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000873626 005__ 20240313094942.0
000873626 037__ $$aFZJ-2020-00869
000873626 1001_ $$0P:(DE-Juel1)174497$$aLayer, Moritz$$b0$$eCorresponding author$$ufzj
000873626 1112_ $$aBernstein Conference 2019$$cBerlin$$d2019-09-17 - 2019-09-20$$wGermany
000873626 245__ $$aLong-Range Neuronal Coordination Near the Breakdown of Linear Stability
000873626 260__ $$c2019
000873626 3367_ $$033$$2EndNote$$aConference Paper
000873626 3367_ $$2BibTeX$$aINPROCEEDINGS
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000873626 520__ $$aExperimental findings suggest that cortical networks operate in a balanced state [1] in which strong recurrent inhibition suppresses single cell input correlations [2,3]. The balanced state, however, only restricts the average correlations in the network, the distribution of correlations between individual neurons is not constrained. We here investigate this distribution and establish a functional relation between the dynamical state of the system and the variance of correlations as a function of cortical distance. The former is characterized by the spectral radius, a measure for how strong a signal is damped while traversing the network. To this end, we develop a theory that captures the heterogeneity of correlations across neurons. Technically, we derive a mean-field theory that assumes the distribution of correlations to be self-averaging; i.e. the same in any realization of the random network. This is possible by taking advantage of the symmetry of the disorder-averaged [4] effective connectivity matrix. We here demonstrate that spatially organized, balanced network models predict rich pairwise correlation structures with spatial extent far beyond the range of direct connections [5]. Massively parallel spike recordings of macaque motor cortex quantitatively confirm this prediction. We show that the range of these correlations depends on the spectral radius, which offers a potential dynamical mechanism to control the spatial range on which neurons cooperatively perform computations.
000873626 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000873626 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x1
000873626 536__ $$0G:(DE-Juel1)HGF-SMHB-2014-2018$$aMSNN - Theory of multi-scale neuronal networks (HGF-SMHB-2014-2018)$$cHGF-SMHB-2014-2018$$fMSNN$$x2
000873626 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x3
000873626 536__ $$0G:(EU-Grant)90251$$aSmartstart - SMARTSTART Training Program in Computational Neuroscience (90251)$$c90251$$x4
000873626 7001_ $$0P:(DE-Juel1)156459$$aDahmen, David$$b1$$ufzj
000873626 7001_ $$0P:(DE-Juel1)144806$$aHelias, Moritz$$b2$$ufzj
000873626 7001_ $$0P:(DE-Juel1)168574$$aDeutz, Lukas$$b3
000873626 7001_ $$0P:(DE-Juel1)168479$$aVoges, Nicole$$b4$$ufzj
000873626 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b5$$ufzj
000873626 7001_ $$0P:(DE-Juel1)144174$$aDiesmann, Markus$$b6$$ufzj
000873626 7001_ $$0P:(DE-Juel1)171408$$aDabrowska, Paulina$$b7$$ufzj
000873626 7001_ $$0P:(DE-HGF)0$$aPapen, Michael von$$b8
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000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)174497$$aForschungszentrum Jülich$$b0$$kFZJ
000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156459$$aForschungszentrum Jülich$$b1$$kFZJ
000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144806$$aForschungszentrum Jülich$$b2$$kFZJ
000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168479$$aForschungszentrum Jülich$$b4$$kFZJ
000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b5$$kFZJ
000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144174$$aForschungszentrum Jülich$$b6$$kFZJ
000873626 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171408$$aForschungszentrum Jülich$$b7$$kFZJ
000873626 9131_ $$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$$x0
000873626 9141_ $$y2019
000873626 9201_ $$0I:(DE-Juel1)INM-6-20090406$$kINM-6$$lComputational and Systems Neuroscience$$x0
000873626 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
000873626 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lTheoretical Neuroscience$$x2
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