A learning method for synthesizing associative memory in neural networks

The paper proposes a learning method for synthesizing associative memory in neural networks. The problem is formulated as determining the weights of the synaptic connections of neural networks such that, for any given set of desired memory vectors, each memory vector becomes an asymptotically stable equilibrium point of the network. We introduce a new architecture of neural networks, hybrid recurrent neural networks, in order to enhance the capability of implementing associative memories. An efficient learning method for synthesizing associative memories is proposed. The proposed method assures that all the memory vectors become asymptotically stable equilibrium points with the prescribed degree of stability. Synthesis examples are presented to demonstrate the applicability and performance of the proposed method