Alexander Mathis

11.4k total citations · 6 hit papers
37 papers, 5.3k citations indexed

About

Alexander Mathis is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Alexander Mathis has authored 37 papers receiving a total of 5.3k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Cognitive Neuroscience, 10 papers in Cellular and Molecular Neuroscience and 9 papers in Computer Vision and Pattern Recognition. Recurrent topics in Alexander Mathis's work include Neural dynamics and brain function (9 papers), Human Pose and Action Recognition (8 papers) and Memory and Neural Mechanisms (8 papers). Alexander Mathis is often cited by papers focused on Neural dynamics and brain function (9 papers), Human Pose and Action Recognition (8 papers) and Memory and Neural Mechanisms (8 papers). Alexander Mathis collaborates with scholars based in United States, Switzerland and Germany. Alexander Mathis's co-authors include Mackenzie Weygandt Mathis, Matthias Bethge, Venkatesh N. Murthy, Pranav Mamidanna, Taiga Abe, Kevin M. Cury, Tanmay Nath, Amir Patel, Andreas V. M. Herz and Martin Stemmler and has published in prestigious journals such as Cell, Physical Review Letters and Nature Communications.

In The Last Decade

Alexander Mathis

34 papers receiving 5.2k citations

Hit Papers

DeepLabCut: markerless pose estimation of user-defined bo... 2018 2026 2020 2023 2018 2019 2022 2022 2019 500 1000 1.5k 2.0k 2.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
    2018 2.5k citations Alexander Mathis, Pranav Mamidanna et al. Nature Neuroscience profile →
  2. Using DeepLabCut for 3D markerless pose estimation across species and behaviors
    2019 763 citations Tanmay Nath, Alexander Mathis et al. profile →
  3. Perspectives in machine learning for wildlife conservation
    2022 348 citations Devis Tuia, Benjamin Kellenberger et al. Nature Communications profile →
  4. Multi-animal pose estimation, identification and tracking with DeepLabCut
    2022 264 citations Jessy Lauer, Mu Zhou et al. Nature Methods profile →
  5. Deep learning tools for the measurement of animal behavior in neuroscience
    2019 257 citations Mackenzie Weygandt Mathis, Alexander Mathis Current Opinion in Neurobiology profile →
  6. Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics
    2024 48 citations Caleb Weinreb, Sherry Lin et al. Nature Methods profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Alexander Mathis United States 19 1.9k 1.4k 808 785 594 37 5.3k
Mackenzie Weygandt Mathis United States 15 1.6k 0.9× 1.1k 0.8× 791 1.0× 674 0.9× 580 1.0× 27 4.9k
Kristin Branson United States 24 859 0.5× 1.3k 1.0× 461 0.6× 326 0.4× 386 0.6× 39 4.2k
Kevin M. Cury United States 4 994 0.5× 955 0.7× 474 0.6× 353 0.4× 235 0.4× 4 2.8k
Taiga Abe United States 3 963 0.5× 755 0.6× 481 0.6× 353 0.4× 238 0.4× 6 2.6k
Pranav Mamidanna Denmark 5 969 0.5× 758 0.6× 474 0.6× 354 0.5× 236 0.4× 7 2.6k
Tony J. Prescott United Kingdom 40 3.9k 2.1× 1.9k 1.4× 236 0.3× 880 1.1× 260 0.4× 160 6.8k
Ilya A. Rybak United States 42 2.5k 1.3× 703 0.5× 922 1.1× 1.2k 1.5× 151 0.3× 130 5.5k
Hillel J. Chiel United States 40 2.1k 1.1× 2.4k 1.8× 365 0.5× 345 0.4× 127 0.2× 168 6.3k
Dale Purves United States 59 3.9k 2.1× 4.5k 3.3× 579 0.7× 805 1.0× 412 0.7× 280 9.8k
Bence P. Ölveczky United States 28 2.2k 1.2× 1.1k 0.8× 270 0.3× 479 0.6× 79 0.1× 44 4.1k

Countries citing papers authored by Alexander Mathis

Since Specialization
Citations

This map shows the geographic impact of Alexander Mathis'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 Alexander Mathis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander Mathis more than expected).

Fields of papers citing papers by Alexander Mathis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexander Mathis. 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 Alexander Mathis. The network helps show where Alexander Mathis may publish in the future.

