LabGym#

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Identifies social behaviors in multi-individual interactions#

 

Distinguishing different social roles of multiple similar-looking interacting individuals

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Distinguishing different interactive behaviors among multiple animal-object interactions

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Distinguishing different social roles of animals in the field with unstable recording environments

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Identifies non-social behaviors#

 

Identifying behaviors in diverse species in various recording environments

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Identifying behaviors with no posture changes such as cells ‘changing color’ and neurons ‘firing’

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Quantifies each user-defined behavior#

Computes a range of motion and kinematics parameters for each behavior. The parameters include count, duration, and latency of behavioral incidents, as well as speed, acceleration, distance traveled, and the intensity and vigor of motions during the behaviors. These parameters are output in spreadsheets.

Also provides visualization of analysis results, including annotated videos/images that visually mark each behavior event, temporal raster plots that show every behavior event of every individual overtime.

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An introduction video for a high-level understanding of what LabGym can do and how it works:

Watch the video

 

A series of tutorial videos focused on details of each module/function in LabGym (more to come!)

 

We have also lauched our LabGym website.

 

Cite LabGym:

  1. Yujia Hu, Carrie R Ferrario, Alexander D Maitland, Rita B Ionides, Anjesh Ghimire, Brendon Watson, Kenichi Iwasaki, Hope White, Yitao Xi, Jie Zhou, Bing Ye. LabGym: Quantification of user-defined animal behaviors using learning-based holistic assessment. Cell Reports Methods. 2023 Feb 24;3(3):100415. doi: 10.1016/j.crmeth.2023.100415. Link

  2. Kelly Goss, Lezio S. Bueno-Junior, Katherine Stangis, Théo Ardoin, Hanna Carmon, Jie Zhou, Rohan Satapathy, Isabelle Baker, Carolyn E. Jones-Tinsley, Miranda M. Lim, Brendon O. Watson, Cédric Sueur, Carrie R. Ferrario, Geoffrey G. Murphy, Bing Ye, Yujia Hu. Quantifying social roles in multi-animal videos using subject-aware deep-learning. bioRxiv. 2024 Jul 10:2024.07.07.602350. doi: 10.1101/2024.07.07.602350. Link