Simple Artificial Neuron Using an Ovonic Threshold Switch Featuring Spike-Frequency Adaptation and Chaotic Activity
Milim Lee, Seong Won Cho, Seon Jeong Kim, Joon Young Kwak, Hyunsu Ju, Yeonjin Yi, Byung-ki Cheong, Suyoun Lee
Abstract
As an essential building block for developing a large-scale brain-inspired computing system, we propose a highly scalable and energy-efficient artificial neuron device composed of an ovonic threshold switch (OTS) and a few passive electrical components. It is found that the proposed neuron device shows not only the basic integrate-and-fire function and the rate-coding property, but also the spike-frequency-adaptation (SFA) property and the chaotic activity of biological neurons, the most common features found in mammalian cortex, but they have been hard to achieve up to now. In addition, it is shown that the energy consumption of the OTS-based neuron device scales with the size of the OTS device, extrapolating both the size and the energy efficiency to the level of a biological neuron in a human brain with state-of-the-art technology. Finally, using the OTS-based neuron device combined with the reservoir computing technique, the spoken-digit recognition task has been performed with a considerable degree of recognition accuracy (94%). These results demonstrate that our OTS-based artificial neuron device is promising for the application in the development of a large-scale brain-inspired computing system.