Minds and Machines 3 (1):53-71 (1993)

Abstract
A rule-based expert system is demonstrated to have both a symbolic computational network representation and a sub-symbolic connectionist representation. These alternate views enhance the usefulness of the original system by facilitating introduction of connectionist learning methods into the symbolic domain. The connectionist representation learns and stores metaknowledge in highly connected subnetworks and domain knowledge in a sparsely connected expert network superstructure. The total connectivity of the neural network representation approximates that of real neural systems and hence avoids scaling and memory stability problems associated with other connectionist models.
Keywords Symbolic AI  connectionist AI  connectionism  neural networks  learning  reasoning  expert networks  expert systems  symbolic models  sub-symbolic models
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DOI 10.1007/BF00974305
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Is the Brain’s Mind a Computer Program?John R. Searle - 1990 - Scientific American 262 (1):26-31.
Could a Machine Think?Paul M. Churchland & Patricia S. Churchland - 1990 - Scientific American 262 (1):32-37.
.J. L. McClelland & D. E. Rumelhart (eds.) - 1987 - MIT Press.

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