Entropy measures are widely applied to quantify the complexity of dynamical systems in
diverse fields. However, the practical application of entropy methods is challenging, due to …

Finding precise signatures of different brain states is a central, unsolved question in
neuroscience. We reformulated the problem to quantify the 'inside out'balance of intrinsic …

A central challenge in neuroscience is how the brain organizes the information necessary to
orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core …

A challenge for physiologists and neuroscientists is to map information transfer between
components of the systems that they study at different scales, in order to derive important …

Network inference algorithms are valuable tools for the study of large-scale neuroimaging
datasets. Multivariate transfer entropy is well suited for this task, being a model-free measure …

We propose a graphical model for representing networks of stochastic processes, the
minimal generative model graph. It is based on reduced factorizations of the joint distribution …

A measure to estimate the direct and directional coupling in multivariate time series is
proposed. The measure is an extension of a recently published measure of conditional …

In the framework of information dynamics, the temporal evolution of coupled systems can be
studied by decomposing the predictive information about an assigned target system into …

Recent advances in computational models of signal propagation and routing in the human
brain have underscored the critical role of white-matter structure. A complementary …

The continuously growing framework of information dynamics encompasses a set of tools,
rooted in information theory and statistical physics, which allow to quantify different aspects …