Integrating parallel computations and sequential symbol processing in neural networks

Presents an analysis of computational processes in neural networks, underlying sequential symbol processing. The main problem addressed in this analysis is that computations in neural networks are massively parallel whereas symbol processing is sequential. It is suggested that the two kinds of processes can be reconciled with each other by the idea of causally constructed representations. Such representations are not assumed to encode symbolic structures and, what follows, structure-sensitive procedures are not applied to them. If computations are massively parallel, then a content-sensitivity principle is used in defining consecutive computational steps on such representations. This idea of computations is naturally implemented by a pattern association mechanism appearing in neural networks