PPV modelling of memristor-based oscillator

TL;DR

This paper introduces a novel method to model the phase response of memristor-based oscillators using PPV, bridging biological concepts with electronic oscillators for efficient large-scale neural network simulations.

Contribution

It establishes a new approach to derive PPV from PRC for memristor oscillators, enabling fast phase dynamics modeling for neural network applications.

Findings

Validated the PPV-PRC relationship with transistor-level simulations

Demonstrated efficient PPV modeling for memristor oscillators

Facilitated fast simulation of large neural networks

Abstract

In this letter, we propose for the first time a method of abstracting the PPV (Perturbation Projection Vector) characteristic of the up-to-date memristor-based oscillators. Inspired from biological oscillators and its characteristic named PRC (Phase Response Curve), we build a bridge between PRC and PPV. This relationship is verified rigorously using the transistor level simulation of Colpitts and ring oscillators, i.e., comparing the PPV converted from PRC and the PPV obtained from accurate PSS+PXF simulation. Then we apply this method to the PPV calculation of the memristor-based oscillator. By keeping the phase dynamics of the oscillator and dropping the details of voltage/current amplitude, the PPV modelling is highly efficient to describe the phase dynamics due to the oscillator coupling, and will be very suitable for the fast simulation of large scale oscillatory neural networks.