Matthew D. Golub
- Cognitive Neuroscience top 2%
- Cellular and Molecular Neuroscience top 5%
- Biomedical Engineering
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Co-authors
- Steven M. ChaseByron M. YuAaron P. BatistaElizabeth C. Tyler‐KabaraDavid SussilloKrishna V. ShenoySaurabh VyasStephen I. Ryu
- Topics
- EEG and Brain-Computer Interfaces (12 papers)Neural dynamics and brain function (8 papers)Motor Control and Adaptation (6 papers)
- Partner nations
- United States
In The Last Decade
Matthew D. Golub
14 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 76
- Cognitive Neuroscience 1.1k
- Cellular and Molecular Neuroscience 487
- Biomedical Engineering 170
- Artificial Intelligence 141
- Electrical and Electronic Engineering 138
Countries citing papers authored by Matthew D. Golub
This map shows the geographic impact of Matthew D. Golub'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 Matthew D. Golub with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew D. Golub more than expected).
Fields of papers citing papers by Matthew D. Golub
This network shows the impact of papers produced by Matthew D. Golub. 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 Matthew D. Golub. The network helps show where Matthew D. Golub may publish in the future.
Co-authorship network of co-authors of Matthew D. Golub
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew D. Golub. A scholar is included among the top collaborators of Matthew D. Golub 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 Matthew D. Golub. Matthew D. Golub is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 44 | |
| 3 | 37 | |
| 4 | Computation Through Neural Population Dynamicsbreakdown → | 279 |
| 5 | 104 | |
| 6 | 14 | |
| 7 | 134 | |
| 8 | 41 | |
| 9 | 61 | |
| 10 | 37 | |
| 11 | Neural constraints on learningbreakdown → | 399 |
| 12 | 55 | |
| 13 | Learning an Internal Dynamics Model from Control Demonstration. | 16 |
| 14 | 20 |
About Matthew D. Golub
Matthew D. Golub is a scholar working on Cognitive Neuroscience, Biophysics and Neurology, having authored 14 papers that have together received 1.2k indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (12 papers), Neural dynamics and brain function (8 papers) and Motor Control and Adaptation (6 papers). The work is most often cited by research in Cognitive Neuroscience (1.1k citations), Cellular and Molecular Neuroscience (487 citations) and Neurology (43 citations). Matthew D. Golub has collaborated with scholars based in United States. Frequent co-authors include Steven M. Chase, Byron M. Yu, Aaron P. Batista, Elizabeth C. Tyler‐Kabara, David Sussillo, Krishna V. Shenoy, Saurabh Vyas, Stephen I. Ryu, Patrick T. Sadtler and Kristin M. Quick. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Nature Neuroscience.
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.