Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A. & Poeppel, D. Neuroscience needs behavior: correcting a reductionist bias. Neuron 93, 480–490 (2017).
Google Scholar
Anderson, D. J. & Perona, P. Toward a science of computational ethology. Neuron 84, 18–31 (2014).
Google Scholar
Egnor, S. E. R. & Branson, K. Computational analysis of behavior. Annu. Rev. Neurosci. 39, 217–236 (2016).
Google Scholar
Datta, S. R., Anderson, D. J., Branson, K., Perona, P. & Leifer, A. Computational neuroethology: a call to action. Neuron 104, 11–24 (2019).
Google Scholar
Falkner, A. L., Grosenick, L., Davidson, T. J., Deisseroth, K. & Lin, D. Hypothalamic control of male aggression-seeking behavior. Nat. Neurosci. 19, 596–604 (2016).
Google Scholar
Ferenczi, E. A. et al. Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior. Science 351, aac9698 (2016).
Google Scholar
Kim, Y. et al. Mapping social behavior-induced brain activation at cellular resolution in the mouse. Cell Rep. 10, 292–305 (2015).
Google Scholar
Gunaydin, L. A. et al. Natural neural projection dynamics underlying social behavior. Cell 157, 1535–1551 (2014).
Google Scholar
Graving, J. M. et al. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 8, e47994 (2019).
Google Scholar
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).
Google Scholar
Pereira, T. D. et al. Fast animal pose estimation using deep neural networks. Nat. Methods 16, 117–125 (2019).
Google Scholar
Geuther, B. Q. et al. Robust mouse tracking in complex environments using neural networks. Commun. Biol. 2, 124 (2019).
Google Scholar
Gris, K. V., Coutu, J.-P. & Gris, D. Supervised and unsupervised learning technology in the study of rodent behavior. Front. Behav. Neurosci. 11, 141 (2017).
Schaefer, A. T. & Claridge-Chang, A. The surveillance state of behavioral automation. Curr. Opin. Neurobiol. 22, 170–176 (2012).
Google Scholar
Robie, A. A., Seagraves, K. M., Egnor, S. E. R. & Branson, K. Machine vision methods for analyzing social interactions. J. Exp. Biol. 220, 25–34 (2017).
Google Scholar
Vu, M.-A. T. et al. A shared vision for machine learning in neuroscience. J. Neurosci. 38, 1601–1607 (2018).
Google Scholar
Goodwin, N. L., Nilsson, S. R. O., Choong, J. J. & Golden, S. A. Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience. Curr. Opin. Neurobiol. 73, 102544 (2022).
Google Scholar
Newton, K. C. et al. Lateral line ablation by ototoxic compounds results in distinct rheotaxis profiles in larval zebrafish. Commun. Biol. 6, 1–15 (2023).
Google Scholar
Jernigan, C. M., Stafstrom, J. A., Zaba, N. C., Vogt, C. C. & Sheehan, M. J. Color is necessary for face discrimination in the Northern paper wasp, Polistes fuscatus. Anim. Cogn. 26, 589–598 (2022).
Google Scholar
Dahake, A. et al. Floral humidity as a signal – not a cue – in a nocturnal pollination system. Preprint at bioRxiv https://doi.org/10.1101/2022.04.27.489805 (2022).
Dawson, M. et al. Hypocretin/orexin neurons encode social discrimination and exhibit a sex-dependent necessity for social interaction. Cell Rep. 42, 112815 (2023).
Google Scholar
Baleisyte, A., Schneggenburger, R. & Kochubey, O. Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes. Cell Rep. 39, 110850 (2022).
Google Scholar
Cruz-Pereira, J. S. et al. Prebiotic supplementation modulates selective effects of stress on behavior and brain metabolome in aged mice. Neurobiol. Stress 21, 100501 (2022).
Google Scholar
Linders, L. E. et al. Stress-driven potentiation of lateral hypothalamic synapses onto ventral tegmental area dopamine neurons causes increased consumption of palatable food. Nat. Commun. 13, 6898 (2022).
Google Scholar
Slivicki, R. A. et al. Oral oxycodone self-administration leads to features of opioid misuse in male and female mice. Addiction Biol. 28, e13253 (2023).
