Topological spin textures in magnetic materials and arrangements of electric dipoles in
ferroelectrics are considered to be promising candidates for next-generation information …

Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …

Physical neuromorphic computing, exploiting the complex dynamics of physical systems,
has seen rapid advancements in sophistication and performance. Physical reservoir …

Physical reservoir computing leverages the dynamical properties of complex physical
systems to process information efficiently, significantly reducing training efforts and energy …

Spin–orbit torque can drive auto-oscillations of propagating spin-wave modes in nano-
constriction spin Hall nano-oscillators. These modes facilitate both long-range coupling and …

Reconfigurable neuromorphic computing holds promise for advancing energy-efficient
neural network implementation and functional versatility. Previous work has focused on …

Learning and pattern recognition inevitably requires memory of previous events, a feature
that conventional CMOS hardware needs to artificially simulate. Dynamical systems …

Quantum reservoir computing (QRC) is a highly promising computational paradigm that
leverages quantum systems as a computational resource for nonlinear information …

Physical reservoirs holding intrinsic nonlinearity, high dimensionality, and memory effects
have attracted considerable interest regarding solving complex tasks efficiently. Particularly …

Synthetic antiferromagnetic structures can exhibit the advantages of high velocity similarly to
antiferromagnets with the additional benefit of being imaged and read-out through …