Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

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) …

Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

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

The simultaneous advances in artificial neural networks and photonic integration
technologies have spurred extensive research in optical computing and optical neural …

We demonstrate the effectiveness of using machine learning for model-free prediction of
spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension …

It has long been known that photonic communication can alleviate the data movement
bottlenecks that plague conventional microelectronic processors. More recently, there has …

Research on optical computing has recently attracted significant attention due to the
transformative advances in machine learning. Among different approaches, diffractive …

We review a novel paradigm that has emerged in analogue neuromorphic optical
computing. The goal is to implement a reservoir computer in optics, where information is …

Reservoir computing, originally referred to as an echo state network or a liquid state
machine, is a brain-inspired paradigm for processing temporal information. It involves …