% 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{Sukiban:863851,
      author       = {Sukiban, Jeyathevy and Voges, Nicole and Dembek, Till A.
                      and Pauli, Robin and Visser-Vandewalle, Veerle and Denker,
                      Michael and Weber, Immo and Timmermann, Lars and Grün,
                      Sonja},
      title        = {{E}valuation of {S}pike {S}orting {A}lgorithms:
                      {A}pplication to {H}uman {S}ubthalamic {N}ucleus
                      {R}ecordings and {S}imulations},
      journal      = {Neuroscience},
      volume       = {414},
      issn         = {0306-4522},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-03822},
      pages        = {168-185},
      year         = {2019},
      abstract     = {An important prerequisite for the analysis of spike
                      synchrony in extracellular recordings is the extraction of
                      single-unit activity from the multi-unit signal. To identify
                      single units, potential spikes are separated with respect to
                      their potential neuronal origins (‘spike sorting’).
                      However, different sorting algorithms yield inconsistent
                      unit assignments, which seriously influences subsequent
                      spike train analyses. We aim to identify the best sorting
                      algorithm for subthalamic nucleus recordings of patients
                      with Parkinson's disease (experimental data ED). Therefore,
                      we apply various prevalent algorithms offered by the
                      ‘Plexon Offline Sorter’ and evaluate the sorting
                      results. Since this evaluation leaves us unsure about the
                      best algorithm, we apply all methods again to artificial
                      data (AD) with known ground truth. AD consists of pairs of
                      single units with different shape similarity embedded in the
                      background noise of the ED. The sorting evaluation depicts a
                      significant influence of the respective methods on the
                      single unit assignments. We find a high variability in the
                      sortings obtained by different algorithms that increases
                      with single units shape similarity. We also find significant
                      differences in the resulting firing characteristics. We
                      conclude that Valley-Seeking algorithms produce the most
                      accurate result if the exclusion of artifacts as unsorted
                      events is important. If the latter is less important
                      (‘clean’ data) the K-Means algorithm is a better option.
                      Our results strongly argue for the need of standardized
                      validation procedures based on ground truth data. The recipe
                      suggested here is simple enough to become a standard
                      procedure.},
      cin          = {INM-6 / INM-10 / IAS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {571 - Connectivity and Activity (POF3-571) / KFO 219
                      Schwerpunktprogramm - KFO 219:
                      Basalganglien-Kortex-Schleifen: Mechanismen pathologischer
                      Interaktionen und ihrer therapeutischen Modulation
                      $(G:DFG-KFO219Schwerpunkt_1510415)$ / DFG project 147522227
                      - Charakterisierung der effektiven Konnektivität
                      motorischer Basalganglien-Kortex-Schleifen durch loklale
                      Feldpotentiale im Nucelus Subthalamicus und EEG-Ableitungen
                      bei Morbus Parkinson (147522227) / DFG project 233510988 -
                      Mathematische Modellierung der Entstehung und Suppression
                      pathologischer Aktivitätszustände in den
                      Basalganglien-Kortex-Schleifen (233510988) / DFG project
                      238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / DFG project
                      238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / DFG project
                      238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA1 - Human Brain
                      Project Specific Grant Agreement 1 (720270)},
      pid          = {G:(DE-HGF)POF3-571 / $G:(DFG)KFO219Schwerpunkt_1510415$ /
                      G:(GEPRIS)147522227 / G:(GEPRIS)233510988 /
                      G:(GEPRIS)238707842 / G:(GEPRIS)238707842 /
                      G:(GEPRIS)238707842 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)785907 / G:(EU-Grant)720270},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31299347},
      UT           = {WOS:000478570500013},
      doi          = {10.1016/j.neuroscience.2019.07.005},
      url          = {https://juser.fz-juelich.de/record/863851},
}