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@INPROCEEDINGS{Bouss:902742,
      author       = {Bouss, Peter and Stella, Alessandra and Palm, Günther and
                      Grün, Sonja},
      title        = {{S}urrogate methods for robust significance evaluation of
                      spike patterns in non-{P}oisson data},
      reportid     = {FZJ-2021-04524},
      year         = {2021},
      abstract     = {Spatio-temporal spike patterns were suggested as
                      indications of active cell assemblies. We developed the
                      SPADE method [1-3] to detect significant spatio-temporal
                      patterns (STPs) with millisecond accuracy. STPs are defined
                      as repeating spike patterns across neurons with potential
                      temporal delays between the spikes. The significance of STPs
                      is derived by comparison to the null hypothesis of
                      independence implemented by surrogate data. SPADE first
                      discretizes the spike trains into bins of a few ms and clips
                      bins with more than 1 spike to 1. The binarized spike trains
                      are examined for STPs by counting repeated patterns using
                      frequent itemset mining. The significance of STPs is
                      evaluated by comparison to pattern counts derived from
                      surrogate data, i.e., modifications of the original data
                      intended to destroy potential spike correlation but under
                      conservation of the firing rate profiles. To avoid false
                      results, surrogate data are required to retain the
                      statistical properties of the original data as close as
                      possible. A classically chosen surrogate technique is
                      Uniform Dithering (UD), which displaces each spike
                      independently according to a uniform distribution. We find
                      that UD surrogates applied to our data (motor cortex)
                      contain fewer spikes than the original data. As a
                      consequence, fewer patterns are expected and, thus, false
                      positives may be generated. We identified as the reason for
                      this spike reduction a change of the ISI distribution: UD
                      surrogates are more Poisson-like than the original data
                      which are in tendency more regular. Thus UD destroys a
                      potential dead time and, therefore, spikes are clipped
                      away.To overcome this problem, we studied several surrogate
                      techniques, in particular methods that consider the ISI
                      distribution, i.e., a modification of UD preserving the dead
                      time, (UDD), (joint-)ISI dithering, trial shifting [4].
                      Another ansatz is a surrogate that shuffles bins of already
                      discretized spike trains within a small window. We examined
                      the surrogates for spike loss, ISI distribution,
                      auto-correlation, and false positives when applied to
                      different ground truth data sets. These are stationary point
                      process models but also non-stationary point processes
                      mimicking the statistical features of the experimental data.
                      It turned out that trial-shuffling [4] best preserves the
                      features of the original data and generates few false
                      positives; we used it then for application to real
                      data.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] Pipa et
                      al. (2008) DOI: 10.1007/s10827-007-0065-3.},
      month         = {Nov},
      date          = {2021-11-08},
      organization  = {Neuroscience 2021 - 50th Annual
                       Meeting, Online (USA), 8 Nov 2021 - 11
                       Nov 2021},
      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          = {5232 - Computational Principles (POF4-523) / 5231 -
                      Neuroscientific Foundations (POF4-523) / 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)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF4-5231 /
                      G:(DE-HGF)ZT-I-0003 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(GEPRIS)368482240},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/902742},
}