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

In the emerging Internet of things cyber-physical system-embedded society, big data
analytics needs huge computing capability with better energy efficiency. Coming to the end …

The recognition of human actions in video streams is a challenging task in computer vision,
with cardinal applications in brain–computer interfaces and surveillance, for example …

The recognition of human actions in videos is one of the most active research fields in
computer vision. The canonical approach consists in a more or less complex preprocessing …

The recent rapid increase in demand for data processing has resulted in the need for novel
machine learning concepts and hardware. Physical reservoir computing and an extreme …

Existing work on coherent photonic reservoir computing (PRC) mostly concentrates on
single-wavelength solutions. In this paper, we discuss the opportunities and challenges …

Reservoir computing is a brain-inspired approach for information processing, well suited to
analog implementations. We report a photonic implementation of a reservoir computer that …

The exploitation of the full structure of multimode light fields enables compelling capabilities
in many fields including classical and quantum information science. We exploit data …

In this paper, a multiple-input multiple-output reservoir computing (RC) system is proposed,
which is composed of multiple nonlinear nodes (Mach–Zehnder modulators) and multiple …

Photonic reservoir computing uses recent advances in machine learning, and in particular
the reservoir computing algorithm, to carry out complex computations optically. Experimental …