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               <dc:title>Ongoing cortical activity at rest: criticality, multistability,/nand ghost attractors</dc:title>
               <dc:creator>Deco, Gustavo</dc:creator>
               <dc:creator>Jirsa, Viktor K.</dc:creator>
               <dc:description>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 (&amp;lt;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 (&amp;lt;/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.</dc:description>
               <dc:description>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.</dc:description>
               <dc:date>2015-01-27T08:32:03Z</dc:date>
               <dc:date>2015-01-27T08:32:03Z</dc:date>
               <dc:date>2012</dc:date>
               <dc:type>info:eu-repo/semantics/article</dc:type>
               <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
               <dc:relation>The Journal of Neuroscience. 2012 Mar;32(10):3366-75</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/EC/FP7/295129</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/EC/FP7/269921</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/ES/3PN/SAF2010-16085</dc:relation>
               <dc:relation>info:eu-repo/grantAgreement/ES/2PN/CSD2007-00012</dc:relation>
               <dc:rights>The work is published under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license, as described at http://creativecommons.org/licenses/by-nc-sa/3.0/</dc:rights>
               <dc:rights>http://creativecommons.org/licenses/by-nc-sa/3.0/</dc:rights>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
               <dc:publisher>Society for Neuroscience</dc:publisher>
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