Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics.

Physical reservoir computing leverages the dynamical properties of complex physical systems to process information efficiently, significantly reducing training efforts and energy consumption. Magnetic skyrmions, topological spin textures, are promising candidates for reservoir computing systems due to their enhanced stability, non-linear interactions and low-power manipulation. Traditional spin-based reservoir computing has been limited to quasi-static detection or real-world data must be rescaled to the intrinsic timescale of the reservoir.

Brownian reservoir computing realized using geometrically confined skyrmion dynamics.

Reservoir computing (RC) has been considered as one of the key computational principles beyond von-Neumann computing. Magnetic skyrmions, topological particle-like spin textures in magnetic films are particularly promising for implementing RC, since they respond strongly nonlinearly to external stimuli and feature inherent multiscale dynamics. However, despite several theoretical proposals that exist for skyrmion reservoir computing, experimental realizations have been elusive until now.

[Distribution of haemoglobin content in human red cells, estimated by scanning cytophotometry (author's transl)].

Haemoglobin content and surface area of human red cells were estimated using a scanning cytophotometer connected to a computer for registration and analysis of the data. The measurements were carried out on fixed, unstained peripheral blood smears at a wavelength of 414 nm. The scanning can be controlled on the screen in order to detect errors and to eliminate extinctions from other sources than the cell examined.