In this paper, we build a general model of memristors suitable for the simulation of event-based systems, such as hardware spiking neural networks, and more generally, neuromorphic computing systems. We extend an existing general model of memristors - the Generalised Metastable Switch Model - to an event-driven setting, eliminating errors associated discrete time approximation, as well as offering potential improvements in terms of computational efficiency for simulation. We introduce the notion of a volatility state variable, to allow for the modelling of memory-dependent and dynamic switching behaviour, succinctly capturing and unifying a variety of volatile phenomena present in memristive devices, including state relaxation, structural disruption, Joule heating, and drift acceleration phenomena. We supply a drift dataset for titanium dioxide memristors and introduce a linear conductance model to simulate the drift characteristics, motivated by a proposed physical model of filament growth. We then demonstrate an approach for fitting the parameters of the event-based model to the drift model.

翻译：本文构建了一种适用于事件驱动系统仿真的通用忆阻器模型，例如硬件脉冲神经网络及更广泛的神经形态计算系统。我们将现有的通用忆阻器模型——广义亚稳态开关模型——扩展至事件驱动框架，消除了离散时间近似带来的误差，并在仿真计算效率方面提供了潜在的改进。我们引入了易失性状态变量的概念，以支持对记忆依赖性和动态开关行为的建模，从而简洁地捕捉并统一了忆阻器件中存在的多种易失现象，包括状态弛豫、结构扰动、焦耳热效应以及漂移加速现象。我们提供了二氧化钛忆阻器的漂移数据集，并基于提出的细丝生长物理模型，引入线性电导模型来模拟漂移特性。随后，我们演示了将事件驱动模型参数拟合至漂移模型的方法。