Rivu Midya
Impact in
- Cellular and Molecular Neuroscience top 0.5%
- Neuroscience and Neural Engineering
- Photoreceptor and optogenetics research
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- Advanced Memory and Neural Computing
- Ferroelectric and Negative Capacitance Devices
Papers in
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- Neuroscience and Neural Engineering 8
- Photoreceptor and optogenetics research 6
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- Neural dynamics and brain function 9
Rivu Midya
19 papers receiving 4.1k citations
Hit Papers
Peers
Comparison fields: 5 of 65
- Cellular and Molecular Neuroscience 2.0k
- Electrical and Electronic Engineering 4.0k
- Polymers and Plastics 679
- Cognitive Neuroscience 912
- Artificial Intelligence 639
Countries citing papers authored by Rivu Midya
This map shows the geographic impact of Rivu Midya'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 Rivu Midya with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rivu Midya more than expected).
Fields of papers citing papers by Rivu Midya
This network shows the impact of papers produced by Rivu Midya. 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 Rivu Midya. The network helps show where Rivu Midya may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Rivu Midya, 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 | 2025 | 0 | |
| 2 | 2024 | 10 | |
| 3 | 2022 | 11 | |
| 4 | 2022 | 3 | |
| 5 | 2022 | 4 | |
| 6 | 2021 | 31 | |
| 7 | 2021 | 28 | |
| 8 | An artificial spiking afferent nerve based on Mott memristors for neurorobotics Hit paper breakdown → | 2020 | 321 |
| 9 | 2020 | 75 | |
| 10 | 2019 | 4 | |
| 11 | 2019 | 30 | |
| 12 | 2019 | 106 | |
| 13 | 2019 | 218 | |
| 14 | Review of memristor devices in neuromorphic computing: materials sciences and device challenges Hit paper breakdown → | 2018 | 416 |
| 15 | 2018 | 8 | |
| 16 | 2018 | 7 | |
| 17 | 2017 | 339 | |
| 18 | Anatomy of Ag/Hafnia‐Based Selectors with 1010 Nonlinearity Hit paper breakdown → | 2017 | 324 |
| 19 | 2017 | 315 | |
| 20 | Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing Hit paper breakdown → | 2016 | 1899 |
About Rivu Midya
Rivu Midya is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 20 papers that have together received 4.1k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (20 papers), Neural dynamics and brain function (9 papers), Neuroscience and Neural Engineering (8 papers), Ferroelectric and Negative Capacitance Devices (7 papers), Photoreceptor and optogenetics research (6 papers), Neural Networks and Reservoir Computing (2 papers), Energy Harvesting in Wireless Networks (1 paper) and Quantum-Dot Cellular Automata (1 paper). The work is most often cited by research in Cellular and Molecular Neuroscience (2.0k citations), Electrical and Electronic Engineering (4.0k citations), Polymers and Plastics (679 citations), Cognitive Neuroscience (912 citations) and Artificial Intelligence (639 citations). Rivu Midya has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include J. Joshua Yang, Qiangfei Xia, Zhongrui Wang, Hao Jiang, Saumil Joshi, Peng Lin, Mark Barnell, Qing Wu, Sergey Savel’ev and R. Stanley Williams. Their work appears in journals such as Advanced Electronic Materials, Nature Communications, Advanced Functional Materials, Journal of Physics D Applied Physics and Advanced Materials.
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.