% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Gutzen:1024075,
      author       = {Gutzen, Robin},
      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     = {FZJ-2024-01955},
      isbn         = {978-3-95806-738-7},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {xii, 252},
      year         = {2024},
      note         = {Dissertation, RWTH Aachen University, 2023},
      abstract     = {Our goal is to better understand the working mechanisms of
                      biologicalneural systems. To this end, describing neural
                      systems as networksprovides a powerful and widely-used
                      analysis approach. The networksyntax of interacting nodes
                      exhibiting joint dynamics facilitates thequantitative
                      characterizations of neural systems across scales.
                      More-over, this approach enables us to construct systematic
                      comparisons ofneural network descriptions across domains.We
                      aim to identify characterizations of neural network activity
                      thatreflect the underlying connectivity and relate to the
                      network’s abil-ity to process information. In this
                      context, we explore characteristicmeasures from experimental
                      and simulated data sources. Concretely,we look at cortical
                      activity data from mice and monkeys, from dif-ferent
                      recording techniques like implanted electrode arrays,
                      laminarprobes, ECoG, and calcium imaging, and further from
                      simulationsof stochastic processes, spiking, and mean-field
                      network models. Weinvestigate activity measures of different
                      complexity, including mea-sures on the level of individual
                      neurons, higher-order measures ofcoordinated spiking
                      activity, and population-level field potential mea-sures
                      describing spatial wave patterns. Such activity
                      characterizationsalways represent an abstraction, and the
                      right level of detail dependson the data type and the
                      question of interest. For a given context, theappropriate
                      abstraction level allows us to integrate and compare dataand
                      models from heterogeneous sources.Evaluating the similarity
                      between such different network descrip-tions is a common
                      demand in computational neuroscience. Extendingthe concept
                      of validation, we formalize and apply cross-domain
                      com-parisons in model vs. experiment, model vs. model, and
                      experiment vs.experiment scenarios. In this framework, we
                      further evaluate and ex-tend existing statistical testing
                      approaches and look at reproducibility,sources of
                      variability, and technical limitations.Through our
                      exploration of network activity characterizations andtheir
                      comparability, we evaluate the relationship between
                      networkconnectivity, activity, and function. Concretely,
                      over the course offive research projects, we implement and
                      demonstrate systematic ap-proaches to validate model
                      simulators, statistically evaluate networkorganization,
                      infer network connectivity from activity data, combinedata
                      sources of wave activity, and relate wave activity to
                      external in-fluences and behavior. With a focus on open and
                      collaborative sciencepractices, we implement our
                      methodologies as reusable open-sourcetools while building
                      upon existing open-source tools and standards.},
      cin          = {IAS-6 / INM-10 / INM-6},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)INM-6-20090406},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / 5232 - Computational Principles
                      (POF4-523) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(DE-HGF)POF4-5232 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-20240516102957373-1285584-9},
      doi          = {10.34734/FZJ-2024-01955},
      url          = {https://juser.fz-juelich.de/record/1024075},
}