@article{(Open Science Index):https://publications.waset.org/pdf/10012716,
	  title     = {Artificial Neurons Based on Memristors for Spiking Neural Networks},
	  author    = {Yan Yu and  Wang Yu and  Chen Xintong and  Liu Yi and  Zhang Yanzhong and  Wang Yanji and  Chen Xingyu and  Zhang Miaocheng and  Tong Yi},
	  country	= {},
	  institution	= {},
	  abstract     = {Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to their high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO2-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO2-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.},
	    journal   = {International Journal of Electronics and Communication Engineering},
	  volume    = {16},
	  number    = {10},
	  year      = {2022},
	  pages     = {437 - 440},
	  ee        = {https://publications.waset.org/pdf/10012716},
	  url   	= {https://publications.waset.org/vol/190},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 190, 2022},
	}