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@PHDTHESIS{Kurth:1000178,
      author       = {Kurth, Anno Christopher},
      othercontributors = {Diesmann, Markus and Honerkamp, Carsten},
      title        = {{C}onstruction of a spiking network model of macaque
                      primary visual cortex: towards digital twins},
      volume       = {107},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
      reportid     = {RWTH-2025-00216},
      isbn         = {978-3-95806-800-1},
      series       = {Schriften des Forschungszentrums Jülich. Reihe Information
                      / information},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Druckausgabe: 2024. - Onlineausgabe: 2025. - Auch
                      veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2025; Dissertation, RWTH Aachen University, 2024},
      abstract     = {Construction of a Spiking Network Model of Macaque Primary
                      Visual Cortex: Towards Digital Twins. 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. As eparation of
                      neural activity into two clusters that can be related to the
                      monkey’s behavior is found that is co-modulated along with
                      top-down 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          = {535500-2 ; 934910 / 130000},
      ddc          = {530},
      cid          = {$I:(DE-82)535500-2_20140620$ / $I:(DE-82)130000_20140620$},
      pnm          = {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) / Impuls- und
                      Vernetzungsfonds},
      pid          = {G:(DE-HGF)SO-092 / G:(EU-Grant)785907 / G:(EU-Grant)945539
                      / G:(GEPRIS)313856816 / G:(DE-HGF)IVF-20140101},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2025-00216},
      url          = {https://publications.rwth-aachen.de/record/1000178},
}