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@PHDTHESIS{Kurth:1037847,
      author       = {Kurth, Anno},
      title        = {{C}onstruction of a {S}piking {N}etwork {M}odel of
                      {M}acaque {P}rimary {V}isual {C}ortex: {T}owards {D}igital
                      {T}wins},
      volume       = {107},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-00990},
      isbn         = {978-3-95806-800-1},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {xvi, 207},
      year         = {2024},
      note         = {Dissertation, RWTH Aachen University, 2024},
      abstract     = {The cerebral cortex of the mammalian brain is composed of
                      an unfathomable amount of neurons that are organized in
                      intricate circuits across several spatial scales. If
                      present, cortical activity reflects higher-level information
                      processing in mammals. One approach to study the
                      relationship between the cortex’ structure and its
                      activity is to represent the studied physical system by a
                      “digital twin”, a computational model in which
                      anatomical and physiological findings can be incorporated.
                      In such digital twins, experiments can be performed and data
                      obtained not feasible using the “physical twin”. This
                      thesis focuses on building a large scale, biologically
                      plausible spiking network model of macaque primary visual
                      cortex. As such, it combines results from the experimental
                      literature and contributes to building ever more
                      sophisticated digital twins of the visual cortex. This quest
                      is embedded into a larger neuroscientific research program
                      aiming at expanding the usage of computer models in
                      Neurosciene. In line with this approach, in this thesis
                      first resting state neural activity recorded from macaque
                      primary visual cortex is analyzed. A separation of neural
                      activity into two clusters that can be related to the
                      monkey’s behavior is found that is co-modulated along with
                      topdown signals from V4. To explore whether this
                      co-modulation might be causative for the separation of
                      states, in silico experiments of a model of the local
                      cortical circuit are conducted. However, this simple model
                      neglects much of the fine structure of visual cortex. Hence,
                      subsequently a large-scale, biologically plausible digital
                      twin of this area is devised. After unifying and integrating
                      a large body of data across multiple sources, simulations of
                      the model reveal unrealistic activity. This motivates a
                      further investigation of cortical connectivity in light of
                      recent advances of reconstruction of microcircuits in the
                      brain. The findings offer potential resolutions for the
                      encountered problems and highlight stark differences between
                      recent and previous reconstructions of local cortical
                      networks. To employ digital twins as research platforms in
                      Neuroscience, simulation technologies need to be readily
                      available for the research community. Such technologies have
                      to be continuously developed and updated to meet the
                      requirements of the researchers. To contribute to this
                      endeavor, in this thesis the performance of the neural
                      simulation tool NEST is assessed and compared with
                      alternative approaches. Additionally, a benchmarking
                      workflow with a view towards neural network simulations is
                      developed that aids the continuous development of spiking
                      neural network simulation technologies.},
      cin          = {IAS-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / ACA -
                      Advanced Computing Architectures (SO-092) / HBP SGA2 - Human
                      Brain Project Specific Grant Agreement 2 (785907) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539) /
                      DFG project G:(GEPRIS)313856816 - SPP 2041: Computational
                      Connectomics (313856816)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(DE-HGF)SO-092 / G:(EU-Grant)785907
                      / G:(EU-Grant)945539 / G:(GEPRIS)313856816},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2504220847268.413844325757},
      doi          = {10.34734/FZJ-2025-00990},
      url          = {https://juser.fz-juelich.de/record/1037847},
}