Data-driven estimation of spatial electrical property of multi-compartment models with neuronal morphology by replica exchange Monte Carlo method

Abstract

One of the neuron models that simulate the electrical activity of neurons, the multi-compartment model, has spatial electrical properties that control nonlinear spatiotemporal dynamics and can reproduce nonlinear electrical responses with high accuracy. However, it is difficult to determine the model parameters in multi-compartment models from membrane potentials, since unknown high dimensional parameters for spatial electrical property should be estimated using incomplete observation data. In this paper, we propose a data-driven method to estimate the spatial electrical properties in the multi-compartment model from membrane potentials observed incompletely. The proposed method employs the replica exchange method using prior information considering morphological smoothness to solve problems of the local optima in the solution space and incompleteness of observation data. We further verify the effectiveness of the proposed method by using simulation data obtained from realistic neuron models.