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As an essential building block for developing a large-scale brain-inspiredcomputing system, we present a highly scalable and energy-efficient artificialneuron device composed of an Ovonic Threshold Switch (OTS) and a few passiveelectrical 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 commonfeatures found in the mammalian cortex, are particularly essential for therealization of the energy-efficient signal processing, learning, and adaptationto environments1-3, but have been hard to achieve up to now. Furthermore, withour OTS-based neuron device employing the reservoir computing techniquecombined with delayed feedback dynamics, spoken-digit recognition task has beenperformed with a considerable degree of recognition accuracy. From a comparisonwith a Mott memristor-based artificial neuron device, it is shown that theOTS-based artificial neuron is much more energy-efficient by about 100 times.These results show that our OTS-based artificial neuron device is promising forthe application in the development of a large-scale brain-inspired computingsystem.

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A highly scalable and energy-efficient artificial neuron using an Ovonic  Threshold Switch (OTS) featuring the spike-frequency adaptation and chaotic  activity