  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.

Disordered Systems and Neural Networks;Emerging Technologies