Abstract
A high-performance, high-capacity dynamic neural memory is proposed which is capable of simultaneous hetero- and autoassociative recall. The proposed memory utilizes two simple layers of neurons which implement a forward mapping and its inverse backward mapping, respectively, such that the range of one mapping is the domain of the other. This dynamic heteroassociative memory (DAM) employs the newly-developed Ho-Kashyap associative memory recording algorithm which optimally distributes the association process of each neural layer over individual neuron weighted-sum and activation function faculties. Various performance characteristics for the proposed DAM are tested and compared to those of correlation- and generalized inverse-recorded DAMs. Simulation results are presented which confirm the superiority of the proposed Ho-Kashyap-recorded DAM over correlation-recorded heteroassociative memories. The Ho-Kashyap recording algorithm's performance is also known to exceed that of the optimal linear associative memory (OLAM) recording technique in the case of binary pattern associative storage and retrieval. The proposed DAM's high performance extends to a wide range of input/output association pattern dimensions and memory storage levels.
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