It is a fundamental challenge to understand how the function of a network is related to its
structural organization. Adaptive dynamical networks represent a broad class of systems that …

Percolation and synchronization are two phase transitions that have been extensively
studied since already long ago. A classic result is that, in the vast majority of cases, these …

The field of neuroscience is facing an unprecedented expanse in the volume and diversity of
available data. Traditionally, network models have provided key insights into the structure …

We study excitation and suppression of chimera states in an ensemble of nonlocally coupled
oscillators arranged in a framework of multiplex network. We consider the homogeneous …

We introduce a practical and computationally not demanding technique for inferring
interactions at various microscopic levels between the units of a network from the …

Adaptive oscillators can learn and encode information in dynamic, plastic states. The
pendulum has recently been proposed as the base oscillator of an adaptive system. In a …

We study both numerically and experimentally the synchronization in an ensemble of
nonidentical neuronlike oscillators described by the FitzHugh–Nagumo equations. The …

Dynamical networks are systems of active elements (nodes) interacting with each other
through links. Examples are power grids, neural structures, coupled chemical oscillators …

We consider competition between layers in adaptive multiplex networks of phase oscillators,
where adaptation principles (which cause intra-layer topology evolution) are inspired by real …

Forecasting a system's behavior is an essential task encountering the complex systems
theory. Machine learning offers supervised algorithms, eg, recurrent neural networks and …