Effective and Stable Neuron Model Optimization Based on Aggregated CMA-ES

Computer simulations have facilitated our understanding of the dynamic behavior of the brain and the effect of the medical treatment such as deep brain stimulation. For improving the simulation model, it is essential to develop a method for optimizing parameters of a neuron model from available experimental data. In this paper, we apply Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to the parameter optimization problem, and compare it with widely used conventional approaches including genetic algorithm (GA) and the Nelder-Mead method. A problem we have observed with CMA-ES is that the performance highly depends on the initial condition. To overcome the problem, we extend CMA-ES by making an aggregation of evolution. We analyze a public dataset recorded from a rat neocortical neuron, which shows that the proposed approach achieves higher performance than the conventional methods.