Abstract
As an essential building block for developing a large-scale brain-inspired computing system, we present a highly scalable and energy-efficient artificial neuron device composed of an Ovonic Threshold Switch (OTS) and a few passive electrical components. It shows not only the basic integrate-and-fire (I&F) function and the rate coding ability, but also the spike-frequency adaptation (SFA) property and the chaotic activity. The latter two, being the most common features found in the mammalian cortex, are particularly essential for the realization of the energy-efficient signal processing, learning, and adaptation to environments1-3, but have been hard to achieve up to now. 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. From a comparison with a Mott memristor-based artificial neuron device, it is shown that the OTS-based artificial neuron is much more energy-efficient by about 100 times. 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.
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