This manuscript serves a specific purpose: to give readers from fields such as material
science, chemistry, or electronics an overview of implementing a reservoir computing (RC) …

Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental
bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

The reservoir computing (RC) framework states that any nonlinear, input-driven dynamical
system (the reservoir) exhibiting properties such as a fading memory and input separability …

Does biological computation happen at some sort of “edge of chaos”, a dynamical regime
somewhere between order and chaos? And if so, is this a fundamental principle that …

Physical reservoir computing (RC) represents a computational framework that exploits
information-processing capabilities of programmable matter, allowing the realization of …

We explore the effect of connectivity and topology on the dynamical behaviour of Reservoir
Computers. At present, considerable effort is taken to design and hand-craft physical …

Abstract We overview Reservoir Computing (RC) with physical systems from an
Unconventional Computing (UC) perspective. We discuss challenges present in both fields …

Inspired by the nonlinear dynamics of neural networks, new unconventional computing
hardware has emerged under the name of physical reservoir computing. In this paradigm …

Modern computers allow a methodical search of possibly billions of experiments and the
exploitation of interactions that are not known in advance. This enables a bottom-up process …