Google Scholar
Miczek, K. A. et al. Excessive alcohol consumption after exposure to two types of chronic social stress: intermittent episodes vs. continuous exposure in C57BL/6J mice with a history of drinking. Psychopharmacology (Berl.) 239, 3287–3296 (2022).
Google Scholar
Cui, Q. et al. Striatal direct pathway targets Npas1+ pallidal neurons. J. Neurosci. 41, 3966–3987 (2021).
Google Scholar
Chen, J. et al. A MYT1L syndrome mouse model recapitulates patient phenotypes and reveals altered brain development due to disrupted neuronal maturation. Neuron 109, 3775–3792 (2021).
Google Scholar
Rigney, N., Zbib, A., de Vries, G. J. & Petrulis, A. Knockdown of sexually differentiated vasopressin expression in the bed nucleus of the stria terminalis reduces social and sexual behaviour in male, but not female, mice. J. Neuroendocrinol. 34, e13083 (2021).
Winters, C. et al. Automated procedure to assess pup retrieval in laboratory mice. Sci. Rep. 12, 1663 (2022).
Google Scholar
Neira, S. et al. Chronic alcohol consumption alters home-cage behaviors and responses to ethologically relevant predator tasks in mice. Alcohol Clin. Exp. Res. 46, 1616–1629 (2022).
Google Scholar
Kwiatkowski, C. C. et al. Quantitative standardization of resident mouse behavior for studies of aggression and social defeat. Neuropsychopharmacology 46, 1584–1593 (2021).
Yamaguchi, T. et al. Posterior amygdala regulates sexual and aggressive behaviors in male mice. Nat. Neurosci. 23, 1111–1124 (2020).
Google Scholar
Nygaard, K. R. et al. Extensive characterization of a Williams syndrome murine model shows Gtf2ird1-mediated rescue of select sensorimotor tasks, but no effect on enhanced social behavior. Genes Brain Behav. 22, e12853 (2023).
Google Scholar
Ojanen, S. et al. Interneuronal GluK1 kainate receptors control maturation of GABAergic transmission and network synchrony in the hippocampus. Mol. Brain 16, 43 (2023).
Google Scholar
Hon, O. J. et al. Serotonin modulates an inhibitory input to the central amygdala from the ventral periaqueductal gray. Neuropsychopharmacology 47, 2194–2204 (2022).
Google Scholar
Murphy, C. A. et al. Modeling features of addiction with an oral oxycodone self-administration paradigm. Preprint at bioRxiv https://doi.org/10.1101/2021.02.08.430180 (2021).
Neira, S. et al. Impact and role of hypothalamic corticotropin releasing hormone neurons in withdrawal from chronic alcohol consumption in female and male mice. J. Neurosci. 43, 7657–7667 (2023).
Google Scholar
Lapp, H. E., Salazar, M. G. & Champagne, F. A. Automated maternal behavior during early life in rodents (AMBER) pipeline. Sci. Rep. 13, 18277 (2023).
Google Scholar
Barnard, I. L. et al. High-THC cannabis smoke impairs incidental memory capacity in spontaneous tests of novelty preference for objects and odors in male rats. eNeuro 10, ENEURO.0115-23.2023 (2023).
Google Scholar
Ausra, J. et al. Wireless battery free fully implantable multimodal recording and neuromodulation tools for songbirds. Nat. Commun. 12, 1968 (2021).
Google Scholar
Friard, O. & Gamba, M. BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations. Methods Ecol. Evol. 7, 1325–1330 (2016).
Google Scholar
Spink, A. J., Tegelenbosch, R. A. J., Buma, M. O. S. & Noldus, L. P. J. J. The EthoVision video tracking system—a tool for behavioral phenotyping of transgenic mice. Physiol. Behav. 73, 731–744 (2001).
Google Scholar
Lundberg, S. shap. https://github.com/shap/shap
Lauer, J. et al. Multi-animal pose estimation, identification and tracking with DeepLabCut. Nat. Methods 19, 496–504 (2022).
Google Scholar
Pereira, T. D. et al. SLEAP: a deep learning system for multi-animal pose tracking. Nat Methods 19, 486–495 (2022).
Segalin, C. et al. The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice. eLife 10, e63720 (2021).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Google Scholar
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2/3 https://journal.r-project.org/articles/RN-2002-022/RN-2002-022.pdf (2022).
