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 …

Magnonics addresses the physical properties of spin waves and utilizes them for data
processing. Scalability down to atomic dimensions, operation in the GHz-to-THz frequency …

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

Dynamic physical systems such as reservoir computing (RC) architectures show a great
prospect in temporal information processing, whereas hierarchical information processing …

Reservoir computing (RC), as a brain-inspired neuromorphic computing algorithm, is
capable of fast and energy-efficient temporal data analysis and prediction. Hardware …

Spintronic nano-synapses and nano-neurons perform neural network operations with high
accuracy thanks to their rich, reproducible and controllable magnetization dynamics. These …

Nonlinear dynamical systems serving reservoir computing enrich the physical
implementation of computing systems. A method for building physical reservoirs from …

Physical reservoir computing has recently been attracting attention for its ability to
substantially reduce the computational resources required to process time series data …

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the
growing energy costs of machine learning. In software-based machine learning, computing …