A Closed-loop Framework to Discriminate Models Using Optimal Control

TL;DR

This paper introduces a closed-loop optimal control framework that iteratively selects inputs to effectively discriminate between candidate models of a system, demonstrated through simulations and electrophysiology experiments.

Contribution

It presents a novel iterative method combining optimal control and model discrimination, enhancing interpretability over purely data-driven approaches.

Findings

Successfully discriminated between models in simulations.

Effectively distinguished models during electrophysiology experiments.

Framework improves model selection accuracy in dynamic systems.

Abstract

Predicting the response of an observed system to a known input is a fruitful first step to accurately control the system's dynamics. Despite the recent advances in fully data-driven algorithms, the most interpretable way to reach this goal is through mechanistic mathematical modeling. Here, we leverage optimal control and propose a closed-loop iterative method to choose among a set of candidate models the one that most accurately predict an observed system. We assume that one has control over an input of the observed system and access to measurements of its response. Our approach is to identify the input control that maximally discriminates the response of the candidate models, allowing us to determine which model is best by comparing such responses with the observed data. We demonstrate our proposed framework in numerical simulations before applying it during an electrophysiology experiment, successfully discriminating between different models for photocurrents produced via opsin dynamics.