A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping.

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 an

SUBMITTER: Huang K 

PROVIDER: S-EPMC8119960 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature