Autoassociative Memory

Autoassociative memory is a memory system that retrieves a complete pattern from a partial or noisy cue by exploiting learned associations within the pattern itself. Unlike heteroassociative memory, which maps from one domain to another (word to meaning, face to name), autoassociative memory maps a pattern onto itself — the output is a cleaned, completed, or reconstructed version of the input.

The canonical implementation is the Hopfield network, in which patterns are stored as stable fixed points of a dynamical system. A partial pattern placed on the network's units will evolve, through recurrent updates, toward the nearest stored memory. The network performs error correction, noise suppression, and pattern completion as emergent properties of its energy landscape. This is not retrieval in the sense of looking up an address. It is reconstruction in the sense of settling into an attractor.

Autoassociative memory reveals that memory and inference are not separate faculties. To remember is to infer the most likely complete pattern given degraded evidence — a form of Bayesian inference implemented in recurrent dynamics.