Lin, Lung-Chang Ouyang, Chen-Sen Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai
Published in
International journal of neural systems
Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosis can lead to mistreatment, affecting patients' lives considerably. Electroencephalography is commonly used for diagnosing epilepsy. Although on electroencephalograms (EEGs), epileptiform discharges (...
Plakias, Spyridon Boutalis, Yiannis S
Published in
International journal of neural systems
This paper introduces a novel fusion neural architecture and the use of a novel Lyapunov theory-based algorithm, for the online approximation of the dynamics of nonlinear systems. The proposed neural system, in combination with the proposed update rule of the neural weights, achieves fast convergence of the identification process, ensuring at the s...
Manzanera, Octavio Martinez Meles, Sanne K Leenders, Klaus L Renken, Remco J Pagani, Marco Arnaldi, Dario Nobili, Flavio Obeso, Jose Oroz, Maria Rodriguez Morbelli, Silvia
...
Published in
International journal of neural systems
Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representat...
Xia, Liang-Yong Wang, Qing-Yong Cao, Zehong Liang, Yong
Published in
International journal of neural systems
Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure-activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse log...
Pregowska, Agnieszka Kaplan, Ehud Szczepanski, Janusz
Published in
International journal of neural systems
The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the firing rates of individual spikes (rate code) or by the precise timing of every spike (temporal code)? Here we compare the loss of information due to correlations for these two possible neural codes. The essence of Shannon's d...
Bernert, Marie Yvert, Blaise
Published in
International journal of neural systems
Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for ...
Sun, Chengfa Cui, Hui Zhou, Weidong Nie, Weiwei Wang, Xiuying Yuan, Qi
Published in
International journal of neural systems
Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To ...
Park, Sang-Eon Laxpati, Nealen G Gutekunst, Claire-Anne Connolly, Mark J Tung, Jack Berglund, Ken Mahmoudi, Babak Gross, Robert E
Published in
International journal of neural systems
The medial septum (MS) is a potential target for modulating hippocampal activity. However, given the multiple cell types involved, the changes in hippocampal neural activity induced by MS stimulation have not yet been fully characterized. We combined MS optogenetic stimulation with local field potential (LFP) recordings from the hippocampus and lev...
Kimiskidis, Vasilios K Schachter, Steven
Published in
International journal of neural systems
Koutlis, Christos Kimiskidis, Vasilios K Kugiumtzis, Dimitris
Published in
International journal of neural systems
The study of connectivity patterns of a system's variables, such as multi-channel electroencephalograms (EEG), is of utmost importance towards a better understanding of its internal evolutionary mechanisms. Here, the problem of estimating the connectivity network from multivariate time series in the presence of prominent unobserved variables is add...
