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 ...
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 (</n0.1 Hz) resting state networks/nemerge as structured noise fluctuations around a stable low firing activity equilibrium state in the presence of latent “ghost” multistable/nattractors. The multistable attractor landscape defines a functionally meaningful dynamic repertoire of the brain network that is inher-/nently present in the neuroanatomical connectivity.
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