Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.
The fact that exposure to severe stress leads to the development of psychiatric disorders serves as the basic rationale for animal models of stress disorders. Clinical and neuroimaging studies have shown that three brain areas involved in learning and memory--the hippocampus, amygdala and prefrontal cortex--undergo distinct structural and functional changes in individuals with stress disorders. These findings from patient studies pose several challenges for animal models of stress disorders. For instance, why does stress impair cognitive function, yet enhance fear and anxiety? Can the same stressful experience elicit contrasting patterns of plasticity in the hippocampus, amygdala and prefrontal cortex? How does even a brief exposure to traumatic stress lead to long-lasting behavioral abnormalities? Thus, animal models of stress disorders must not only capture the unique spatio-temporal features of structural and functional alterations in these brain areas, but must also provide insights into the underlying neuronal plasticity mechanisms. This Review will address some of these key questions by describing findings from animal models on how stress-induced plasticity varies across different brain regions and thereby gives rise to the debilitating emotional and cognitive symptoms of stress-related psychiatric disorders.
Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having extremely similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, a popular open source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for robust multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity directly to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.
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