Abstract—
The organization of associative memory plays a crucial role in the efficiency of intelligent systems, software, and practical applications that require analysis and processing of big data and knowledge. A logical architecture of a neuro-like hierarchical pattern associative memory system, which provides storage of incoming information in the form of so-called patterns, is proposed and described. Patterns are hierarchically organized memory elements that make it possible to recover data from its part and find elements within automatically determined contexts on the basis of previously stored information. Modern approaches to the organization of associative memory in intelligent systems are considered. The main elements of pattern memory, i.e., pattern, neuron, mediator, and synapse, are defined. The basic concepts and principles of functioning of hierarchical pattern associative memory systems are described, and examples of practical implementation are given. Based on the principles described, it is possible to create specific memory versions for practical tasks, such as processing text, graphic, audio, and video information. Shared memory can be used to generalize various information flows into common logical chains of memories. It is concluded that the capabilities of the proposed hierarchical pattern memory systems for handling data make it possible to store information in intelligent systems and model the thinking process of an expert in the course of logical reasoning.
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ACKNOWLEDGMENTS
We thank S.P. Smirnov, Candidate of Engineering Sciences, for scientific ideas and participation in discussions during the research.
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Translated by O. Pismenov
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Stepanyan, I.V. Neural Network Modeling and Organization of a Hierarchical Associative Memory System. J. Mach. Manuf. Reliab. 50, 735–742 (2021). https://doi.org/10.3103/S1052618821080148
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DOI: https://doi.org/10.3103/S1052618821080148