decoding-natural-behavior-from-neuroethological-embedding

Decoding Natural Behavior from Neuroethological Embedding

Animals engage with their natural environment through rich and dynamic brain activity. Understanding how neural population dynamics encode naturalistic ...

Southern University of Science and Technology

Chinese Academy of Sciences

Deep learning-based methods have advanced animal pose estimation, enhancing accuracy, and efficiency in quantifying animal behavior. However, these methods frequently experience tracking drift, where noise-induced jumps in body point estimates compromise reliability. Here, we present the anti-drift pose tracker (ADPT), a transformer-based tool that mitigates tracking drift in behavioral analysis. Extensive experiments across cross-species datasets-including proprietary mouse and monkey recordings and public  and macaque datasets-demonstrate that ADPT significantly reduces drift and surpasses existing models like DeepLabCut and SLEAP in accuracy. Moreover, ADPT achieved 93.16% identification accuracy for 10 unmarked mice and 90.36% accuracy for freely interacting unmarked mice, which can be further refined to 99.72%, enhancing both anti-drift performance and pose estimation accuracy in social interactions. With its end-to-end design, ADPT is computationally efficient and suitable for real-time analysis, offering a robust solution for reproducible animal behavior studies. The ADPT code is available at https://github.com/tangguoling/ADPT.

Yaning Han

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JoVE Journal Publications

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