A simplified computational memory model from information processing

Lanhua Zhang, Dongsheng Zhang, Yuqin Deng, Xiaoqian Ding, Yan Wang, Yiyuan Tang, Baoliang Sun

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number37470
JournalScientific reports
Volume6
DOIs
StatePublished - Nov 23 2016

Fingerprint Dive into the research topics of 'A simplified computational memory model from information processing'. Together they form a unique fingerprint.

Cite this