An artificial spiking afferent nerve based on Mott memristors for neurorobotics

By Xumeng Zhang, Ye Zhuo, Q. Luo, Zuheng Wu, Rivu Midya, Zhongrui Wang, Wenhao Song, Rui Wang, N. Upadhyay, Yilin Fang and 7 more. Nature Communications, 2020

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Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbOx Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future. Though artificial sensory systems based on electronic devices have been realized, further transformation of data into spikes is required for neural network optimization. Here, based on NbOx Mott memristors, the authors report artificial spiking afferent nerves for accessing spiking systems and demonstrate spiking mechanoreceptor systems.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f135d5414e6583b4861098511d8898815d6c7af4