural networkssymbolic processingconnectionist modelsconnectionist revolutionhybrid connectionist modelscognitive scienceartificial intelligenceCLARION
Type
Chapter
Information
The Cambridge Handbook of Artificial Intelligence , pp. 108 - 127
Publisher: Cambridge University Press
Print publication year: 2014

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References

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