Runchun Wang

512 citations
28 papers · 300 · h-index 10

Impact in

Papers in

Runchun Wang

26 papers receiving 294 citations

Peers

Runchun Wang
Comparison fields: 5 of 30
  • Cognitive Neuroscience 159
  • Cellular and Molecular Neuroscience 110
  • Electrical and Electronic Engineering 255
  • Artificial Intelligence 78
  • Signal Processing 20
Replace Daniel Ben Dayan Rubin with:
Daniel Ben Dayan Rubin United States
X. Arreguit Switzerland
Kristofor D. Carlson United States
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Man Yao China
Kshitij Dhoble New Zealand
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Citations per field
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Citations per year

Countries citing papers authored by Runchun Wang

Since Specialization
Citations

This map shows the geographic impact of Runchun Wang'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 Runchun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Runchun Wang more than expected).

Fields of papers citing papers by Runchun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Runchun Wang. 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 Runchun Wang. The network helps show where Runchun Wang may publish in the future.

Co-authors

The 14 scholars most cited alongside Runchun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Runchun Wang Line = papers co-authored together Runchun Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201356
2 201736
3 201425
4 201622
5 201716
6 201114
7 201514
8 201513
9 201411
10 20189
11 20149
12 20168
13 20217
14 20187
15 20147
16 20127
17 20197
18 20117
19 20186
20 20185

About Runchun Wang

Runchun Wang is a scholar working on Electrical and Electronic Engineering, Cognitive Neuroscience, Cellular and Molecular Neuroscience, Artificial Intelligence and Signal Processing, having authored 28 papers that have together received 300 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (20 papers), Neural dynamics and brain function (11 papers), Neuroscience and Neural Engineering (10 papers), Neural Networks and Applications (7 papers), Neural Networks and Reservoir Computing (5 papers), Hearing Loss and Rehabilitation (5 papers), Ferroelectric and Negative Capacitance Devices (4 papers) and Acoustic Wave Phenomena Research (4 papers). The work is most often cited by research in Cognitive Neuroscience (159 citations), Cellular and Molecular Neuroscience (110 citations), Electrical and Electronic Engineering (255 citations), Artificial Intelligence (78 citations) and Signal Processing (20 citations). Runchun Wang has collaborated with scholars based in Australia, United States and India. Frequent co-authors include André van Schaik, Tara Julia Hamilton, Jonathan Tapson, Chetan Singh Thakur, Gregory Cohen, Klaus M. Stiefel, Ying Xu, Alistair McEwan, Craig Jin and Saeed Afshar. Their work appears in journals such as IEEE Transactions on Circuits and Systems I Regular Papers, Frontiers in Neuroscience, IEEE Transactions on Biomedical Circuits and Systems and Applied Sciences.

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.

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