中国物理B ›› 2017, Vol. 26 ›› Issue (11): 118502-118502.doi: 10.1088/1674-1056/26/11/118502

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    下一篇

An improved memristor model for brain-inspired computing

Errui Zhou(周二瑞), Liang Fang(方粮), Rulin Liu(刘汝霖), Zhenseng Tang(汤振森)   

  1. State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China
  • 收稿日期:2017-05-10 修回日期:2017-08-17 出版日期:2017-11-05 发布日期:2017-11-05
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 61332003) and High Performance Computing Laboratory, China (Grant No. 201501-02).

An improved memristor model for brain-inspired computing

Errui Zhou(周二瑞), Liang Fang(方粮), Rulin Liu(刘汝霖), Zhenseng Tang(汤振森)   

  1. State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China
  • Received:2017-05-10 Revised:2017-08-17 Online:2017-11-05 Published:2017-11-05
  • Contact: Liang Fang E-mail:lfang@nudt.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 61332003) and High Performance Computing Laboratory, China (Grant No. 201501-02).

摘要: Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors' performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover, the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.

关键词: memristor, drift diffusion model, synaptic, brain-inspired computing

Abstract: Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors' performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover, the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.

Key words: memristor, drift diffusion model, synaptic, brain-inspired computing

中图分类号:  (Nanoelectronic devices)

  • 85.35.-p
87.19.lv (Learning and memory) 87.19.lw (Plasticity)