Simple Behavioral Analysis (SimBA)

Several excellent computational frameworks exist that enable high-throughput and consistent tracking of freely moving unmarked animals. SimBA introduce and distribute a plug-and play pipeline that enables users to use these pose-estimation approaches in combination with behavioral annotation for the generation of supervised machine-learning behavioral predictive classifiers.

SimBA was developed for the analysis of complex social behaviors, but includes the flexibility for users to generate predictive classifiers across other behavioral modalities with minimal effort and no specialized computational background.

SimBA has a variety of extended functions for large scale batch video pre-processing, generating descriptive statistics from movement features, and interactive modules for user-defined regions of interest and visualizing classification probabilities and movement patterns.

Project Author(s)

Simon Nilsson: Jia Jie Chhong; Sophia Hwang; Nastacia Goodwin; Sam A Golden

https://github.com/sgoldenlab/simba

Project Video

https://www.youtube.com/watch?v=Frq6mMcaHBc&list=PLi5Vwf0hhy1R6NDQJ3U28MOUJPfl2YWYl&index=2&t=0s


This post was automatically generated by Simon Nilsson


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