English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We devise a machine learning technique to solve the general problem ofinferring network links that have time-delays. The goal is to do this purelyfrom time-series data of the network nodal states. This task has applicationsin fields ranging from applied physics and engineering to neuroscience andbiology. To achieve this, we first train a type of machine learning systemknown as reservoir computing to mimic the dynamics of the unknown network. Weformulate and test a technique that uses the trained parameters of thereservoir system output layer to deduce an estimate of the unknown networkstructure. Our technique, by its nature, is non-invasive, but is motivated bythe widely-used invasive network inference method whereby the responses toactive perturbations applied to the network are observed and employed to infernetwork links (e.g., knocking down genes to infer gene regulatory networks). Wetest this technique on experimental and simulated data from delay-coupledopto-electronic oscillator networks. We show that the technique often yieldsvery good results particularly if the system does not exhibit synchrony. Wealso find that the presence of dynamical noise can strikingly enhance theaccuracy and ability of our technique, especially in networks that exhibitsynchrony.

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