Model of auto associative memory that stores and retrieves data regardless of their orthogonality, randomness or size

The use of autoassociative memory to store nonorthogonal nonrandom data is discussed. The basic model of autoassociative memory examined is the Hopfield network. Hopfield networks are content-addressable memories processing all the emergent properties. A description of associative memory and a proof that it can also be used as a content-addressable memory, without any further computation than standard, are given. The results of simulations demonstrate an improved behavior when associative memory is used as content-addressable memory instead of Hopfield networks. The final model, a combination of Hopfield networks and associative memory, is discussed in detail, and a model of shared content-addressable memory (SCAM) based on this model is discussed. The problem of storing the same pattern twice and at the same time is addressed, and a solution is proposed to the problem of associating two patterns of activity in an autoassociative memory