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Chin. Phys. B, 2017, Vol. 26(11): 118502    DOI: 10.1088/1674-1056/26/11/118502

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)
State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China
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.
Keywords:  memristor      drift diffusion model      synaptic      brain-inspired computing  
Received:  10 May 2017      Revised:  17 August 2017      Accepted manuscript online: 
PACS:  85.35.-p (Nanoelectronic devices) (Learning and memory)  
  87.19.lw (Plasticity)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61332003) and High Performance Computing Laboratory, China (Grant No. 201501-02).
Corresponding Authors:  Liang Fang     E-mail:

Cite this article: 

Errui Zhou(周二瑞), Liang Fang(方粮), Rulin Liu(刘汝霖), Zhenseng Tang(汤振森) An improved memristor model for brain-inspired computing 2017 Chin. Phys. B 26 118502

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