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@PHDTHESIS{Quaglio:877615,
      author       = {Quaglio, Pietro},
      title        = {{D}etection and {S}tatistical {E}valuation of {S}pike
                      {P}atterns in {P}arallel {E}lectrophysiological
                      {R}ecordings},
      volume       = {217},
      school       = {RWTH Aachen},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2020-02330},
      isbn         = {978-3-95806-468-3},
      series       = {Schriften des Forschungszentrums Jülich. Reihe
                      Schlüsseltechnologien / Key Technologies},
      pages        = {128 S.},
      year         = {2020},
      note         = {RWTH Aachen, Diss., 2020},
      abstract     = {The computational processes deployed by the brain to
                      represent, process and transmit information are largely
                      unknown. Cell assemblies (highly inter-connected groups of
                      neurons) have been hypothesized to be the building block of
                      the computational processes in the cerebral network. The
                      coordination of spikes emission among neuronsat millisecond
                      temporal scale is one of the possible mechanisms of
                      information coding and a signature of assembly activation.
                      In particular,specific temporally precise spike sequences in
                      the input can reliably cause a spike emission in a
                      post-synaptic neuron. Evidences of coordination of the
                      spiking activity at milliseconds precision have been
                      collected in the past, yet such studies present two
                      mainlimitations: in most cases they consider few neurons
                      recorded in parallel and the correlation analysis are
                      limited to spike synchronicity. Recent developments of the
                      recording devices overcome the first limitation. Modern
                      electrophysiological technologies enable to obtain the
                      spiking activity of hundreds of neurons in parallel, a
                      number which is destined to grow. The size of the current
                      available data requires optimized computational analysis
                      technique and sophisticated statistical approaches. In this
                      work we address the second limitation, developing a method
                      to detect spatio temporal patterns of spikes in large
                      parallel recordings. In particular we extend the Spike
                      Pattern Detection and Evaluation(SPADE) method, originally
                      limited to synchronous patterns detection,to search for any
                      repeated sequence of spikes. SPADE can be summarized in two
                      steps: a) extraction of all the repeated spike sequences
                      using the frequent item-set mining framework, b) statistical
                      evaluation of the significance of the mined sequences in
                      respect to the null hypothesis of independent spike
                      emissions in time. We extensively refined and validated the
                      method using ground-truth artificial data designed to
                      resemble experimental data to test the statistical
                      performances of the method. We then made the python
                      implementation of SPADE publicly available online as a
                      submodule of the Electorphysiological Analysis Toolkit
                      (Elephant). We applied SPADE to in-vivo parallel recordings
                      of neuronal activity in the motor area of two macaque
                      monkeys performing a reach-to-grasp task, finding a large
                      number of significant spike patterns. We then investigated
                      the statistical features of the detected patterns in terms
                      of neuronal composition, temporal occurrences and relation
                      to behavior. Most of the patterns occur during the reach
                      movement of the task and the yare formed by two to four
                      different neurons. Furthermore the neurons forming the
                      patterns differ for different grip types, hinting to a high
                      specificity of the patterns to the different behavioral
                      contexts. In the last part of this work we compare SPADE to
                      other existing methods in the context of a more general
                      review of methods for the analysis of correlations in
                      parallel spiketrains. In particular we argue for the
                      importance of a thorough comparison of the different methods
                      and for the integration different methodologies that
                      highlight different aspects of the correlation structure of
                      the data. In summary we show that SPADE robustly detects and
                      selects significant precise spike sequences and that
                      multiple significant patterns repeat during the execution of
                      a reach to grasp task. Nevertheless the spatio-temporal
                      patterns alone do not guarantee a complete description of
                      the correlation structure of the data, hence we present and
                      compare alternative correlation analysis methods for
                      parallel spike trains.},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2020072240},
      url          = {https://juser.fz-juelich.de/record/877615},
}