Feedback identification of conductance-based models

Thiago B. Burghi (Corresponding author), Maarten Schoukens, Rodolphe Sepulchre

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Article number109297
Number of pages13
JournalAutomatica
Volume123
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Closed-loop identification
  • Contraction analysis
  • Neuronal models
  • Nonlinear system identification
  • Prediction error methods

Fingerprint Dive into the research topics of 'Feedback identification of conductance-based models'. Together they form a unique fingerprint.

Cite this