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

Yifang Xu; Leslie Collins

doi:10.1117/12.497265

30 April 2003 Stochastic resonance in the electrically stimulated auditory nerve: predictions using a stochastic model of neural responsiveness

Yifang Xu, Leslie Collins

Author Affiliations +

Proceedings Volume 5110, Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems; (2003) https://doi.org/10.1117/12.497265
Event: SPIE's First International Symposium on Fluctuations and Noise, 2003, Santa Fe, New Mexico, United States

Abstract

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.

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Yifang Xu and Leslie Collins "Stochastic resonance in the electrically stimulated auditory nerve: predictions using a stochastic model of neural responsiveness", Proc. SPIE 5110, Fluctuations and Noise in Biological, Biophysical, and Biomedical Systems, (30 April 2003); https://doi.org/10.1117/12.497265

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KEYWORDS

Stochastic processes

Neurons

Nerve

Data modeling

Electrodes

Statistical modeling

Convolution

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