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@PHDTHESIS{Gutzen:981487,
      author       = {Gutzen, Robin},
      othercontributors = {Grün, Sonja Annemarie and Kampa, Björn M. and Kowalski,
                          Julia},
      title        = {{A}nalysis and quantitative comparison of neural network
                      dynamics on a neuron-wise and population level},
      volume       = {102},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
      reportid     = {RWTH-2024-03073},
      isbn         = {978-3-95806-738-7},
      series       = {Schriften des Forschungszentrums Jülich. Reihe
                      Information/information},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2024},
      note         = {Druckausgabe: 2024. - Onlineausgabe: 2024. - Auch
                      veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2023},
      abstract     = {Our goal is to better understand the working mechanisms of
                      biological neural systems. To this end, describing neural
                      systems as networks provides a powerful and widely-used
                      analysis approach. The network syntax of interacting nodes
                      exhibiting joint dynamics facilitates the quantitative
                      characterizations of neural systems across scales. Moreover,
                      this approach enables us to construct systematic comparisons
                      of neural network descriptions across domains. We aim to
                      identify characterizations of neural network activity that
                      reflect the underlying connectivity and relate to the
                      network's ability to process information. In this context,
                      we explore characteristic measures from experimental and
                      simulated data sources. Concretely, we look at cortical
                      activity data from mice and monkeys, from different
                      recording techniques like implanted electrode arrays,
                      laminar probes, ECoG, and calcium imaging, and further from
                      simulations of stochastic processes, spiking, and mean-field
                      network models. We investigate activity measures of
                      different complexity, including measures on the level of
                      individual neurons, higher-order measures of coordinated
                      spiking activity, and population-level field potential
                      measures describing spatial wave patterns. Such activity
                      characterizations always represent an abstraction, and the
                      right level of detail depends on the data type and the
                      question of interest. For a given context, the appropriate
                      abstraction level allows us to integrate and compare data
                      and models from heterogeneous sources. Evaluating the
                      similarity between such different network descriptions is a
                      common demand in computational neuroscience. Extending the
                      concept of validation, we formalize and apply cross-domain
                      comparisons in model vs. experiment, model vs. model, and
                      experiment vs. experiment scenarios. In this framework, we
                      further evaluate and extend existing statistical testing
                      approaches and look at reproducibility, sources of
                      variability, and technical limitations. Through our
                      exploration of network activity characterizations and their
                      comparability, we evaluate the relationship between network
                      connectivity, activity, and function. Concretely, over the
                      course of five research projects, we implement and
                      demonstrate systematic approaches to validate model
                      simulators, statistically evaluate network organization,
                      infer network connectivity from activity data, combine data
                      sources of wave activity, and relate wave activity to
                      external influences and behavior. With a focus on open and
                      collaborative science practices, we implement our
                      methodologies as reusable open-source tools while building
                      upon existing open-source tools and standards.},
      cin          = {163110 / 160000},
      ddc          = {570},
      cid          = {$I:(DE-82)163110_20180110$ / $I:(DE-82)160000_20140620$},
      pnm          = {HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / HAF - Helmholtz Analytics Framework
                      (ZT-I-0003) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(DE-HGF)ZT-I-0003 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2024-03073},
      url          = {https://publications.rwth-aachen.de/record/981487},
}