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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Identifying causal relationships is a challenging yet crucial problem in manyfields of science like epidemiology, climatology, ecology, genomics, economicsand neuroscience, to mention only a few. Recent studies have demonstrated thatordinal partition transition networks (OPTNs) allow inferring the couplingdirection between two dynamical systems. In this work, we generalize thisconcept to the study of the interactions among multiple dynamical systems andwe propose a new method to detect causality in multivariate observational data.By applying this method to numerical simulations of coupled linear stochasticprocesses as well as two examples of interacting nonlinear dynamical systems(coupled Lorenz systems and a network of neural mass models), we demonstratethat our approach can reliably identify the direction of interactions and theassociated coupling delays. Finally, we study real-world observationalmicroelectrode array electrophysiology data from rodent brain slices toidentify the causal coupling structures underlying epileptiform activity. Ourresults, both from simulations and real-world data, suggest that OPTNs canprovide a complementary and robust approach to infer causal effect networksfrom multivariate observational data.

Causal coupling inference from multivariate time series based on ordinal partition transition networks