CUED Publications database

Feedback identification of conductance-based models

Burghi, TB and Schoukens, M and Sepulchre, R (2020) Feedback identification of conductance-based models. Automatica, 123. ISSN 0005-1098

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Abstract

© 2020 Elsevier Ltd 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.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 26 Oct 2020 20:07
Last Modified: 02 Sep 2021 05:47
DOI: 10.1016/j.automatica.2020.109297