The exquisite ability of the brain to perform adaptable, efficient, and robust computation in changing environment is attributed to complex connectivity structure of underlying networks. At multiple scales ranging from microscopic cell-to-cell connectivity to a large-scale network of brain regions, biological neural networks are characterized by topological complexities observed in both anatomical connectomes as well as in functional connectivity constructed from correlated neural activities. We use data-driven modeling to probe the precise connections between network complexity and computation, in close collaboration with experimental neuroscientists.

Neurons communicate diverse information of sensory stimuli, decision signals, and motor commands, using their rich dynamics at both single cell and population levels. Their operating modes characterized by different spiking patterns are determined by various biophysiological factors including intrinsic properties of individual neurons such as ion channels and synaptic variables as well as network properties defining population composition and connectivity among heterogeneous neurons. In addition, sensory stimuli, behavioral states, and relevant tasks to perform shape neural dynamics at both the local and global scales. We investigate how changes in these variables instigate and shape transitions between different phases of dynamics in single neurons represented by spiking as well as in neural populations described by excitatory-inhibitory balanced dynamics and synchronizability.