Our lab’s goal is to understand how animals and people learn in terms of changes in the brain’s neural activity. To do this, we take three general approaches:
- theory (e.g., normative models: “How should someone learn this task?”)
- machine learning (e.g., artificial agents trained with reinforcement learning)
- neural data analysis (e.g., statistical models of high-dimensional neural activity)
Some of our past and current work considers learning in the following contexts:
- neural control of movement in motor cortex, using brain-computer interfaces (BCI)
- reinforcement learning in the dopamine system, using recurrent neural network (RNN) models
You can browse our publications for summaries of our work in this area.
Moving forward, our lab has two additional research interests: meta-learning (“learning to learn”), and the emergence of probabilistic representations in the brain.