Estimating the Parameters of Fitzhugh-Nagumo Neurons From Neural Spiking Data

dc.authorid Doruk, Ozgur/0000-0002-9217-0845
dc.authorscopusid 8503734100
dc.authorscopusid 57212465314
dc.authorwosid Doruk, Ozgur/T-9951-2018
dc.contributor.author Doruk, Resat Ozgur
dc.contributor.author Abosharb, Laila
dc.contributor.other Electrical-Electronics Engineering
dc.date.accessioned 2024-07-05T15:41:37Z
dc.date.available 2024-07-05T15:41:37Z
dc.date.issued 2019
dc.department Atılım University en_US
dc.department-temp [Doruk, Resat Ozgur; Abosharb, Laila] Atilim Univ, Dept Elect & Elect Engn, TR-06836 Ankara, Turkey en_US
dc.description Doruk, Ozgur/0000-0002-9217-0845 en_US
dc.description.abstract 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. en_US
dc.identifier.citationcount 7
dc.identifier.doi 10.3390/brainsci9120364
dc.identifier.issn 2076-3425
dc.identifier.issue 12 en_US
dc.identifier.pmid 31835351
dc.identifier.scopus 2-s2.0-85076719896
dc.identifier.uri https://doi.org/10.3390/brainsci9120364
dc.identifier.uri https://hdl.handle.net/20.500.14411/3456
dc.identifier.volume 9 en_US
dc.identifier.wos WOS:000506873500001
dc.institutionauthor Doruk, Reşat Özgür
dc.language.iso en en_US
dc.publisher Mdpi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 14
dc.subject neuron modeling en_US
dc.subject Fitzhugh-Nagumo Model en_US
dc.subject Poisson processes en_US
dc.subject inhomogeneous Poisson en_US
dc.subject neural spiking en_US
dc.subject maximum likelihood estimation en_US
dc.title Estimating the Parameters of Fitzhugh-Nagumo Neurons From Neural Spiking Data en_US
dc.type Article en_US
dc.wos.citedbyCount 12
dspace.entity.type Publication
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