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@INPROCEEDINGS{Layer:873626,
      author       = {Layer, Moritz and Dahmen, David and Helias, Moritz and
                      Deutz, Lukas and Voges, Nicole and Grün, Sonja and
                      Diesmann, Markus and Dabrowska, Paulina and Papen, Michael
                      von},
      title        = {{L}ong-{R}ange {N}euronal {C}oordination {N}ear the
                      {B}reakdown of {L}inear {S}tability},
      reportid     = {FZJ-2020-00869},
      year         = {2019},
      abstract     = {Experimental 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.},
      month         = {Sep},
      date          = {2019-09-17},
      organization  = {Bernstein Conference 2019, Berlin
                       (Germany), 17 Sep 2019 - 20 Sep 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) / PhD no Grant - Doktorand ohne
                      besondere Förderung (PHD-NO-GRANT-20170405) / 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:(DE-Juel1)PHD-NO-GRANT-20170405 / G:(EU-Grant)90251},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/873626},
}