We systematize the approach to the investigation of deep neural network landscapes by
basing it on the geometry of the space of implemented functions rather than the space of …

An interesting feature of the brain is its ability to respond to disparate sensory signals from
the environment in unique ways depending on the environmental context or current brain …

Reservoir computing (RC) is a simple and efficient model-free framework for forecasting the
behavior of nonlinear dynamical systems from data. Here, we show that there exist …

A key challenge of nonlinear dynamics and network science is to understand how higher-
order interactions influence collective dynamics. Although many studies have approached …

Traditional neural network models of associative memories were used to store and retrieve
static patterns. We develop reservoir-computing based memories for complex dynamical …

Nonlinear systems that model physical experiments often have many equilibrium
configurations, and the number of these static solutions grows with the number of degrees of …

Power systems dominated by renewable energy encounter frequently large, random
disturbances, and a critical challenge faced in power-system management is how to …

Decision making can be tough. Transferring the choice to an unbiased authority, like a coin
or a die, may help to relieve the pressure. Indeed, for just a few dollars, you can buy a …

Nonlinear complex network-coupled systems typically have multiple stable equilibrium
states. Following perturbations or due to ambient noise, the system is pushed away from its …

Macroscopic magnets can easily be manipulated and positioned so that interactions
between themselves and with external fields induce interesting dynamics and equilibrium …