ArticleNature communications2021
A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed.
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- [Establishment and evaluation of rat and mouse models with addictive behaviors].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Review
- Synaptic Dysfunction in the Anterior Cingulate Cortex Underlies Pain-Anxiety Comorbidity in a Mandibular Asymmetry Mouse Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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- DNA replication fork speed acts as a pacer in cortical neurogenesis.Nature communications · 2025Article
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Authors and funding
10 authors.
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Abstract
Animal behavior usually has a hierarchical structure and dynamics. Therefore, to understand how the neural system coordinates with behaviors, neuroscientists need a quantitative description of the hierarchical dynamics of different behaviors. However, the recent end-to-end machine-learning-based methods for behavior analysis mostly focus on recognizing behavioral identities on a static timescale or based on limited observations. These approaches usually lose rich dynamic information on cross-scale behaviors. Here, inspired by the natural structure of animal behaviors, we address this challenge by proposing a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behavior into the feature space. In addition, we characterize the animal 3D kinematics with our low-cost and efficient multi-view 3D animal motion-capture system. Finally, we demonstrate that this framework can monitor spontaneous behavior and automatically identify the behavioral phenotypes of the transgenic animal disease model. The extensive experiment results suggest that our framework has a wide range of applications, including animal disease model phenotyping and the relationships modeling between the neural circuits and behavior.
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