Goodwin, N. L., Nilsson, S. R. O. & Golden, S. A. Rage against the machine: advancing the study of aggression ethology via machine learning. Psychopharmacology 237, 2569–2588 (2020).
Google Scholar
Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).
Google Scholar
Ribeiro, M. T., Singh, S., & Guestrin, C. ‘Why should I trust you?’: explaining the predictions of any classifier. Preprint at arXiv https://doi.org/10.48550/arXiv.1602.04938 (2016).
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In Proc. of the 34th International Conference on Machine Learning 3319–3328 (MLR Press, 2017).
Hatwell, J., Gaber, M. M. & Azad, R. M. A. CHIRPS: explaining random forest classification. Artif. Intell. Rev. 53, 5747–5788 (2020).
Google Scholar
Lundberg, S. & Lee, S.-I. A unified approach to interpreting model predictions. Preprint at arXiv https://doi.org/10.48550/arXiv.1705.07874 (2017).
Verma, S., Dickerson, J. & Hines, K. Counterfactual explanations for machine learning: a review. Preprint at arXiv https://doi.org/10.48550/arXiv.2010.10596 (2020).
Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).
Google Scholar
Takahashi, A. et al. Establishment of a repeated social defeat stress model in female mice. Sci. Rep. 7, 12838 (2017).
Google Scholar
Hashikawa, K. et al. Esr1+ cells in the ventromedial hypothalamus control female aggression. Nat. Neurosci. 20, 1580–1590 (2017).
Google Scholar
Newman, E. L. et al. Fighting females: neural and behavioral consequences of social defeat stress in female mice. Biol. Psychiatry 86, 657–668 (2019).
Google Scholar
Aubry, A. V. et al. Sex differences in appetitive and reactive aggression. Neuropsychopharmacology 47, 1746–1754 (2022).
Google Scholar
Golden, S. A., Covington, H. E., Berton, O. & Russo, S. J. A standardized protocol for repeated social defeat stress in mice. Nat. Protoc. 6, 1183–1191 (2011).
Google Scholar
Shemesh, Y. & Chen, A. A paradigm shift in translational psychiatry through rodent neuroethology. Mol. Psychiatry 28, 993–1003 (2023).
Google Scholar
Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S. & Branson, K. JAABA: interactive machine learning for automatic annotation of animal behavior. Nat. Methods 10, 64–67 (2013).
Google Scholar
Bordes, J. et al. Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress. Nat. Commun. 14, 4319 (2023).
Google Scholar
Winters, C., Gorssen, W., Wöhr, M. & D’Hooge, R. BAMBI: a new method for automated assessment of bidirectional early-life interaction between maternal behavior and pup vocalization in mouse dam-pup dyads. Front. Behav. Neurosci. 17, 1139254 (2023).
Lundberg, S. M., Erion, G. G. & Lee, S.-I. Consistent individualized feature attribution for tree ensembles. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03888 (2019).
Covert, I. C., Lundberg, S. & Lee, S.-I. Explaining by removing: a unified framework for model explanation. J. Mach. Learn. Res. 22, 1–90 (2021).
Lorbach, M., Poppe, R. & Veltkamp, R. C. Interactive rodent behavior annotation in video using active learning. Multimed. Tools Appl. 78, 19787–19806 (2019).
Google Scholar
Tillmann, J. F., Hsu, A. I., Schwarz, M. K. & Yttri, E. A. A-SOiD, an active-learning platform for expert-guided, data-efficient discovery of behavior. Nat. Methods 21, 703–711 (2024).
Google Scholar
Whiteway, M. R. et al. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput. Biol. 17, e1009439 (2021).
Google Scholar
Sun, J. J. et al. Task Programming: Learning Data Efficient Behavior Representations. In Proc IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2875–2884 (2021).
MABe 2022. Multi-agent behavior: representation, modeling, measurement, and applications. https://sites.google.com/view/mabe22/home
Sun, J. J. et al. The multi-agent behavior dataset: mouse dyadic social interactions. Preprint at arXiv https://doi.org/10.48550/arXiv.2104.02710 (2021).
OpenBehavior. About the OpenBehavior Project and the open source movement. https://edspace.american.edu/openbehavior/
Mouse Phenome Database. https://phenome.jax.org/about
Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns (N Y) 4, 100804 (2023).
