Computational Semantics of a Neural Network System for Thought Process Simulation and its Applications

概要

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One major defect in neural network models in earlier research is nonclarity of computational Semantics. This paper describes a neural network system for thoutht process simulation called "Repetition of Association and Concentration Model". Repetition of concept association is simulated by a neural network model with a feedback loop. However such a loop results in a stable state which is contradictory to the dynamics of the thought process. In this model, dynamics are maintained by nonlinearity to simulate consciousness, that is, thought energy distribution is enhanced to facilitate concept recollection. Computational semantics of this model are defined as a parallel production system by correspondence of the synaptic connections between neurons to production rules. Three types of learning facilities are described as automatic knowledge acquisition. The relevance of this model is suggested by simulating the learning procedures for recognition of relations between concepts.
社団法人人工知能学会の論文
1990-09-01