Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

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 …

Photonic delay systems have revolutionized the hardware implementation of Recurrent
Neural Networks and Reservoir Computing in particular. The fundamental principles of …

Microwave photonic approaches for the generation of microwave signals have attracted
substantial attention in recent years, thanks to the significant advantages brought by …

This book is devoted to a comprehensive compilation of the first hardware platforms
employed for photonic reservoir computing. Reservoir computing is a machine learning …

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 …

Physical reservoir computing is a promising way to develop efficient artificial intelligence
using physical devices exhibiting nonlinear dynamics. Although magnetic materials have …

Physical reservoir computing, which is a promising method for the implementation of highly
efficient artificial intelligence devices, requires a physical system with nonlinearity, fading …

A dynamical system is an information processing apparatus that encodes input streams from
the external environment to its state and processes them through state transitions. The …

Limit cycle oscillators have the potential to be resourced as reservoir computers due to their
rich dynamics. Here, a Hopf oscillator is used as a physical reservoir computer by discarding …