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@PHDTHESIS{Kleinjohann:994159,
      author       = {Kleinjohann, Alexander},
      othercontributors = {Grün, Sonja Annemarie and Kampa, Björn M.},
      title        = {{M}odeling experimental data: required pre-processing and
                      workflows},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2024-09140},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2023},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University 2024; Dissertation, RWTH Aachen University, 2023},
      abstract     = {In this work, we formulate and apply a model to
                      experimental data. We validate the model, use it to explain
                      the experimental results, and calculate constraints for the
                      parameters of the model using the experimental data. The
                      formulation of an accurate model at a proper abstraction
                      level and its application to real experimental data requires
                      several important prerequisites; the experimental data has
                      to be pre-processed adequately, it has to be cleaned of
                      artefacts which could impact the results, and the provenance
                      of the pre-processing and the applied analysis methods as
                      well as the metadata of the recording has to be readily
                      available and searchable to be taken into account in the
                      formulation of the model. To this end, we first explore
                      artefacts in electrophysiological data which could impact
                      our results. We group them into multiple artefact types,
                      propose removal methods for all types, and evaluate the
                      removal methods to check their performance. We then build
                      upon a pre-processing workflow for electrophysiological data
                      which incorporates the artefact removal methods which have
                      proven to be successful in the previous step by improving,
                      expanding, and generalising the workflow. Data
                      pre-processing and artefact removal is a growing demand in
                      computational neuroscience as experiments become more and
                      more complex and more data of different modalities can be
                      recorded simultaneously. We propose a common framework for
                      data handling in electrophysiology, design a checklist for
                      designing a rigorous acquisition and pre-processing
                      workflow, and apply it to an example use-case. In the final
                      step, we formulate a spatial model of the embedding of
                      synfire chains in a cortical network and apply it to
                      spatio-temporal spike patterns detected in macaque primary
                      motor cortex during a delayed reach-to-grasp task. First, we
                      validate that synfire chain activity can be detected as
                      spatio-temporal spike patterns in this experimental setup
                      despite the massive sub-sampling of the underlying neuronal
                      populations. Building upon this, we fit the model to the
                      experimental results, show that synfire chains can explain
                      the observed spike pattern statistics, and calculate
                      constraints of the model parameters given the experimental
                      results: the groups of the synfire chains have to contain
                      many neurons and have to be broadly distributed in space.},
      cin          = {163110 / 160000},
      ddc          = {570},
      cid          = {$I:(DE-82)163110_20180110$ / $I:(DE-82)160000_20140620$},
      pnm          = {HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / HMC - Helmholz Metadata Collaboration
                      $(HMC_20200306)$},
      pid          = {G:(EU-Grant)945539 / $G:(DE-HGF)HMC_20200306$},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2024-09140},
      url          = {https://publications.rwth-aachen.de/record/994159},
}