Online Bayesian approach for estimation and control of neural system

We propose a data-driven method for estimating and controling neural systems. The model parameters and latent variables are estimated online simultaneously based on stochastic EM algorithm and sequential Monte Carlo even when model parameters are unknown. Moreover, the control signal is determined from estimated values based on model predictive control. We verified the effectiveness of the proposed method using simulation data.