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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Spin waves propagating through a stripe magnetic domain structure and their applications to reservoir computing
Ryosho Nakane, Akira Hirose, Gouhei Tanaka
Physical Review Research (2021) Vol. 3, Iss. 3
Open Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

A perspective on physical reservoir computing with nanomagnetic devices
D. A. Allwood, Matthew O. A. Ellis, David Griffin, et al.
Applied Physics Letters (2023) Vol. 122, Iss. 4
Open Access | Times Cited: 43
Physical reservoir computing with emerging electronics
Xiangpeng Liang, Jianshi Tang, Ya‐Nan Zhong, et al.
Nature Electronics (2024) Vol. 7, Iss. 3, pp. 193-206
Closed Access | Times Cited: 43
2D Magnetic heterostructures: spintronics and quantum future
Bingyu Zhang, Pengcheng Lu, Roozbeh Tabrizian, et al.
npj Spintronics (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 22
Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks
Satoshi Sunada, Tomoaki Niiyama, Kazutaka Kanno, et al.
Physical Review Letters (2025) Vol. 134, Iss. 1
Closed Access | Times Cited: 1
Pattern recognition with neuromorphic computing using magnetic field–induced dynamics of skyrmions
Tomoyuki Yokouchi, Satoshi Sugimoto, Bivas Rana, et al.
Science Advances (2022) Vol. 8, Iss. 39
Open Access | Times Cited: 52
Reservoir computing on a silicon platform with a ferroelectric field-effect transistor
Kasidit Toprasertpong, Eishin Nako, Zeyu Wang, et al.
Communications Engineering (2022) Vol. 1, Iss. 1
Open Access | Times Cited: 46
Pattern recognition in reciprocal space with a magnon-scattering reservoir
Lukas Körber, Christopher Heins, Tobias Hula, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 27
Performance Enhancement of a Spin-Wave-Based Reservoir Computing System Utilizing Different Physical Conditions
Ryosho Nakane, Akira Hirose, Gouhei Tanaka
Physical Review Applied (2023) Vol. 19, Iss. 3
Open Access | Times Cited: 22
Magnetization Vector Rotation Reservoir Computing Operated by Redox Mechanism
Wataru Namiki, Daiki Nishioka, Takashi Tsuchiya, et al.
Nano Letters (2024) Vol. 24, Iss. 15, pp. 4383-4392
Open Access | Times Cited: 8
Experimental Demonstration of High‐Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multidetection
Wataru Namiki, Daiki Nishioka, Yu Yamaguchi, et al.
Advanced Intelligent Systems (2023) Vol. 5, Iss. 12
Open Access | Times Cited: 21
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Heng Zhang, Danilo Vasconcellos Vargas
IEEE Access (2023) Vol. 11, pp. 81033-81070
Open Access | Times Cited: 20
Information Processing Capacity of Spintronic Oscillator
Sumito Tsunegi, Tomoyuki Kubota, Akira Kamimaki, et al.
Advanced Intelligent Systems (2023) Vol. 5, Iss. 9
Open Access | Times Cited: 13
Analytic-signal-based input-output modeling inspires passband signal learning for spin-wave reservoir computing
Jiaxuan Chen, Yicheng Song, Akira Hirose
Physical Review Applied (2025) Vol. 23, Iss. 3
Closed Access
Spintronic reservoir computing with interpretable nonlinearity
Jiaxuan Chen, Yicheng Song, Akira Hirose
Physical Review Research (2025) Vol. 7, Iss. 1
Open Access
Simulation platform for pattern recognition based on reservoir computing with memristor networks
Gouhei Tanaka, Ryosho Nakane
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 21
Universal scaling between wave speed and size enables nanoscale high-performance reservoir computing based on propagating spin-waves
Satoshi Iihama, Yuya Koike, Shigemi Mizukami, et al.
npj Spintronics (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 3
An organized view of reservoir computing: a perspective on theory and technology development
Gisya Abdi, Tomasz Mazur, Konrad Szaciłowski
Japanese Journal of Applied Physics (2024) Vol. 63, Iss. 5, pp. 050803-050803
Open Access | Times Cited: 3
Fast physical reservoir computing, achieved with nonlinear interfered spin waves
Wataru Namiki, Daiki Nishioka, Takashi Tsuchiya, et al.
Neuromorphic Computing and Engineering (2024) Vol. 4, Iss. 2, pp. 024015-024015
Open Access | Times Cited: 3
Opto-magnonic reservoir computing coupling nonlinear interfered spin wave and visible light switching
Wataru Namiki, Yu Yamaguchi, Daiki Nishioka, et al.
Materials Today Physics (2024) Vol. 45, pp. 101465-101465
Open Access | Times Cited: 2
Asymmetric transition of electrical resistance in an all-solid-state redox device with Fe3O4 and Li-ion electrolyte thin films for physical reservoir computing
Wataru Namiki, Takashi Tsuchiya, Daiki Nishioka, et al.
Japanese Journal of Applied Physics (2024) Vol. 63, Iss. 3, pp. 03SP13-03SP13
Open Access | Times Cited: 1
Nonlinear phase shifts induced by pumping spin waves in magnonic crystals
Roman V. Haponchyk, А. Б. Устинов
Applied Physics Letters (2023) Vol. 122, Iss. 21
Closed Access | Times Cited: 4
Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets
Kaito Kobayashi, Yukitoshi Motome
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 3
Film-penetrating transducers applicable to on-chip reservoir computing with spin waves
Jiaxuan Chen, Ryosho Nakane, Gouhei Tanaka, et al.
Journal of Applied Physics (2022) Vol. 132, Iss. 12
Open Access | Times Cited: 5
Numerical simulation of performance of magnonic reservoir computer based on active-ring oscillator
А. В. Кондрашов, А. А. Никитин, А. А. Никитин, et al.
Journal of Magnetism and Magnetic Materials (2022) Vol. 563, pp. 169968-169968
Closed Access | Times Cited: 4
Proposal of Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals
Junya Kato, Gouhei Tanaka, Ryosho Nakane, et al.
2022 International Joint Conference on Neural Networks (IJCNN) (2022), pp. 1-6
Closed Access | Times Cited: 4
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