Theoretical prediction of dynamic range and intensity discrimination for electrical noise-modulated pulse-train stimuli

This work investigates dynamic range and intensity discrimination for electrical noise-modulated pulse-train stimuli using a stochastic auditory nerve model, Based on a hypothesized monotonic relationship between loudness and the number of spikes, theoretical prediction of the most uncomfortable level was determined by comparing spike counts to a fixed threshold. However, no specific rule for determining this fixed number has previously been suggested. Our work determines the most uncomfortable level based on the excitation pattern of the basilar membrane in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven at an uncomfortable stimulus level in a normal ear is related to the most uncomfortable spiking number. The intensity discrimination limens are predicted using signal detection theory via the probability mass function (PMF) of the neural response and via experimental simulations. The results show that the uncomfortable level for a pulse-train stimulus increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli increases with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, the overall intensity coding performance does not improve significantly.