Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

Reservoir computing (RC) is a neural computing paradigm especially well-suited for
learning dynamical systems by leveraging an untrained reservoir layer, providing high …

Prediction of multi-dimensional time-series data, which may represent such diverse
phenomena as climate changes or financial markets, remains a challenging task in view of …

The field of wireless communication is currently being pushed to new boundaries with the
emergence of 6G technology. This advanced technology requires substantially increased …

The unprecedented dissemination of edge devices is accompanied by a growing demand
for neuromorphic chips that can process time-series data natively without cloud support …

5G and beyond 5G wireless communication has revolutionized our daily lives. However, the
increased bandwidth and data rate transfer in 5G present challenges, particularly in the …

Updating the state of reservoir nodes is one of the essential operations of reservoir
computing (RC), which highly affects the system's performance. In an echo state network …

Pushing the frontiers of time-series information processing in ever-growing edge devices
with stringent resources has been impeded by the system's ability to process information …

We propose look-up tables networks-based reservoir computing (LUTNet-RC). This work is
the first trial of applying LUTNets to RC. LUTNet-RC consists of a LUT-based reservoir layer …

In this paper, a small-world network-based reservoir computing (SWN-RC) is introduced to a
micro-electromechanical system (MEMS) mirror-based laser scanner to achieve high …