The reliability of brain function is stunning. Hundreds of thousands to billions of neurons come together and give rise to cognition and behavior, and somehow, these interactions are consistent for decades. This robustness is all the more impressive given that cellular components are inconstant, proteins turn over in seconds to hours, and experience is a powerful modifier. Despite this, our ability to recognize objects, control our limbs, and recall complex ideas is so consistent that it is largely taken for granted in daily life. At the same time, it is failures in the reliability of neural networks that define many pathologies, such as Alzheimer’s disease and other degenerative conditions.

The goal of our group is to understand how neural networks generate robust computation and dynamics. In particular, we employ systems and computational neuroscience techniques to study the self-organization of complex brain activity in freely behaving animals.

We seek to address an important set of research questions: do networks of neurons self-organize to an optimal computational regime? What are the genes, cell-types, and mechanisms that, when distributed in a large network, promote stable computation? How does behavior influence the expression of these mechanisms? Do the latent spaces underlying network dynamics change with time? How do individual neurons contribute to complex and variable behavior across a lifetime? In pursuit of these types of questions, we are developing methods to allow the recording of thousands of neurons for the lifetime of an animal, as well as improving the computational tools available to meaningfully measure and describe dynamics in large populations of neurons.

Our lab sits in the heart of the College of Arts and Sciences at Washington University in Saint Louis, and we are composed of graduates students through DBBS, undergraduate researchers, technicians, postdocs, and senior scientists.