Journal
NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s41467-021-22970-y
Keywords
-
Categories
Funding
- Key Area R&D Program of Guangdong Province [2018B030338001, 2018B030331001]
- National Key R&D Program of China [2018YFA0701403]
- National Natural Science Foundation of China [NSFC 31500861, NSFC 31630031, NSFC 91732304, NSFC 31930047]
- Chang Jiang Scholars Program
- International Big Science Program Cultivating Project of CAS [172644KYS820170004]
- Strategic Priority Research Program of Chinese Academy of Science [XDB32030100]
- Youth Innovation Promotion Association of the Chinese Academy of Sciences [2017413]
- CAS Key Laboratory of Brain Connectome and Manipulation [2019DP173024]
- Shenzhen Government Basic Research Grants [JCYJ20170411140807570, JCYJ20170413164535041]
- Science, Technology and Innovation Commission of Shenzhen Municipality [JCYJ20160429185235132]
- Helmholtz-CAS joint research grant [GJHZ1508]
- Guangdong Provincial Key Laboratory of Brain Connectome and Behavior [2017B030301017]
- Ten Thousand Talent Program
- Guangdong Special Support Program
- Key Laboratory of SIAT [2019DP173024]
- Shenzhen Key Science and Technology Infrastructure Planning Project [ZDKJ20190204002]
Ask authors/readers for more resources
The study introduces a parallel and multi-layered framework to learn the hierarchical dynamics of animal behavior and map behavior into the feature space objectively. Experimental results suggest that this framework has wide applications, including modeling animal disease model phenotypes and relationships between neural circuits and behavior.
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. Animal behavior usually has a hierarchical structure and dynamics. Here, the authors propose a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behaviour into the feature space.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available