Figure 1. LN models and 
–
curves for gain-scaling (GS) and nongain-scaling (NGS) neurons.
A. The nonlinearities in the LN model framework for a GS (red) (
pS/µm2 and 
pS/µm2) and a NGS (blue) (
pS/µm2 and 
pS/µm2) neuron simulated as conductance-based model neurons (Eq. 2). The nonlinearities were computed using Bayes' rule: 
, where 
is the neuron's mean firing rate and 
is the linearly filtered stimulus (see also Eq. 7 in Methods). B. The same nonlinearities as A, in stimulus units scaled by 
(magnitude of stimulus fluctuations). The nonlinearities overlap for GS neurons over a wide range of 
. C–D. The 
–
curves for a NGS (C) and a GS neuron (D) for different values of 
. E. The output entropy as a function of the mean (DC) and 
(amplitude of fast fluctuations). F. Information about the output firing rate of the neurons as a function of 
.