The rapid development of information technology has led to urgent requirements for high
efficiency and ultralow power consumption. In the past few decades, neuromorphic …

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

Complex-valued neural networks have many advantages over their real-valued
counterparts. Conventional digital electronic computing platforms are incapable of executing …

Neuromorphic computing can potentially solve the von Neumann bottleneck of current
mainstream computing because it excels at self‐adaptive learning and highly parallel …

Photonic systems for high-performance information processing have attracted renewed
interest. Neuromorphic silicon photonics has the potential to integrate processing functions …

Recently, research interest in brain‐inspired neuromorphic computing based on robust and
intelligent artificial neural networks has surged owing to the ability of such technology to …

Nanophotonics has been an active research field over the past two decades, triggered by
the rising interests in exploring new physics and technologies with light at the nanoscale. As …

There has been recent interest in neuromorphic photonics, a field with the promise to access
pivotal and unexplored regimes of machine intelligence. Progress has been made on …

Artificial neural networks are computational network models inspired by signal processing in
the brain. These models have dramatically improved performance for many machine …

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 …