Abstract:
The study presents the development of an alternative noise current term and novel voltage dependent
current noise algorithm for conductance based stochastic auditory nerve fibre (ANF) models. ANFs
are known to have significant variance in threshold stimulus which affects temporal characteristics
such as latency. This variance is primarily caused by the stochastic behaviour or microscopic
fluctuations of the node of Ranvier’s voltage dependent sodium channels of which the intensity is a
function of membrane voltage. Though easy to implement and low in computational cost, existing
current noise models have two deficiencies: it is independent of membrane voltage and it is unable to
inherently determine the noise intensity required to produce in vivo measured discharge probability
functions. The proposed algorithm overcomes these deficiencies whilst maintaining its low
computational cost and ease of implementation compared to other conductance and Markovian based
stochastic models. The algorithm is applied to a Hodgkin-Huxley based compartmental cat ANF
model and validated via comparison of the threshold probability and latency distributions to
measured cat ANF data. Simulation results show the algorithm’s adherence to in vivo stochastic
fibre characteristics such as an exponential relationship between the membrane noise and
transmembrane voltage, a negative linear relationship between the log of the relative spread of the
discharge probability and the log of the fibre diameter and a decrease in latency with an increase in
stimulus intensity.
