Approaching limitations of digital computing technologies have spurred research in
neuromorphic and other unconventional approaches to computing. Here we argue that if we …

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

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

This manuscript serves a specific purpose: to give readers from fields such as material
science, chemistry, or electronics an overview of implementing a reservoir computing (RC) …

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring
materials to emerge as versatile functional platforms, from reconfigurable magnonics to …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

A machine-learning approach called “reservoir computing” has been used successfully for
short-term prediction and attractor reconstruction of chaotic dynamical systems from time …

Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

In this paper, the performance of three machine-learning methods for predicting short-term
evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz …

In this paper, we provide a novel approach to the architectural design of deep Recurrent
Neural Networks using signal frequency analysis. In particular, focusing on the Reservoir …