Estimation of single-neuron model parameters from spike train data

Authors:
Randall D. Hayes;John H. Byrne;Steven J. Cox;Douglas A. Baxter
Affiliations:
Department of Neurobiology and Anatomy, Center for Computational Biomedicine, The University of Texas-Houston Medical School, 6431 Fannin Street, Houston, TX 77031, USA and W.M. Keck Center for Co ...;Department of Neurobiology and Anatomy, Center for Computational Biomedicine, The University of Texas-Houston Medical School, 6431 Fannin Street, Houston, TX 77031, USA;Computational and Applied Mathematics, Rice University, 6100 Main Street, Houston, TX 77005, USA;Department of Neurobiology and Anatomy, Center for Computational Biomedicine, The University of Texas-Houston Medical School, 6431 Fannin Street, Houston, TX 77031, USA
Venue:
Neurocomputing
Year:
2005

Citing 2
Cited 1

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Abstract

Estimating parameters for models of neurons requires a quantitative comparison between the model output and empirical data. The present study compares three error functions: voltage time-series (VTS), cumulative voltage integrals (CVI), and phase histograms (PH). In two test cases, predefined models were used to produce target data and to compare the efficacy of the three error functions when they were used to recover the target data. In a third example, empirical data were used to parameterize a model. VTS was found to be inferior, whereas as CVI and PH were similar and effective. Reliable parameters were derived from analyzing as few as two data sets.