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The effect of channel blocking on first spike timing

Uzun, Rukiye | Özer, Mahmut


We investigate the first-spike response latency dynamics of a single Hodgkin-Huxley neuron model with a tunable intensity of intrinsic noise and fraction of blocked voltage-gated sodium and potassium ion channels embedded in biological membranes. In contrast to previous studies, we consider a biophysically realistic neuron model which contains stochastic ion channels. We show that potassium ion channels play a key role than sodium ion channels on the appearance of the noise delayed decay (NDD) effect in the first-spike timing.

Effects of autapse and channel blockage on firing regularity in a biological neuronal network

Uzun, Rukiye | Özer, Mahmut

Proceedings | 2017 | Istanbul University - Journal of Electrical and Electronics Engineering17 , pp.3069 - 3073

In this paper; the effects of autapse (a kind of self-synapse formed between the axon of the soma of a neuron and its own dendrites) and ion channel blockage on the firing regularity of a biological small-world neuronal network, consists of stochastic Hodgkin-Huxley neurons, are studied. In this study, it is assumed that all of the neurons on the network have a chemical autapse and a constant membrane area. Obtained results indicate that there are different effects of channel blockage and parameters of the autapse on the regularity of the network, thus on the temporal coherence of the network. It is found that the firing regularity . . .of the network is decreased with the sodium channel blockage while increased with potassium channel blockage. Besides, it is determined that regularity of the network augments with the conductance of the autapse Daha fazlası Daha az

Comparison of artificial neural network and regression models to diagnose of knee disorder in different postures using surface electromyography

Uzun, Rukiye | Erkaymaz, Okan | Şenyer Yapıcı, İrem

Article | 2018 | Gazi University Journal of Science31 ( 1 ) , pp.100 - 110

The surface electromyography (sEMG) is useful tool to diagnose of knee disorder in clinical environments. It assists in designing the clinical decision support systems based classification. These systems exhibit complex structure because of sEMG data obtained at different postures at this study. In this context, we have researched the classification performance of each posture using artificial neural network (ANN) and logistic regression (LR) models and have showed that the classification success of the model used sitting posture data is higher than other postures (gait and standing). We have promoted this finding by using machine l . . .earning and statistical methods. The results show that the proposed models can classify with over 95% of success, and also the ANN model has higher performance than the LR model. Our ANN model outperforms reported studies in literature. The accuracy results indicate that the models used the only sitting posture data can exhibit successful classification for the knee disorder. Therefore, the usage of complex dataset is prevented for diagnosing knee disorder. © 2018, Gazi University Eti Mahallesi. All rights reserved Daha fazlası Daha az

Computer-assisted diagnosis of vertebral column diseases by adaptive neuro-fuzzy inference system

Uzun, Rukiye | İşler, Yalçın | Erkaymaz, Okan | Kocadayı, Yasemin

Proceedings | 2018 | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 , pp.1 - 4

In this study, a clustering algorithm based on adaptive neural fuzzy inference system (ANFIS) was used for computer-assisted diagnosis of the vertebral column disorder from machine learning databases of UCI (University of California Irvine). Features of pelvic incidence, pelvic tilt and lumbar lordosis angle given in this dataset was applied to the inputs of the algorithm. The performance of algorithm was evaluated using mean square error and regression coefficient criteria to discriminate patients with vertebral column disease from healthy subjects. As a result, the classification performance of 90.82% was obtained by using the gen . . .erated model of ANFIS. © 2018 IEEE Daha fazlası Daha az

Can scale-freeness offset delayed signal detection in neuronal networks?

Uzun, Rukiye | Özer, Mahmut | Perc, Matjaž

Article | 2014 | EPL105 ( 6 ) , pp.1 - 4

First-spike latency following stimulus onset is of significant physiological relevance. Neurons transmit information about their inputs by transforming them into spike trains, and the timing of these spike trains is in turn crucial for effectively encoding that information. Random processes and uncertainty that underly neuronal dynamics have been shown to prolong the time towards the first response in a phenomenon dubbed noise-delayed decay. Here we study whether Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise might have shorter response times to external stimuli just above threshold if placed on a scale-free netw . . .ork. We show that the heterogeneity of the interaction network may indeed eradicate slow responsiveness, but only if the coupling between individual neurons is sufficiently strong. Increasing the average degree also favors a fast response, but it is less effective than increasing the coupling strength. We also show that noise-delayed decay can be offset further by adjusting the frequency of the external signal, as well as by blocking a fraction of voltage-gated sodium or potassium ion channels. For certain conditions, we observe a double peak in the response time depending on the intensity of intrinsic noise, indicating competition between local and global effects on the neuronal dynamics. © Copyright EPLA, 2014 Daha fazlası Daha az

