Resting brains never rest: Computational insights into potential cognitive architectures

Gustavo Deco
Universitat Pompeu Fabra

Resting-state networks (RSNs), which have become a large focus in neuroimaging research, can be best 
simulated by large-scale cortical models when networks teeter on the edge of instability. In this state the 
functional networks are in a low firing stable state while they are continuously pulled towards multiple 
other configurations. Small extrinsic perturbations can shape task-related network dynamics, while 
perturbations from intrinsic noise generate excursions reflecting the range of available functional 
networks. This is distinctly advantageous for the efficiency and speed of network mobilization. Thus, the 
resting state reflects the dynamical capabilities of the brain, which emphasizes the vital interplay of time 
and space. We propose a new theoretical framework for RSNs that can serve as a fertile ground for 
empirical testing.