Deep learning tools for the measurement of animal behavior in neuroscience
Open accessAbstract
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capturing the postures of animals-pose estimation - has been rapidly advancing with new deep learning methods. While challenges still remain, we envision that the fast-paced development of new deep learning tools will rapidly change the landscape of realizable real-world neuroscience.
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Marker-less DL methods for pose estimation have tremendously improved over the last years and already impacted biology.
“For pose estimation, marker-less methods based on DL have tremendously improved over the last years and already impacted biology105.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [105]
Modern ML has rapidly changed laboratory-based non-invasive observation of animals.
“In recent years, modern ML has rapidly changed laboratory-based non-invasive observation of animals76,105.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [105]
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Kaplan, O., Yamamoto, G., Taketomi, T., et al. (2019) · doi:10.1109/apmar.2019.8709267 · Cited by 4 · see citing works
Detection and Tracking of Multiple Mice Using Part Proposal NetworksPreprint
Jiang, Z., Liu, Z., Chen, L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02831 · Cited by 4 · see citing works
Playing magic tricks to deep neural networks untangles human deceptionPreprint
Zaghi-Lara, R., Gea, M. Á., Camı́, J., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.07446 · Cited by 2 · see citing works
Yue, Y., Xu, P., Liu, Z., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/786822 · Cited by 1 · see citing works
Flagged first, then by citations
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Sorted by citations
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works
Self-citationAnipose: A toolkit for robust markerless 3D pose estimation
Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324 · see citing works
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.017 · Cited by 231 · see citing works
Self-citationNeural circuits regulating prosocial behaviors
Walsh, J. J., Christoffel, D. J., & Malenka, R. C. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-022-01348-8 · Cited by 72 · see citing works
Automated segmentation of insect anatomy from micro‐CT images using deep learning
Toulkeridou, E., Gutierrez, C. E., Baum, D., Doya, K., & Economo, E. P. (2023) · Natural Sciences · doi:10.1002/ntls.20230010 · Cited by 13 · see citing works
Transferring Dense Pose to Proximal Animal Classes
Sanakoyeu, A., Khalidov, V., McCarthy, M. S., Vedaldi, A., & Neverova, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00528 · Cited by 13 · see citing works
- Authors
- Mathis, M. W., & Mathis, A.
- Affiliations
- Harvard University (US)
- Corresponding author
- Mathis, Mackenzie Weygandt
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 60
- Pages
- 1-11
- ISSN
- 0959-4388 (ISSN-L)
- OpenAlex
- W2990943996
- Cited by
- 450
- References
- 98
- Citations by year
- 2026: 47 · 2025: 80 · 2024: 65 · 2023: 83 · 2022: 74 · 2021: 61 · 2020: 40
- FWCI
- 4.64
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Rowland Institute at Harvard
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- pose estimation, deep learning, computer vision
- MeSH terms
- Deep Learning, Animals, Behavior, Animal, Neurosciences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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