SimBA for Behavioral Neuroscientists
Friday, July 16, 2021
07:00 UTC
Dr. Sam A. Golden
University of Washington, Department of Biological Structure
Recording
Open recording
Hosted by Open Source Neuro70 minutes
Abstract
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.
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