2012/09/30 by Andrea Duggento, Tomislav Stankovski, Peter V. E. McClintock +1 · 1 citation
Physics and Astronomy · #physics.data-an #nlin.AO #physics.bio-ph #physics.med-ph
paper · pdf · doi:10.1103/physreve.86.061126
published as Phys. Rev. E 86, 061126 (2012) [15 pages] · 15 pages, 13 figures, published in Physical Review E
arxiv created 2013/01/11 · arxiv updated 2013/01/14
Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. (Phys. Rev. Lett. 109 024101, 2012) introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time- evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically-generated data, data from an analog electronic circuit, and cardio-respiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.