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 …

Time-delayed optoelectronic oscillators are at the center of a large body of scientific
literature. The complex behavior of these nonlinear oscillators has been thoroughly explored …

We present a deep learning model, DE-LSTM, for the simulation of a stochastic process with
an underlying nonlinear dynamics. The deep learning model aims to approximate the …

In this article, we address the issue of modeling and estimating changes in the state of the
spatio-temporal dynamical systems based on a sequence of observations like video frames …

The deep time-delay reservoir computing concept utilizes unidirectionally connected
systems with time-delays for supervised learning. We present how the dynamical properties …

The concept of reservoir computing emerged from a specific machine learning paradigm
characterized by a three-layered architecture (input, reservoir, and output), where only the …

The widespread development and use of neural networks have significantly enriched a wide
range of computer algorithms and promise higher speed at lower cost. However, the …

Deep neural networks are among the most widely applied machine learning tools showing
outstanding performance in a broad range of tasks. We present a method for folding a deep …

We study two identical FitzHugh-Nagumo oscillators which are coupled with one or two
different time delays. If only a single-delay coupling is used, the length of the delay …

In this paper, we study effects of partial time delays on phase synchronization in Watts-
Strogatz small-world neuronal networks. Our focus is on the impact of two parameters …