کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405766 678028 2016 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Parameter identifiability and identifiable combinations in generalized Hodgkin–Huxley models
ترجمه فارسی عنوان
شناسایی پارامترها و ترکیب های قابل شناسایی در مدل هاجکین ـ هاکسلی تعمیم یافته
کلمات کلیدی
شناسایی؛ برآورد پارامتر؛ مدل هوچکین-هاکسلی؛ گیره ولتاژ
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• We consider the structural identifiability of the Hodgkin–Huxley equations.
• The product of all steady-state parameters with conductance is identifiable.
• When initial conditions are known, the steady-state parameters can be identified.
• The time constants of the generalized model are identifiable.
• The input–output equation can be used to compute the time constants.

The use of Hodgkin–Huxley (HH) equations abounds in the literature, but the identifiability of the HH model parameters has not been broadly considered. Identifiability analysis addresses the question of whether it is possible to estimate the model parameters for a given choice of measurement data and experimental inputs. Here we explore the structural identifiability properties of a generalized form of HH from voltage clamp data. Through a scaling argument, we conclude that the steady-state gating variables are not identifiable from voltage clamp data, and then further show that their product together with the conductance term forms an identifiable combination. We additionally show that these parameters become identifiable when the initial conditions for each of the gating variables are known. The time constants for each gating variable are shown to be identifiable, and a novel method for estimating them is presented. Finally, the exponents of the gating variables are shown to be identifiable in the two-gate case, and we conjecture these to be identifiable in the general case. These results are broadly applicable to models using HH-like formalisms, and show in general which parameters and combinations of parameters are possible to estimate from voltage clamp data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 199, 26 July 2016, Pages 137–143
نویسندگان
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