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@PHDTHESIS{Bos:685856,
      author       = {Bos, Hannah},
      othercontributors = {Helias, Moritz and Kampa, Björn M. and Einevoll, Gaute T.},
      title        = {{C}onnectivity structure induced dynamics and correlations
                      in spiking neural networks},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      reportid     = {RWTH-2017-02567},
      pages        = {1 Online-Ressource (vii, 131 Seiten) : Illustrationen,
                      Diagramme},
      year         = {2017},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2017},
      abstract     = {The cerebral cortex exhibits distinct connectivity patterns
                      on different length scales. Long range connections between
                      cortical areas are mostly excitatory, while connectivity
                      within the areas is excitatory and inhibitory. Additionally,
                      cortical areas exhibit a layered structure across depth.
                      This laminar structure is accompanied by specific
                      connectivity patterns between populations of neurons located
                      in the layers. Experimental studies link the laminar
                      structure to dynamical phenomena, like specific
                      oscillations, which are more prominent in some layers than
                      in others. In this thesis, I present several studies that
                      relate the laminar connectivity as well as the connectivity
                      on the single neuron level to the structure of correlation
                      in the network activity. This question has so far been
                      approached by theoretical studies using either
                      phenomenological population models or simplified
                      connectivity structure. First, I present a theoretical
                      framework that describes the dynamics of randomly connected
                      spiking neurons by their population dynamics. This
                      dimensionality reduction facilitates the analysis of
                      phenomena that arise from the laminar structure. Next, I
                      derive a sensitivity measure which identifies dynamically
                      relevant connectivity motifs within the network. Applying
                      these theoretical tools to a model that describes a
                      microcircuit in area V1, I predict population rate spectra,
                      identify the sub-circuits that generate the observed
                      oscillations and link the results to experimental findings.
                      Extending the theoretical framework and the model by slow
                      synaptic currents sheds light on their role in both,
                      synchronizing and desynchronizing population activity.
                      Inspired by experimental studies, which probe circuits by
                      oscillatory stimuli, I analyze how these stimuli are
                      processed by neuronal networks and how the network response
                      is shaped by the connectivity structure. The effect of the
                      connectivity within the populations on the stationary
                      correlation structure is studied by means of a binary
                      network. This sheds light on features that are neglected
                      when summarizing neuronal activity in populations, like
                      heterogeneities of neuronal activities and correlations
                      between neurons. This study illustrates how network activity
                      is decorrelated in the input to each neuron and how the
                      connectivity structure on the single cell level shapes the
                      decay time of stimuli induced network responses. Throughout
                      this work, I employ simulations of neuronal networks to
                      support approximations made in the derivation of theoretical
                      framework and furthermore to model interactions which are
                      not analytically tractable. I therefore document how I
                      unified the connection routines in the neural simulator NEST
                      and implemented the new connection framework which allows
                      for the creation of connectivity structures with randomized
                      connections as well as randomized synaptic parameters. In
                      summary, this thesis relates laminar connectivity patterns
                      to experimentally observed dynamical phenomena. The
                      methodology introduced here facilitates the understanding of
                      how connectivity structure of neuronal networks, on the
                      population level as well as on the single cell level, shapes
                      the dynamics emerging from the network.},
      cin          = {136930 / 130000},
      ddc          = {530},
      cid          = {$I:(DE-82)136930_20160614$ / $I:(DE-82)130000_20140620$},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:82-rwth-2017-025673},
      doi          = {10.18154/RWTH-2017-02567},
      url          = {https://publications.rwth-aachen.de/record/685856},
}