Doruk, Reşat Özgür

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https://hdl.handle.net/20.500.14411/6826
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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
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Profesör Doktor
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resat.doruk@atilim.edu.tr
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Electrical-Electronics Engineering
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0000-0002-9217-0845
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8503734100
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-0-QDnwAAAAJ
WoS Researcher ID
T-9951-2018

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Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi3
Journal of Biological Physics2
Computer Methods and Programs in Biomedicine2
Turkish Journal of Electrical Engineering and Computer Sciences2
Frontiers in Neural Circuits1
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Author: Doruk, R. OzgurSubject: Excitatory neuron

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    Article
    Citation - WoS: 7
    Citation - Scopus: 7
    Fitting of Dynamic Recurrent Neural Network Models To Sensory Stimulus-Response Data
    (Springer, 2018) Doruk, R. Ozgur; Zhang, Kechen
    We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time-dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) that have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using the maximum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynamical neuron network models allows us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data are generated by a phased cosine Fourier series having a fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus component size, and sample size are applied in order to examine the effect of the stimulus to the identification process. Results are presented in tabular and graphical forms at the end of this text. In addition, to demonstrate the success of this research, a study involving the same model, nominal parameters and stimulus structure, and another study that works on different models are compared to that of this research.