The ongoing activity of the brain at rest, i.e., under no stimulation and in absence of any task, is astonishingly highly structured into/nspatiotemporal patterns. These spatiotemporal patterns, called resting state networks, display low-frequency characteristics (<0.1 Hz)/nobserved typically in the BOLD-fMRI signal of human subjects. We aim here to understand the origins of resting state activity through/nmodeling via a global spiking attractor network of the brain. This approach offers a realistic mechanistic model at the level of each single/nbrain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses. Integrating the biologically realistic diffusion/ntensor imaging/diffusion spectrum imaging-based neuroanatomical connectivity into the brain model, the resultant emerging resting/nstate functional connectivity of the brain network fits quantitatively best the experimentally observed functional connectivity in humans/nwhen the brain network operates at the edge of instability. Under these conditions, the slow fluctuating (

G.D. was supported by the European Union Grant EC005-024, by SAF2010-16085 and the “La Marato”/nFoundation,andbytheCONSOLIDER-INGENIO2010ProgrammeCSD2007-00012.The research reported here in was/nsupported by the Brain Network Recovery Group through the James S. McDonnell Foundation and the FP7-ICT/nBrainScales.