title: Predicting the threshold of pulse-train electrical stimuli using a stochastic auditory nerve model: the effects of stimulus noise.
abstract: The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in some human subjects (Zeng et al., 2000). In this paper, thresholds for noise-modulated pulse-train stimuli are predicted utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. The neural refractory effect 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, a recursive method using the conditional probability is utilized to track the neural responses to each successive pulse. A neural spike count rule has been presented for both threshold and intensity discrimination under the assumption that auditory perception occurs via integration over a relatively long time period (Bruce et al., 1999). An alternative approach originates from the hypothesis of the multilook model (Viemeister and Wakefield, 1991), which argues that auditory perception is based on several shorter time integrations and may suggest an NofM model for prediction of pulse-train threshold. This motivates analyzing the neural response to each individual pulse within a pulse train, which is considered to be the brief look. A logarithmic rule is hypothesized for pulse-train threshold. Predictions from the multilook model are shown to match trends in psychophysical data for noise-free stimuli that are not always matched by the long-time integration rule. Theoretical predictions indicate that threshold decreases as noise variance increases. Theoretical models of the neural response to pulse-train stimuli not only reduce calculational overhead but also facilitate utilization of signal detection theory and are easily extended to multichannel psychophysical tasks.
authors.affiliation: Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708-0291, USA.
authors.forename: Yifang
authors.initials: Y
authors.lastname: Xu
authors.forename: Leslie M
authors.initials: LM
authors.lastname: Collins
mesh_terms: D000465:Algorithms; D000818:Animals; D003056:Cochlear Nerve / Q000502:physiology*; D003198:Computer Simulation; D004056:Differential Threshold / Q000502:physiology; D004558:Electric Stimulation / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D009412:Nerve Fibers / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes