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
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:firstname.lastname@example.org).
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).