Auto-associative networks in the enigma of memory and language processing

This manuscript provides a comprehensive overview of the role of auto-associative networks (AANs) in the neurobiological process of cognitive behavior. The structural organization of the brain is largely governed by the Hebbian postulate, “cells that fire together, wire together.” This organization is supported by assemblies that use both ascending feedforward and descending feedback projections, as well as recursive loops that allow for the enhancement of human capacity for such cognition. Detailed analysis reveals that AANs enable the prime and functional retrieval of human memory and language. Their attractor system facilitates pattern completion from noisy and partial cues, enabling reliable recall of stored information. This winner-take-all approach calls for the closest match to be pulled from prior expectations. Oscillatory controls, long-term potentiation, and NMDA gating all contribute to this process, pushing our brains to not only replay events but also predict and simulate those of the future. This can be conceptualized from a symbolic standpoint using real-world items (RWIs), tokens that represent abstractions and can be paired via spreading activation. AANs support language by organizing symbolic RWIs into stable, retrievable assemblies. This context suggests that these associative dynamics are the foundation for driving the fluidity of human memory, language, and predictive thought.