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@ARTICLE{vanMeegen:902103,
      author       = {van Meegen, Alexander and van Albada, Sacha J.},
      title        = {{M}icroscopic theory of intrinsic timescales in spiking
                      neural networks},
      journal      = {Physical review research},
      volume       = {3},
      number       = {4},
      issn         = {2643-1564},
      address      = {College Park, MD},
      publisher    = {APS},
      reportid     = {FZJ-2021-04034},
      pages        = {043077},
      year         = {2021},
      abstract     = {A complex interplay of single-neuron properties and the
                      recurrent network structure shapes the activity of cortical
                      neurons. The single-neuron activity statistics differ in
                      general from the respective population statistics, including
                      spectra and, correspondingly, autocorrelation times. We
                      develop a theory for self-consistent second-order
                      single-neuron statistics in block-structured sparse random
                      networks of spiking neurons. In particular, the theory
                      predicts the neuron-level autocorrelation times, also known
                      as intrinsic timescales, of the neuronal activity. The
                      theory is based on an extension of dynamic mean-field theory
                      from rate networks to spiking networks, which is validated
                      via simulations. It accounts for both static variability,
                      e.g., due to a distributed number of incoming synapses per
                      neuron, and temporal fluctuations of the input. We apply the
                      theory to balanced random networks of generalized linear
                      model neurons, balanced random networks of leaky
                      integrate-and-fire neurons, and a biologically constrained
                      network of leaky integrate-and-fire neurons. For the
                      generalized linear model network with an error function
                      nonlinearity, a novel analytical solution of the colored
                      noise problem allows us to obtain self-consistent firing
                      rate distributions, single-neuron power spectra, and
                      intrinsic timescales. For the leaky integrate-and-fire
                      networks, we derive an approximate analytical solution of
                      the colored noise problem, based on the Stratonovich
                      approximation of the Wiener-Rice series and a novel
                      analytical solution for the free upcrossing statistics.
                      Again closing the system self-consistently, in the
                      fluctuation-driven regime, this approximation yields
                      reliable estimates of the mean firing rate and its variance
                      across neurons, the interspike-interval distribution, the
                      single-neuron power spectra, and intrinsic timescales. With
                      the help of our theory, we find parameter regimes where the
                      intrinsic timescale significantly exceeds the membrane time
                      constant, which indicates the influence of the recurrent
                      dynamics. Although the resulting intrinsic timescales are on
                      the same order for generalized linear model neurons and
                      leaky integrate-and-fire neurons, the two systems differ
                      fundamentally: for the former, the longer intrinsic
                      timescale arises from an increased firing probability after
                      a spike; for the latter, it is a consequence of a prolonged
                      effective refractory period with a decreased firing
                      probability. Furthermore, the intrinsic timescale attains a
                      maximum at a critical synaptic strength for generalized
                      linear model networks, in contrast to the minimum found for
                      leaky integrate-and-fire networks.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {530},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / 5232 -
                      Computational Principles (POF4-523) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      DFG project 347572269 - Heterogenität von Zytoarchitektur,
                      Chemoarchitektur und Konnektivität in einem großskaligen
                      Computermodell der menschlichen Großhirnrinde (347572269) /
                      PhD no Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)POF4-5232 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(GEPRIS)347572269 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000713153600008},
      doi          = {10.1103/PhysRevResearch.3.043077},
      url          = {https://juser.fz-juelich.de/record/902103},
}