Research map / Probabilistic and scientific modeling / Dynamical systems
Learning interpretable control inputs and dynamics underlying animal locomotion
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Proposes a method to learn interpretable control inputs and dynamics underlying animal locomotion from tracked behavioral data, as an alternative to clustering-based motor primitive analysis.
Problem
Understanding how the brain orchestrates movement from high-resolution animal tracking data; existing approaches such as cluster analysis for identifying stereotypical motor primitives are limited for capturing control structure.
Approach
Learns interpretable control inputs and their dynamics directly from animal locomotion trajectories, moving beyond purely descriptive motor primitive clustering toward a model of the control process. Details beyond the abstract are not available.
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