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Mesoscopic segregation of excitation and inhibition in a brain network model
Daniel Malagarriga12 Alessandro E. P. Villa2 Jordi García-Ojalvo3 Antonio J. Pons1
1Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Edifici Gaia, Rambla Sant Nebridi 22, 08222 Terrassa, Spain and 2Neuroheuristic Research Group, Faculty of Business and Economics, University of Lausanne, CH-1015 Lausanne, Switzerland 3Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Dr. Aiguader 88, 08003 Barcelona, Spain

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized irregular regime. Even though we also consider other topologies, here we focus on a heterogeneous topology network, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.