Principles of Associative Computation
Currently neural networks are used in many different domains. But are neural networks also suitable for modeling problem solving, a domain which is traditionally reserved for the symbolic approach? This central question of cognitive science is answered in this paper. It is affirmed by a corresponding neural network model. The model has the same behavior as a symbolic model. However, also additional properties resulting from the distributed representation emerge. It is shown by comparison of those additional abilities with the basic behavior of the model, that the additional properties lead to a significant algorithmic improvement. This is verified by statistical hypothesis testing.
KeywordsShort Term Memory Associative Memory Heuristic Function Frame Problem Statistical Hypothesis Testing
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