Jan-Matthis Lueckmann
- Cognitive Neuroscience top 10%
- Social Psychology top 10%
- Cellular and Molecular Neuroscience
- Clinical Psychology
- Artificial Intelligence
- Co-authors
- Jakob H. MackeSteven R. KraaijeveldWouter WolfAna LevordashkaKipling D. WilliamsGiacomo BassettoKaan ÖcalMarcel Nonnenmacher
- Topics
- Neural dynamics and brain function (5 papers)Gaussian Processes and Bayesian Inference (3 papers)Visual perception and processing mechanisms (2 papers)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Jan-Matthis Lueckmann
10 papers receiving 405 citations
Peers
Comparison fields: 5 of 98
- Cognitive Neuroscience 155
- Social Psychology 114
- Cellular and Molecular Neuroscience 69
- Clinical Psychology 58
- Artificial Intelligence 58
Countries citing papers authored by Jan-Matthis Lueckmann
This map shows the geographic impact of Jan-Matthis Lueckmann'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 Jan-Matthis Lueckmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan-Matthis Lueckmann more than expected).
Fields of papers citing papers by Jan-Matthis Lueckmann
This network shows the impact of papers produced by Jan-Matthis Lueckmann. 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 Jan-Matthis Lueckmann. The network helps show where Jan-Matthis Lueckmann may publish in the future.
Co-authorship network of co-authors of Jan-Matthis Lueckmann
This figure shows the co-authorship network connecting the top 25 collaborators of Jan-Matthis Lueckmann. A scholar is included among the top collaborators of Jan-Matthis Lueckmann 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 Jan-Matthis Lueckmann. Jan-Matthis Lueckmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 24 | |
| 2 | 116 | |
| 3 | 14 | |
| 4 | 1 | |
| 5 | 26 | |
| 6 | Likelihood-free inference with emulator networks | 15 |
| 7 | Flexible statistical inference for mechanistic models of neural dynamics | 29 |
| 8 | 7 | |
| 9 | 136 | |
| 10 | 45 |
About Jan-Matthis Lueckmann
Jan-Matthis Lueckmann is a scholar working on Cognitive Neuroscience, Statistics and Probability and Developmental Neuroscience, having authored 10 papers that have together received 413 indexed citations. Recurring topics across this work include Neural dynamics and brain function (5 papers), Gaussian Processes and Bayesian Inference (3 papers) and Visual perception and processing mechanisms (2 papers). The work is most often cited by research in Cognitive Neuroscience (155 citations), Applied Psychology (33 citations) and Developmental Neuroscience (26 citations). Jan-Matthis Lueckmann has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Jakob H. Macke, Steven R. Kraaijeveld, Wouter Wolf, Ana Levordashka, Kipling D. Williams, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Pedro J. Gonçalves and Chaitanya Chintaluri. Their work appears in journals such as Journal of Neuroscience, Scientific Reports and eLife.
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.