% 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{Stella:908746,
      author       = {Stella, Alessandra and Bouss, Peter and Palm, Günther and
                      Grün, Sonja},
      title        = {{C}omparing {S}urrogates to {E}valuate {P}recisely {T}imed
                      {H}igher-{O}rder {S}pike {C}orrelations},
      journal      = {eNeuro},
      volume       = {9},
      number       = {3},
      issn         = {2373-2822},
      address      = {Washington, DC},
      publisher    = {Soc.},
      reportid     = {FZJ-2022-02804},
      pages        = {ENEURO.0505-21.2022 -},
      year         = {2022},
      abstract     = {The generation of surrogate data, i.e., the modification of
                      data to destroy a certain feature, can be considered as the
                      implementation of a null-hypothesis whenever an analytical
                      approach is not feasible. Thus, surrogate data generation
                      has been extensively used to assess the significance of
                      spike correlations in parallel spike trains. In this
                      context, one of the main challenges is to properly construct
                      the desired null-hypothesis distribution and to avoid
                      altering the single spike train statistics. A classical
                      surrogate technique is uniform dithering (UD), which
                      displaces spikes locally and uniformly distributed, to
                      destroy temporal properties on a fine timescale while
                      keeping them on a coarser one. Here, we compare UD against
                      five similar surrogate techniques in the context of the
                      detection of significant spatiotemporal spike patterns. We
                      evaluate the surrogates for their performance, first on
                      spike trains based on point process models with constant
                      firing rate, and second on modeled nonstationary artificial
                      data to assess the potential detection of false positive
                      (FP) patterns in a more complex and realistic setting. We
                      determine which statistical features of the spike trains are
                      modified and to which extent. Moreover, we find that UD
                      fails as an appropriate surrogate because it leads to a loss
                      of spikes in the context of binning and clipping, and thus
                      to a large number of FP patterns. The other surrogates
                      achieve a better performance in detecting precisely timed
                      higher-order correlations. Based on these insights, we
                      analyze experimental data from the pre-/motor cortex of
                      macaque monkeys during a reaching-and-grasping task.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / GRK 2416: 
                      MultiSenses-MultiScales: Novel approaches to decipher neural
                      processing in multisensory integration (368482240) / HBP
                      SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / HAF - Helmholtz Analytics Framework
                      (ZT-I-0003) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) /
                      Open-Access-Publikationskosten Forschungszentrum Jülich
                      (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(GEPRIS)368482240 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      G:(DE-HGF)ZT-I-0003 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35584914},
      UT           = {WOS:000817093700001},
      doi          = {10.1523/ENEURO.0505-21.2022},
      url          = {https://juser.fz-juelich.de/record/908746},
}