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Rate-coding neurons are often characterized by their tuning curve, that is, the average firing rate, T(x), as a function of stimulus intensity, x. However the substantial natural variability in firing rate that often occurs for a fixed stimulus provides a limitation on the fidelity of firing rate encoding of stimuli. Consequently, stimulus-dependent variance in firing rate, V(x), is crucial in studies of tuning curve optimality. Information theory can be used to quantify such limits and to address the question of finding the tuning curve that maximizes information rate.

Optimal sigmoidal tuning curves for intensity encoding sensory neurons with quasi-Poisson variability