The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

Transfer entropy (TE) is an information-theoretic measure which has received recent
attention in neuroscience for its potential to identify effective connectivity between neurons …

What can we learn from the collective dynamics of a complex network about its interaction
topology? Taking the perspective from nonlinear dynamics, we briefly review recent …

Reconstructing complex networks from measurable data is a fundamental problem for
understanding and controlling collective dynamics of complex networked systems. However …

Inferring the network topology from dynamical observations is a fundamental problem
pervading research on complex systems. Here, we present a simple, direct method for …

Determining interrelatedness structure of various entities from multiple time series data is of
significant interest to many areas. Knowledge of such a structure can aid in identifying cause …

The communication among neuronal populations, reflected by transient synchronous
activity, is the mechanism underlying the information processing in the brain. Although it is …

Modeling and identification of high dimensional systems, involving signals with many
components, poses severe challenges to off-the-shelf techniques for system identification …

We study the relationship between dynamical properties and interaction patterns in complex
oscillator networks in the presence of noise. A striking finding is that noise leads to a …

We propose a method of analysis of dynamical networks based on a recent measure of
Granger causality between time series, based on kernel methods. The generalization of …