Artificial intelligence (AI) is accelerating the development of unconventional computing
paradigms inspired by the abilities and energy efficiency of the brain. The human brain …

Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

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

Reservoir computing is a relatively recent computational paradigm that originates from a
recurrent neural network and is known for its wide range of implementations using different …

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

Echo state networks (ESN) provide an architecture and supervised learning principle for
recurrent neural networks (RNNs). The main idea is (i) to drive a random, large, fixed …

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage
legitimate credentials with trusted tools already deployed in a network environment, making …

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

Physical reservoir computing is a computational paradigm that enables spatiotemporal
pattern recognition to be performed directly in matter. The use of physical matter leads the …

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 …