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@INPROCEEDINGS{Stella:885610,
      author       = {Stella, Alessandra and Bouss, Peter and Palm, Günther and
                      Grün, Sonja},
      title        = {{R}eoccurring spatio-temporal spike patterns in parallel
                      spike trains},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2020-03962},
      year         = {2020},
      abstract     = {We designed the statistical method SPADE [1,2,3] to detect
                      ms-precise significant spatio-temporal spike patterns (STPs)
                      across neurons in electrophysiological data. SPADE first
                      discretizes the spike trains by binning and clipping into
                      0-1 sequences. These parallel sequences are then mined for
                      candidate patterns through Frequent Itemset Mining [4]. The
                      patterns found and their counts are evaluated for
                      significance using surrogates.A classical choice for
                      surrogate generation is uniform dithering (UD) [5], i.e.,
                      uniform independent displacement of each spike around its
                      original position. In this work, however, we evidence that
                      UD surrogate spike trains do not conserve the ISI
                      distribution and the regularity of the original data.
                      Moreover, we observe that the combination of spike train
                      binarization and UD causes a reduction of the surrogates’
                      spike count, yielding an overestimation of the significance
                      of mined patterns (leading to false positives). Thus, our
                      objective is to investigate alternatives to UD for the
                      significance test of spatio-temporal patterns in spike train
                      data.Therefore, we present five (novel as well as
                      established) surrogate techniques and contrast them to UD:
                      uniform dithering with refractoriness, ISI-dithering,
                      joint-ISI dithering [6,8], spike train shifting [7,8] and
                      bin shuffling. We compare these surrogate techniques in
                      terms of the different statistical features of the original
                      spike trains that they preserve, and we test them on
                      randomly generated data reflecting the statistics of our
                      experimental data.We see that only the UD leads to a large
                      number of false positive patterns. Moreover, when analyzing
                      experimental data from monkey pre-/motor cortex we detect
                      many behavior-related patterns and find that all methods
                      besides UD lead to very similar results in terms of the
                      found patterns and their compositions, underlining the
                      robustness of the extracted patterns.AcknowledgementsThe
                      project is funded by the Helmholtz Association Initiative
                      and Networking Fund (ZT-I-0003), by Human Brain Project HBP
                      Grant No. 785907 (SGA2 and SGA3), and by RTG2416
                      MultiSenses-MultiScales (DFG).References Torre et al.
                      (2016), 10.1523/jneurosci.4375-15.2016 Quaglio et al.
                      (2017), 10.3389/fncom.2017.00041 Stella et al. (2019),
                      10.1016/j.biosystems.2019.104022 Picado-Muiño et al.
                      (2013), 10.3389/fninf.2013.00009 Date et al. (1998) Gerstein
                      (2004) Pipa et al. (2008), 10.1007/s10827-007-0065-3 Louis
                      et al. (2010), $10.1007/978-1-4419-5675-0_17$ Brochier et
                      al. (2018), 10.1038/sdata.2018.55},
      month         = {Sep},
      date          = {2020-09-29},
      organization  = {Bernstein Conference 2020, Online
                       (Online), 29 Sep 2020 - 2 Oct 2020},
      subtyp        = {After Call},
      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) / HAF -
                      Helmholtz Analytics Framework (ZT-I-0003) / HBP SGA2 - Human
                      Brain Project Specific Grant Agreement 2 (785907) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539) /
                      GRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze
                      zur Aufklärung neuronaler multisensorischer Integration
                      (368482240) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)ZT-I-0003 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(GEPRIS)368482240 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.12751/NNCN.BC2020.0231},
      url          = {https://juser.fz-juelich.de/record/885610},
}