Matthew T. Kaufman
- Cognitive Neuroscience top 0.2%
- Cellular and Molecular Neuroscience top 0.5%
- Biomedical Engineering top 5%
- Electrical and Electronic Engineering top 10%
- Social Psychology top 2%
- Co-authors
- Krishna V. ShenoyMark M. ChurchlandStephen I. RyuJohn P. CunninghamAnne K. ChurchlandPaul NuyujukianJustin FosterGary H. Glover
- Topics
- Neural dynamics and brain function (26 papers)EEG and Brain-Computer Interfaces (17 papers)Motor Control and Adaptation (12 papers)
- Journals
- NatureCellNature Communications
- Partner nations
- United StatesUnited KingdomPortugal
In The Last Decade
Matthew T. Kaufman
33 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 128
- Cognitive Neuroscience 5.2k
- Cellular and Molecular Neuroscience 2.4k
- Biomedical Engineering 758
- Electrical and Electronic Engineering 581
- Social Psychology 440
Countries citing papers authored by Matthew T. Kaufman
This map shows the geographic impact of Matthew T. Kaufman'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 T. Kaufman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew T. Kaufman more than expected).
Fields of papers citing papers by Matthew T. Kaufman
This network shows the impact of papers produced by Matthew T. Kaufman. 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 T. Kaufman. The network helps show where Matthew T. Kaufman may publish in the future.
Co-authorship network of co-authors of Matthew T. Kaufman
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew T. Kaufman. A scholar is included among the top collaborators of Matthew T. Kaufman 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 T. Kaufman. Matthew T. Kaufman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 2 | |
| 4 | 14 | |
| 5 | 42 | |
| 6 | Single-trial neural dynamics are dominated by richly varied movementsbreakdown → | 557 |
| 7 | 22 | |
| 8 | 99 | |
| 9 | 71 | |
| 10 | 133 | |
| 11 | 37 | |
| 12 | 232 | |
| 13 | 297 | |
| 14 | Cortical activity in the null space: permitting preparation without movementbreakdown → | 451 |
| 15 | 4 | |
| 16 | 388 | |
| 17 | 323 | |
| 18 | 53 | |
| 19 | 317 | |
| 20 | Distributed Neural Representation of Expected Value | 5 |
About Matthew T. Kaufman
Matthew T. Kaufman is a scholar working on Cognitive Neuroscience, General Decision Sciences and Cellular and Molecular Neuroscience, having authored 35 papers that have together received 6.0k indexed citations. Recurring topics across this work include Neural dynamics and brain function (26 papers), EEG and Brain-Computer Interfaces (17 papers) and Motor Control and Adaptation (12 papers). The work is most often cited by research in Cognitive Neuroscience (5.2k citations), Cellular and Molecular Neuroscience (2.4k citations) and General Decision Sciences (165 citations). Matthew T. Kaufman has collaborated with scholars based in United States, United Kingdom and Portugal. Frequent co-authors include Krishna V. Shenoy, Mark M. Churchland, Stephen I. Ryu, John P. Cunningham, Anne K. Churchland, Paul Nuyujukian, Justin Foster, Gary H. Glover, Brian Knutson and Jonathan Taylor. Their work appears in journals such as Nature, Cell and Nature Communications.
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.