Summary
2023
Session Number:C3L-1
Session:
Number:C3L-15
Sequential Monte Carlo Framework for Simultaneously Estimating and Controlling Nonlinear Neuronal Dynamics
Omi Taketo, Omori Toshiaki,
pp.525-528
Publication Date:2023-09-21
Online ISSN:2188-5079
Summary:
Estimating and controlling nonlinear neuronal system are crucial for understanding the neuronal system and brain functions. However, it is challenging to determine appropriate time-series input for 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. For estimating the hidden state and its dynamics, we derive an online algorithm based on the sequential Monte Carlo method and the expectation-maximization algorithm. In addition, we constitute the feedback control law by employing the Monte Carlo method based model predictive control. We verify the effectiveness of the proposed method using simulation environments. The results suggest that with the proposed method we can simultaneously estimate the latent variables and the parameters and control neuronal state toward the desired firing pattern.