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Source: PubMed
Nature
10, 2019;574 (7776): 137-138.
Deep learning powers a motion-tracking revolution.
Roberta Kwok
Author Information
PMID: 31570871
DOI: 10.1038/d41586-019-02942-5
Abstract
No abstract text available.
Keywords
Animal behaviour
Computational biology and bioinformatics
Conservation biology
Ecology
References
Di Santo, V., Blevins, E. L. & Lauder, G. V. J. Exp. Biol. 220, 705–712 (2017). [PMID:
27965272
]
Zhang, W. & Yartsev, M. M. Cell 178, 413–428 (2019). [PMID:
31230710
]
Graving, J. M. et al. eLife https://doi.org/10.7554/eLife.47994 (2019). [DOI:
10.7554/eLife.47994
]
Mathis, A., Yüksekgönül, M., Rogers, B., Bethge, M., & Mathis, M. W. Preprint at https://arxiv.org/abs/1909.11229 (2019).
Heras, F. J. H., Romero-Ferrero, F., Hinz, R. C. & de Polavieja, G. G. PLoS Comput. Biol. 15, e1007354 (2019). [PMID:
31518357
]
MeSH Term
Algorithms
Animals
Behavior, Animal
Deep Learning
Humans
Mice
Movement
Open Access Publishing
Posture
Software
Journal Article
17
CITATIONS
17
Total citations
5
Recent citations
4.3
Field Citation Ratio
0.37
Relative Citation Ratio
Word Cloud
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biology
Deep
learning
powers
motion-tracking
revolution
Animal
behaviour
Computational
bioinformatics
Conservation
Ecology
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