Okan Erkaymaz

Impact of synaptic noise and conductance state on spontaneous cortical firing

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.

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.

Cell counting and recognition of immunohistochemically dyed seminiferous tubules with feed-forward neural network

In this study, the features of the seminiferous tubule sections were extracted and the presence of the cells and cell stain types detected with the help of the feed forward artificial neural network. By looking at the section view with a small window, 78 features were extracted from the pixels seen by the window and used as an input to the artificial neural network. Artificial neural network outputs are decides presence of the cell and the staining of the cell. The results obtained with the artificial neural network were determined by using the connected component labeling method. The results obtained with the help of the user and the results obtained with the artificial neural network were compared. It has been shown that the proposed ANN model performs cell counting process comparable to the literature (%76 accuracy).

Determination of the physiological effects of diabetic retinopathy disease from Video-Oculography (VOG) signals using discrete wavelet transform

The insulin hormone secreted from the pancreas gland in the body is not present in sufficient amount, or because they do not fit, which is defined as the elevation of blood glucose “Diabetes Mellitus (Diabetes)”. “Diabetic Retinopathy” is the most common in diabetes-related eye diseases. It had done damages in the retina that detect light on behind the eye as a result of changes in the arteries that is one of the reasons that makes blindness (loss of vision) in people. In this study, horizontal and vertical Video-Oculography (VOG) signals captured by using internal tracking camera in Metrovision MonPackOne Electrooculography device. In order to filter the noise from the signals, the wavelet transform method was used. Obtained signals have shown that the signals of diabetic retinopathy patients have higher amplitude and irregular characteristic than the signals obtained from healthy groups. In both groups, significant Daubechies-6 wavelet coefficients (A6-D6) gave better results than Daubechies-4 wavelet coefficients (A4-D4). Obtained data as a result of using wavelet transform sheds light on feature extraction and classification in proposed future works.

Effect of the Correlation in Synaptic Background Inputs on the Regularity of Neocortical Neuron Firing Activity

Neocortical neurons are subject to synaptic background activity. During activated states of the brain, it becomes very intense and affects the input-output characteristics of the neuron. In this study, the effect of the correlation in synaptic background inputs on the regularity of the cortical neuron firing activity is examined. For this purpose, a recently proposed point-conductance model was used. The model considers single-compartment neuron with global excitatory and inhibitory conductances that represent the sum of a large number of synaptic inputs. Twenty-eight different values of the correlation coefficient were used in the simulations. We changed the correlation of the background activity without affecting the average conductance due to the background activity. Therefore, the neuron received the same amount of random input with different correlation at each trial. For each value of the correlation, a simulation of 200 second duration was conducted and it was repeated 100 times. In order to examine the regularity of spike sequences obtained at each trial, the coefficient of variation of interspikes intervals was computed. Obtained results suggest that the neocortical neuron can detect the change of correlation in its input and encode it by increasing the regularity of the firing for the increasing correlation.