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@INPROCEEDINGS{Ito:1015175,
      author       = {Ito, Junji and Grün, Sonja},
      title        = {{A} method for detecting spatio-temporal patterns of
                      neuronal spikes based on principal component analysis},
      reportid     = {FZJ-2023-03577},
      year         = {2023},
      abstract     = {Previous studies have suggested functional implications of
                      spatio-temporal patterns (STPs) of neuronal spikes [1-3]
                      with millisecond precision. Existing methods for STP
                      detection are based on counting the occurrences of each STP
                      found in the given data, and hence suffer from the
                      combinatorial explosion of the number of STPs to be
                      considered, which can lead to massively multiple testing
                      that deteriorates the detection power. Here we propose an
                      alternative approach to STP detection, which is based on a
                      principal component analysis (PCA) of spike train data and
                      can circumvent the above-mentioned difficulties. Required
                      parameters of the method are the width $\tau$ of the
                      analysis time window (i.e., the maximum possible duration of
                      STPs to be detected) and the allowed temporal imprecision
                      $\sigma$ of the spike times within an STP. We first convolve
                      parallel spike trains (of N neurons) with a Gaussian kernel
                      of the standard deviation $\sigma$, thereby translating the
                      spike trains into N parallel time series of instantaneous
                      spike density functions. Next, we arbitrarily choose one of
                      the N neurons, and for each spike of this neuron, we cut out
                      a segment from the spike density time series starting at the
                      spike time and ending at $\tau$ after it. We collect such
                      segments (each of which is an N-by-$\tau f_s$ matrix; $f_s$:
                      data sampling rate) for all the spikes of the chosen neuron
                      (with M the number of spikes). We then concatenate rows of
                      each of the segment matrices to reduce it into an N$\tau
                      f_s$-dimensional row vector. Finally, we stuck these row
                      vectors into an M-by-N$\tau f_s$ matrix, which we use as the
                      data matrix for PCA. If the spike trains contain recurrences
                      of an STP starting with a spike of the chosen neuron, the
                      temporal modulations of spike density caused by the spikes
                      in the STP are confined to specific columns of the data
                      matrix, and hence the principal components (PCs) of this
                      data matrix capture the correlated modulation of spike
                      density along those columns. Application of the method to
                      synthetic spike train data (10 neurons firing Poisson spikes
                      at 10 Hz; 3 out of the 10 neuronsparticipating in an STP
                      occurring at 0.5 Hz) successfully detects the STP as the
                      first PC. We study the sensitivity and robustness of the
                      proposed method by applying it to synthetic data with
                      various parameter combinations and with temporally
                      non-stationary and spatially inhomogeneous firing rates, and
                      compare the performance to existing methods of STP
                      detection.},
      month         = {Sep},
      date          = {2023-09-26},
      organization  = {Bernstein Conference 2023, Berlin
                       (Germany), 26 Sep 2023 - 29 Sep 2023},
      subtyp        = {After Call},
      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          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / HAF - Helmholtz Analytics Framework (ZT-I-0003) /
                      JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)ZT-I-0003 / G:(DE-Juel1)JL
                      SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1015175},
}