Hannah Choi (University of Washington)
The complex connectivity structure unique to the brain network is believed to underlie its robust and efficient coding capability. The recent development of the structural mouse brain network available at the Allen Mouse Brain Connectivity Atlas, makes it possible to conduct in-depth analyses on connections between structure and computation in the brain network. In this talk, I will discuss computational strategies that can be inferred from the architecture of the mesoscopic mouse brain network constructed from viral tracing experiments. First, I will explore how network synchrony depends on complex connectivity structures of the whole-brain network. By simulating large-scale brain dynamics using a data-driven network of phase oscillators, we show that complexities added to the spatially embedded whole-brain connectome by sparse long-range connections, enable rapid transitions between local and global synchronizations. This result implicates computational roles of strong distal connections in the brain, which may be important for the brain?s exceptional ability to rapidly switch between modular and global computations?such rapid transition is known to be impaired in pathological brains (e.g. Alzheimer?s disease). The recent expansion of the Allen Mouse Brain Connectivity data includes cell-type and layer-specific cortical connectivity, constructed from viral tracing experiments in Cre-transgenic mice. In the second part of the talk, I will introduce an unsupervised method to find the hierarchical organization of the mouse cortical & thalamic network, based on the layer-specific connectivity. The implemented method discovers the hierarchy of the mouse brain areas based on their anatomical connectivity patterns, and provides a measure of ?hierarchy scores? for different connectomes. The uncovered hierarchy provides insights into the direction of information flows in the mouse brain, which has been less well-defined compared to the primate brain.