Abstract This Review Article provides an overview of chaos in laser diodes by surveying
experimental achievements in the area and explaining the theory behind the phenomenon …

The increasing demands on information processing require novel computational concepts
and true parallelism. Nevertheless, hardware realizations of unconventional computing …

The paradigm of deterministic chaos has influenced thinking in many fields of science.
Chaotic systems show rich and surprising mathematical structures. In the applied sciences …

This book sets out to build bridges between the domains of photonic device physics and
neural networks, providing a comprehensive overview of the emerging field of" …

Abstract Long Short-Term Memory (LSTM) is able to solve many time series tasks
unsolvable by feed-forward networks using fixed size time windows. Here we find that …

The NARX network is a dynamical neural architecture commonly used for input–output
modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX …

Chaos, or exponential sensitivity to small perturbations, appears everywhere in nature.
Moreover, chaos is predicted to play diverse functional roles in living systems. A method for …

Publisher Summary This chapter reviews the progress in the field of synchronization and
communication with chaotic laser systems. The origin of chaotic dynamics in lasers is …

In this paper we introduce a multiscale symbolic information-theory approach for
discriminating nonlinear deterministic and stochastic dynamics from time series associated …

Reservoir computing is a paradigm in machine learning whose processing capabilities rely
on the dynamical behavior of recurrent neural networks. We present a mixed analog and …