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

Artificial intelligence (AI) is gaining strength, and materials science can both contribute to
and profit from it. In a simultaneous progress race, new materials, systems, and processes …

Reservoir computing offers a powerful neuromorphic computing architecture for
spatiotemporal signal processing. To boost the power efficiency of the hardware …

Reservoir computing is a highly efficient network for processing temporal signals due to its
low training cost compared to standard recurrent neural networks, and generating rich …

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and
interact to process information. Taking inspiration from this behaviour to realize high-density …

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 …

The booming development of artificial intelligence (AI) requires faster physical processing
units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged …

Reservoir computing is a relatively recent computational paradigm that originates from a
recurrent neural network and is known for its wide range of implementations using different …

This paper reviews some recent progress in the field of neuromorphic photonics, with a
particular focus on scalability. We provide a framework for understanding the underlying …