%0 Journal Article
	%A 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
	%D 2022
	%J International Journal of Electronics and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 190, 2022
	%T Artificial Neurons Based on Memristors for Spiking Neural Networks
	%U https://publications.waset.org/pdf/10012716
	%V 190
	%X 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.
	%P 437 - 440