Recurrent auto-associative networks and sequential processing
نویسنده
چکیده
A novel connectionist architecture that develops static representations of structured sequences is presented. The model is based on SRNs trained on an autoassociation 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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