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Learn about cutting-edge behavioral analysis techniques through this 29-minute webinar presented by Dr. Eric Yttri from Carnegie Mellon University. Discover how B-SOiD (Behavioral Segmentation of Open Field in DeepLabCut) and A-SOiD algorithms enable automated quantification and segmentation of naturalistic animal behaviors using both supervised and unsupervised machine learning approaches. Explore the development of novel pattern recognition systems that can learn and segment behavioral motifs automatically, providing researchers with tools to move beyond traditional reductionist approaches to studying animal behavior. Examine how these computational methods add interpretability to pose estimation data and enable the identification of spontaneous, naturalistic behaviors in freely moving animals. Understand the neurobehavioral relationships revealed through simultaneous recordings from motor cortex and striatum during open-field exploration, including how different brain areas represent behavioral features with varying levels of complexity and dimensionality. Investigate the limitations of traditional linear analysis tools like PCA and GPFA when applied to naturalistic settings, and learn why nonlinear approaches are necessary for understanding complex behavioral representations, particularly in the dorsal striatum. Gain insights into how coordinated information translation between brain areas orchestrates complex, ethological behaviors and how these findings advance our understanding of neural circuit dynamics in motor control and decision-making. The presentation includes opportunities to earn PACE credits for continuing education through the Labroots platform.
