ArticleNature communications2024
Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- An Efficient Biological Codon Recognition Reservoir Computing System Based on Low-Energy Epitaxial HfAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bias-Engineered Synthetic Antiferromagnets Hosting Sub-20 nm Zero-Field Skyrmions at Room Temperature.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unconventional skyrmions in synthetic antiferromagnets.Nature materials · 2026Article
- Observation of Topological Chirality Switching Induced Freezing of a Skyrmion Crystal.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Reservoir computing from collective dynamics of active colloidal oscillators.Communications AI & computing · 2026Article
- Photothermal skyrmion tweezer: programmable optical manipulation of magnetic topological quasiparticles.Nature communications · 2025Article
- Real-time observation of topological defect dynamics mediating two-dimensional skyrmion lattice melting.Nature nanotechnology · 2025Article
- Effects of Chiral Polypeptides on Skyrmion Stability and Dynamics.Nano letters · 2025Article
- Fiber Memristor-Based Physical Reservoir Computing for Multimodal Sleep Monitoring.Research (Washington, D.C.) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
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. We address this challenge by time-multiplexed skyrmion reservoir computing, that allows for aligning the reservoir's intrinsic timescales to real-world temporal patterns. Using millisecond-scale hand gestures recorded with Range-Doppler radar, we feed voltage excitations directly into our device and detect the skyrmion trajectory evolution. This method scales down to the nanometer level and demonstrates competitive or superior performance compared to energy-intensive software-based neural networks. Our hardware approach's key advantage is its ability to integrate sensor data in real-time without temporal rescaling, enabling numerous applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.