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IJMLC 2021 Vol.11(5): 350-356 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.5.1060

Applications of Memristors in Neural Networks and Neuromorphic Computing: A Review

Ye-Guo Wang

Abstract—Memristor is one of the best choices for neuromorphic computing because of its synapse-like structure and function. The single memristor with ion dynamics enables emulations of diverse synaptic plasticity significant for learning and memory. Moreover, several memristive crossbar arrays show low power consumption, high precision and high efficiency on physically achieving algorithmic functions. Although a large number of experiments have demonstrated great potential of memristive devices in the field of computer architecture design and integrated circuits, there is still a long way to go for their practical industrialization. This review concentrates on the application and function of memristors, as well as some critical challenges and perspectives on their future development.

Index Terms—Memristor, neuromorphic computing, neural network, oxide, synaptic plasticity.

Ye-Guo Wang is with the Qingdao University of Science and Technology, China (e-mail:1608070226@mails.qust.edu.cn).

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Cite: Ye-Guo Wang, "Applications of Memristors in Neural Networks and Neuromorphic Computing: A Review," International Journal of Machine Learning and Computing vol. 11, no. 5, pp. 350-356, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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