<p><b>A</b>, Granger causality (GC) scores as a function of time used for fitting. <b>B</b>, GC scores as a function of the maximal time lag used for fitting (same color scheme as in <b>A</b>). <b>C</b>, Network inference for all four data sets, using the Granger causality score. The physiologically nonexistent connection does not correspond to the weakest one in any case. Horizontal scatter is for visualization only. <b>D</b>, Coupling strengths (CS) and Granger causality scores (GC) are uncorrelated for the point process model. For the point process model, the strength of the coupling can be either defined by the net integral of the interaction filter (horizontal axis) or by the statistical Granger causality score (vertical axis). The sca...
Granger causality (GC) is expressed as change in abundance of response variable (in standard deviati...
Classical multivariate approaches based on Granger causality (GC) which estimate functional connecti...
Multivariable dynamical processes are characterized by complex cause and effect relationships among ...
<div><p>Multivariate neural data provide the basis for assessing interactions in brain networks. Amo...
International audienceEffective connectivity can be modeled and quantified with a number of techniqu...
<p>(A) The Granger causality matrix shows how much each neuron interacts one another. (B) The causa...
Granger causality analysis is becoming central for the analysis of interactions between neural popul...
Granger causality (GC) is a very popular tool for assessing the presence of directional interactions...
Granger causality (GC) is a very popular tool for assessing the presence of directional interactions...
<p>The results of Granger analyses on relative T<sub>IS</sub> (T<sub>IS</sub><sup>rel</sup>) and con...
The notion of Granger causality between two time series examines if the prediction of one series cou...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Granger causality (GC) is a method for determining whether and how two time series exert causal infl...
Since the operative definition given by C. W. J. Granger of an idea expressed by N. Wiener, the Wien...
Granger causality (GC) is expressed as change in abundance of response variable (in standard deviati...
Classical multivariate approaches based on Granger causality (GC) which estimate functional connecti...
Multivariable dynamical processes are characterized by complex cause and effect relationships among ...
<div><p>Multivariate neural data provide the basis for assessing interactions in brain networks. Amo...
International audienceEffective connectivity can be modeled and quantified with a number of techniqu...
<p>(A) The Granger causality matrix shows how much each neuron interacts one another. (B) The causa...
Granger causality analysis is becoming central for the analysis of interactions between neural popul...
Granger causality (GC) is a very popular tool for assessing the presence of directional interactions...
Granger causality (GC) is a very popular tool for assessing the presence of directional interactions...
<p>The results of Granger analyses on relative T<sub>IS</sub> (T<sub>IS</sub><sup>rel</sup>) and con...
The notion of Granger causality between two time series examines if the prediction of one series cou...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...
Granger causality (GC) is a method for determining whether and how two time series exert causal infl...
Since the operative definition given by C. W. J. Granger of an idea expressed by N. Wiener, the Wien...
Granger causality (GC) is expressed as change in abundance of response variable (in standard deviati...
Classical multivariate approaches based on Granger causality (GC) which estimate functional connecti...
Multivariable dynamical processes are characterized by complex cause and effect relationships among ...