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Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data
A theoretical and computational study on the estimation of the parameters of a single Fitzhugh–Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contai...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956007/ https://www.ncbi.nlm.nih.gov/pubmed/31835351 http://dx.doi.org/10.3390/brainsci9120364 |
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author | Doruk, Resat Ozgur Abosharb, Laila |
author_facet | Doruk, Resat Ozgur Abosharb, Laila |
author_sort | Doruk, Resat Ozgur |
collection | PubMed |
description | A theoretical and computational study on the estimation of the parameters of a single Fitzhugh–Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contain no amplitude information. The goal can be achieved by applying a maximum likelihood estimation approach where the likelihood function is derived from point process statistics. The firing rate of the neuron was assumed as a nonlinear map (logistic sigmoid) relating it to the membrane potential variable. The stimulus data were generated by a phased cosine Fourier series having fixed amplitude and frequency but a randomly shot phase (shot at each repeated trial). Various values of amplitude, stimulus component size, and sample size were applied to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms, which also include statistical analysis (mean and standard deviation of the estimates). We also tested our model using realistic data from a previous research (H1 neurons of blowflies) and found that the estimates have a tendency to converge. |
format | Online Article Text |
id | pubmed-6956007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69560072020-01-23 Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data Doruk, Resat Ozgur Abosharb, Laila Brain Sci Article A theoretical and computational study on the estimation of the parameters of a single Fitzhugh–Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contain no amplitude information. The goal can be achieved by applying a maximum likelihood estimation approach where the likelihood function is derived from point process statistics. The firing rate of the neuron was assumed as a nonlinear map (logistic sigmoid) relating it to the membrane potential variable. The stimulus data were generated by a phased cosine Fourier series having fixed amplitude and frequency but a randomly shot phase (shot at each repeated trial). Various values of amplitude, stimulus component size, and sample size were applied to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms, which also include statistical analysis (mean and standard deviation of the estimates). We also tested our model using realistic data from a previous research (H1 neurons of blowflies) and found that the estimates have a tendency to converge. MDPI 2019-12-09 /pmc/articles/PMC6956007/ /pubmed/31835351 http://dx.doi.org/10.3390/brainsci9120364 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Doruk, Resat Ozgur Abosharb, Laila Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data |
title | Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data |
title_full | Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data |
title_fullStr | Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data |
title_full_unstemmed | Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data |
title_short | Estimating the Parameters of Fitzhugh–Nagumo Neurons from Neural Spiking Data |
title_sort | estimating the parameters of fitzhugh–nagumo neurons from neural spiking data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956007/ https://www.ncbi.nlm.nih.gov/pubmed/31835351 http://dx.doi.org/10.3390/brainsci9120364 |
work_keys_str_mv | AT dorukresatozgur estimatingtheparametersoffitzhughnagumoneuronsfromneuralspikingdata AT abosharblaila estimatingtheparametersoffitzhughnagumoneuronsfromneuralspikingdata |