Prediction of psychophysical measurements for electrical pulse -train stimuli using a stochastic auditory nerve model: Implications for cochlear implants

Two approaches have been proposed to reduce the synchrony of the neural response to electrical stimuli in cochlear implants. One is to add noise to the stimulus, and the other is to use a high-rate pulse-train carrier. In this work, hypotheses regarding the efficacy of these two approaches are investigated using computational models of neural responsiveness. We predict existing psychophysical data to verify our methods, and we also predict yet to be measured psychophysical data to guide the design of future psychophysical experiments.;We use a stochastic model to examine the neural response to noise-free pulse trains, noise-modulated pulse trains, and low frequency sinusoidally modulated high-rate pulse-train stimuli. The refractory effect associated with the neural response is described using a Markov model for a noise-free pulse-train stimulus, and a closed-form solution for the steady state neural response is provided. For noise-modulated pulse-train stimuli and low frequency stimuli with high-rate pulse-train carriers, we propose a recursive method using the conditional probability to track the neural responses to successive pulses.;Based on the statistics of the neural response, psychophysical measurements are predicted. Regarding threshold prediction, we hypothesize a logarithmic rule for the pulse-train threshold. Predictions from a previously suggested multi-look model match trends in psychophysical data for noise-free stimuli; these data do not always match the predictions generated from a long temporal integration rule. Theoretical predictions indicate that the threshold decreases as noise variance increases and that the threshold increases as pulse rate increases for stimuli with a high-rate carrier.;In predicting dynamic range, we determine the uncomfortable level (UCL) based on the excitation pattern of the neural response in a normal ear. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. The UCL for the high-rate pulse-train stimulus increases as the pulse rate increases. Combining threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise, and that the dynamic range for high-rate pulse-train stimuli should increase with pulse rate.;Intensity discrimination limens (IDL) are studied for noise-free pulse trains, noise-modulated pulse trains, and stimuli with high-rate carriers. For noise-free and noise-modulated pulse-train stimuli, we predict the performance using signal detection theory via the probability mass function (PMF) of the neural response as well as experimental simulations. For stimuli with high-rate carriers, IDL is predicted via model simulations. Our predictions indicate that intensity discrimination under noise degrades, while it improves for high-rate carriers. Therefore, the overall intensity coding performance might improve for a strategy using high-rate carriers but not when noise is added. Psychophysical data are required to validate these hypotheses.