A general auto-associative memory model

This paper attempts to establish a theory for a general auto-associative memory model. We start by defining a new concept called supporting function to replace the concept of energy function. The latter relies on an assumption of symmetric connection weights, which is used in the conventional Hopfield auto-associative memory, but not evidenced in any biological memories. We then formulate the information retrieval or recalling process as a dynamic system by making use of the supporting function, explore its stability and attraction conditions, and develop an algorithm for learning the attraction condition based upon Rosenblatt´s perceptron rule. The effectiveness of the learning algorithm is evidenced by some outstanding experiment results