Our research is divided into two separate but related streams. The first
 goal is to understand how neural networks generate reliable function, 
cognition, and behavior. We seek to address the following 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? and, how do individual 
neurons contribute to complex and variable behavior across a lifetime?

In parallel, we are exploring systems-level explanations of neurological 
disease. At the root of any disease is a failure of stabilizing 
mechanisms to assert healthy patterns. Traditionally, these failures are
 viewed through a highly reductionist lens: genes and proteins are the 
usual suspects. However, these explanations have little space for the 
role of environment and experience. We are combining theoretical 
physics, applied math, and systems biology to test novel explanations 
and interventions in neurodegeneration and psychiatric disorders.

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