Feedback identification of conductance-based models

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

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
38 Downloads (Pure)


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
Publication statusPublished - Jan 2021


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


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