Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

The effects of stochastic neural activity in a model predicting intensity perception with cochlear implants: low-rate stimulation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
5 Author(s)
Bruce, I.C. ; Bionic Ear Inst., Johns Hopkins Univ., Baltimore, MD, USA ; White, M.W. ; Irlicht, L.S. ; O'Leary, S.J.
more authors

Most models of auditory nerve response to electrical stimulation are deterministic, despite significant physiological evidence for stochastic activity. Furthermore, psychophysical models and analyses of physiological data using deterministic descriptions do not accurately predict many psychophysical phenomena. Here, the authors investigate whether inclusion of stochastic activity in neural models improves such predictions. To avoid the complication of interpulse interactions and to enable the use of a simpler and faster auditory nerve model the authors restrict their investigation to single pulses and low-rate (<200 pulses/s) pulse trains. They apply signal detection theory to produce direct predictions of behavioral threshold, dynamic range and intensity difference limen. Specifically, the authors investigate threshold versus pulse duration (the strength-duration characteristics), threshold and uncomfortable loudness (and the corresponding dynamic range) versus phase duration, the effects of electrode configuration on dynamic range and on strength-duration, threshold versus number of pulses (the temporal-integration characteristics), intensity difference limen as a function of loudness, and the effects of neural survival on these measures. For all psychophysical measures investigated, the inclusion of stochastic activity in the auditory nerve model was found to produce more accurate predictions.

Published in:

Biomedical Engineering, IEEE Transactions on  (Volume:46 ,  Issue: 12 )