In this project the PIs will develop a theoretical framework to understand information processing in brain networks. The theoretical developments will be tested with experiments done in the collaborating lab of Canals (Alicante) by observation of the hemodynamic and electrical neural activity in animal with micro-electric stimulation in in-vivo animal experiments. A vast corpus of theoretical analysis and experimental data will serve to analyze the brain as a network of networks. This will involve a novel theoretical framework conceived to robustly determine how modules dynamically form and share information at different scales. The network analysis will reveal the brain nodes that are essential to control brain functionality in terms of super-spreaders and super-inhibitor nodes, cascading effects, robustness and vulnerability to node failure. The mathematical framework of the PIs challenges current thinking regarding the functional structure of the brain as a small-world and scale-free network, which is defined by short paths, large local clustering and a single degree distribution. Small-world networks have been proposed to solve a basic conundrum: the brain needs to form modules which ought to be sufficiently independent to guarantee functional specialization and sufficiently connected to bind multiple processors for efficient information transfer. However, this structure presents an intrinsic tension between shortcuts generating small-worlds and the persistence of modularity; a global property unrelated to local clustering. In this project the PIs depart from the current thinking in brain functional structure, replacing the concept of small-world by that of hierarchical Networks of Networks that describes the brain as a set of hierarchical modules made of weak/strong links. The broader significance of the proposed theory for the large-scale organization of the brain extends the mathematical theory of networks to radically novel information processing systems. The findings from this research will have implications, not only for systems neuroscience, but also for a number of complex systems ranging from technological, social to biological networks. This proposal represents a symbiosis between the labs of two physicists with expertise in statistical physics and complex networks and a neuroscientist. Such a setting will provide interdisciplinary and international opportunities to students involved in this project. Further broader educational impacts include involvement of underrepresented minority students from CCNY and curriculum development.
