Figure 4.

Global Input-Output Transformation in L2/3 Pyramidal Neurons

(A) Presynaptic inhibitory (black) and excitatory input spike trains (colors, as in Figure 2I) used for fitting the biophysical model to experimental data (Figure 2).

(B) The somatic membrane potential in the biophysical model (black) and the output of the hLN model with linear integration and a global nonlinearity (blue) in response to the input shown in (A). Parameters of the biophysical model and the inputs were identical to that shown in Figure 2, except that somatic active conductances were removed.

(C) Prediction accuracy (variance explained) of hLN models with increasing complexity. Bottom shows the architectures of different hLN models and table summarizing their main properties (cf.Figures 3B–3D). Gray lines show individual datapoints and boxplots show median, quartiles, and range of the data. ^∗p < 0.005, ^∗∗p < 10⁻⁷.

(D) The nonlinearity of the input-output transformation (blue) and the distribution of linearly integrated synaptic inputs (gray) in the one-subunit model for ten different simulations.

(E) Mean of model predictions as a function of the measured response (colored lines). Gray histogram shows the distribution of the measured response; black dashed diagonal shows identity line. Yellow shaded area indicates the standard deviation of the 23-subunit model’s prediction.