TY - JOUR

T1 - Dynamical Bayesian inference of time-evolving interactions

T2 - from a pair of coupled oscillators to networks of oscillators

AU - Duggento, Andrea

AU - Stankovski, Tomislav

AU - McClintock, Peter V. E.

AU - Stefanovska, Aneta

N1 - ©2012 American Physical Society

PY - 2012/12/21

Y1 - 2012/12/21

N2 - 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. DOI: 10.1103/PhysRevE.86.061126

AB - 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. DOI: 10.1103/PhysRevE.86.061126

KW - STATES

KW - CARDIORESPIRATORY SYSTEM

KW - POPULATIONS

KW - COHERENCE

KW - EEG

KW - PHASE SYNCHRONIZATION

KW - ANESTHESIA

KW - ENTRAINMENT

U2 - 10.1103/PhysRevE.86.061126

DO - 10.1103/PhysRevE.86.061126

M3 - Journal article

VL - 86

JO - Physical Review E

JF - Physical Review E

SN - 1539-3755

IS - 6

M1 - 061126

ER -