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.
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