Our Research Interests

Machine learning for neural data analysis

Probabilistic latent variable models, deep neural networks, and other optimization-based frameworks. How can we use tools like these to extract meaning from recordings of large neural populations? What new statistical techniques are needed to test hypotheses about neural computation?

Decision making and motor control

How do populations of neurons generate decisions based on sensory evidence, and how do these decision-making processes flexibly interlace with the action selection and execution processes that they inform?

Population-level changes in neural activity during learning

How do populations of neurons change their joint patterns of activity during learning? What are the population-level mechanisms driving those changes, and what are the brain's learning rules that update synaptic strengths? What are the limitations of these rules and mechanisms, and how can we overcome them to facilitate faster learning to higher levels of proficiency?

Brain-computer interfaces (BCIs)

BCIs translate intracortical recordings into signals for driving prosthetic devices, such as robotic limbs or computer cursors. We focus on using BCIs as a powerful testbed for basic neuroscientific discovery (in contrast to their more-traditional application toward restoring movement and communication for those with spinal cord injuries, neurodegenerative diseases or limb amputations).