Computation of the passive electrical parameters of neurons using a system model

Abstract

Time-domain analysis of voltage responses to current pulse stimulation has been used to estimate the electrotonic parameters of neurons using the signal model. Errors are likely to accumulate from various steps of the analysis due to noise and electrode artifacts. A system model, which has inherent noise immunity and filtering properties, is presented here. This model employs frequency-domain analysis of the input impedance of a neuronal model (an RC cable). The resistances and capacitances of the system model are estimated from the cell-input impedance using an optimization strategy. Using the expression for the input impedance, any specified number of equalizing time constants can be computed exactly. The accessibility to these equalizing time constants 1) provides greater insight into the charge equalization along the length and circumference of the cable, and 2) improves the estimation of all other passive parameters including the electrotonic length. Thus, the system model approach allows information to be extracted more directly and accurately than the signal model approach.