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On the validity of electric brain signal predictions based on population firing rates

Fig 10

Effect of spike-train statistics on population kernel errors.

A: Dependence of the LFP amplitude on the recording depth for various presynaptic spike-train ensembles generated by the MIP and by the Brunel network model (see legend and main text), for the kernels corresponding to the default case. MIP spike trains are characterized by the firing rate ν and the spike-train correlation coefficient . Brunel spike trains are obtained from the “AI” and the “SI slow” regime. Solid and dashed lines refer to the ground-truth signal and the LFP predicted by the kernel method. B: Same as panel A, but showing the absolute prediction error. Solid and dotted lines represent results obtained from simulations and theory, respectively. C: Same as panel B, but showing the relative prediction error. D: The maximum error across depth for MIP spike trains with different firing rates ν and correlations f. E: Same as panel D but showing the maximum relative error across depth.

doi: https://doi.org/10.1371/journal.pcbi.1012303.g010