To detect directional couplings from time series various measures based on distances in reconstructed state spaces were introduced. These measures can, however, be biased by asymmetries in the dynamics' structure, noise color, or noise level, which are ubiquitous in experimental signals. Using theoretical reasoning and results from model systems we identify the various sources of bias and show that most of them can be eliminated by an appropriate normalization. We furthermore diminish the remaining biases by introducing a measure based on ranks of distances. This rank-based measure outperforms existing distance-based measures concerning both sensitivity and specificity for directional couplings. Therefore, our findings are relevant for a re...
Inferring the topology of a network using the knowledge of the signals of each of the interacting un...
Source code in MATLAB format (.m)This page provides the source code underlying the manuscript: Andr...
We investigate the properties of a recently introduced asymmetric association measure, called inner ...
To detect directional couplings from time series various measures based on distances in reconstructe...
Source code in MATLAB format (.m)This page provides the source code underlying the manuscript: Chi...
The detection of directional couplings between dynamics based onmeasured spike trains is a crucial p...
We compare two conceptually different approaches to the detection of weak directional couplings betw...
The characterization of interactions between coupled dynamics from their signals is important for th...
Abstract—In the study of complex systems, one of the primary concerns is the characterization and qu...
The detection of causal influences is a topical problem in time series analysis. A traditional appro...
We compare two conceptually different approaches to the detection of weak directional couplings betw...
In the study of complex systems, one of the primary concerns is the characterization and quantificat...
Quantitative characterization of nonlinear directional couplings between stochastic oscillators from...
<p>The top panel shows sample time series for driving and driven variables. As is changed from , ...
International audienceWe propose a fast nonlinear method for assessing quantitatively both the exist...
Inferring the topology of a network using the knowledge of the signals of each of the interacting un...
Source code in MATLAB format (.m)This page provides the source code underlying the manuscript: Andr...
We investigate the properties of a recently introduced asymmetric association measure, called inner ...
To detect directional couplings from time series various measures based on distances in reconstructe...
Source code in MATLAB format (.m)This page provides the source code underlying the manuscript: Chi...
The detection of directional couplings between dynamics based onmeasured spike trains is a crucial p...
We compare two conceptually different approaches to the detection of weak directional couplings betw...
The characterization of interactions between coupled dynamics from their signals is important for th...
Abstract—In the study of complex systems, one of the primary concerns is the characterization and qu...
The detection of causal influences is a topical problem in time series analysis. A traditional appro...
We compare two conceptually different approaches to the detection of weak directional couplings betw...
In the study of complex systems, one of the primary concerns is the characterization and quantificat...
Quantitative characterization of nonlinear directional couplings between stochastic oscillators from...
<p>The top panel shows sample time series for driving and driven variables. As is changed from , ...
International audienceWe propose a fast nonlinear method for assessing quantitatively both the exist...
Inferring the topology of a network using the knowledge of the signals of each of the interacting un...
Source code in MATLAB format (.m)This page provides the source code underlying the manuscript: Andr...
We investigate the properties of a recently introduced asymmetric association measure, called inner ...