Figure 8. EIF models reproduce cortical recordings. All parts: mature (P7) cell (color: relative input SD, σv/vth−vo = {0.27, 0.35, 0.54, 0.70}), EIF model with fixed parameters across all input conditions (black). A, Scaled input–output relations (σv/vth−vo = 0.35 excluded for clarity). EIF model predicts input–output relations: D̄M = 0.18 ± 0.02 (sampling floor ≈ 0.1). EIF model predicts breakdown of gain scaling for small σ. Data and model show nearly perfect gain scaling for larger σ, as expected from analysis of the steady-state voltage distributions. B, Mean firing rate versus input SD: data (●), model (♦, black line); shown in physical units and intrinsic model units. Consistent with our theoretical understanding of gain scaling, the breakdown in perfect gain scaling occurs at small σ where the mean rate is not yet approximately linear in σ, indicating that the voltage distribution does not scale as required (Eq. 29). C, EIF model goodness of fit in mature population (P7, n = 6) is consistent with example shown. Mean rate model versus mean rate data (black line gives equality). Not shown: coincidence factor, 〈Γ〉 = 0.59 ± 0.07; 〈D̄M〉 = 0.36 ± 0.21. D–G, Voltage traces (color code as in A) for fixed mean input (μ = σ1/2 = 21 pA) for an example mature cell. Effective resting potential is vo = −45 mV and EIF model threshold is vth = −24 mV. Same input time series for D–G. Goodness of fit: coincidence factors Γ = {0.64, 0.71, 0.66, 0.60} ± 0.02 (Eq. 22; Kistler et al., 1997).