The role of ion channel blocking on synchronization-induced termination in excitatory neuronal networks

Palabaş, Tuğba | Gürleyen, Hatice Hilal | Uzuntarla, Muhammet

Proceedings | 2019 | TIPTEKNO 2019 - Tip Teknolojileri Kongresi

It is known that activity is terminated abruptly as a result of strong synchronization in bistable neuron populations when there is sufficient current stimulation. The aim of this study is to investigate the effect of ion channel blocking on the phenomenon of spontaneous termination of ongoing activity in the bistable neural network connected by excitatory chemical synapses using stochastic Hodgkin-Huxley (H-H) equations. The obtained results show that significant changes in the dynamics of neurons occur due to the blocking of potassium ion channels at different rates depending on the coupling strength. As the coupling of synaptic i . . .nteraction increases, the synchronization between neurons increases and the activity terminates. Simulation results showed that sodium ion channels are not effective on this phenomenon. © 2019 IEEE Daha fazlası Daha az

Classification of cervical cancer data and the effect of random subspace algorithms on classification performance

Erkaymaz, Okan | Palabaş, Tuğba

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

Computer assisted automatic diagnostic systems are used for the purpose of speeding up diagnosis and treatment and helping to make the right decision. In this study, cervical cancer is identified using four basic classifiers: Naive Bayes (NB), k-Nearest Neighbor (kNN), Multilayer Perceptron (MLP) and Decision Trees (KA-C4.5) algorithms and random subspaces ensemble algorithm. Gain Ratio Attribute Evaluation (GRAE) feature extraction algorithm is applied to contribute to classification performance. The classification results obtained with all datasets and reduced datasets are compared with respect to performance criteria such as accu . . .racy, Root Mean Square Error (RMSE), Sensitivity, Specificity performance criteria. According to the obtained performance analysis, it is seen that the classification performance with the random subspace ensemble algorithm using the kNN basic classifier on the reduced data set is the highest (%95.51). © 2018 IEEE Daha fazlası Daha az

6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.