% 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”.

@INPROCEEDINGS{Yegenoglu:811244,
      author       = {Yegenoglu, Alper and Quaglio, Pietro and Grün, Sonja and
                      Torre, Emiliano and Endres, Dominik},
      title        = {{S}patio {T}emporal {S}pike {P}attern {D}etection in
                      {M}assively {P}arallel {S}pike {T}rains using {F}ormal
                      {C}oncept {A}nalysis},
      reportid     = {FZJ-2016-03745},
      year         = {2016},
      abstract     = {Cortical neurons form a highly interwoven network. The
                      observations that i) spike time coordination atmillisecond
                      precision shapes synaptic efficacy [1], ii) neurons emit a
                      spike more reliably upon synchronousthan asynchronous input
                      [2], and iii) synchronous input may result from pre-synaptic
                      spikes emitted at dif-ferent times but arriving
                      simultaneously at the post-synaptic site, suggest that
                      millisecond-precise temporalsequences of spikes emitted from
                      several neurons at successive times may play a role in
                      cortical process-ing. Today modern electrophysiological
                      techniques enable to record hundreds of neurons
                      simultaneouslyincreasing the chances to observe neurons
                      involved in temporal spike sequences, or “spatio-temporal
                      pat-terns” (STPs). Here we propose a method to investigate
                      the presence of STPs in such massively parallelspike train
                      (MPST) data.The analysis of MPST data faces computational
                      and statistical challenges due to the immense numberof
                      possible patterns to evaluate. Existing methods to analyze
                      correlations in MPST data [3-5] typicallyfocus on spike
                      synchrony, a special type of STP. Our suggested method
                      analyzes and extracts STPs ina more general sense by using
                      Formal Concept Analysis. A temporal window is slid along the
                      data andSTPs within each window are extracted. Patterns
                      involved in as many windows and neurons as
                      possible(“formal concepts” [6], also called closed item
                      sets in the data mining community [7]) are identified.
                      MPSTdata typically contain a large number of chance
                      patterns, simply due to the background activity of
                      manyneurons. To further identify non-chance STPs, we apply
                      either a stability analysis [8] of the patterns, or
                      ananalysis of their statistical significance analogous to
                      that proposed in [5].We test our method on ground truth MPST
                      data generated by stochastic simulations. A variety
                      ofparameters affects the performance of the method (in terms
                      of false positive and false negative detections),such as the
                      number of STP occurrences, the number of neurons involved in
                      each pattern and variousfeatures of the background activity
                      which are typical of real data, like firing rate variability
                      over time andacross neurons, and inter-spike interval
                      regularity. We demonstrate the robustness of our method to
                      theseparameters by simulating a number of scenarios which
                      replicate such features. Our results show that themethod is
                      suited for the analysis of STPs in massively parallel spike
                      trains thereby offering the possibilityto relate such
                      patterns to behavior and show their computational
                      relevance.},
      month         = {Jun},
      date          = {2016-06-07},
      organization  = {INM Retreat 2016, Juelich (Germany), 7
                       Jun 2016 - 8 Jun 2016},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / JARA-BRAIN},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      $I:(DE-82)080010_20140620$},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP - The
                      Human Brain Project (604102) / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017) / DFG
                      project 238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)604102 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(GEPRIS)238707842},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/811244},
}