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@ARTICLE{Pipa:137486,
      author       = {Pipa, Gordon and Grün, Sonja and van Vreeswijk, Carl},
      title        = {{I}mpact of {S}pike {T}rain {A}utostructure on
                      {P}robability {D}istribution of {J}oint {S}pike {E}vents},
      journal      = {Neural computation},
      volume       = {25},
      number       = {5},
      issn         = {1530-888X},
      address      = {Cambridge, Mass.},
      publisher    = {MIT Press},
      reportid     = {FZJ-2013-03923},
      pages        = {1123 - 1163},
      year         = {2013},
      abstract     = {The discussion whether temporally coordinated spiking
                      activity really exists and whether it is relevant has been
                      heated over the past few years. To investigate this issue,
                      several approaches have been taken to determine whether
                      synchronized events occur significantly above chance, that
                      is, whether they occur more often than expected if the
                      neurons fire independently. Most investigations ignore or
                      destroy the autostructure of the spiking activity of
                      individual cells or assume Poissonian spiking as a model.
                      Such methods that ignore the autostructure can significantly
                      bias the coincidence statistics. Here, we study the
                      influence of the autostructure on the probability
                      distribution of coincident spiking events between tuples of
                      mutually independent non-Poisson renewal processes. In
                      particular, we consider two types of renewal processes that
                      were suggested as appropriate models of experimental spike
                      trains: a gamma and a log-normal process. For a gamma
                      process, we characterize the shape of the distribution
                      analytically with the Fano factor (FFc). In addition, we
                      perform Monte Carlo estimations to derive the full shape of
                      the distribution and the probability for false positives if
                      a different process type is assumed as was actually present.
                      We also determine how manipulations of such spike trains,
                      here dithering, used for the generation of surrogate data
                      change the distribution of coincident events and influence
                      the significance estimation. We find, first, that the width
                      of the coincidence count distribution and its FFc depend
                      critically and in a nontrivial way on the detailed
                      properties of the structure of the spike trains as
                      characterized by the coefficient of variation CV. Second,
                      the dependence of the FFc on the CV is complex and mostly
                      nonmonotonic. Third, spike dithering, even if as small as a
                      fraction of the interspike interval, can falsify the
                      inference on coordinated firing.},
      cin          = {INM-6 / IAS-6},
      ddc          = {004},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / HASB - Helmholtz Alliance on Systems
                      Biology (HGF-SystemsBiology) / BRAINSCALES - Brain-inspired
                      multiscale computation in neuromorphic hybrid systems
                      (269921)},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-Juel1)HGF-SystemsBiology /
                      G:(EU-Grant)269921},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000316992800001},
      pubmed       = {pmid:23470124},
      doi          = {10.1162/NECO_a_00432},
      url          = {https://juser.fz-juelich.de/record/137486},
}