A simplified computational memory model from information processing.

Journal: Scientific reports
Published Date: Nov 23, 2016

Abstract

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 bi-modular 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 mapping the node and edge, and then the bi-modular network is delineated with intra-modular 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.

Authors

  • Lanhua Zhang
    College of Information and Engineering, Taishan Medical University, Taian 271016, China.
  • Dongsheng Zhang
    College of Radiology, Taishan Medical University, Taian 271016, China.
  • Yuqin Deng
    Department of Sport Psychology, School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China.
  • Xiaoqian Ding
    Centre for Psychological Health and Education, Dalian Nationalities University, Dalian 116600, China.
  • Yan Wang
    College of Animal Science and Technology, Beijing University of Agriculture, Beijing, China.
  • Yiyuan Tang
    Department of Psychological Sciences, Texas Tech University, TX 79409, USA.
  • Baoliang Sun
    Key Lab of cerebral microcirculation in Universities of Shandong, Taishan Medical University, Taian 271016, China.

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