High Capacity Recurrent Associative Memories

Various algorithms for constructing weight matrices for Hopfield-type associative memories are reviewed, including ones with much higher capacity than the basic model. These alternative algorithms either iteratively approximate the projection weight matrix or use simple perceptron learning. An experimental investigation of the performance of networks trained by these algorithms is presented, including measurements of capacity, training time and their ability to correct corrupted versions of the training patterns. The most effective versions of these models are identified