Characterizing Features of Motor Learning in Action Selection and Execution of Mice

Behavioral neuroscience faces the inherent challenge of linking neural changes with observable behavior. Recent methodological advances now offer more effective ways to connect these domains. This project, conducted within the Petersson Lab (research program: The corticostriatal system in health and disease), investigates the potential convergence of inputs from the posterior parietal cortex (PPC) and secondary motor cortex (M2) onto the striatum, potentially underlying context-dependent action control. To support this neurobiological inquiry, a behavioral analysis framework has been developed, tailored to train mice in a custom version of the skilled movement and grasping task. The scope of this project was to develop an analytical framework to analyze and process large volumes of behavioral data to extract metrics on task performance, strategy, and ethological markers. Several algorithms were implemented to quantify learning dynamics and identify learning stages across individual subjects. Findings confirm an overall learning effect, with individual differences in acquisition speed and strategy. This work contributes to a methodological toolbox for classifying motor learning stages in mice and enhances the interpretation of behavioral data about striatal circuit function.