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@INPROCEEDINGS{Voges:841132,
      author       = {Voges, Nicole and Dabrowska, Paulina and Senk, Johanna and
                      Hagen, Espen and Riehle, Alexa and Brochier, Thomas and
                      Grün, Sonja},
      title        = {{C}haracterization of resting state dynamics in monkey
                      motor cortex},
      reportid     = {FZJ-2017-08232},
      year         = {2017},
      abstract     = {Nowadays, modeling studies of cortical network dynamics aim
                      to include realistic assumptions on structural and
                      functional properties of the corresponding neurons [1,2].
                      Such models often do not consider functional aspects but
                      rather describe the “ground”, “idle”, or “resting
                      state” of the cortical network, typically characterized as
                      asynchronous irregular spiking [2]. However, for model
                      validation, i.e., for a concrete comparison of experimental
                      versus model data aiming at a more realistic model, one
                      needs to compare this cortical state to the corresponding
                      experimental data. Therefore we performed a “resting
                      state” experiment (this term is adapted from human fMRI
                      studies where it is defined as brain activity observed when
                      the subject is at rest [3]). We recorded the neuronal
                      activity from macaque monkey motor cortex with a chronically
                      implanted 4x4mm2 100 electrode Utah Array (Blackrock
                      Microsystems) for 15min, while the monkey was sitting in a
                      chair without any task or given stimulus. This is in
                      contrast to most neurophysiological studies that focus on a
                      task- or stimulus-specific analysis [e.g. 4]. Based on a
                      video recording of the monkey during the neuronal recording,
                      we differentiate between “resting” intervals and
                      intervals when the monkey spontaneously moved.The goal of
                      this study is to thoroughly characterize the simultaneous
                      spiking activity recorded from 146 single units during
                      resting state. To enable a detailed comparison to simulated
                      spiking data, we subdivide the single units into putative
                      excitatory and inhibitory neurons based on their spike
                      shapes [5]. We apply common statistical measures, e.g.,
                      firing rate, (local) coefficient of variation for single
                      unit characterization, and we also compute the pairwise fine
                      temporal correlation by correlation coefficients. These
                      measures are calculated in two ways: averaged over time and
                      single units, as well as averaged over time but separately
                      for each single unit (except for the correlation
                      coefficients). Comparing the distributions of these measures
                      from the two behavioral states we do not find any difference
                      – when averaging over single units. However, when focusing
                      on non-averaged, single unit data we notice that some
                      neurons increase their firing rates systematically when the
                      monkey moves compared to rest, whereas others decrease or do
                      not change their rates. Thus, there was seemingly no
                      difference on the population level, but significant
                      differences on the level of individual neurons. Moreover, we
                      observe a strong correlation between a few neuronal units,
                      independent of their cortical distance, while others show
                      lower or no correlation. Our next steps are to characterize
                      if such findings are particularly different for excitatory
                      and inhibitory neurons. Further, we aim to study the
                      underlying network mechanisms. One possibility would be to
                      re-consider the balancing effects of inhibition and
                      recurrence [5,6].},
      month         = {Jul},
      date          = {2017-07-15},
      organization  = {Computational Neuroscience Society,
                       Antwerpen (Belgium), 15 Jul 2017 - 20
                       Jul 2017},
      cin          = {INM-6 / IAS / INM-10 / AMU / JARA-BRAIN},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)VDB1106 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-588b)1043886400 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {571 - Connectivity and Activity (POF3-571) / DFG project
                      238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA1 - Human Brain Project
                      Specific Grant Agreement 1 (720270) / Smartstart -
                      SMARTSTART Training Program in Computational Neuroscience
                      (90251)},
      pid          = {G:(DE-HGF)POF3-571 / G:(GEPRIS)238707842 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)720270 /
                      G:(EU-Grant)90251},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/841132},
}