Simultaneously estimating and controlling nonlinear neuronal dynamics based on sequential Monte Carlo framework

Abstract

Estimating and controlling nonlinear neuronal system are crucial for understanding the neuronal dynamics and brain functions. However, it is challenging to control the nonlinear system including unobservable state and unknown dynamics. We propose a framework for estimating and controlling an individual neuron by leveraging the sequential Monte Carlo method (SMC). We derive an online algorithm based on the expectation-maximization algorithm and constitute the control law by employing the SMC-based model predictive control. We verify the effectiveness of the proposed method using simulation environments. The results suggest we can simultaneously estimate the latent variables and the parameters and control neuronal state.