Rukiye Uzun

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

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 network. 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.

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

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 determined. 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.

Influences of autapse and channel blockage on multiple coherence resonance in a single neuron

We study how the spiking regularity of a single stochastic Hodgkin–Huxley neuron is effected in the presence of ion channel blocking and autaptic connection. In this study, we consider a chemical autapse expressed by its coupling strength and delay time. It is found that the neuron exhibits multiple coherence resonance (MCR) behavior induced by autaptic time delay at an appropriate level of ion channel blocking and autaptic coupling strength. This MCR behavior increases with the decrement of working potassium ion channels, whereas it decreases or completely disappears with the increment of a fraction of sodium ion channels blocking, regardless of autaptic coupling strength. Furthermore, this behavior is more explicit at intermediate autaptic coupling strength regardless of the ion channel blocking type. We briefly discuss the obtained results with the underlying reasons in terms of ion channel blocking type and autapse parameters. We also showed that ion channel noise, thus membrane patch size, should be at an optimal level to obtain MCR behavior otherwise, this behavior would be destroyed. The obtained results also showed that autaptic time delay is more operative on regularity than its coupling strength regardless of ion channel blocking. Considering the importance of spiking regularity on neuronal information processing, our results may help to understand the intersection of ion channel blocking and autaptic connections of a single neuron.

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

In this paper; the effects of autapse (a kind of synapse formed between the axon of the soma of a neuron and its own dendrites) and ion channel blocking 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 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. 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.

The effect of channel blocking on first spike timing

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.

Yeni bir organik ara tabaka esaslı Schottky diyodun elektronik karakterizasyonu

2-oxo-2-(2-tiyenilmetoksi) etil-2-metakrilat (TMOEM) ve 2-tiyenilmetil 4-vinilbenzil eter (TMVBE) yan zincirinde tiyofen iceren yeni metakrilik ve stirenik monomerlerdir. Bu calismada, yaklasik esit mol oranlarinda TMOEM ve TMVBE monomerlerinden sentezlenen poli(TMVBE-ko-TMOEM) kopolimeri metal/ara tabaka/yariiletken (MAY) Schottky diyot yapimi icin kullanildi. Elde edilen MAY diyodun akim-gerilim olcumleri gerceklestirildi. Akim-gerilim ( I-V ) grafiginden diyodun iyi bir dogrultucu davranisi ve yeterli ters beslem doyum akimi gosterdigi gozlendi. Ileri beslem I - V karakteristigi kullanilarak yapilan hesaplamalardan, Ag/poli(TMVBE-ko-TMOEM)/ p -Si MAY diyodunun idealite faktoru ve bariyer yuksekligi sirasiyla 2.34 ve 0.75 eV olarak bulundu. Ag/poli(TMVBE-ko-TMOEM)/ p -Si MAY diyodu icin bulunan bariyer yuksekliginin geleneksel Ag/ p -Si Schottky diyoduna gore daha yuksek oldugu goruldu. Ayrica, Cheung-Cheung methodu da idealite faktoru, bariyer yuksekligi ve seri direnc degerlerinin bulunmasi icin kullanildi ve bulunan sonuclar I-V metodu ile karsilastirildi.

Effect of channel blocking on spike propagation in myelinated axons

Voltage-gated ion channels embedded in neuronal membranes are of great importance in the generation and propagation of electrical signals in the excitable membranes. These channels fluctuate randomly between open and closed states. Blockage of a given channel type is crucial to understand the impact of specific ion channel type on the neuronal dynamics for a given cell size. In this study, the impact of channel blocking on the spike propagation through myelinated axons is examined by using a compartmental stochastic axon model. Potassium channel blocking increases the transmission reliability while sodium channel blocking decreases it. On the other hand, potassium channel blocking decreases the thresold value for the electrical coupling between the nodes of Ranvier while sodium channel blocking increases it. Results also show that an increase in transmission reliability results in the increase in the spike train regularity.

Effect of channel noise on spike propagation in myelinated axons

Voltage-gated ion channels embedded in neuronal membranes are stochastic devices fluctuating randomly between open and close states. These fluctuations results in noisy membrane currents and subthreshold oscillations. The intensity of the ion channel noise is inversely related to the cell size (or the total number of ion channels): the smaller the cell size, the larger the intensity of channel noise. When the cell size is large enough, stochastic effects of the channel noise becomes negligible and the collective dynamics approaches the deterministic description. However, when the cell size is small, their stochastic behaviors have significant impacts on the membrane dynamics. The generated spikes (or action potentials) are propagated through an axon. In many vertebrates, the propagation of the spikes is mediated by myelinated axons, where the voltage-gated ion channels are accumulated in the nodes of Ranvier. Therefore, the stochastic behavior of the channels in these nodes affects the spike propagation. In this study, the impact of channel noise on the spike propagation through myelinated axons is examined by using a compartmental stochastic axon model.