Block-splitting type morphological associative memory for practical applications

From an engineering viewpoint, associative memory is one of the most valuable brain functions. A new type of associative memory, morphological associative memory (MAM), has been proposed. The MAM achieves a high perfect recall rate by using a kernel image as an index for pattern recalling. The kernel images, however, are difficult to design for a large number of stored patterns. We developed a block-splitting type morphological associative memory (BMAM) with no need of kernel images. In this paper, the architecture of the BMAM is described and its performance is discussed based on the results of autoassociation experiments.