NCSL approaches understanding brain function in health and disease with a dynamical systems and control perspective. Specifically, we use mathematical representations to characterize how environmental stimuli (images, sounds, etc.) or exogenous stimuli (e.g. drugs, stimulation treatment) impact neural activity and behavior, carefully accounting for latent variables that exist but that are difficult to measure experimentally. For example, when faced with financial decision-making tasks, we understand that human behaviors (choices) depends not only on the options (inputs) but on internal biases (emotions, confidence etc.). Internal biases are difficult to measure experimentally, but can be estimated via dynamical models. These techniques allow neuroscientists to explain variable and dynamical complex behaviors that arise in the presence of static options, and most importantly to then identify neural correlates that capture both stimuli and latent variables.

Today, our lab focuses on developing computational, data-driven, and biological approaches to advance the knowledge of decision making and motor control, and the treatment of epilepsy, chronic pain, and insomnia.