The exquisite ability of the brain to perform adaptable, efficient, and robust computation in changing environments is attributed to complex connectivity structures of underlying networks. Across multiple scales ranging from microscopic cell-to-cell connectivity to a large-scale network of brain regions, biological neural networks are characterized by their topological complexities. The distinctive topological motifs of the anatomical connectivity affect network dynamics and neural coding. On the other hand, functional connectivity constructed from correlated neural activities, is shaped by the underlying anatomical network structures as well as stimulus complexity and behavioral states. Functional networks inferred from population activities, furthermore, reflect information transmissions among neurons, neuronal populations, and brain regions.

We use data-driven modeling to probe the precise connections between topology and computation in both functional and anatomical networks of neurons, 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 are characterized by different spiking patterns. These patterns are determined by various biological factors: intrinsic properties of individual neurons such as ion channels and synaptic variables, and network properties defining population composition and connectivity of spatially-structured heterogeneous neurons. In addition, sensory stimuli, internal states, and behavioral tasks shape neural dynamics at both the local and global scales.

We investigate how changes in local (intrinsic) and global (network) variables instigate transitions across different phases of single-neuron spiking dynamics as well as population dynamics described by excitatory-inhibitory balance, synchronizability, sensitivity, and dimensionality.