Graph Theory Approaches for Network Analysis of Neuronal Signals and Behavior
How does the brain encode behavior? What are the internal mechanisms affecting neuronal network organization and configuration at different scales? I will introduce novel approaches for analysis of networks based on viewing neuronal activity as high dimensional observations of a dynamic system, with the ultimate goal of revealing the intrinsic mechanisms giving rise to biological processes. I will introduce our new analytical framework for investigating time-varying functional connectivity in neural networks based on Graph Theory and Riemannian Geometry. By extracting the latent dynamics of functional connectivity I demonstrate how local circuits and global cortical networks encode spontaneous behaviors. Furthermore, I will present how this analytic framework discovers spatio-temporal patterns of functional cortical activity closely linked to behavior
