Parameter-extraction of a two-compartment model for whole-cell data analysis.
Neuronal modeling of patch-clamp data is based on approximations which are valid under specific assumptions regarding cell properties and morphology. Certain cells, which show a biexponential capacitance transient decay, can be modeled with a two-compartment model. However, for parameter-extraction in such a model, approximations are required regarding the relative sizes of the various model parameters. These approximations apply to certain cell types or experimental conditions and are not valid in the general case. In this paper, we present a general method for the extraction of the parameters in a two-compartment model without assumptions regarding the relative size of the parameters. All the passive electrical parameters of the two-compartment model are derived in terms of the available experimental data. The experimental data is obtained from a DC measurement (where the command potential is a hyperpolarizing DC voltage) and an AC measurement (where the command potential is a sinusoidal stimulus on a hyperpolarized DC potential) performed on the cell under test. Computer simulations are performed with a circuit simulator, XSPICE, to observe the effects of varying the two-compartment model parameters on the capacitive transients of the current response. Our general solution for the parameter-estimation of a two-compartment model may be used to model any neuron, which has a biexponential capacitive current decay. In addition, our model avoids the need for simplifying and perhaps erroneous approximations. Our equations may be easily implemented in hardware/software compensation schemes to correct the recorded currents for any series resistance or capacitive transient errors. Our general solution reduces to the results of previous researchers under their approximations.
Pandey
Santosh
S
Sherman Fairchild Center, Lehigh University, Bethlehem, PA 18015, USA. skp3@lehigh.edu
White
Marvin H
MH
D000465:Algorithms; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004558:Electric Stimulation; D004594:Electrophysiology / Q000706:statistics & numerical data*; D008959:Models, Neurological; D015233:Models, Statistical; D018408:Patch-Clamp Techniques