Analysis of Parallel Spike Trains

Volume 7 of the series Springer Series in Computational Neuroscience pp 103-127

Dependence of Spike-Count Correlations on Spike-Train Statistics and Observation Time Scale

  • Tom TetzlaffAffiliated withDepartment of Mathematical Sciences and Technology, Norwegian University of Life Sciences Email author 
  • , Markus DiesmannAffiliated withLaboratory for Computational Neurophysics, RIKEN Brain Science InstituteBernstein Center for Computational Neuroscience, Albert-Ludwig University


Spiking activity is typically measured by counting the number of spikes in a certain time interval. The length of this interval, the “bin size”, varies considerably across studies. In this chapter, we provide a mathematical framework to relate the spike-count statistics to the statistics of the underlying point processes. We show that spike-count variances, covariances, and correlation coefficients generally depend in a nontrivial way on the bin size and on the spike-train auto- and cross-correlation structure. The spike-count coherence, in contrast, constitutes a correlation measure independent of bin size.