Core brain functions—perception, action, decision-making—depend on complex patterns of neural activity coordinated within local microcircuits and across brain regions. Recently, massively-parallel neurotechnologies enabled activity recordings from thousands of neurons on the brain-wide scale and provided detailed maps of the brain-wide anatomical connectivity. These large-scale datasets reveal dynamic activity patterns that are highly variable in time and widely distributed across structured brain networks. How this widespread activity emerges from anatomical connectivity and how it gives rise to behavior is not well understood.

My lab uses computational and theoretical approaches to investigate how coordinated activity arises from distributed neural circuitry to drive behavioral and cognitive functions. We develop mathematical models and data analysis methods to reveal distributed circuit mechanisms from rich experimental data. We employ and extend tools and ideas from diverse fields, including statistical mechanics, machine learning, dynamical systems theory, and information theory. Our work benefits from close collaborations with experimental neuroscience laboratories collecting neurophysiological data in animals engaged in sophisticated tasks, such as attention, decision-making, and learning.