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 …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional
materials to perform complex computational tasks. Magnetic metamaterials are exciting …

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 …

Neural networks have revolutionized the area of artificial intelligence and introduced
transformative applications to almost every scientific field and industry. However, this …

Reservoir Computing is a relatively recent computational framework based on a large
Recurrent Neural Network with fixed weights. Many physical implementations of Reservoir …

We present both an overview and a perspective of recent experimental advances and
proposed new approaches to performing computation using memristors. A memristor is a 2 …

We study the computational potential of a spin-wave (SW) substrate by applying two metrics
known from reservoir computing. At low intensities, SW scatterers can perform linear …

Physical reservoir computing is a promising way to develop efficient artificial intelligence
using physical devices exhibiting nonlinear dynamics. Although magnetic materials have …