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@INPROCEEDINGS{Kleinjohann:886075,
      author       = {Kleinjohann, Alexander and Sprenger, Julia and Essink,
                      Simon and Rotter, Stefan and Grün, Sonja},
      title        = {{M}odeling {T}emporally {P}recise {S}pike {A}rtefacts to
                      {S}tudy {T}heir {I}mpact on {S}pike {C}orrelation
                      {A}nalyses},
      reportid     = {FZJ-2020-04261},
      year         = {2020},
      abstract     = {Due to technical advances, the number of neurons recorded
                      in parallel increases drastically. This development reveals
                      new types of artefacts: Common noise and cross-talk are
                      observed in the raw parallel recording signals [1-3], as
                      well as hyper-synchronous spike events at sampling rate
                      precision in sorted spike data (‚synchrofacts‘; 3-5).
                      These likely originate from environmental electromagnetic
                      signals that couple into the recording signals. Here we
                      concentrate on synchrofacts and their effects on results of
                      spike data analyses, such as cross-correlation and
                      higher-order synchronous or spatio-temporal spike
                      patterns.In experimental data sets, e.g. recorded in macaque
                      M1/PM1 with 100-electrode Utah arrays and manually spike
                      sorted, we noticed synchrofacts in population histograms at
                      a bin size matching the sampling rate (30 kHz). The
                      complexity distribution [6], i.e. the histogram of
                      synchronous events of a certain size (number of spikes
                      across neurons) and their counts, shows entries up to 60,
                      far larger than predicted by independent data of the same
                      rate. Not all channels participate equally, but this is not
                      related to the spatial electrode distribution.To
                      systematically study the effects of the synchrofacts on
                      analysis results, we formulate a stochastic data generation
                      model in which we have control over synchrofacts,
                      ‚neuronal‘ correlations and firing rates. We model
                      background activity as independent Poisson processes and
                      inject ‚neuronal‘ correlations and synchrofacts each
                      formulated by separate Compound Poisson Processes (CPPs,
                      7-8). A CPP generates synchronous events with event sizes
                      given by its amplitude distribution and inserts these spikes
                      randomly into the neuronal spike trains. To model the
                      observed synchrofacts we adapt the spike assignment with a
                      non-uniform assignment distribution.In the next step, we
                      apply various analysis methods to the artificial data to
                      determine how synchrofacts affect the analysis results.
                      Questions we are going to address are: a) does the presence
                      of synchrofacts decrease the detectability of neuronal
                      correlation activity, b) which type of correlation activity
                      (pairwise or higher-order) is more diluted, and c) which
                      synchrofact parameters (rate, correlation order,
                      distribution over neurons, distribution over time) are
                      mostly affecting the results. In order to do this we compare
                      the analysis results from data with and without
                      synchrofacts. This allows us to propose suitable methods of
                      dealing with them.ReferencesMusial P, Baker S, Gerstein G,
                      King E, Keating J (2002) Signal-to-noise ratio improvement
                      in multiple electrode recording, Journal of Neuroscience
                      Methods, Volume 115, Issue 1, Pages 29-43, ISSN 0165-0270,
                      10.1016/S0165-0270(01)00516-7Dann B, Michaels JA,
                      Schaffelhofer S, Scherberger H (2016) Uniting functional
                      network topology and oscillations in the fronto-parietal
                      single unit network of behaving primates, eLife
                      2016;5:e15719, 10.7554/eLife.15719Essink S, Kleinjohann A,
                      Barthélemy F, Ito J, Riehle A, Brochier T, Grün S (2019)
                      Detection and Removal of Artefacts in Multi-Channel
                      Electrophysiology Recordings, Bernstein Conference 2019,
                      10.12751/nncn.bc2019.0068Sprenger J (2014) Spatial
                      Dependence of the Spike-Related Component of the Local Field
                      Potential in Motor Cortex (Master’s thesis, RWTH
                      Aachen).Torre E, Quaglio P, Denker M, Brochier T, Riehle A,
                      Grün S (2016) Synchronous Spike Patterns in Macaque Motor
                      Cortex during an Instructed-Delay Reach-to-Grasp Task, J.
                      Neurosci. 36(32):8329-8340,
                      10.1523/JNEUROSCI.4375-15.2016Grün S, Abeles M, Diesmann M
                      (2008) Impact of Higher-Order Correlations on Coincidence
                      Distributions of Massively Parallel Data, in: Marinaro M,
                      Scarpetta S, Yamaguchi Y (eds) Dynamic Brain - from Neural
                      Spikes to Behaviors, NN 2007, Lecture Notes in Computer
                      Science, vol 5286. Springer, Berlin,
                      $10.1007/978-3-540-88853-6_8Kuhn$ A, Aertsen A, Rotter S
                      (2003) Higher-Order Statistics of Input Ensembles and the
                      Response of Simple Model Neurons, Neural Computation 15:1,
                      67-101, 10.1162/089976603321043702Staude B, Grün S, Rotter
                      S (2010) Higher-Order Correlations and Cumulants, in: Grün
                      S, Rotter S (eds) Analysis of Parallel Spike Trains,
                      Springer Series in Computational Neuroscience, vol 7,
                      Springer, Boston, MA, $10.1007/978-1-4419-5675-0_12$},
      month         = {Sep},
      date          = {2020-09-29},
      organization  = {Bernstein Conference 2020, Berlin
                       (Germany), 29 Sep 2020 - 1 Oct 2020},
      subtyp        = {Other},
      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          = {571 - Connectivity and Activity (POF3-571) / 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) / DFG
                      project 322093511 - Kognitive Leistung als Ergebnis
                      koordinierter neuronaler Aktivität in unreifen
                      präfrontal-hippokampalen Netzwerken (322093511) / DFG
                      project 238707842 - Kausative Mechanismen mesoskopischer
                      Aktivitätsmuster in der auditorischen
                      Kategorien-Diskrimination (238707842) / DFG project
                      237833830 - Optogenetische Analyse der für kognitive
                      Fähigkeiten zuständigen präfrontal-hippokampalen
                      Netzwerke in der Entwicklung (237833830) / HBP SGA2 - Human
                      Brain Project Specific Grant Agreement 2 (785907) / PhD no
                      Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(GEPRIS)238707842 / G:(GEPRIS)322093511 /
                      G:(GEPRIS)238707842 / G:(GEPRIS)237833830 /
                      G:(EU-Grant)785907 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.12751/NNCN.BC2020.0095},
      url          = {https://juser.fz-juelich.de/record/886075},
}