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@PHDTHESIS{Stella:853123,
      author       = {Stella, Alessandra},
      othercontributors = {Grün, Sonja Annemarie and Kampa, Björn M.},
      title        = {{H}igher-order correlation analysis in massively parallel
                      recordings in behaving monkey},
      volume       = {81},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH, Zentralbibliothek, Verlag},
      reportid     = {RWTH-2022-08615},
      isbn         = {978-3-95806-640-3},
      series       = {Schriften des Forschungszentrums Jülich. Reihe Information
                      = Information},
      pages        = {1 Online-Ressource : Illustrationen, Diagramme},
      year         = {2022},
      note         = {Druckausgabe: 2022. - Onlineausgabe: 2022. - Auch
                      veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2022},
      abstract     = {It has been hypothesized that information processing in the
                      cortical network evolves through the subsequent activation
                      of groups of neurons called cell assemblies, and correlated
                      activity is thought to be the signature of their
                      activation.Numerous studies have assessed the presence of
                      precisely-timed spatio-temporal spike patterns (STPs),
                      defined here as sequences of spikes emitted by a set of
                      neurons with fixed time delays between the spikes, repeating
                      in the same configuration in all occurrences. SPADE
                      (Spatio-temporal PAttern Detection and Evaluation) was
                      introduced asan analysis method for the detection of
                      synchronous patterns, and then extended for the detection of
                      spike patterns with temporal delays in parallel spike
                      trains. However, the method was evaluated for the STP
                      detection on simple artificial data, and not yet applied on
                      experimental spike trains.In this thesis we introduce an
                      extension of the original statistical test of SPADE,
                      accounting for the temporal duration of the patterns, the
                      order of correlation and the frequency of pattern
                      occurrence. In this way, we assess that statistical
                      performances are strongly improved. Additionally, we propose
                      an optimized implementation of the mining algorithm of
                      SPADE. We test the implementation on a wide range of
                      different hardware and on real experimental data, showing
                      that it results to be between one and two orders of
                      magnitude faster and more memory efficient.We also propose
                      five artificial data sets, reproducing with increasing
                      degree the statistical complexity of experimental data,
                      still being completely artificial and generated by point
                      process models. Such data sets may be employed as ground
                      truth for analysis methods of parallel spike trains.
                      Furthermore, we compare different surrogate techniques to
                      evaluate their effect on parallel spike trains statistics
                      and on the evaluation of STP significance. Our results show
                      that the most classical method of uniform dithering fails as
                      an appropriate surrogate, since it leads to underestimation
                      of significance. Thus, we propose an alternative method with
                      better performance.Finally, we analyze with SPADE
                      experimental data from the neural activity recorded from the
                      motor cortex of two macaque monkeys, trained to execute a
                      reaching-and-grasping task. We find that significant STPs
                      occur in all phases of the behavior, and are highly specific
                      to the behavioral context, suggesting that different cell
                      assemblies are active in the context of different behaviors.
                      Moreover, our analysis reveals neurons that are involved in
                      several patterns in different behavioral contexts, and are
                      not clustered in space.},
      cin          = {163110 / 160000},
      ddc          = {570},
      cid          = {$I:(DE-82)163110_20180110$ / $I:(DE-82)160000_20140620$},
      pnm          = {GRK 2416: MultiSenses-MultiScales: Novel approaches to
                      decipher neural processing in multisensory integration
                      (368482240) / Open-Access-Publikationskosten
                      Forschungszentrum Jülich (OAPKFZJ) (491111487) / 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:(GEPRIS)368482240 / G:(GEPRIS)491111487 /
                      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-2022-08615},
      url          = {https://publications.rwth-aachen.de/record/853123},
}