On the stochastic properties of the neural encoding mechanism of sound intensity

Liz C. Chang, Willy Wong

Abstract


The stochastic properties of the neural encoding mechanism of sound intensity were described. The homogeneous Poisson process was modified and extended to give a more realistic stochastic model that expressed the properties of auditory neurons quantitatively. A command program was written to control the different parameter values to study the effects of the different components on the mean-variance ratio. It was found that the ratio was a monotonic increasing function of dead time under a fixed firing rate, while it increased with increasing values of firing rate.

Keywords


Computer simulation; Functions; Mathematical models; Parameter estimation; Poisson distribution; Probability density function; Random processes; Signal encoding; Statistical methods; Neural encoding; Poisson process; Pulse-number distribution (PND); Stochastic properties

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