A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping.
A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping.
复制标题
用于动物自发行为映射的分层 3D 运动学习框架
DOI：
10.1038/s41467-021-22970-y
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发表时间：
2021-05-13
影响因子：
16.6
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Huang K;Han Y;Chen K;Pan H;Zhao G;Yi W;Li X;Liu S;Wei P;Wang L
动物的行为通常具有层次结构和动态。因此，为了理解神经系统如何与行为协调，神经科学家需要对不同行为的层次动力学进行定量描述。然而，最近的端到端的基于机器学习的行为分析方法主要集中在识别静态时间尺度上的行为身份或基于有限的观察。这些方法通常会丢失跨尺度行为的丰富动态信息。在这里，受动物行为的自然结构的启发，我们通过提出一个并行和多层的框架来学习分层动态并生成一个客观的度量来将行为映射到特征空间来解决这一挑战。此外，我们的特点与我们的低成本和高效的多视角三维动物运动捕捉系统的动物三维运动学。最后，我们证明了该框架可以监测自发行为和自动识别转基因动物疾病模型的行为表型。大量的实验结果表明，我们的框架具有广泛的应用，包括动物疾病模型表型和神经回路与行为之间的关系建模。
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.