(a–c) Static NL model responses. (a) The input nonlinearity of NL model is chosen to be a Hill function with n = 1. (b) Filter of NL model, measured directly from the data. (c) NL model responses vs. projected stimulus. While these curves appear to change slope with increasing mean stimulus, mean responses also tend to increase (purple … yellow). (d–f) Varying NL model responses, where the KD of the input nonlinearity is allowed to vary with the mean stimulus. (d) Input nonlinearities for stimuli with different mean (colors). The KD of each curve is set to the mean stimulus of that trial. (e) Filter of NL model, same as in (b). (f) Model responses vs. projected stimulus. Note that, like in the data (cf. Figure 2e), the mean response remains relatively invariant with mean stimulus, and that curves get shallower with increasing mean stimulus. (g) Comparison of steady state gain (slope of functions shown in (a) and (d)) when KD is fixed (black) and when KD is allowed to vary with the mean stimulus (red). When KD is fixed, the the relationship between gain and mean stimulus approaches a power law with exponent 2 (gain ~ ). However, when varies with the mean stimulus, the steady state gain ~ , which is the Weber-Fechner Law.