Doruk, Reşat Özgür
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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
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
Email Address
resat.doruk@atilim.edu.tr
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Scholarly Output
27
Articles
14
Citation Count
21
Supervised Theses
12
26 results
Scholarly Output Search Results
Now showing 1 - 10 of 26
Article Citation Count: 1Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models(Frontiers Media Sa, 2019) Doruk, R. Ozgur; Zhang, Kechen; Electrical-Electronics EngineeringWe present an adaptive stimulus design method for efficiently estimating the parameters of a dynamic recurrent network model with interacting excitatory and inhibitory neuronal populations. Although stimuli that are optimized for model parameter estimation should, in theory, have advantages over nonadaptive random stimuli, in practice it remains unclear in what way and to what extent an optimal design of time-varying stimuli may actually improve parameter estimation for this common type of recurrent network models. Here we specified the time course of each stimulus by a Fourier series whose amplitudes and phases were determined by maximizing a utility function based on the Fisher information matrix. To facilitate the optimization process, we have derived differential equations that govern the time evolution of the gradients of the utility function with respect to the stimulus parameters. The network parameters were estimated by maximum likelihood from the spike train data generated by an inhomogeneous Poisson process from the continuous network state. The adaptive design process was repeated in a closed loop, alternating between optimal stimulus design and parameter estimation from the updated stimulus-response data. Our results confirmed that, compared with random stimuli, optimally designed stimuli elicited responses with significantly better likelihood values for parameter estimation. Furthermore, all individual parameters, including the time constants and the connection weights, were recovered more accurately by the optimal design method. We also examined how the errors of different parameter estimates were correlated, and proposed heuristic formulas to account for the correlation patterns by an approximate parameter-confounding theory. Our results suggest that although adaptive optimal stimulus design incurs considerable computational cost even for the simplest excitatory-inhibitory recurrent network model, it may potentially help save time in experiments by reducing the number of stimuli needed for network parameter estimation.Article Citation Count: 7Estimating the Parameters of Fitzhugh-Nagumo Neurons from Neural Spiking Data(Mdpi, 2019) Doruk, Resat Ozgur; Abosharb, Laila; Electrical-Electronics EngineeringA 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.Article Citation Count: 6Fitting of dynamic recurrent neural network models to sensory stimulus-response data(Springer, 2018) Doruk, R. Ozgur; Zhang, Kechen; Electrical-Electronics EngineeringWe 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.Article Citation Count: 0Fitzhugh-Nagumo Modelleri İçin Çatallanma Denetimi(2018) Doruk, Reşat Özgür; Ihnısh, Hamza; Electrical-Electronics EngineeringBu yazıda tekil Fitzhugh-Nagumo (FN) nöron modelleri için teorik bir çatallanma denetim çalışması sunulmaktadır. Değişmekte olan parametreler için çatallanma analizleri MATLAB üzerinde çalışan MATCONT uygulaması ile yapılmıştır. Söz konusu analizde 5 Hopf (H) ve 1 adette Sınır Noktası/Eyer Dü˘gümü (LP) olgusuna rastlanmıştır. Hopf tipi çatallanmalar izdüşümsel denetim ile desteklenmiş arındırma süzgeçleri kullanılarak sağlanmıştır. Arındırma süzgeçleri birinci ve ikinci derece olarak uygulanmıştır. Birinci derece süzgeç ikinci dereceye göre daha avantajlı oldu˘gu anlaşılmıştır. Birinci derece süzgeç hem daha uygulanabilir olmakta hem de daha hızlı davranmaktadır. LP türü çatallanmalar için derecesinden bağımsız olarak arındırma süzgecinden yapılan çıktı geri beslemesi başarılı olamamakta ve bu nedenle birini derece süzgecle beraber birde zar potansiyelinden ek bir geri besleme alınmaktadır. Bunun dezavantajı süzgecin yüksek geçirgen niteli˘ginin bozulmasına neden olmakta ve LP denge noktasının korunmasına olanak vermemektedir. Bu soruna çözüm olması için doğrusal olmayan bir denetleyici tasarımıda gösterilmektedir. Bunun tek dezavantajı orjinal denge noktaları korunamaktadır. Sonuçlar benzetimlerle desteklenmektedir.Article Citation Count: 0Geri Adımlama Tekni˘gi ile Bir DC Motorun Konum ve Hız Kontrolü(2018) Doruk, Reşat Özgür; Zuglem, Ahmed; Electrical-Electronics EngineeringBu çalışmada Lyapunov’un ikinci kararlılık yönteminin bir özyinelemeli biruyarlaması olan geri adımlama yöntemi fırçalı bir doğru akım motorunun denetimineuygulanmaktadır. Bozucu etkilerden bağımsız bir ortamda hem hız, hem de konumdenetiminde başarı ile uygulanabildiği görülen yöntemin bozucu etkiler altındakiperformasını inceleyebilmek için hem teorik hem de benzetim tabanlı analizler yapılmıştır.Teorik