Effect of input noise on neuronal firing rate

When neurons are driven with a noisy input, the mean and the variance of the stimulus modulate the firing rate. Previous studies have shown that in linear-nonlinear model neurons the mean firing rate obtained in response to a noisy input is the average rate that would be obtained from an ensemble of constant currents. In this work, we study the firing rate of several neuron models, focusing on its dependence on the amount of input noise. We find that for models with monotonic activation curves, the theory provides a good qualitative approximation of the firing rate. For neurons with non-monotonic activation curves, however, the theory fails. The discrepancies between the theory and the simulations appear because rapidly fluctuating stimuli involve intrinsically dynamical processes that cannot be interpreted as an ensemble of constant stimuli.