A simplified computational memory model from information processing

Lanhua Zhang, Dongsheng Zhang, Yuqin Deng, Xiaoqian Ding, Yan Wang, Yiyuan Tang, Baoliang Sun
2016 Scientific Reports  
This paper is intended to propose a computational model for memory from the view of information processing. The model, called simplified memory information retrieval network (SMIRN), is a bimodular hierarchical functional memory network by abstracting memory function and simulating memory information processing. At first meta-memory is defined to express the neuron or brain cortices based on the biology and graph theories, and we develop an intra-modular network with the modeling algorithm by
more » ... pping the node and edge, and then the bi-modular network is delineated with intramodular and inter-modular. At last a polynomial retrieval algorithm is introduced. In this paper we simulate the memory phenomena and functions of memorization and strengthening by information processing algorithms. The theoretical analysis and the simulation results show that the model is in accordance with the memory phenomena from information processing view. and have a detailed introduction to memory modeling and retrieval in section "Model". Then we simulate the numerical memory algorithm and compare the retrieval efficiency to test the model in section "Simulation" and explain the memory phenomena with this model in section "Discussion". At last we draw the conclusion of our simplified model and point out the shortage and the possible future research of our model.
doi:10.1038/srep37470 pmid:27876847 pmcid:PMC5120294 fatcat:uvp6nvmnw5cjfdpzej2z2zko3e