Stochastic noise analysis of neural interface front end

A time-domain methodology for noise analysis of neural interface front-end with arbitrary deterministic neuron model excitations is presented. Rather than estimating noise behavior by a population of realizations, the neural interface front-end is described as a set of stochastic differential equations and closure approximations are introduced to obtain the noise variances, covariances and cross-correlations between any electrical quantity and any stochastic source as a function of time. Statistical simulation shows that the proposed method offer an accurate and an efficient solution closely approximating those from a time-domain Monte Carlo analysis.

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