Fig. 2
From: Inferring and validating mechanistic models of neural microcircuits based on spike-train data
Estimation results for background input statistics using in vitro and in vivo data. a Examples of recorded membrane voltage in response to fluctuating input with indicated mean μI and standard deviation σI (vertical bar marks μI ± σI) together with ISI histogram and density pISI that corresponds to the fitted leaky I&F model for a PYR (top) and an INT (bottom). Method 1 was used for parameter estimation. b Estimated input parameters (μ, σ) versus empirical input statistics (μI, σI) with Pearson correlation coefficient ϱ indicated for an example PYR and INT each. Note that since we considered only spikes, and not the membrane potential, we did not estimate the input resistance and rest potential; therefore, the input parameters were defined up to arbitrary offset and scale factors. c ϱ for input mean and standard deviation for all seven PYRs and six INTs. d Histograms of AIC difference (ΔAIC) between the Poisson and I&F models across all stimuli for all PYRs (top) and all INTs (bottom). e Examples of ISI histograms from in vivo recordings and densities pISI from fitted I&F neurons. f Estimates of parameters for the background input together with contour lines of equal spike rate and ISI CV calculated from pISI. g Histogram of AIC difference between the Poisson and I&F models across all cells and conditions
