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@ARTICLE{Zaytsev:256185,
      author       = {Zaytsev, Yury and Morrison, Abigail and Deger, Moritz},
      title        = {{R}econstruction of recurrent synaptic connectivity of
                      thousands of neurons from simulated spiking activity},
      journal      = {Journal of computational neuroscience},
      volume       = {39},
      number       = {1},
      issn         = {1573-6873},
      address      = {Dordrecht [u.a.]},
      publisher    = {Springer Science + Business Media B.V},
      reportid     = {FZJ-2015-06170},
      pages        = {77 - 103},
      year         = {2015},
      abstract     = {Dynamics and function of neuronal networks are determined
                      by their synaptic connectivity. Current experimental methods
                      to analyze synaptic network structure on the cellular level,
                      however, cover only small fractions of functional neuronal
                      circuits, typically without a simultaneous record of
                      neuronal spiking activity. Here we present a method for the
                      reconstruction of large recurrent neuronal networks from
                      thousands of parallel spike train recordings. We employ
                      maximum likelihood estimation of a generalized linear model
                      of the spiking activity in continuous time. For this model
                      the point process likelihood is concave, such that a global
                      optimum of the parameters can be obtained by gradient
                      ascent. Previous methods, including those of the same class,
                      did not allow recurrent networks of that order of magnitude
                      to be reconstructed due to prohibitive computational cost
                      and numerical instabilities. We describe a minimal model
                      that is optimized for large networks and an efficient scheme
                      for its parallelized numerical optimization on generic
                      computing clusters. For a simulated balanced random network
                      of 1000 neurons, synaptic connectivity is recovered with a
                      misclassification error rate of less than 1 $\%$ under ideal
                      conditions. We show that the error rate remains low in a
                      series of example cases under progressively less ideal
                      conditions. Finally, we successfully reconstruct the
                      connectivity of a hidden synfire chain that is embedded in a
                      random network, which requires clustering of the network
                      connectivity to reveal the synfire groups. Our results
                      demonstrate how synaptic connectivity could potentially be
                      inferred from large-scale parallel spike train recordings.},
      cin          = {JSC / IAS-6 / JARA-HPC / INM-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      $I:(DE-82)080012_20140620$ / I:(DE-Juel1)INM-6-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / W2Morrison - W2/W3
                      Professorinnen Programm der Helmholtzgemeinschaft
                      (B1175.01.12) / SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(DE-HGF)B1175.01.12 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000357488700006},
      pubmed       = {pmid:26041729},
      doi          = {10.1007/s10827-015-0565-5},
      url          = {https://juser.fz-juelich.de/record/256185},
}