A new method is introduced for analysis of interactions between time-dependent coupled oscillators, based on the signals they generate. It distinguishes unsynchronized dynamics from noise-induced phase slips and enables the evolution of the coupling functions and other parameters to be followed. It is based on phase dynamics, with Bayesian inference of the time-evolving parameters achieved by shaping the prior densities to incorporate knowledge of previous samples. The method is tested numerically and applied to reveal and quantify the time-varying nature of cardiorespiratory interactions
In the present work, we present a new algorithm for assessing causality in uni-directionally coupled...
A Bayesian framework for parameter inference in non-stationary, nonlinear, stochastic, dynamical sys...
Markovian analysis is applied to derive nonlinear stochastic equations for the reconstruction of hea...
In view of the current availability and variety of measured data, there is an increasing demand for ...
Biological systems are never isolated, usually oscillatory, and invariably subject to noise and fluc...
Synchronization is a widespread phenomenon that occurs among interacting oscillatory systems. It fac...
Interacting dynamical systems abound in nature, with examples ranging from biology and population dy...
The usefulness of the information extracted from biomedical data relies heavily on the underlying th...
Measurements of interaction intensity are generally achieved by observing responses to perturbations...
Networks of interacting oscillators abound in nature, and one of the prevailing challenges in scienc...
We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set o...
First, we introduce nonautonomous oscillator—a self-sustained oscillator subject to external perturb...
The synchronized phase of globally coupled identical nonlinear oscillators subject to noise fluctua...
We discuss the advent of coupling functions as a new dimension in the analysis of cardiorespiratory ...
The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems ...
In the present work, we present a new algorithm for assessing causality in uni-directionally coupled...
A Bayesian framework for parameter inference in non-stationary, nonlinear, stochastic, dynamical sys...
Markovian analysis is applied to derive nonlinear stochastic equations for the reconstruction of hea...
In view of the current availability and variety of measured data, there is an increasing demand for ...
Biological systems are never isolated, usually oscillatory, and invariably subject to noise and fluc...
Synchronization is a widespread phenomenon that occurs among interacting oscillatory systems. It fac...
Interacting dynamical systems abound in nature, with examples ranging from biology and population dy...
The usefulness of the information extracted from biomedical data relies heavily on the underlying th...
Measurements of interaction intensity are generally achieved by observing responses to perturbations...
Networks of interacting oscillators abound in nature, and one of the prevailing challenges in scienc...
We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set o...
First, we introduce nonautonomous oscillator—a self-sustained oscillator subject to external perturb...
The synchronized phase of globally coupled identical nonlinear oscillators subject to noise fluctua...
We discuss the advent of coupling functions as a new dimension in the analysis of cardiorespiratory ...
The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems ...
In the present work, we present a new algorithm for assessing causality in uni-directionally coupled...
A Bayesian framework for parameter inference in non-stationary, nonlinear, stochastic, dynamical sys...
Markovian analysis is applied to derive nonlinear stochastic equations for the reconstruction of hea...