Dynamical Bayesian inference of time-evolving interactions: from a pair of coupled oscillators to networks of oscillators.
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 cardiorespiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.
Medical Physics Section, Faculty of Medicine, Tor Vergata University, Rome, Italy.
Andrea
A
Duggento
Tomislav
T
Stankovski
Peter V E
PV
McClintock
Aneta
A
Stefanovska
D000465:Algorithms; D001499:Bayes Theorem; D002319:Cardiovascular System; D004560:Electricity; D004562:Electrocardiography / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D016011:Normal Distribution; D009991:Oscillometry / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors