% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Ito:864173,
      author       = {Ito, Junji and Lucrezia, Emanuele and Palm, Günther and
                      Grün, Sonja},
      title        = {{D}etection and evaluation of bursts in terms of novelty
                      and surprise},
      journal      = {Mathematical biosciences and engineering},
      volume       = {16},
      number       = {6},
      issn         = {1547-1063},
      address      = {Springfield, Mo.},
      publisher    = {Inst.},
      reportid     = {FZJ-2019-04039},
      pages        = {6990-7008},
      year         = {2019},
      abstract     = {The detection of bursts and also of response onsets is
                      often of relevance in understanding neurophysiological data,
                      but the detection of these events is not a trivial task. We
                      build on a method that was originally designed for burst
                      detection using the so-called burst surprise as a measure.
                      We extend this method and provide a proper significance
                      measure. Our method consists of two stages. In the first
                      stage we model the neuron’s interspike interval (ISI)
                      distribution and make an i.i.d. assumption to formulate our
                      null hypothesis. In addition we define a set of
                      ’surprising’ events that signify deviations from the
                      null hypothesis in the direction of ’burstiness’. Here
                      the so-called (strict) burst novelty is used to measure the
                      size of this deviation. In the second stage we determine the
                      significance of this deviation. The (strict) burst surprise
                      is used to measure the significance, since it is the
                      negative logarithm of the significance probability. After
                      showing the consequences of a non-proper null hypothesis on
                      burst detection performance, we apply the method to
                      experimental data. For this application the data are divided
                      into a period for parameter estimation to express a proper
                      null hypothesis (model of the ISI distribution), and the
                      rest of the data is analyzed by using that null hypothesis.
                      We find that assuming a Poisson process for experimental
                      spike data from motor cortex is rarely a proper null
                      hypothesis, because these data tend to fire more regularly
                      and thus a gamma process is more appropriate. We show that
                      our burst detection method can be used for rate change onset
                      detection, because a deviation from the null hypothesis
                      detected by (strict) burst novelty also covers an increase
                      of firing rate.},
      month         = {Sep},
      date          = {2018-09-09},
      organization  = {Neural Coding 2018, Turin (Italy), 9
                       Sep 2018 - 14 Sep 2018},
      cin          = {INM-10 / INM-6 / IAS-6},
      ddc          = {510},
      cid          = {I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)INM-6-20090406 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {89571 - Connectivity and Activity (POF2-89571) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      DFG project 238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / GRK 2416 - GRK 2416:
                      MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240) / DFG
                      project 238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842)},
      pid          = {G:(DE-HGF)POF2-89571 / G:(EU-Grant)785907 /
                      G:(GEPRIS)238707842 / G:(GEPRIS)368482240 /
                      G:(GEPRIS)238707842},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000487331700043},
      pubmed       = {pmid:31698600},
      doi          = {10.3934/mbe.2019351},
      url          = {https://juser.fz-juelich.de/record/864173},
}