Estimating parameters and predicting membrane voltages with conductance-based neuron models

C.D. Meliza, M. Kostuk, H. Huang, A. Nogaret, D. Margoliash, H.D.I. Abarbanel

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)
159 Downloads (Pure)

Abstract

Recent results demonstrate techniques for fully quantitative, statistical inference of the dynamics of individual neurons under the Hodgkin-Huxley framework of voltage-gated conductances. Using a variational approximation, this approach has been successfully applied to simulated data from model neurons. Here, we use this method to analyze a population of real neurons recorded in a slice preparation of the zebra finch forebrain nucleus HVC. Our results demonstrate that using only 1,500ms of voltage recorded while injecting a complex current waveform, we can estimate the values of 12 state variables and 72 parameters in a dynamical model, such that the model accurately predicts.
Original languageEnglish
Pages (from-to)495-516
Number of pages22
JournalBiological Cybernetics
Volume108
Issue number4
Early online date25 Jun 2014
DOIs
Publication statusPublished - 1 Aug 2014

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