A phenomenological memristor model for short-term/long-term memory

被引：37

作者：

Chen, Ling ^{[1
]}

Li, Chuandong ^{[1
]}

Huang, Tingwen ^{[2
]}

Ahmad, Hafiz Gulfam ^{[1
]}

Chen, Yiran ^{[3
]}

机构：

[1] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China

[2] Texas A&M Univ Qatar, Doha, Qatar

[3] Univ Pittsburgh, Pittsburgh, PA 15261 USA

来源：

PHYSICS LETTERS A | 2014年 / 378卷 / 40期

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

Memristor; Ion diffusion; Short-term memory; Long-term memory; Forgetting effect; SYNCHRONIZATION; SYNAPSE; DEVICE;

D O I：

10.1016/j.physleta.2014.08.018

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Memristor is considered to be a natural electrical synapse because of its distinct memory property and nanoscale. In recent years, more and more similar behaviors are observed between memristors and biological synapse, e.g., short-term memory (STM) and long-term memory (LTM). The traditional mathematical models are unable to capture the new emerging behaviors. In this article, an updated phenomenological model based on the model of the Hewlett-Packard (HP) Labs has been proposed to capture such new behaviors. The new dynamical memristor model with an improved ion diffusion term can emulate the synapse behavior with forgetting effect, and exhibit the transformation between the STM and the LTM. Further, this model can be used in building new type of neural networks with forgetting ability like biological systems, and it is verified by our experiment with Hopfield neural network. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：2924 / 2930

页数：7

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