incelemede girdiden-duruma kararlılık kuramından yararlanılmıştır. Bu noktadagirdi bozucu etkileri (bozucu torklar) temsil etmektedir. Yöntem uygulandığında, denetimkazançlarının seçiminde bir alt sınırın var olduğu ve bozucu etkilerden bağışık ortamdaolduğu gibi serbest seçilmesinin uygun olmayabileceği anlaşılmaktadır. Benzetimlerdeise bozucu etkiler rastgele sinyaller olarak modellenmiş olup, denetim kazançlarıyükseltildiğinde bozucu etkilerin baskılanabildiği gözlemlenmektedir. Geri adımlamatekniğinin bozucu etkiler altında kararlılık analizi ile birlikte doğru akım motorunundenetimine uygulanması literatüre önemli bir katkı sunmaktadır.Article Citation Count: 0Automatic control of Hypothalamus-Pituitary-Adrenal axis dynamics(Elsevier Ireland Ltd, 2019) Doruk, R. Ozgur; Mohsin, Ahmed H.; Electrical-Electronics EngineeringBackground and Objective: In this study, a presentation is made for the automatic control of the hypothalamus-pituitary-adrenal axis which plays an important role in the immune stress responses and the circadian rhythms of mammalian organisms. Methods: Control approaches are implemented on a novel second order nonlinear system which accepts adrenocorticotropin hormone as an input and models the variation of plasma concentrations of adrenocorticotropin and cortisol respectively. The control methods are based on back-stepping and input-output feedback linearization techniques. The controllers adjust the adrenocorticotropin injection to maintain the daily rhythm of the cortisol concentration. In accordance with the periodicity of biological clock mechanism, we provide a sinusoidally varying cortisol reference to the controllers. Results: Numerical simulations are performed (on MATLAB) to demonstrate the closed loop performance of the controllers. Major concerns in the selection of the control gains are chattering and negative concentration in responses. The simulation results showed that one can successfully find gain levels which do not lead to those issues. However, the gains lie in different ranges for back-stepping and feedback linearization based controllers. Conclusion: The results showed that, both back-stepping and feedback linearization based controllers fulfilled their duty of synchronization of the cortisol concentration to a reference daily periodic rhythm. In addition to that, the risk of negative valued adrenocorticotropin injection can be eliminated by properly choosing the controller gains. (C) 2019 Elsevier B.V. All rights reserved.Article Citation Count: 0Bozucu torklar altında izdüşümsel doğru akım motoru kontrolü(Gazi Univ, Fac Engineering Architecture, 2018) Doruk, Reşat Özgür; Zuglam, İsmail; Electrical-Electronics EngineeringBu çalışmada, izdüşümsel doğrusal kareselservo geri beslemesi (P-LQSF) yöntemiyle tasarlanmış bir birdoğru akım (DC) motoru denetim yaklaşımı sunulmaktadır. Tasarlanan denetleyicinin kararlılığı girdidenhale-kararlılıkyaklaşımından yola çıkarak incelenmektedir. İzdüşümsel kontrol yöntemi, tam durumdeğişkeni geri beslemeli bir denetleyicinin özdeğer spektrumunu çıktı geri beslemesi kullanarak yaklaşıkolarak elde etmeyi amaçlar. Tasarlanan denetleyicilerin kararlılık analizi hem teorik hem de sayısalbenzetim yoluyla incelenecektir. Temel doğrusal kararlılığın yanı sıra, bozucu etkilerin kapalı döngüyü birdış girdi olarak etkilemesinden yola çıkarak girdiden-çıktıya-kararlılık kavramından yararlanılması olanaklıolabilmektedir. Sonuç olarak bir bozucu etkiden-hale-kararlılık yaklaşımı ortaya çıkmaktadır. Tasarımlar,elde edilen bu yaklaşımla incelenecektir. Performanslar ise sayısal benzetimler yoluyla görülecektir.Article Citation Count: 1Neuron modeling: estimating the parameters of a neuron model from neural spiking data(2018) Doruk, Reşat Özgür; Electrical-Electronics EngineeringWe 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.Article Citation Count: 0Fitting 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; Electrical-Electronics EngineeringThis 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).Article Citation Count: 5Control of Hopf Bifurcations in Hodgkin-Huxley Neurons by Automatic Temperature Manipulation(Anka Publisher, 2018) Doruk, Resat Ozgur; Electrical-Electronics EngineeringThe purpose of this research is to revisit the bifurcation control problem in Hodgkin-Huxley neurons. As a difference from the classical membrane potential feedback to manipulate the external current injection, we will actuate the temperature of the neural environment to control the bifurcations. In order to achieve this a linear feedback from the membrane potential is established to generate a time varying temperature profile. The considered bifurcating parameter is the external current injection. Upon finishing the controllers, the bifurcation analysis against the changes in external current injection is repeated in order to see the possibility of relapse of any bifurcation phenomena at nearby points. In addition to that, simulations are also provided to show the performances of the controllers.
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