Daniel Belkin
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- Neuroscience and Neural Engineering 3
- Photoreceptor and optogenetics research 1
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- Advanced Memory and Neural Computing 5
- Ferroelectric and Negative Capacitance Devices 4
- Cognitive Neuroscience top 5%
- Neural dynamics and brain function 1
- Hardware and Architecture top 10%
- Artificial Intelligence top 5%
- Quantum Computing Algorithms and Architecture 1
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- Iron and Steelmaking Processes 1
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- Laser-Plasma Interactions and Diagnostics 1
- Cited by
- Cellular and Molecular NeuroscienceElectrical and Electronic EngineeringCognitive Neuroscience
- Partner nations
- United StatesRussiaUnited Kingdom
In The Last Decade
Daniel Belkin
7 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 51
- Cellular and Molecular Neuroscience 552
- Electrical and Electronic Engineering 1.3k
- Cognitive Neuroscience 287
- Hardware and Architecture 76
- Artificial Intelligence 278
Countries citing papers authored by Daniel Belkin
This map shows the geographic impact of Daniel Belkin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Belkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Belkin more than expected).
Fields of papers citing papers by Daniel Belkin
This network shows the impact of papers produced by Daniel Belkin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Belkin. The network helps show where Daniel Belkin may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Daniel Belkin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 4 | |
| 2 | 2021 | 3 | |
| 3 | 2020 | 0 | |
| 4 | 2019 | 4 | |
| 5 | Reinforcement learning with analogue memristor arraysbreakdown → | 2019 | 289 |
| 6 | Efficient and self-adaptive in-situ learning in multilayer memristor neural networksbreakdown → | 2018 | 711 |
| 7 | 2018 | 41 | |
| 8 | 2017 | 339 |
About Daniel Belkin
Daniel Belkin is a scholar working on Cellular and Molecular Neuroscience, Electrical and Electronic Engineering and Nuclear and High Energy Physics, having authored 8 papers that have together received 1.4k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (5 papers), Ferroelectric and Negative Capacitance Devices (4 papers), Neuroscience and Neural Engineering (3 papers), Quantum Computing Algorithms and Architecture (1 paper), Photoreceptor and optogenetics research (1 paper), Neural dynamics and brain function (1 paper), Iron and Steelmaking Processes (1 paper) and Laser-Plasma Interactions and Diagnostics (1 paper). The work is most often cited by research in Cellular and Molecular Neuroscience (552 citations), Electrical and Electronic Engineering (1.3k citations) and Cognitive Neuroscience (287 citations). Daniel Belkin has collaborated with scholars based in United States, Russia and United Kingdom. Frequent co-authors include Mark Barnell, Qiangfei Xia, Can Li, Hao Jiang, Qing Wu, Yunning Li, J. Joshua Yang, Zhongrui Wang, R. Stanley Williams and Miao Hu. Their work appears in journals such as Nature Communications, PRX Quantum, Nature Electronics, Russian Journal of Nondestructive Testing and arXiv (Cornell University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.