State and parameter estimation for canonic models of neural oscillators

I.Y. Tyukin, E. Steur, H. Nijmeijer, D. Fairhurst, I. Song, A. Semyanov, C. Leeuwen, van

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)

Abstract

We consider the problem of how to recover the state and parameter values of typical model neurons, such as Hindmarsh-Rose, FitzHugh-Nagumo, Morris-Lecar, from in-vitro measurements of membrane potentials. In control theory, in terms of observer design, model neurons qualify as locally observable. However, unlike most models traditionally addressed in control theory, no parameter-independent diffeomorphism exists, such that the original model equations can be transformed into adaptive canonic observer form. For a large class of model neurons, however, state and parameter reconstruction is possible nevertheless. We propose a method which, subject to mild conditions on the richness of the measured signal, allows model parameters and state variables to be reconstructed up to an equivalence class. © 2010 World Scientific Publishing Company.
Original languageEnglish
Pages (from-to)193-207
JournalInternational Journal of Neural Systems
Volume20
Issue number3
DOIs
Publication statusPublished - 2010

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