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Araştırmacılar

Weak signal propagation through noisy feedforward neuronal networks
Optical signal processing of interference fringes by Hartley transform method
Propagation of firing rate in a feedforward network with stochastic Hodgkin-Huxley neurons
Discretization approach to EEG signal classification using multilayer perceptron neural network model [EEG i·şaretlerinin çok-katmanli algilayici yapay sinir agi modeli ile siniflandirilmasinda ayriklaştirma yaklaşimi]

- Mühendislik Fakültesi 4
- Elektrik - Elektronik Mühendisliği Bölümü 4
- Fakülteler 4
- Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD 4
- Bildiri Koleksiyonu (Elektrik - Elektronik Mühendisliği Bölümü) 3
- Scopus İndeksli Yayınlar Koleksiyonu 3
- WoS İndeksli Yayınlar Koleksiyonu 3
- Bildiri Koleksiyonu (Fizik Bölümü) 1
- Fen Edebiyat Fakültesi 1
- Fizik Bölümü 1
- Makale Koleksiyonu (Elektrik - Elektronik Mühendisliği Bölümü) 1 Daha fazlası Daha az

Özer, Mahmut | Perc, Matjaž | Uzuntarla, Muhammet | Koklukaya, Etem

Article | 2010 | NeuroReport21 ( 5 ) , pp.338 - 343

We determine under which conditions the propagation of weak periodic signals through a feedforward Hodgkin-Huxley neuronal network is optimal. We find that successive neuronal layers are able to amplify weak signals introduced to the neurons forming the first layer only above a certain intensity of intrinsic noise. Furthermore, we show that as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the feedforward neuronal network, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted. © 2010 Wolters Kluwer Health | Lippincott Wil . . .liams & Wilkins Daha fazlası Daha az

Kaya, Hakan | Saraç, Zehra | Özer, Mahmut | Taşkın, Halit

Proceedings | 2010 | Proceedings of SPIE - The International Society for Optical Engineering7746 , pp.338 - 343

In this paper, the processing of interference fringes is achieved by Hartley transform method. The experimental and simulated interference fringe patterns are used for the signal analysis. Phase results are presented. These are compared with phase obtained by Fourier transform method. Disadvantages and advantages of Hartley transform method used for the evaluation of interference fringe patterns are given. © 2010 SPIE.

Uzuntarla, Muhammet | Özer, Mahmut | Köklükaya, Etem

Proceedings | 2010 | ANALYSIS OF BIOMEDICAL SIGNALS AND IMAGES , pp.122 - 128

We study firing rate propagation in a feedforward network with multiple layers. Neurons in the network are modeled by using stochastic Hodgkin-Huxley neuronal model, which considers stochastic behaviour of voltage-gated ion channels embedded in neuronal membranes. In the model, ion channel noise due to its stochastic behaviour is related to the cell size in such a way that the noise strength increases with decreasing the cell size, mimicking the actual biophysical conditions. An external additive current noise is also injected into the first layer of the network. Therefore, neurons in the first layer are subject to both internal and . . . external noise while neurons in the subsequent layers are subject to only internal noise. It is shown that the efficient transmission of firing rates requires an appropriate intrinsic noise level in the network if the input firing rate or the external noise strength is higher than a critical value Daha fazlası Daha az

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Proceedings | 2010 | 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010 , pp.122 - 128

Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron Neural Network (MLPNN) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval were selected on amplitude axis of each EEG segment. EEG signals were rearranged by way of that each amplitude value was shifted to the center point closest to itself. Equal width discretization (EWD) method was used for rearrangement process. . . .Wavelet coefficients of each segment of EEG signals were computed by using discrete wavelet transform (DWT). The mean, the standard deviation and the entropy of these coefficients was used as the inputs of MLPNN model. The model was protected from the overfitting by cross validation. Two different classification experiments were implemented by the same MLPNN model: 1) the classification of healthy volunteers, epilepsy patients during seizure and epilepsy patients during a seizure-free interval, 2) the classification of epilepsy patients during seizure and seizure-free interval. MLPNN model classified EEG signals with the accuracy of 99.60% in first experiment and 100% in second experiment. It is observed that MLPNN classification of EEG signals after rearrangement in amplitude axis provides better results. ©2010 IEEE Daha fazlası Daha az

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