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@ARTICLE{Helias:834092,
      author       = {Helias, Moritz and Kühn, Tobias},
      title        = {{L}ocking of correlated neural activity to ongoing
                      oscillations},
      journal      = {PLoS Computational Biology},
      volume       = {13},
      number       = {6},
      issn         = {1553-7358},
      address      = {San Francisco, Calif.},
      publisher    = {Public Library of Science},
      reportid     = {FZJ-2017-04093},
      pages        = {e1005534},
      year         = {2017},
      abstract     = {Population-wide oscillations are ubiquitously observed in
                      mesoscopic signals of cortical activity. In these network
                      states a global oscillatory cycle modulates the propensity
                      of neurons to fire. Synchronous activation of neurons has
                      been hypothesized to be a separate channel of signal
                      processing information in the brain. A salient question is
                      therefore if and how oscillations interact with spike
                      synchrony and in how far these channels can be considered
                      separate. Experiments indeed showed that correlated spiking
                      co-modulates with the static firing rate and is also tightly
                      locked to the phase of beta-oscillations. While the
                      dependence of correlations on the mean rate is well
                      understood in feed-forward networks, it remains unclear why
                      and by which mechanisms correlations tightly lock to an
                      oscillatory cycle. We here demonstrate that such correlated
                      activation of pairs of neurons is qualitatively explained by
                      periodically-driven random networks. We identify the
                      mechanisms by which covariances depend on a driving periodic
                      stimulus. Mean-field theory combined with linear response
                      theory yields closed-form expressions for the
                      cyclostationary mean activities and pairwise zero-time-lag
                      covariances of binary recurrent random networks. Two
                      distinct mechanisms cause time-dependent covariances: the
                      modulation of the susceptibility of single neurons (via the
                      external input and network feedback) and the time-varying
                      variances of single unit activities. For some parameters,
                      the effectively inhibitory recurrent feedback leads to
                      resonant covariances even if mean activities show
                      non-resonant behavior. Our analytical results open the
                      question of time-modulated synchronous activity to a
                      quantitative analysis.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {570},
      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) / HBP SGA1 - Human
                      Brain Project Specific Grant Agreement 1 (720270) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / Helmholtz Young Investigators Group
                      (HGF-YoungInvestigatorsGroup)},
      pid          = {G:(DE-HGF)POF3-574 / G:(DE-HGF)POF3-571 /
                      G:(EU-Grant)720270 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-HGF)HGF-YoungInvestigatorsGroup},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000404565400013},
      pubmed       = {pmid:28604771},
      doi          = {10.1371/journal.pcbi.1005534},
      url          = {https://juser.fz-juelich.de/record/834092},
}