M.A. Jiménez-Montaño
- Molecular Biology
- Cognitive Neuroscience top 10%
- Statistical and Nonlinear Physics top 5%
- Artificial Intelligence
- Economics and Econometrics top 10%
- Co-authors
- I. D. ZimmermanPaul E. RappA. M. AlbanoLutz MolgedeyRalf SteuerW. EbelingNiv CohenKathryn E. Korslund
- Topics
- Fractal and DNA sequence analysis (5 papers)Neural Networks and Applications (4 papers)Neural dynamics and brain function (3 papers)
- Journals
- Journal of NeurosciencePhysics Letters APhysica A Statistical Mechanics and its Applications
- Partner nations
- MexicoUnited StatesAustralia
In The Last Decade
M.A. Jiménez-Montaño
12 papers receiving 381 citations
Peers
Comparison fields: 5 of 69
- Molecular Biology 138
- Cognitive Neuroscience 137
- Statistical and Nonlinear Physics 131
- Artificial Intelligence 89
- Economics and Econometrics 80
Countries citing papers authored by M.A. Jiménez-Montaño
This map shows the geographic impact of M.A. Jiménez-Montaño'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 M.A. Jiménez-Montaño with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M.A. Jiménez-Montaño more than expected).
Fields of papers citing papers by M.A. Jiménez-Montaño
This network shows the impact of papers produced by M.A. Jiménez-Montaño. 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 M.A. Jiménez-Montaño. The network helps show where M.A. Jiménez-Montaño may publish in the future.
Co-authorship network of co-authors of M.A. Jiménez-Montaño
This figure shows the co-authorship network connecting the top 25 collaborators of M.A. Jiménez-Montaño. A scholar is included among the top collaborators of M.A. Jiménez-Montaño 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 M.A. Jiménez-Montaño. M.A. Jiménez-Montaño 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 | 32 | |
| 3 | 54 | |
| 4 | 8 | |
| 5 | 5 | |
| 6 | 5 | |
| 7 | 111 | |
| 8 | 72 | |
| 9 | 12 | |
| 10 | 18 | |
| 11 | 37 | |
| 12 | 33 |
About M.A. Jiménez-Montaño
M.A. Jiménez-Montaño is a scholar working on History and Philosophy of Science, Computational Theory and Mathematics and Cognitive Neuroscience, having authored 12 papers that have together received 391 indexed citations. Recurring topics across this work include Fractal and DNA sequence analysis (5 papers), Neural Networks and Applications (4 papers) and Neural dynamics and brain function (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (131 citations), Cognitive Neuroscience (137 citations) and Economics and Econometrics (80 citations). M.A. Jiménez-Montaño has collaborated with scholars based in Mexico, United States and Australia. Frequent co-authors include I. D. Zimmerman, Paul E. Rapp, A. M. Albano, Lutz Molgedey, Ralf Steuer, W. Ebeling, Niv Cohen, Kathryn E. Korslund, Christopher J. Cellucci and Tomas Watanabe. Their work appears in journals such as Journal of Neuroscience, Physics Letters A and Physica A Statistical Mechanics and its Applications.
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.