Recurrent auto-associative networks and sequential processing

Abstract

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.