Delay-based reservoir computing has gained a lot of attention due to the relative simplicity
with which this concept can be implemented in hardware. However, unnecessary constraints …

Deep learning has been revolutionizing information processing in many fields of science
and engineering owing to the massively growing amounts of data and the advances in deep …

Microwave photonic approaches for the generation of microwave signals have attracted
substantial attention in recent years, thanks to the significant advantages brought by …

In the reservoir computing literature, the information processing capacity is frequently used
to characterize the computing capabilities of a reservoir. However, it remains unclear how …

The concept of reservoir computing emerged from a specific machine learning paradigm
characterized by a three-layered architecture (input, reservoir, and output), where only the …

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …

The deep time-delay reservoir computing concept utilizes unidirectionally connected
systems with time-delays for supervised learning. We present how the dynamical properties …

The implementation of 5G increases the demand for data acquisition, thus increasing the
pressure of data processing. Although artificial neural network shows great potential in …

Task specific hyperparameter tuning in reservoir computing is an open issue, and is of
particular relevance for hardware implemented reservoirs. We investigate the influence of …

Microring resonators (MRRs) are promising devices for time-delay photonic reservoir
computing, but the impact of the different physical effects taking place in the MRRs on the …