Richárd Forster (2017.01.01-2019.08.31)

CERN

Publication: Parallel Louvain Community Detection Optimized for GPUs

Abstract: Network analysis became a fundamental tool in understanding the structural and functional organization of the brain. The connectome of the cerebral cortex, the most complex part of the brain, is best known at the large scale, which, as a network, represents the connectivity between the different cortical areas or sub-regions. However, in reality brain areas and sub-regions are connected via numerous parallel pathways formed by populations of neurons residing within the areas, sub-regions. This mesoscale, or so called columnar network architecture of the cerebral cortex is not known, largely because of serious experimental limitations. However, graph theory provides some tools to approach the blueprint of the mesoscale cortical network.

The goal of the Neuroscience project is the application of graph theory and network analytic tools to explore the hidden structure of the large scale cortical network relevant to the mesoscale organization. To this end we study the network of the combinations of the incoming and outgoing edges of the different areas, which represent the interactions within the cortical network forming the interaction network. Considering cortical functioning, interactions or transfer between the inputs and the outputs is the basic operation of the areas. The interaction network is the two-hop representation of the network, which can be computed by graph derivations. Importantly, the derived network preserves the topological features of the original network but increases the size by orders of magnitude. The collaboration has been focusing on the exploration of the architecture of the interaction network. We aim to understand both the global organization of the interaction network as well as the role of the particular areas in regard to the specific pathways of the cortical signal flow.

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