UC Berkeley

A key challenge in neuroscience is to synthesize our understanding of neural structure, dynamics, and information processing in both health and disease. While substantial progress has been made toward such a synthesis at the microscopic scale of single neurons and their connections, signicant work remains to be done at the macroscopic scale of interacting brain regions. While the eld has successfully mapped the macroscale connections bridging cortical columns and regions, and has also systematically described basic features of macroscale cortical electrodynamics across perceptual, cognitive, and brain states, neuroscientists still currently lack a mathematically-specic understanding of how these macroscale networks and electrodynamics underpin large-scale neural computation and communication. Toward the end of advancing our mathematical understanding of this relationship between large-scale brain networks, dynamics, and information ow, we here present: a tool for quantifying, in bits, how much information is integrated across large-scale brain networks; a tool for tracking the presence and degree of chaos in neural electrodynamics; and evidence that macroscale cortical circuits optimize their information-carrying capacity during conscious states by operating near edge-of-chaos criticality.