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arXiv · 2002.09626

Feedback Identification of conductance-based models

Thiago B. Burghi·Maarten Schoukens·Rodolphe Sepulchre
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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.

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Thiago B. Burghi, Maarten Schoukens, Rodolphe Sepulchre. 2020-02-22. Feedback Identification of conductance-based models. https://doi.org/10.1016/j.automatica.2020.109297

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