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@INPROCEEDINGS{Stella:885609,
      author       = {Stella, Alessandra and Bouss, Peter and Palm, Günther and
                      Grün, Sonja},
      title        = {{C}omparison of surrogate techniques for evaluation of
                      spatio-temporal patterns in case of regular data},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2020-03961},
      year         = {2020},
      abstract     = {To identify active cell assemblies we developed a method to
                      detect significant spatio-temporal spike patterns (STPs).
                      The method, called SPADE [1,2,3], identifies repeating
                      ms-precise spike patterns across neurons. SPADE first
                      discretizes the spike trains in exclusive bins (defining the
                      pattern precision, e.g. 5ms) and clips the bin content to 1
                      if more than 1 spike is therein. Second, STPs are mined by
                      Frequent Itemset Mining [4], and their counts are evaluated
                      for significance through comparison to surrogate data. The
                      distribution of the pattern counts in the surrogate data
                      provides p-values for determining the significance of
                      grouped patterns. The surrogate data implement the
                      null-hypothesis of independence, and a classical choice is
                      to apply uniform dithering (UD) [7], i.e. independent,
                      uniformly distributed displacement of each spike (e.g. in a
                      range of +/- 5 times the bin width [1]). This approach does
                      not maintain the absolute refractory period and a
                      potentially existing ISI regularity. The binarization leads
                      in the surrogates to a higher probability of more than 1
                      spike per bin, and thus by the consecutive clipping to a
                      reduction of the spike count (up to $12\%,$ in particular
                      for high firing rates) as compared to the original data.
                      This may cause false positives (cmp. [9]). Therefore, we
                      explored further methods for surrogate generation. To not
                      have different spike counts in the original and the
                      surrogate data, bin-shuffling shuffles the bins after
                      binning the original data. To keep the refractory period
                      (RP) uniform dithering with refractory period (UD-RP) does
                      not allow dithered spikes within a short time interval after
                      each spike. Dithering according to the ISI distribution
                      (ISI-D) [e.g. 7] or the Joint-ISI distribution (J-ISI-D) [5]
                      conserves the ISI and ISI/J-ISI distributions, respectively.
                      Spike-train shifting (ST-Shift) [6,7] moves the whole spike
                      train, trial by trial, by a random amount, thereby only
                      affecting the relation of spike trains to each other. Thus
                      all of these implement different null-hypotheses, as
                      summarized in the table below. It shows the
                      non-/preservation (no/yes) of features in the various
                      surrogates compared to the original data. We applied all
                      surrogate methods (within SPADE) and compared their results
                      using artificial, and experimental spike data simultaneously
                      recorded in pre-/motor cortex of a macaque monkey performing
                      a reach-to-grasp task [8]. We find that all methods besides
                      UD lead to very similar results in terms of number of
                      patterns and their composition. UD results in a much larger
                      number of patterns, in particular if neurons have very high
                      firing rates and exhibit regular spike trains. We conclude
                      that the reduction in the spike count using UD increases the
                      false positive rate for spike trains with CV<1 and/or high
                      firing rates, the other methods are much less affected, the
                      least spike train shifting. References:[1] Torre et al
                      (2016) DOI: 10.1523/jneurosci.4375-15.2016[2] Quaglio et al
                      (2017) DOI: 10.3389/fncom.2017.00041[3] Stella et al (2019)
                      DOI: 10.1016/j.biosystems.2019.104022[4] Picado-Muiño et al
                      (2013). DOI: 10.3389/fninf.2013.00009[5] Gerstein (2004)[6]
                      Pipa et al (2008) DOI: 10.1007/s10827-007-0065-3[7] Louis et
                      al (2010) DOI: $10.1007/978-1-4419-5675-0_17[8]$ Brochier et
                      al (2018) DOI: 10.1038/sdata.2018.55[9] Pipa et al
                      (2013)DOI: $10.1162/NECO_a_00432$},
      month         = {Jul},
      date          = {2020-07-18},
      organization  = {Conference of Computational
                       Neuroscience 2020, Online (Online), 18
                       Jul 2020 - 23 Jul 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},
      url          = {https://juser.fz-juelich.de/record/885609},
}