Recommended Reading 📚

📕 DAY 1

We suggest you to start the learning week with two reviews, the classic Toward a science of computational ethology by Anderson and Perona [AP14], as well as A primer on motion capture with deep learning: principles, pitfalls, and perspectives [MSLM20].

If you’re interested in Ecology you may also want to check out [TKB+22].

📙 DAY 2

For Day 2 make sure to check out the first DeepLabCut publication [MMC+18], as well as the DeepLabCut Protocol [NMC+19].

📘 DAY 3

Today you should read the DLC-live paper [KLS+20] and the multi-animal DeepLabCut paper [LZY+22].

📗 DAY 4

For unsupervised behavioral analysis read the first MoSeq paper [WJI+15].

References

[AP14]

David J. Anderson and Pietro Perona. Toward a science of computational ethology. Neuron, 84(1):18–31, October 2014. URL: https://doi.org/10.1016/j.neuron.2014.09.005, doi:10.1016/j.neuron.2014.09.005.

[KLS+20]

Gary A Kane, Goncalo Lopes, Jonny L Saunders, Alexander Mathis, and Mackenzie W Mathis. Real-time, low-latency closed-loop feedback using markerless posture tracking. eLife, December 2020. URL: https://doi.org/10.7554/elife.61909, doi:10.7554/elife.61909.

[LZY+22]

Jessy Lauer, Mu Zhou, Shaokai Ye, William Menegas, Steffen Schneider, Tanmay Nath, Mohammed Mostafizur Rahman, Valentina Di Santo, Daniel Soberanes, Guoping Feng, Venkatesh N. Murthy, George Lauder, Catherine Dulac, Mackenzie Weygandt Mathis, and Alexander Mathis. Multi-animal pose estimation, identification and tracking with DeepLabCut. Nature Methods, 19(4):496–504, April 2022. URL: https://doi.org/10.1038/s41592-022-01443-0, doi:10.1038/s41592-022-01443-0.

[MMC+18]

Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21(9):1281–1289, August 2018. URL: https://doi.org/10.1038/s41593-018-0209-y, doi:10.1038/s41593-018-0209-y.

[MSLM20]

Alexander Mathis, Steffen Schneider, Jessy Lauer, and Mackenzie Weygandt Mathis. A primer on motion capture with deep learning: principles, pitfalls, and perspectives. Neuron, 108(1):44–65, October 2020. URL: https://doi.org/10.1016/j.neuron.2020.09.017, doi:10.1016/j.neuron.2020.09.017.

[NMC+19]

Tanmay Nath, Alexander Mathis, An Chi Chen, Amir Patel, Matthias Bethge, and Mackenzie Weygandt Mathis. Using DeepLabCut for 3d markerless pose estimation across species and behaviors. Nature Protocols, 14(7):2152–2176, June 2019. URL: https://rdcu.be/bHpHN, doi:10.1038/s41596-019-0176-0.

[TKB+22]

Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W Mathis, Frank van Langevelde, Tilo Burghardt, and others. Perspectives in machine learning for wildlife conservation. Nature communications, 13(1):1–15, 2022.

[WJI+15]

Alexander B. Wiltschko, Matthew J. Johnson, Giuliano Iurilli, Ralph E. Peterson, Jesse M. Katon, Stan L. Pashkovski, Victoria E. Abraira, Ryan P. Adams, and Sandeep Robert Datta. Mapping sub-second structure in mouse behavior. Neuron, 88(6):1121–1135, December 2015. URL: https://doi.org/10.1016/j.neuron.2015.11.031, doi:10.1016/j.neuron.2015.11.031.