Discontinuous Galerkin finite element method for solving population density functions of cortical pyramidal and thalamic neuronal populations. (1st February 2015) - Huang, Chih-Hsu; Lin, Chou-Ching K.; Ju, Ming-Shaung
Abstract: Compared with the Monte Carlo method, the population density method is efficient for modeling collective dynamics of neuronal populations in human brain. In this method, a population density function describes the probabilistic distribution of states of all neurons in the population and it is governed by a hyperbolic partial differential equation. In the past, the problem was mainly solved by using the finite difference method. In a previous study, a continuous Galerkin finite element method was found better than the finite difference method for solving the hyperbolic partial differential equation; however, the population density function often has discontinuity and both methods suffer from a numerical stability problem. The goal of this study is to improve the numerical stability of the solution using discontinuous Galerkin finite element method. To test the performance of the new approach, interaction of a population of cortical pyramidal neurons and a population of thalamic neurons was simulated. The numerical results showed good agreement between results of discontinuous Galerkin finite element and Monte Carlo methods. The convergence and accuracy of the solutions are excellent. The numerical stability problem could be resolved using the discontinuous Galerkin finite element method which has total-variation-diminishing property. The efficient approach will be employed to simulate the electroencephalogram or dynamics of thalamocortical network which involves three populations, namely, thalamic reticular neurons, thalamocortical neurons and cortical pyramidal neurons. Highlights: Discontinuous Galerkin FEM resolves stability problem in simulation of neural systems. Discontinuous Galerkin FEM excels Monte Carlo simulation for LIF and LIFB models. Discontinuous Galerkin FEM can simulate EEG which involves three populations.