We are the Systems Neuroscience & Artificial Intelligence Lab (SNAIL) at the University of Washington, aka "Golub Lab". Our research focuses on the intersection of neuroscience and machine learning. We develop computational models and tools for understanding how single-trial neural population activity drives our abilities to generate movements, make decisions, and learn from experience.
Our Research
Machine learning for neuroscience
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 design the most efficient experiments and the most interpretable models for testing hypotheses about neural computation?
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 mechanisms, and how can we overcome those limitations to facilitate faster learning to higher levels of proficiency?
Decision making and motor control
How do populations of neurons generate decisions based on context and noisy sensory evidence, and how do these decision-making processes flexibly interlace with the action selection and execution processes that they inform?
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.