Stochastic resonance in the electrically stimulated auditory nerve: Predictions using a stochastic model of neural responsiveness

The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in human subjects. In this paper, thresholds for noise-modulated pulse-train stimuli are predicted by utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. A neural spike count comparison rule has been presented for both threshold and intensity discrimination under the assumption that loudness is a monotonic function of the number of neuron spikes. An alternative approach which we have pursued involves analyzing the neural response to each individual pulse within a pulse train to investigate the threshold behavior. The refractory effect is described using a Markov model for a noise-free pulse-train stimulus. A recursive method using the conditional probability is utilized to track the neural responses to each successive pulse for a noise-modulated pulse-train stimulus. After determining the stochastic properties of the auditory nerve response to each pulse within the pulse train, a logarithmic rule is hypothesized for pulse-train threshold and the predictions are shown to match psychophysical data not only for noise-free stimuli but also for noise-modulated stimuli. Results indicate that threshold decreases as noise variance increases.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering