Literature#
Reading List#
Anderson, D. J., & Perona, P. (2014). Toward a Science of Computational Ethology. Neuron, 84(1), 18–31. https://doi.org/10.1016/j.neuron.2014.09.005
Bohnslav, J. P., Wimalasena, N. K., Clausing, K. J., Dai, Y. Y., Yarmolinsky, D. A., Cruz, T., Kashlan, A. D., Chiappe, M. E., Orefice, L. L., Woolf, C. J., & Harvey, C. D. (2021). DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels. ELife, 10, e63377. https://doi.org/10.7554/eLife.63377
Calhoun, A., & Hady, A. E. (2021). What is behavior? No seriously, what is it? [Preprint]. Animal Behavior and Cognition. https://doi.org/10.1101/2021.07.04.451053
Dunn, T. W. (2021). Geometric deep learning enables 3D kinematic profiling across species and environments. Nature Methods, 18, 17. https://doi.org/10.1038/s41592-021-01106-6
Gomez-Marin, A., Paton, J. J., Kampff, A. R., Costa, R. M., & Mainen, Z. F. (2014). Big behavioral data: Psychology, ethology and the foundations of neuroscience. Nature Neuroscience, 17(11), 1455–1462. https://doi.org/10.1038/nn.3812
Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021). Anipose: A toolkit for robust markerless 3D pose estimation. Cell Reports, 36(13), 109730. https://doi.org/10.1016/j.celrep.2021.109730
Levitis, D. A., Lidicker Jr, W. Z., & Freund, G. (2009). Behavioural biologists do not agree on what constitutes behaviour. Animal behaviour, 78(1), 103-110. https://doi.org/10.1016/j.anbehav.2009.03.018
Luxem, K., Mocellin, P., Fuhrmann, F., Kürsch, J., Remy, S., & Bauer, P. (2022). Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion. https://doi.org/10.1101/2020.05.14.095430
Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018). DeepLabCut: Markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21(9), 1281–1289. https://doi.org/10.1038/s41593-018-0209-y
Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W. (2019). Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols, 14(7), 2152–2176. https://doi.org/10.1038/s41596-019-0176-0
Pereira, T. D., Shaevitz, J. W., & Murthy, M. (2020). Quantifying behavior to understand the brain. Nature Neuroscience, 23(12), 1537–1549. https://doi.org/10.1038/s41593-020-00734-z
Recommended Reading#
Computer Age Statistical Inference
Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781316576533
ISBN: 9781316576533
Measuring Behaviour (An Introductory Guide) 4th Edition
Bateson, M., & Martin, P. (2021). Measuring Behaviour: An Introductory Guide (4th ed.). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108776462
ISBN: 9781108745727
Finding Research Papers#
What to do with papers?
First, you probably just hope to be privileged enough to be affiliated to an institution that can provide access to these journals.
Assuming this is will not be the case at some stage in our career, you should - as a good researcher - make use of any database you can find that may have listed the paper you are looking for, this may include visiting local libraries. If this is still not working,you don’t stop there and - as a better researcher - start scraping the web for loose pdf files with e.g. Google Scholar, Research Gate and even in the authors’ personal websites.
If this still fails, fight the system.
Fig. 1 Figure from Bohannon (2016) in Science ‘Who’s downloading pirated papers? Everyone.’. Accessed from Sci-Hub via https://sci-hub.mksa.top/10.1126/science.352.6285.508.#