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000873634 005__ 20240313094942.0
000873634 037__ $$aFZJ-2020-00877
000873634 1001_ $$0P:(DE-Juel1)176938$$aZeraati, Roxana$$b0
000873634 1112_ $$aVerhandlungen der Deutschen Physikalischen Gesellschaft$$cRegensburg$$d2019-03-31 - 2019-04-05$$wGermany
000873634 245__ $$aCritical avalanches in a spatially structured model of cortical On-Off dynamics
000873634 260__ $$c2019
000873634 3367_ $$033$$2EndNote$$aConference Paper
000873634 3367_ $$2DataCite$$aOther
000873634 3367_ $$2BibTeX$$aINPROCEEDINGS
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000873634 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1580903783_7428$$xOther
000873634 500__ $$aRoxana Zeraati was employed at the FZJ through the project SMARTSTART Computational Neuroscience, DB001423.
000873634 520__ $$aSpontaneous cortical activity unfolds across different spatial scales. On a local scale of individual columns, activity spontaneously transitions between episodes of vigorous (On) and faint (Off) spiking, synchronously across cortical layers. On a wider spatial scale of interacting columns, activity propagates as neural avalanches, with sizes distributed as an approximate power-law with exponential cutoff, suggesting that brain operates close to a critical point. We investigate how local On-Off dynamics can coexist with critical avalanches. To this end, we developed a branching network model capable of capturing both of these dynamics. Each unit in the model represents a cortical column, that spontaneously transitions between On and Off episodes and has spatially structured connections to other columns. We found that models with local connectivity do not exhibit critical dynamics in the limit of a large system size. While for a critical network, it is expected that the cut-off of the avalanche-size distribution scales with the system size, in models with nearest-neighbor connectivity, it stays constant above a characteristic size. We demonstrate that the scaling can be recovered by increasing the radius of connections or by rewiring a small fraction of local connections to long-range random connections. Our results highlight the possible role of long-range connections in defining the operating regime of the brain dynamics.
000873634 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000873634 536__ $$0G:(EU-Grant)90251$$aSmartstart - SMARTSTART Training Program in Computational Neuroscience (90251)$$c90251$$x1
000873634 7001_ $$0P:(DE-HGF)0$$aEngel, Tatiana$$b1
000873634 7001_ $$0P:(DE-HGF)0$$aLevina, Anna$$b2
000873634 909CO $$ooai:juser.fz-juelich.de:873634$$pec_fundedresources$$pVDB$$popenaire
000873634 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176938$$aForschungszentrum Jülich$$b0$$kFZJ
000873634 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000873634 9141_ $$y2019
000873634 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000873634 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x1
000873634 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x2
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