Search bioRxiv⌕ Search

Biology subjects

Olveczky, B. P.

Publications and source records attributed to Olveczky, B. P..

2 recordsLinked to original sources

Similar striatal activity exerts different control over automatic and flexible motor sequences

The ability to sequence movements in response to new task demands enables rich and adaptive behavior. Such flexibility, however, is computationally costly and can result in halting performances. Practicing the same motor sequence repeatedly can render its execution precise, fast, and effortless, i.e., automatic. The basal ganglia are thought to underlie both modes of sequence execution, yet whether and how their contributions differ is unclear. We parse this in rats trained to perform the same motor sequence in response to cues and in an overtrained, or automatic, condition. Neural recordings in the sensorimotor striatum revealed a kinematic code independent of execution mode. While lesions affected the detailed kinematics similarly across modes, they disrupted high-level sequence structure for automatic, but not visually-guided, behaviors. These results suggest that the basal ganglia contribute to learned movement kinematics and are essential for automatic motor skills but can be dispensable for sensory-guided motor sequences.

neuroscience↗

The PAIR-R24M Dataset for Multi-animal 3D Pose Estimation

Understanding the biological basis of social and collective behaviors in animals is a key goal of the life sciences, and may yield important insights for engineering intelligent multi-agent systems. A critical step in interrogating the mechanisms underlying social behaviors is a precise readout of the 3D pose of interacting animals. While approaches for multi-animal pose estimation are beginning to emerge, they remain challenging to compare due to the lack of standardized training and benchmark datasets. Here we introduce the PAIR-R24M (Paired Acquisition of Interacting oRganisms - Rat) dataset for multi-animal 3D pose estimation, which contains 24.3 million frames of RGB video and 3D ground-truth motion capture of dyadic interactions in laboratory rats. PAIR-R24M contains data from 18 distinct pairs of rats and 24 different viewpoints. We annotated the data with 11 behavioral labels and 3 interaction categories to facilitate benchmarking in rare but challenging behaviors. To establish a baseline for markerless multi-animal 3D pose estimation, we developed a multi-animal extension of DANNCE, a recently published network for 3D pose estimation in freely behaving laboratory animals. As the first large multi-animal 3D pose estimation dataset, PAIR-R24M will help advance 3D animal tracking approaches and aid in elucidating the neural basis of social behaviors.

animal behavior and cognition↗