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On the validity of electric brain signal predictions based on population firing rates
Fig 4
Error in population kernel predictions depend on kernel heterogeneity and spike-train correlations.
Each column shows 1000 single-cell kernels with different amplitude standard deviations 
(top), and different levels of spike-train correlations (middle). Spike trains with varying levels of correlations were generated through Multiple Interaction Processes (MIP) [42], controlled by the parameter f, where f = 0 corresponds to uncorrelated homogeneous Poisson processes, while f = 1 corresponds to fully correlated (identical) spike trains (see Methods). The mean firing rate is shown in black, and the standard deviation in gray. The toy LFP is calculated (bottom). Relative error Erel, quantified by the normalized standard deviation of the difference between the ground truth signal and the population kernel signal (see Methods), vanishes for identical kernels, regardless of correlation (first column). For variable kernels with some correlation, the kernel approach will result in some relative error (second column). For variable kernels and zero correlation, the relative error will be large (third column). For perfect correlation, the relative error vanishes regardless of kernel variability (fourth column).
doi: https://doi.org/10.1371/journal.pcbi.1012303.g004