Marcel Nonnenmacher
- Cognitive Neuroscience top 10%
- Neural dynamics and brain function 4
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- Model Reduction and Neural Networks 3
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- Meteorological Phenomena and Simulations 2
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- Gaussian Processes and Bayesian Inference 2
- Neural Networks and Applications 1
- Machine Learning and Algorithms 1
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- Physiological and biochemical adaptations 1
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- Soil Moisture and Remote Sensing 1
- Co-authors
- Jakob H. MackeDavid S. GreenbergKaan ÖcalPedro J. GonçalvesGiacomo BassettoJan-Matthis LueckmannChaitanya ChintaluriTim P. Vogels
- Journals
- Weather and Forecasting (1 paper)PLoS Computational Biology (1 paper)Journal of Advances in Modeling Earth Systems (1 paper)
- Partner nations
- GermanyAustriaUnited Kingdom
In The Last Decade
Marcel Nonnenmacher
8 papers receiving 221 citations
Peers
Comparison fields: 5 of 77
- Cognitive Neuroscience 111
- Statistical and Nonlinear Physics 37
- Cellular and Molecular Neuroscience 45
- Statistics and Probability 19
- General Decision Sciences 4
Countries citing papers authored by Marcel Nonnenmacher
This map shows the geographic impact of Marcel Nonnenmacher'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 Marcel Nonnenmacher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcel Nonnenmacher more than expected).
Fields of papers citing papers by Marcel Nonnenmacher
This network shows the impact of papers produced by Marcel Nonnenmacher. 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 Marcel Nonnenmacher. The network helps show where Marcel Nonnenmacher may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Marcel Nonnenmacher, 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 | 2021 | 18 | |
| 2 | 2021 | 5 | |
| 3 | 2020 | 116 | |
| 4 | 2019 | 18 | |
| 5 | 2017 | 31 | |
| 6 | Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations | 2017 | 4 |
| 7 | Flexible statistical inference for mechanistic models of neural dynamics | 2017 | 29 |
| 8 | 2015 | 3 |
About Marcel Nonnenmacher
Marcel Nonnenmacher is a scholar working on Statistical and Nonlinear Physics, Cognitive Neuroscience and Artificial Intelligence, having authored 8 papers that have together received 224 indexed citations. Recurring topics across this work include Neural dynamics and brain function (4 papers), Model Reduction and Neural Networks (3 papers), Meteorological Phenomena and Simulations (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Physiological and biochemical adaptations (1 paper), Neural Networks and Applications (1 paper), Soil Moisture and Remote Sensing (1 paper) and Machine Learning and Algorithms (1 paper). The work is most often cited by research in Cognitive Neuroscience (111 citations), Statistical and Nonlinear Physics (37 citations) and Cellular and Molecular Neuroscience (45 citations). Marcel Nonnenmacher has collaborated with scholars based in Germany, Austria and United Kingdom. Frequent co-authors include Jakob H. Macke, David S. Greenberg, Kaan Öcal, Pedro J. Gonçalves, Giacomo Bassetto, Jan-Matthis Lueckmann, Chaitanya Chintaluri, Tim P. Vogels, Michael Deistler and Sara Ann Haddad. Their work appears in journals such as Weather and Forecasting, PLoS Computational Biology, Journal of Advances in Modeling Earth Systems, eLife and arXiv (Cornell University).
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.