A stochastic model of the electrically stimulated auditory nerve: single-pulse response.
Most models of neural response to electrical stimulation, such as the Hodgkin-Huxley equations, are deterministic, despite significant physiological evidence for the existence of stochastic activity. For instance, the range of discharge probabilities measured in response to single electrical pulses cannot be explained at all by deterministic models. Furthermore, there is growing evidence that the stochastic component of auditory nerve response to electrical stimulation may be fundamental to functionally significant physiological and psychophysical phenomena. In this paper we present a simple and computationally efficient stochastic model of single-fiber response to single biphasic electrical pulses, based on a deterministic threshold model of action potential generation. Comparisons with physiological data from cat auditory nerve fibers are made, and it is shown that the stochastic model predicts discharge probabilities measured in response to single biphasic pulses more accurately than does the equivalent deterministic model. In addition, physiological data show an increase in stochastic activity with increasing pulse width of anodic/cathodic biphasic pulses, a phenomenon not present for monophasic stimuli. These and other data from the auditory nerve are then used to develop a population model of the total auditory nerve, where each fiber is described by the single-fiber model.
Bruce
I C
IC
Bionic Ear Institute, University of Melbourne, VIC, Australia. ibruce@bme.jhu.edu
White
M W
MW
Irlicht
L S
LS
O'Leary
S J
SJ
Dynes
S
S
Javel
E
E
Clark
G M
GM
D000200:Action Potentials; D000818:Animals; D001309:Auditory Threshold; D002415:Cats; D004558:Electric Stimulation; D004566:Electrodes; D016014:Linear Models; D008959:Models, Neurological; D009412:Nerve Fibers / Q000502:physiology*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013269:Stochastic Processes; D000159:Vestibulocochlear Nerve / Q000502:physiology*