Sukbin Lim
Impact in
- Cognitive Neuroscience top 5%
- Neural dynamics and brain function
- Memory and Neural Mechanisms
- EEG and Brain-Computer Interfaces
-
- Neuroscience and Neuropharmacology Research
- Neuroscience and Neural Engineering
Papers in
-
- Neural dynamics and brain function 13
- Memory and Neural Mechanisms 3
- Visual perception and processing mechanisms 3
-
- Neuroscience and Neural Engineering 4
- Neuroscience and Neuropharmacology Research 3
- Neurobiology and Insect Physiology Research 1
- Co-authors
- Mark S. Goldman (5 shared papers)John Rinzel (1 shared paper)Yali Amit (1 shared paper)David L. Sheinberg (1 shared paper)David J. Freedman (1 shared paper)Nicolas Brunel (1 shared paper)Jillian L. McKee (1 shared paper)Alexander A. Chubykin (1 shared paper)
- Journals
- Journal of Neuroscience (2 papers)Nature Neuroscience (2 papers)eLife (2 papers)iScience (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesChinaSouth Korea
In The Last Decade
Sukbin Lim
13 papers receiving 299 citations
Peers
Comparison fields: 5 of 50
- Cognitive Neuroscience 267
- Cellular and Molecular Neuroscience 138
- Statistical and Nonlinear Physics 42
- Sensory Systems 11
- Neurology 14
Countries citing papers authored by Sukbin Lim
This map shows the geographic impact of Sukbin Lim'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 Sukbin Lim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sukbin Lim more than expected).
Fields of papers citing papers by Sukbin Lim
This network shows the impact of papers produced by Sukbin Lim. 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 Sukbin Lim. The network helps show where Sukbin Lim may publish in the future.
Co-authors
The 13 scholars most cited alongside Sukbin Lim, 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 | 2013 | 127 | |
| 2 | 2015 | 60 | |
| 3 | 2014 | 33 | |
| 4 | 2009 | 21 | |
| 5 | 2011 | 15 | |
| 6 | 2017 | 13 | |
| 7 | 2021 | 12 | |
| 8 | 2019 | 9 | |
| 9 | 2021 | 6 | |
| 10 | 2012 | 3 | |
| 11 | 2023 | 2 | |
| 12 | 2022 | 2 | |
| 13 | 2024 | 1 | |
| 14 | 2025 | 0 |
About Sukbin Lim
Sukbin Lim is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics and Sensory Systems, having authored 14 papers that have together received 304 indexed citations. Recurring topics across this work include Neural dynamics and brain function (13 papers), Advanced Memory and Neural Computing (7 papers), Neuroscience and Neural Engineering (4 papers), Memory and Neural Mechanisms (3 papers), Neuroscience and Neuropharmacology Research (3 papers), Visual perception and processing mechanisms (3 papers), stochastic dynamics and bifurcation (2 papers) and Neurobiology and Insect Physiology Research (1 paper). The work is most often cited by research in Cognitive Neuroscience (267 citations), Cellular and Molecular Neuroscience (138 citations), Statistical and Nonlinear Physics (42 citations), Sensory Systems (11 citations) and Neurology (14 citations). Sukbin Lim has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Mark S. Goldman, John Rinzel, Yali Amit, David L. Sheinberg, David J. Freedman, Nicolas Brunel, Jillian L. McKee, Alexander A. Chubykin, Emre Aksay and Shirui Chen. Their work appears in journals such as Journal of Neuroscience, Nature Neuroscience, eLife, iScience and Scientific Reports.
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.