In our lab, we use the tools of computational neuroscience to study how behaviors are learned and how this learning is implemented in neural circuits.

Learned motor behaviors require the shaping of time-varying patterns of neural activity. Understanding how such learning occurs is challenging because multiple brain areas are involved and because such behaviors may involve multiple timescales, from low-level limb movements to the cognitive level of goal-driven planning. By developing models of the subcortical and cortical neural circuits that control behavior, using data from experimental collaborators to inform and test these models, and drawing on recent advances in neural-network implementations of reinforcement learning, our research aims to advance understanding of how learned motor behaviors are implemented in the brain.