A method for estimation of parameters in a neural model with noisy measurements

In this article, we establish a method for the estimation of parameters of a three-dimensional Hindmarsh-Rose (HR) neural model under noisy environment. It has been assumed that all the parameters are unknown and are expressed as the time-varying sinusoidal functions for the membrane voltage recordings. We apply the method to present the stochastic nature of parameters, applied current and membrane voltage. The proposed method shows that the estimation procedure needs a large number of time scales for which the solution will be more accurate and it provides the relation between the parameters. The stochastic 3D HR model is used, and the mean and variances are calculated. Our analysis explains how the parameters are estimated and it helps us to select an optimal simulation procedure. The estimation procedure is also derived for a particular case when the parameters are constants instead of time-varying functions. This paper reports the application of estimation technique to experimental studies in neural computation.