The spontaneous activity of the brain shows different features at different scales. On one hand, neuroimaging studies show that long-range correlations are highly structured in spatiotemporal patterns, known as resting-state networks, on the other hand, neurophysiological reports show that short-range correlations between neighboring neurons are low, despite a large amount of shared presynaptic inputs. Different dynamical mechanisms of local decorrelation have been proposed, among which is feedback inhibition. Here, we investigated the effect of locally regulating the feedback inhibition on the global dynamics of a large-scale brain model, in which the long-range connections are given by diffusion imaging data of human subjects. We used simulations and analytical methods to show that locally constraining the feedback inhibition to compensate for the excess of long-range excitatory connectivity, to preserve the asynchronous state, crucially changes the characteristics of the emergent resting and evoked activity. First, it significantly improves the model's prediction of the empirical human functional connectivity. Second, relaxing this constraint leads to an unrealistic network evoked activity, with systematic coactivation of cortical areas which are components of the default-mode network, whereas regulation of feedback inhibition prevents this. Finally, information theoretic analysis shows that regulation of the local feedback inhibition increases both the entropy and the Fisher information of the network evoked responses. Hence, it enhances the information capacity and the discrimination accuracy of the global network. In conclusion, the local excitation–inhibition ratio impacts the structure of the spontaneous activity and the information transmission at the large-scale brain level.

G.D. was supported by the ERC Advanced Grant: DYSTRUCTURE (no. 295129), by the Spanish Research Project SAF2010-16085 and by the CONSOLIDER-INGENIO 2010 Program CSD2007-00012, the Foundation “La Marato” (Catalonia), and the FP7-ICT BrainScales and The Human Brain Project FET-Flagship. P.H was supported by Leenaards Foundation. M.C. was supported by NIH Grants R01HD061117 and R01MH096482. The research reported herein was supported by the Brain Network Recovery Group through the James S. McDonnell Foundation. We thank H. Sompolinsky and M. Lechón for critical comments and valuable discussions