Google Scholar
Dankert, H., Wang, L., Hoopfer, E. D., Anderson, D. J. & Perona, P. Automated monitoring and analysis of social behavior in Drosophila. Nat. Methods 6, 297–303 (2009).
Google Scholar
de Chaumont, F. et al. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning. Nat. Biomed. Eng. 3, 930–942 (2019).
Google Scholar
Giancardo, L. et al. Automatic visual tracking and social behaviour analysis with multiple mice. PLoS ONE 8, e74557 (2013).
Google Scholar
Hong, W. et al. Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning. Proc. Natl Acad. Sci. USA 112, E5351–E5360 (2015).
Google Scholar
Goodwin, N. L. et al. Simple behavioral analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience. Nat. Neurosci. (in the press).
Bohnslav, J. P. et al. DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels. eLife 10, e63377 (2021).
Google Scholar
Gerós, A., Magalhães, A. & Aguiar, P. Improved 3D tracking and automated classification of rodents’ behavioral activity using depth-sensing cameras. Behav. Res. 52, 2156–2167 (2020).
Google Scholar
Harris, C., Finn, K. R., Kieseler, M.-L., Maechler, M. R. & Tse, P. U. DeepAction: a MATLAB toolbox for automated classification of animal behavior in video. Sci. Rep. 13, 2688 (2023).
Google Scholar
Hu, Y. et al. LabGym: quantification of user-defined animal behaviors using learning-based holistic assessment. Cell Rep. Methods 3, 100415 (2023).
Marks, M. et al. Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments. Nat. Mach. Intell. 4, 331–340 (2022).
Google Scholar
Branson, K., Robie, A. A., Bender, J., Perona, P. & Dickinson, M. H. High-throughput ethomics in large groups of Drosophila. Nat. Methods 6, 451–457 (2009).
Google Scholar
Jia, Y. et al. Selfee, self-supervised features extraction of animal behaviors. eLife 11, e76218 (2022).
Google Scholar
Berman, G. J., Choi, D. M., Bialek, W. & Shaevitz, J. W. Mapping the stereotyped behaviour of freely moving fruit flies. J. R. Soc. Interface 11, 20140672 (2014).
Google Scholar
Arakawa, T. et al. Automated estimation of mouse social behaviors based on a hidden Markov model. In Hidden Markov Models: Methods and Protocols (eds Westhead, D. R. & Vijayabaskar, M. S.) 185–197 (Humana Press, 2017).
Chen, Z. et al. AlphaTracker: a multi-animal tracking and behavioral analysis tool. Front. Behav. Neurosci. 17, 1111908 (2023).
Google Scholar
Huang, K. et al. A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping. Nat. Commun. 12, 2784 (2021).
Google Scholar
Luxem, K. et al. Identifying behavioral structure from deep variational embeddings of animal motion. Commun. Biol. 5, 1267 (2022).
Google Scholar
Nandi, A., Virmani, G., Barve, A. & Marathe, S. DBscorer: an open-source software for automated accurate analysis of rodent behavior in forced swim test and tail suspension test. eNeuro 8, ENEURO.0305-21.2021 (2021).
Gabriel, C. J. et al. BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking. eLife 11, e74314 (2022).
Google Scholar
Golden, S. A. et al. Epigenetic regulation of RAC1 induces synaptic remodeling in stress disorders and depression. Nat. Med. 19, 337–344 (2013).
Google Scholar
Burgos-Artizzu, X. P., Dollar, P., Lin D., Anderson, D. J. & Perona, P. CRIM13 (Caltech Resident-Intruder Mouse 13) (1.0). CaltechDATA. https://doi.org/10.22002/D1.1892 (2021).
Karashchuk, P., Tuthill, J. C. & Brunton, B. W. The DANNCE of the rats: a new toolkit for 3D tracking of animal behavior. Nat. Methods 18, 460–462 (2021).
Google Scholar
Branson, K. APT. https://github.com/kristinbranson/APT
Lee, W., Fu, J., Bouwman, N., Farago, P. & Curley, J. P. Temporal microstructure of dyadic social behavior during relationship formation in mice. PLoS ONE 14, e0220596 (2019).
Google Scholar
