% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Dahmen:828725,
      author       = {Dahmen, David and Diesmann, Markus and Helias, Moritz},
      title        = {{D}istributions of covariances as a window intothe
                      operational regime of neuronal networks},
      reportid     = {FZJ-2017-02591},
      year         = {2017},
      abstract     = {Massively parallel recordings of spiking activity in
                      cortical networks show that spike count covariances vary
                      widely across pairs of neurons [Ecker et al., Science
                      (2010)]. Their low average is well understood [Renart et
                      al., Science (2010), Tetzlaff et al., PLoS CB (2012)], but
                      an explanation for the wide distribution in relation to the
                      static (quenched) disorder of the connectivity in recurrent
                      random networks was so far elusive. Starting from spin-glass
                      techniques [Sompolinsky and Zippelius, Phys. Rev. B (1982)]
                      and a generating function representation for the joint
                      probability distribution of the network activity [Chow and
                      Buice, J. Math. Neurosci. (2015)], we derive a finite-size
                      mean-field theory that reduces a disordered to a highly
                      symmetric network with fluctuating auxiliary fields. The
                      exposed analytical relation between the statistics of
                      connections and the statistics of pairwise covariances shows
                      that both, average and dispersion of the latter, diverge at
                      a critical coupling. At this point, a network of nonlinear
                      units transits from regular to chaotic dynamics. Applying
                      these results to recordings from the mammalian brain
                      suggests its operation close to this edge of criticality.},
      month         = {Mar},
      date          = {2017-03-22},
      organization  = {NWG meeting 2017, Göttingen
                       (Germany), 22 Mar 2017 - 25 Mar 2017},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / 571 -
                      Connectivity and Activity (POF3-571) / 331 - Signalling
                      Pathways and Mechanisms in the Nervous System (POF2-331) /
                      MSNN - Theory of multi-scale neuronal networks
                      (HGF-SMHB-2014-2018) / HBP SGA1 - Human Brain Project
                      Specific Grant Agreement 1 (720270) / SMHB - Supercomputing
                      and Modelling for the Human Brain (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-571 /
                      G:(DE-HGF)POF2-331 / G:(DE-Juel1)HGF-SMHB-2014-2018 /
                      G:(EU-Grant)720270 / G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/828725},
}