Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests

TL;DR

This paper introduces a machine learning approach using reservoir computing to infer network links with delays from time-series data, applicable to various fields and tested on opto-electronic oscillator networks.

Contribution

It presents a novel non-invasive method for delay-coupled network inference using reservoir computing trained on experimental and simulated data.

Findings

Technique yields accurate network inference results.

Dynamical noise can improve inference accuracy.

Method performs well especially in non-synchronous systems.

Abstract

We devise a machine learning technique to solve the general problem of inferring network links that have time-delays. The goal is to do this purely from time-series data of the network nodal states. This task has applications in fields ranging from applied physics and engineering to neuroscience and biology. To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network. We formulate and test a technique that uses the trained parameters of the reservoir system output layer to deduce an estimate of the unknown network structure. Our technique, by its nature, is non-invasive, but is motivated by the widely-used invasive network inference method whereby the responses to active perturbations applied to the network are observed and employed to infer network links (e.g., knocking down genes to infer gene regulatory networks). We test this technique on experimental and simulated data from delay-coupled opto-electronic oscillator networks. We show that the technique often yields very good results particularly if the system does not exhibit synchrony. We also find that the presence of dynamical noise can strikingly enhance the accuracy and ability of our technique, especially in networks that exhibit synchrony.