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@MASTERSTHESIS{Canova:185738,
      author       = {Canova, Carlos},
      title        = {{S}tatistical {A}ssessment and {N}euronal {C}omposition of
                      {A}ctive {S}ynfire {C}hains},
      school       = {Universität Tübingen},
      type         = {Dipl.},
      reportid     = {FZJ-2014-07163},
      pages        = {75},
      year         = {2014},
      note         = {bitte dem korrekten Teilprojekt von : SPP 1665:
                      Aufschlüsselung und Manipulation neuronaler Netzwerke im
                      Gehirn von Säugetieren: Von korrelativen zur kausalen
                      Analyse zuordnen; Universität Tübingen, Diplomarbeit,
                      2014},
      abstract     = {The synfire chain (SFC) model has been suggested (Abeles,
                      1982, 1991) as a network model for cortical cell assemblies
                      (Hebb, 1949). It is composed of consecutive groups of
                      neurons, where each group is connected to the next in a
                      feedforward fashion by a large number of convergent and
                      divergent inputs. This connectivity structure enables stable
                      propagation of packets of synchronous spiking activity
                      through the network after stimulation of the first group
                      (Diesmann et al., 1999a). Recent advances in
                      electrophysiological recording techniques enable to record
                      one hundred or more individual neurons simultaneously,
                      thereby increasing the chance to detect active cell
                      assemblies. Schrader et al. (2008) and Gerstein et al.
                      (2012) suggested a method based on an intersection matrix to
                      detect active SFCs. Each entry in the matrix contains the
                      degree of overlap of identical neurons being active at two
                      different time bins. If a particular SFC isa ctivated twice,
                      a diagonal structure, composed of consecutive time bins of
                      high intersection values, appears in the matrix. Further
                      convolution of the matrix with a diagonal linear filter
                      enhances diagonal structures as a whole compared to isolated
                      high intersection values due to chance overlaps, allowing to
                      distinguish among the two. Several features of the data are
                      expected to interfere with the analysis. First, the contrast
                      between diagonal structures and the rest of the matrix can
                      be severely diminished by high firing rates of the neurons,
                      thereby increasing the intersection values of individual
                      background pixels. Second, undersampling of the system
                      and/or stochastic participation in assembly activation may
                      lead to diagonal structures that fluctuate in intensity or
                      even become discontinuous. Third, diagonal structures may
                      become wiggly due to interference of the analysis bin width
                      and the propagation speed of the SFC. This thesis introduces
                      a quantitative statistical analysis method for the presence
                      of SFCsbuilt on the original approach which features an
                      automatic identification of the neurons participating in the
                      SFC. In the first stage, six steps are performed in addition
                      to the construction of the original intersection matrix
                      (step 1 in Figure 6.1). Diagonal structures are enhanced by
                      convolving the intersection matrix with a block filter,
                      which enables to cope with wiggly structures (step 2). Then,
                      a statistical test is performed to assess if there are
                      significant sequences of high intersection values (step 3)
                      by generating multiple realizations of surrogate matrices.
                      In these surrogates, the positions of the original matrix
                      entries are randomized before filtering, thereby
                      implementing the null hypothesis of repeated synchronous
                      activations of specific neuronal groups but not in a
                      consecutive manner in time. The entries of all surrogate
                      matrices form thedistribution of matrix entries under the
                      stated null hypothesis. By choosing an upper quantile (e.g.
                      $0.1\%),$ a threshold is defined to identify statistically
                      significant entries (step 3). The resulting matrix is
                      converted into a mask of regions of interest (step 4), which
                      is then applied to the original intersection matrix (step
                      5). A second threshold is calculated from a further set of
                      surrogate matrices which are generated by dithering the
                      spike times, in order to implement a null hypotheses of no
                      spike synchrony in the data. The masked intersection matrix
                      is thresholded with this new significance level, yielding
                      the final matrix in step 6. Ideally, this final matrix
                      contains only significant entries which are the signature of
                      repeated SFC activity. The second stage of the method
                      characterizes the significant entries from the final matrix.
                      Using a clustering algorithm on those entries, the diagonal
                      structures are labeled as repetitions of an active synfire
                      chains (step 7). The IDs of the neurons participating in the
                      chain(s) are then extracted to identify the neurons
                      composing the SFC and their group membership (step 8). The
                      method was calibrated using stochastic simulations
                      consisting of repeating consecutive synchronous spike
                      patterns embedded in otherwise independent data. Performance
                      results from the calibrations show that in realistic
                      parameter regimes, e.g. with realistic spike rates and
                      downsampled networks, the new analysis method is able to
                      successfully identify large portions (> $90\%)$ of the
                      embedded SFCs while having low false positive and false
                      negative levels. Possible applications to
                      electrophysiological data were demonstrated, identifying
                      where the method has to be improved for practical use.},
      keywords     = {Unveröffentlichte Hochschulschrift (GND)},
      cin          = {INM-6 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828},
      pnm          = {331 - Signalling Pathways and Mechanisms in the Nervous
                      System (POF2-331) / 89571 - Connectivity and Activity
                      (POF2-89571) / HBP - The Human Brain Project (604102) / SMHB
                      - Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017)},
      pid          = {G:(DE-HGF)POF2-331 / G:(DE-HGF)POF2-89571 /
                      G:(EU-Grant)604102 / G:(DE-Juel1)HGF-SMHB-2013-2017},
      typ          = {PUB:(DE-HGF)10},
      url          = {https://juser.fz-juelich.de/record/185738},
}