Humans and animals coordinate behavior across many spatial and temporal scales, from rapid movements to long-term goals and habits. How the brain links these different levels of behavior remains poorly understood.
The Action Computation Lab investigates how distributed brain circuits learn representations that connect perception, action, and internal goals, to support multiscale control and policy learning. We develop theoretical and computational models and test them in large-scale neural and behavioral datasets.
Our work aims to uncover general principles of hierarchical action representation, multi-area computation, and embodied learning, while also inspiring new approaches to flexible and continual learning in artificial systems.

The lab aims to:
1) understand how the brain learns hierarchical representations of actions, from individual movements to long-term goals
2) determine how computations distributed across cortical, hippocampal, and basal ganglia circuits are coordinated to support multiscale behavior
3) uncover the learning and adaptation mechanisms that allow behavior to remain flexible in changing environments.
We combine theoretical neuroscience, machine learning, and the analysis of large-scale neural and behavioral data. By developing interpretable computational models and testing their predictions in experimental datasets, we aim to reveal general principles of action representation, multi-area computation, and embodied learning.