Yazar "Ozer, Mahmut" seçeneğine göre listele

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    Detection of directional eye movements based on the electrooculogram signals through an artificial neural network
    (Pergamon-Elsevier Science Ltd, 2015) Erkaymaz, Hande; Ozer, Mahmut; Orak, Ilhami Muharrem
    The electrooculogram signals are very important at extracting information about detection of directional eye movements. Therefore, in this study, we propose a new intelligent detection model involving an artificial neural network for the eye movements based on the electrooculogram signals. In addition to conventional eye movements, our model also involves the detection of tic and blinking of an eye. We extract only two features from the electrooculogram signals, and use them as inputs for a feed-forwarded artificial neural network. We develop a new approach to compute these two features, which we call it as a movement range. The results suggest that the proposed model have a potential to become a new tool to determine the directional eye movements accurately. (C) 2015 Elsevier Ltd. All rights reserved.
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    Electrooculogram and Diplopia Controlled Fuzzy Direction Detect System
    (Ieee, 2014) Erkaymaz, Hande; Orak, I. Muharrem; Ozer, Mahmut
    Coding of the organ movements have been seen commonly in the hardware designing which is useful for humanity in scientific researches. 5 sense organs which reveal different data with cellular structures, is the most important focus point. Especially, Eye is known as photosensitive sensor organ of living creature. There is a potential difference between cornea and retina of eye. This potential difference is known as electrooculogram (FOG). Nowadays, FOG signals are commonly used in these hardware designs. Obtained potential differences from FOG signals have been converted coded directional movements. Thus, in this study, 4 basic direction movements have been tried to detect with a fuzzy controlled model. It is shown that the fuzzy controlled system as determine the direction can be used successfully. In addition, eye direction movements of squint person are obtained by the system.
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    EOG Based Intelligent Direction Detect System with Pre-Filtering Algorithm
    (Ieee, 2015) Erkaymaz, Hande; Ozer, Mahmut; Kaya, Ceren; Orak, I. Muharrem
    Nowadays, artificial movements have been obtained by utilizing other organs for paralyzed patients. Especially the usage of eye movements for giving message to outside world became popular as a scientific subject. In studies according to eye movements, the Electrooculogram (EOG) signal is used. In this study, the vertical and horizontal FOG signals taken from electrodes, placed around the eyes, have been modelled by using Artificial Neural Networks (ANN) which is one of artificial intelligent technique. The system can sense four main directions (Right, Left, Up and Down) at the same time it can also detect blinking movements. Firstly, the signals have been pre-filtered, amplified and classified by ANN. The performance of recommended model has been demonstrated by analyzing statistical accuracy and confusion matrix according to the features of obtained signal. It has been seen that eye movements can be successfully determined by designed model.
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    Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems
    (Tubitak Scientific & Technological Research Council Turkey, 2014) Erkaymaz, Okan; Ozer, Mahmut; Yumusak, Nejat
    Since feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.
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    Impact of synaptic noise and conductance state on spontaneous cortical firing
    (Lippincott Williams & Wilkins, 2007) Ozer, Mahmut; Graham, Lyle J.; Erkaymaz, Okan; Uzuntarla, Muhammet
    Cortical neurons in-vivo operate in a continuum of overall conductance states, depending on the average level of background synaptic input throughout the dendritic tree. We compare how variability, or fluctuations, in this input affects the statistics of the resulting 'spontaneous' or 'background' firing activity, between two extremes of the mean input corresponding to a low-conductance (LC) and a high-conductance (HC) state. In the HC state, we show that both firing rate and regularity increase with increasing variability. In the LC state, firing rate also increases with input variability, but in contrast to the HC state, firing regularity first decreases and then increases with an increase in the variability. At high levels of input variability, firing regularity in both states converge to similar values.
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    Impact of The Ion Channel Blockage on the Collective Spiking Regularity of a Scale-Free Neuronal Network
    (Elsevier Science Bv, 2012) Yilmaz, Ergin; Ozer, Mahmut; Cavusoglu, Abdullah
    The voltage-gated ion channels embedded in biological membranes play crucial roles on the generation and transmission of action potentials. Therefore, understanding of impacts of each ion channel is of great importance for the dynamics of neuronal networks. Among the others, one method on this way is to block a specific ion channel type while keeping the remaining ion channel types active across the membrane and to observe their impact on neuronal dynamics. In this study, we study the effects of sodium and potassium channels blockage on the collective spiking regularity of a scale-free neuronal network with stochastic Hodgkin-Huxley neurons, and investigate how the dependence of the collective spiking regularity on the membrane area or cell size varies with the coupling constant between neurons. Results reveal that the collective spiking regularity exhibits coherence resonance (CR) depending on the channel blockage scaling factor and the cell size, where potassium channel blockage enhances the collective spiking regularity whereas sodium channel blockage decreases it. We show that there is a lower limit for the coupling constant which warrants the CR behavior. We also show that the maximal regularity is obtained for a smaller cell size with the increasing the value of the coupling constant.
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    Performance Analysis of A Feed-Forward Artifical Neural Network With Small-World Topology
    (Elsevier Science Bv, 2012) Erkaymaz, Okan; Ozer, Mahmut; Yumusak, Nejat
    Feed Forward Artificial Neural Networks are the most widely used models to explain the information processing mechanism of the brain. Network topology plays a key role in the performance of the feed forward neural networks. Recently, the small-world network topology has been shown to meet the properties of the real life networks. Therefore, in this study, we consider a feed forward artificial neural network with small-world topology and analyze its performance on classifying the epilepsy. In order to obtain the small-world network, we follow the Watts-Strogatz approach. An EEG dataset taken from healthy and epileptic patients is used to test the performance of the network. We also consider different numbers of neurons in each layer of the network. By comparing the performance of small-world and regular feed forward artificial neural networks, it is shown that the Watts-Strogatz small-world network topology improves the learning performance and decreases the training time. To our knowledge, this is the first attempt to use small-world topology in a feed forward artificial neural network to classify the epileptic case.
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    Stochastic Multi-Resonances on Scale-Free Hodgkin-Huxley Neuronal Networks via Pacemaker
    (Turgut Ozal Univ, 2012) Yilmaz, Ergin; Ozer, Mahmut
    Contradictory to the intuitive belief, noise has constructive effects on the dynamical processes in nonlinear systems. In this paper, we investigated the effects of the frequency of stimuli, which is introduced to the one of the neuron that acts as a pacemaker and the information transmission delay on the stochastic resonance phenomena depends on channel noise in scale-free Hodgkin-Huxley (HH) neuronal networks. The obtained results reveal that irrespective of the placing of the pacemaker within the network there exists several frequencies at which the correlation between the rhythm of pacemaker and the response of the network resonantly depends on channel noise intensity. The results also demonstrated that proper values of delays can trigger stochastic multiresonances occurring that at every multiple of the pacemaker's oscillation period.