Co-authorship network of co-authors of Alexander Mathis

This figure shows the co-authorship network connecting the top 25 collaborators of Alexander Mathis. A scholar is included among the top collaborators of Alexander Mathis 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 Alexander Mathis. Alexander Mathis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhou, Mu, et al.. (2026). Individual identification of brown bears using pose-aware metric learning. Current Biology. 36(3). 645–659.e14.
2.
Mathis, Mackenzie Weygandt & Alexander Mathis. (2025). Joint modelling of brain and behaviour dynamics with artificial intelligence. Nature reviews. Neuroscience. 27(2). 87–100.
3.
Rußwurm, Marc, et al.. (2024). WildCLIP: Scene and Animal Attribute Retrieval from Camera Trap Data with Domain-Adapted Vision-Language Models. International Journal of Computer Vision. 132(9). 3770–3786. 6 indexed citations
4.
Mathis, Mackenzie Weygandt, et al.. (2024). Decoding the brain: From neural representations to mechanistic models. Cell. 187(21). 5814–5832. 12 indexed citations
5.
Weinreb, Caleb, Sherry Lin, Mohammed Abdal Monium Osman, et al.. (2024). Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nature Methods. 21(7). 1329–1339. 48 indexed citations breakdown →
6.
Lauer, Jessy, Mu Zhou, Shaokai Ye, et al.. (2022). Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods. 19(4). 496–504. 264 indexed citations breakdown →
7.
Tuia, Devis, Benjamin Kellenberger, Sara Beery, et al.. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications. 13(1). 792–792. 348 indexed citations breakdown →
8.
Tsutsui‐Kimura, Iku, Yu Xie, Alexander Mathis, et al.. (2022). Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction. Neuron. 110(22). 3789–3804.e9. 50 indexed citations
9.
Mathis, Alexander, et al.. (2021). Measuring and modeling the motor system with machine learning. Current Opinion in Neurobiology. 70. 11–23. 32 indexed citations
10.
Mathis, Alexander, et al.. (2020). ImageNet performance correlates with pose estimation robustness and generalization on out-of-domain data. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2 indexed citations
11.
Mathis, Alexander, Steffen Schneider, Jessy Lauer, & Mackenzie Weygandt Mathis. (2020). A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives. Neuron. 108(1). 44–65. 148 indexed citations
12.
Mathis, Alexander, Pranav Mamidanna, Kevin M. Cury, et al.. (2018). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience. 21(9). 1281–1289. 2550 indexed citations breakdown →
13.
Herz, Andreas V. M., Alexander Mathis, & Martin Stemmler. (2017). Periodic population codes: From a single circular variable to higher dimensions, multiple nested scales, and conceptual spaces. Current Opinion in Neurobiology. 46. 99–108. 9 indexed citations
14.
Li, Ying, Alexander Mathis, Benjamin F. Grewe, et al.. (2017). Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice. Cell. 171(5). 1176–1190.e17. 178 indexed citations
15.
Stemmler, Martin, Alexander Mathis, & Andreas V. M. Herz. (2015). Connecting multiple spatial scales to decode the population activity of grid cells. Science Advances. 1(11). e1500816–e1500816. 93 indexed citations
16.
Mathis, Alexander, Andreas V. M. Herz, & Martin Stemmler. (2013). Multiscale codes in the nervous system: The problem of noise correlations and the ambiguity of periodic scales. Physical Review E. 88(2). 22713–22713. 21 indexed citations
17.
Mathis, Alexander, Andreas V. M. Herz, & Martin Stemmler. (2012). Optimal Population Codes for Space: Grid Cells Outperform Place Cells. Neural Computation. 24(9). 2280–2317. 88 indexed citations
18.
Mathis, Alexander, Andreas V. M. Herz, & Martin Stemmler. (2012). Resolution of Nested Neuronal Representations Can Be Exponential in the Number of Neurons. Physical Review Letters. 109(1). 18103–18103. 39 indexed citations
19.
Schmid, W., Alexander Mathis, & U. Keller. (2002). NOWCASTING THE RISK OF SNOWFALL AND FREEZING RAIN WITH RADAR AND GROUND DATA. 2 indexed citations
20.
Arnold, Pierre, et al.. (1999). Clinical, Serologic, and Parasitologic Follow-Up after Long-Term Allopurinol Therapy of Dogs Naturally Infected with Leishmania infantum. Journal of Veterinary Internal Medicine. 13(4). 330–330. 14 indexed citations

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.

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