Doruk, Reşat Özgür

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Profile URL
https://hdl.handle.net/20.500.14411/6826
Name Variants
R.Ö.Doruk
Reşat Özgür Doruk
D.,Resat Ozgur
R. Ö. Doruk
R., Doruk
Doruk, Resat Ozgur
Doruk,R.O.
R.,Doruk
Doruk R.
D.,Reşat Özgür
özgür Doruk R.
Reşat Özgür, Doruk
R. O. Doruk
Özgür Doruk R.
R.O.Doruk
Doruk,R.Ö.
D., Reşat Özgür
D., Resat Ozgur
Resat Ozgur, Doruk
Doruk,Resat Ozgur
Doruk, Reşat Özgür
Doruk, R. Ozgur
Job Title
Profesör Doktor
Email Address
resat.doruk@atilim.edu.tr
Main Affiliation
Electrical-Electronics Engineering
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Website
ORCID ID
0000-0002-9217-0845
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
-0-QDnwAAAAJ
WoS Researcher ID
T-9951-2018

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This researcher does not have a Scopus ID.
Documents

20

Citations

78

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Scholarly Output

33

Articles

16

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171/2205

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WoS Citation Count

40

Scopus Citation Count

51

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JournalCount
Turkish Journal of Electrical Engineering and Computer Sciences2
Computer Methods and Programs in Biomedicine2
Journal of Biological Physics2
Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi2
Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi1
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Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Fitting a Recurrent Dynamical Neural Network To Neural Spiking Data: Tackling the Sigmoidal Gain Function Issues
    (Tubitak Scientific & Technological Research Council Turkey, 2019) Doruk, Reşat Özgür
    This is a continuation of a recent study (Doruk RO, Zhang K. Fitting of dynamic recurrent neural networkmodels to sensory stimulus-response data. J Biol Phys 2018; 44: 449-469), where a continuous time dynamical recurrentneural network is fitted to neural spiking data. In this research, we address the issues arising from the inclusion ofsigmoidal gain function parameters to the estimation algorithm. The neural spiking data will be obtained from the samemodel as that of Doruk and Zhang, but we propose a different model for identification. This will also be a continuoustime recurrent neural network, but with generic sigmoidal gains. The simulation framework and estimation algorithmsare kept similar to that of Doruk and Zhang so that we can have a solid base to compare the results. We evaluatethe estimation performance in two different ways. First, we compare the firing rate responses of the original and theestimated model. We find that responses of both models to the same stimuli are similar. Secondly, we evaluate variationsof the standard deviations of the estimates against a number of samples and stimulus parameters. They show a similarpattern to that of Doruk and Zhang. We thus conclude that our model serves as a reasonable alternative provided thatfiring rate is the response of interest (to any stimulus).
  • Thumbnail Image
    Article
    Neuron Modeling: Estimating the Parameters of a Neuron Model From Neural Spiking Data
    (2018) Doruk, Reşat Özgür
    We present a modeling study aiming at the estimation of the parameters of a single neuron model from neuralspiking data. The model receives a stimulus as input and provides the firing rate of the neuron as output. The neuralspiking data will be obtained from point process simulation. The resultant data will be used in parameter estimationbased on the inhomogeneous Poisson maximum likelihood method. The model will be stimulated by various forms ofstimuli, which are modeled by a Fourier series (FS), exponential functions, and radial basis functions (RBFs). Tabulatedresults presenting cases with different sample sizes (# of repeated trials), stimulus component sizes (FS and RBF),amplitudes, and frequency ranges (FS) will be presented to validate the approach and provide a means of comparison.The results showed that regardless of the stimulus type, the most effective parameter on the estimation performanceappears to be the sample size. In addition, the lowest variance of the estimates is obtained when a Fourier series stimulusis applied in the estimation.