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.