A simple and highly-scalable artificial neuron using an Ovonic Threshold Switch (OTS)

A scalable and low power-consuming artificial neuron is an essential building block for developing a brain-inspired computing system. Among various features of a biological neuron in the mammalian cortex, the spike-frequency adaptation and chaotic activities are very important ingredients for the realization of the energy-efficient signal processing, learning, and adaptation to environments, which are hard to be achieved up to now. In this work, we have demonstrated those features in a simple artificial neuron device composed of an Ovonic Threshold Switch (OTS) and a few passive electrical components. Furthermore, with our OTS-based neuron device employing the reservoir computing technique combined with delayed feedback dynamics, spoken-digit recognition task has been performed with a considerable degree of recognition accuracy. These results show that our OTS-based artificial neuron device is promising for the application in the development of a large-scale brain-inspired computing system.