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 …

Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …

Hardware implementation tailored to requirements in reservoir computing would facilitate
lightweight and powerful temporal processing. Capacitive reservoirs would boost power …

Spintronics is a growing research field that focuses on exploring materials and devices that
take advantage of the electron's “spin” to go beyond charge based devices. The most …

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 has attracted considerable attention due to its low training cost.
However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing …

Neuromorphic computing is a promising computing paradigm toward building next-
generation artificial intelligence machines, in which diverse types of synaptic plasticity play …

The brain's remarkable and efficient information processing capability is driving research
into brain-inspired (neuromorphic) computing paradigms. Artificial aqueous ion channels …

Reservoir computing (RC) is a computational architecture capable of efficiently processing
temporal information, which allows low-cost hardware implementation. However, the …

Neuromorphic computing can process large amounts of information in parallel and provides
a powerful tool to solve the von Neumann bottleneck. Constructing an artificial neural …