Modelling and Emulation of Afferent-Like Excitatory-Inhibitory Spiking Using Ovonic Threshold Devices for Neuromorphic Oscillatory Computing.

Spike-based computing has attracted increasing attention for efficient information processing, creating demand for neuromorphic hardware capable of generating and processing spikes. Ovonic threshold switching (OTS) devices are promising spiking neurons because their threshold-switching behavior and negative differential resistance enable compact spike-generation circuits, while their scalability supports dense integration. However, a circuit-level design framework linking defect dynamics with conduction behavior remains lacking. Here, we experimentally demonstrate an intensity-tuned artificial neuron based on a GeAsTe OTS device integrated with a resistor-capacitor (RC) circuit. The neuron shows a non-monotonic excitatory-self-inhibitory response with applied voltage, phenomenologically analogous to excitatory-inhibitory behavior in biological neurons. The spiking frequency initially increases and then decreases beyond an inflection voltage point. This transition arises intrinsically from competition between RC charging dynamics and conductive filament retention in the OTS device, rather than from a separate inhibitory pathway. The neuron achieves a spiking frequency of up to 1.6 MHz. The behavior is supported by simulations using a dynamical model for diffusive filament-based memristors, accounting for resistance changes from filament formation and rupture. Furthermore, a physics-informed compact SPICE model combining defect-state transitions and filament evolution reproduces OTS switching and predicts neuron dynamics under varying temperatures, internal resistances, and circuit conditions. Finally, circuit simulations of a coupled OTS neuron network demonstrate vertex coloring with phase convergence within approximately 5 μs, highlighting the potential of this framework for compact, energy-efficient brain-inspired computing.