Recurrent auto-associative networks and sequential processing

A novel connectionist architecture that develops static representations of structured sequences is presented. The model is based on simple recurrent networks trained on an auto-association task in a way that guarantees the development of unique static representations. The model can be applied in modeling natural language, cognition, etc.

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