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@INPROCEEDINGS{Berling:865759,
      author       = {Berling, David and Tetzlaff, Tom and Kleinjohann, Alexander
                      and Stella, Alessandra and Diesmann, Markus and Grün,
                      Sonja},
      title        = {{C}an {S}patio-{T}emporal {S}pike {P}atterns {F}ound in
                      {E}xperimental {D}ata be {E}xplained by the {S}ynfire
                      {C}hain {M}odel},
      school       = {RWTH Aachen},
      reportid     = {FZJ-2019-05075},
      year         = {2019},
      abstract     = {To investigate cortical network interactions during a
                      reach-to-grasp task [1], we analyzed spatio-temporal
                      patterns (STPs) in massively parallel spike data. Using the
                      SPADE analysis [2,3], we found significant STPs in about 100
                      simultaneously recorded single units. For each of the four
                      task types, we observe up to 50 patterns during the movement
                      period. The STPs differ in spatial and temporal arrangement
                      of spikes, and are composed of 2 to 6 units which belong to
                      the same set of maximal 10 units. Here, we investigate if
                      the characteristics of the found STPs can be explained by a
                      simple assembly network model, the synfire chain (SFC) model
                      [4].In the SFC model, neurons form groups connected in a
                      feedforward, highly convergent-divergent manner. Synchronous
                      stimulation of neurons in the first group results in volleys
                      of spikes reliably propagating through the chain [5]. Spike
                      recordings from a subset of cells in this model would reveal
                      recurring STPs similar to those observed in the data,
                      provided the same SFCs are repeatedly stimulated.We
                      investigate if the observed STP statistics is consistent
                      with a network model where SFCs are spatially distributed in
                      accordance with biologically realistic connection
                      probabilities [6,7]. In the context of this model, we
                      evaluate the probability of observing multiple neurons
                      involved in the same STP by means of a 10x10 Utah electrode
                      array spanning 4x4 mm2 of cortical space. We explore how
                      model parameters such as the neuron density, the distance
                      dependence of lateral connections between cortical neurons
                      and the spatial reach of extracellular electrodes constrain
                      the spatial arrangement of SFCs (see figure) and the number
                      of observable SFC neurons.In future work, we will equip the
                      current network model with a temporal dynamics [8], and
                      further, embed it into a balanced network [similar to 9] to
                      study the temporal characteristics of STPs.},
      month         = {Sep},
      date          = {2019-09-17},
      organization  = {Bernstein Conference 2019, Berlin
                       (Germany), 17 Sep 2019 - 20 Sep 2019},
      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) / 574 - Theory,
                      modelling and simulation (POF3-574) / GRK 2416 - GRK 2416:
                      MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240) / HAF -
                      Helmholtz Analytics Framework (ZT-I-0003) / HBP SGA2 - Human
                      Brain Project Specific Grant Agreement 2 (785907) / Advanced
                      Computing Architectures $(aca_20190115)$ / PhD no Grant -
                      Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(GEPRIS)368482240 / G:(DE-HGF)ZT-I-0003 /
                      G:(EU-Grant)785907 / $G:(DE-Juel1)aca_20190115$ /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/865759},
}