Peng Yan
- Electrical and Electronic Engineering top 2%
- Cellular and Molecular Neuroscience top 5%
- Cognitive Neuroscience top 5%
- Artificial Intelligence top 5%
- Polymers and Plastics top 10%
- Co-authors
- Qiangfei XiaPeng LinCan LiYunning LiJ. Joshua YangQing WuZhongrui WangR. Stanley Williams
- Topics
- Advanced Memory and Neural Computing (12 papers)Neuroscience and Neural Engineering (8 papers)Ferroelectric and Negative Capacitance Devices (8 papers)
- Cited by
- Cellular and Molecular NeuroscienceElectrical and Electronic EngineeringCognitive Neuroscience
- Partner nations
- United StatesChinaSwitzerland
In The Last Decade
Peng Yan
12 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 50
- Electrical and Electronic Engineering 1.5k
- Cellular and Molecular Neuroscience 636
- Cognitive Neuroscience 294
- Artificial Intelligence 286
- Polymers and Plastics 155
Countries citing papers authored by Peng Yan
This map shows the geographic impact of Peng Yan'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 Peng Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Yan more than expected).
Fields of papers citing papers by Peng Yan
This network shows the impact of papers produced by Peng Yan. 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 Peng Yan. The network helps show where Peng Yan may publish in the future.
Co-authorship network of co-authors of Peng Yan
This figure shows the co-authorship network connecting the top 25 collaborators of Peng Yan. A scholar is included among the top collaborators of Peng Yan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peng Yan. Peng Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 19 | |
| 3 | 22 | |
| 4 | 0 | |
| 5 | 80 | |
| 6 | 239 | |
| 7 | Reinforcement learning with analogue memristor arraysbreakdown → | 289 |
| 8 | Efficient and self-adaptive in-situ learning in multilayer memristor neural networksbreakdown → | 711 |
| 9 | 41 | |
| 10 | 71 | |
| 11 | 35 | |
| 12 | 8 | |
| 13 | 70 |
About Peng Yan
Peng Yan is a scholar working on General Engineering, Cellular and Molecular Neuroscience and Electrical and Electronic Engineering, having authored 13 papers that have together received 1.6k indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (12 papers), Neuroscience and Neural Engineering (8 papers) and Ferroelectric and Negative Capacitance Devices (8 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (636 citations), Electrical and Electronic Engineering (1.5k citations) and Cognitive Neuroscience (294 citations). Peng Yan has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include Qiangfei Xia, Peng Lin, Can Li, Yunning Li, J. Joshua Yang, Qing Wu, Zhongrui Wang, R. Stanley Williams, Miao Hu and Ning Ge. Their work appears in journals such as Advanced Materials, Nature Communications and Applied Physics Letters.
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.