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@INPROCEEDINGS{Bouss:894204,
      author       = {Bouss, Peter and Stella, Alessandra and Palm, Günther and
                      Grün, Sonja},
      title        = {{S}urrogate methods for spike pattern detection in
                      non-{P}oisson data},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2021-03095},
      year         = {2021},
      abstract     = {In order to detect significant spatio-temporal spike
                      patterns (STPs) at ms-precision, we developed the SPADE
                      method[1-3]. SPADE enables the detection and evaluation of
                      spatio-temporal patterns (STPs), i.e., spike patterns across
                      neurons and with temporal delays. For the significance
                      assessment of STPs, surrogates are generated to implement
                      the null hypothesis. Here we demonstrate the requirements
                      for appropriate surrogates.SPADE first discretizes the spike
                      trains into bins of a few ms width. The discretization also
                      includes clipping, i.e., if a bin is occupied by 1 or more
                      spikes, its content is set to 1. The binarized spike trains
                      are then mined for STPs with Frequent Itemset Mining,
                      counting identical patterns. For the assessment of these
                      patterns' significance, surrogata spike trains are used. The
                      surrogate data are mined as the original data resulting in a
                      p-value spectrum for the significance
                      evaluation[3].Surrogate data are modifications of the
                      original data where potential time-correlations are
                      destroyed and thus implement the null hypothesis of
                      independence. For that purpose, the surrogate data need to
                      keep the statistical features of the original data as
                      similar as possible to avoid false positives. A classical
                      choice for a surrogate is uniform dithering (UD), which
                      independently displaces each individual spike according to a
                      uniform distribution. We show that UD makes the spike trains
                      more Poisson-like and does not preserve a potential dead
                      time after the spikes. As a consequence, more spikes are
                      clipped away as compared to the original data. Thus, UD
                      surrogate data reduce the expectation for the patterns.To
                      overcome this problem, we evaluate different surrogate
                      techniques. The first is a modification of UD that preserves
                      the dead time. Further, we employ (joint-)ISI dithering,
                      preserving the (joint-)ISI distribution[4]. Another
                      surrogate is based on shuffling bins of already discretized
                      spike data within a small window. Lastly, we evaluate trial
                      shifting that shifts the whole spike trains against the
                      others, trial by trial, according to a uniform distribution.
                      To evaluate the effect of the different surrogate methods on
                      significance assessment, we first analyze the surrogate
                      modifications on different types of stochastic spike models,
                      such as Poisson spike trains, Gamma spike trains but also
                      Poisson spike trains with dead time[5]. We find that all
                      surrogates but UD are robust to clipping. Trial shifting is
                      the technique that preserves best the statistical features
                      of the spike trains. Further, we analyze artificial data
                      sets for the occurrence of false-positive patterns. These
                      data sets were generated with non-stationary firing rates
                      and interval statistics taken from an experimental data set
                      but are otherwise independent. We find many false positives
                      for UD but all other surrogates show a consistently low
                      number of false-positive patterns. Based on these results,
                      we conclude with a recommendation on which surrogate method
                      to use.References1. 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. Gerstein (2004).5.
                      Deger at al. (2011). DOI: 10.1007/s10827-011-0362-8.},
      month         = {Jun},
      date          = {2021-06-28},
      organization  = {30th Annual Computational Neuroscience
                       Meeting, Online (Online), 28 Jun 2021 -
                       7 Jul 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/894204},
}