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@INPROCEEDINGS{Layer:873622,
      author       = {Layer, Moritz and Dahmen, David and Deutz, Lukas and
                      Dabrowska, Paulina and Voges, Nicole and Grün, Sonja and
                      Diesmann, Markus and Helias, Moritz and Papen, Michael von},
      title        = {{F}ocus {S}ession: {C}ollective {D}ynamics in {N}eural
                      {N}etworks; {L}ong-{R}ange {C}ollective {D}ynamics in the
                      {B}alanced {S}tate},
      reportid     = {FZJ-2020-00865},
      year         = {2019},
      abstract     = {Experimental findings suggest, that cortical networks
                      operate in a balanced state, in which strong recurrent
                      inhibition suppresses single cell input correlations. The
                      balanced state, however, only restricts the average
                      correlations in the network, the distribution of
                      correlations between individual inputs is not constrained.
                      We here investigate this distribution and establish a
                      functional relation between the distance to criticality and
                      the spatial dependence of the statistics of correlations.
                      Therefore, we develop a mean-field theory that goes beyond
                      self-averaging quantities by taking advantage of the
                      symmetry of the disorder-averaged effective connectivity
                      matrix. We demonstrate that spatially organized, balanced
                      networks can show rich pairwise correlation structures,
                      extending far beyond the range of direct connections.
                      Strikingly, the range of these correlations depends on the
                      distance of the network dynamics to a critical point. This
                      relation between the operational regime of the network and
                      the range of correlations is a potential dynamical mechanism
                      that controls the spatial range on which neurons
                      cooperatively perform computations. In the future we will
                      compare our results with data from multi channel recordings
                      to infer new constraints on realistic network models.},
      month         = {Mar},
      date          = {2019-03-31},
      organization  = {DPG Spring Meetings 2019, Regensburg
                       (Germany), 31 Mar 2019 - 5 Apr 2019},
      subtyp        = {Other},
      cin          = {INM-6 / INM-10 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / GRK
                      2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur
                      Aufklärung neuronaler multisensorischer Integration
                      (368482240) / MSNN - Theory of multi-scale neuronal networks
                      (HGF-SMHB-2014-2018) / Smartstart - SMARTSTART Training
                      Program in Computational Neuroscience (90251)},
      pid          = {G:(DE-HGF)POF3-574 / G:(GEPRIS)368482240 /
                      G:(DE-Juel1)HGF-SMHB-2014-2018 / G:(EU-Grant)90251},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/873622},
}