A self-organizing neural network for locus-addressable associative memory

A self-organizing neural network model for locus-addressable associative memory and binary pattern recognition is presented. The net may be used for either autoassociative or heteroassociative tasks. Locus addressability is suggested as a possible mechanism for retrieval of memories without any external cues in the form of partial or corrupted exemplar patterns. The architecture, which uses competitive dynamics, embodies a parallel search scheme which updates itself adaptively as the learning progresses. A thresholding mechanism ensures the learning of new exemplars. Upon saturation of the memory capacity, the net thereafter responds to new patterns by recalling exemplars in its memory that are nearest in Hamming distance to the presented input. The stability-plasticity problem is overcome by fast learning and irreversibility of connection-weight changes. This architecture overcomes the orthogonality and linear independence constraints that limit other models