Effects of autapse and ion channel block on the collective firing activity of Newman–Watts small-world neuronal networks

Uzun, Rukiye | Yılmaz, Ergin | Özer, Mahmut

Article | 2017 | Physica A: Statistical Mechanics and its Applications486 , pp.386 - 396

An autapse is a special kind of synapse established between the axon and dendrites of the same neuron. In the present study, we have investigated the cooperative effects of autapse and ion channel block on the collective firing regularity of Newman–Watts small-world networks of stochastic Hodgkin–Huxley neurons. We obtain autaptic time delay induced multi-coherence resonance (MCR) phenomenon in the absence of ion channel block. When the ion channel block is considered, we find that this autaptic delay induced MCR phenomenon enhances with the increasing of potassium channel block, whereas it weakens with the increasing of sodium chan . . .nel block at weak and intermediate autaptic conductance regimes. However, at strong autaptic conductance regime neither sodium nor potassium channel block have a significant effect on the collective firing regularity of the network. Besides, we investigate the effects of the coupling strength, the network randomness and the cell size on the regularity. We obtain an optimal coupling strength value and an optimal cell size leading to a more prominent MCR effect. We also show that the MCR phenomenon increases with the increasing of network randomness in potassium channel block, but it needs to a minimum network randomness for its appearing in case of sodium channel block. © 2017 Elsevier B.V Daha fazlası Daha az

Noise-delayed decay in the response of a scale-free neuronal network

Uzuntarla, Muhammet | Uzun, Rukiye | Yılmaz, Ergin | Özer, Mahmut | Perc, Matjaž

Article | 2013 | Chaos, Solitons and Fractals56 , pp.202 - 208

Noise-delayed decay occurs when the first-spike latency of a periodically forced neuron exhibits a maximum at particular noise intensity. Here we investigate this phenomenon at the network level, in particular by considering scale-free neuronal networks, and under the realistic assumption of noise being due to the stochastic nature of voltage-gated ion channels that are embedded in the neuronal membranes. We show that noise-delayed decay can be observed at the network level, but only if the synaptic coupling strength between the neurons is weak. In case of strong coupling or in a highly interconnected population the phenomenon vanis . . .hes, thus indicating that delays in signal detection can no longer be resonantly prolonged by noise. We also find that potassium channel noise plays a more dominant role in the occurrence of noise-delayed decay than sodium channel noise, and that poisoning the neuronal membranes may weakens or intensify the phenomenon depending on targeting. © 2013 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Effects of autaptic coupling stregth and ion channel blockage on firing regularity in a Hodgkin-Huxley neuron

Uzun, Rukiye | Özer, Mahmut

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 , pp.202 - 208

We investigate how firing dynamics of neuron changes with the autpse's coupling strength for different ratio of the blockage of potassium and sodium ion channels embedded in membranes affects, with using a biophysically more realistic Hodgkin-Huxley neuron model. In the study, we assume that neuron has an excitatory chemical autapse. It is found that the neuron dynamics does not change for too small coupling strength whether it increases after a certain coupling strength' value in case of different ion channel blockage case. Besides, it is determined that potassium ion channel blockage has a healing effect whereas sodium ion channel . . . blockage has a destructive effect on the neuron dynamics increasing with the autaptic coupling strength. © 2017 IEEE Daha fazlası Daha az

Detection of knee abnormality from surface EMG signals by artificial neural networks

Erkaymaz, Okan | Senyer, İrem | Uzun, Rukiye

Proceedings | 2017 | 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 , pp.202 - 208

Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was de . . .termined. The highest classification performance of proposed model was obtained in rate fiction 80%-20% and 70%-30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals. © 2017 IEEE Daha fazlası Daha az

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