Abstract
In this paper representation in connectionist symbol processing is addressed. There is a putative view that symbol processing requires structural representations and sensitive-to-structure procedures. Here it is proposed that symbolic information processing is based on causal nonstructural representations when computation is massively-parallel. Such representations are formed causally and need not be structural in the sense of constituent symbolic structures. A method of causal representation construction is presented along with a simple example. An implementation of the method in Simple Recurrent Network is shown.
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Pozarlik, R. (1998). Connectionist Symbol Processing with Causal Representations. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_25
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DOI: https://doi.org/10.1007/978-1-4471-1546-5_25
Publisher Name: Springer, London
Print ISBN: 978-3-540-76208-9
Online ISBN: 978-1-4471-1546-5
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