Feedback identification of conductance-based models. (January 2021)

• Record Type: Journal Article
• Authors: Burghi, Thiago B.; Schoukens, Maarten; Sepulchre, Rodolphe
• Is Part Of: Automatica. Volume 123(2021)
• Publication Date: 2021-01
• Subjects: Nonlinear system identification -- Closed-loop identification -- Prediction error methods -- Contraction analysis -- Neuronal models; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2020.109297
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise. While the nonlinear system exhibits excitability, bifurcations, and limit-cycle oscillations, we prove consistency of the parameter estimation procedure under output feedback. Hence, this paper provides a rigorous framework for the application of conventional nonlinear system identification methods to discrete-time stochastic neuronal systems. The main result exploits the elementary property that conductance-based models of neurons have an exponentially contracting inverse dynamics. This property is implied by the voltage-clamp experiment, which has been the fundamental modeling experiment of neurons ever since the pioneering work of Hodgkin and Huxley.