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International Journal of Neural Systems

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https://read.qxmd.com/read/30975004/introduction
#1
Vasilios K Kimiskidis, Steven Schachter
No abstract text is available yet for this article.
April 11, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30961408/hypothermia-masks-most-of-the-effects-of-rapid-cycling-vns-on-rat-hippocampal-electrophysiology
#2
Wouter Van Lysebettens, Kristl Vonck, Lars Emil Larsen, Mathieu Sprengers, Evelien Carrette, Charlotte Bouckaert, Jean Delbeke, Wytse Jan Wadman Paul Boon, Robrecht Raedt
AIM: Vagus nerve stimulation (VNS) modulates hippocampal dentate gyrus (DG) electrophysiology and induces hypothermia in freely moving rats. This study evaluated whether hippocampal (CA1) electrophysiology is similarly modulated and to what extent this is associated with VNS-induced hypothermia. METHODS: Six freely moving rats received a first 4 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mspace/> </mml:math> h session of rapid cycling VNS (7 <mml:math xmlns:mml="http://www...
February 14, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30929575/strong-weak-pruning-for-brain-network-identification-in-connectome-wide-neuroimaging-application-to-amyotrophic-lateral-sclerosis-disease-stage-characterization
#3
Angela Serra, Paola Galdi, Emanuele Pesce, Michele Fratello, Francesca Trojsi, Gioacchino Tedeschi, Roberto Tagliaferri, Fabrizio Esposito
Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the "hub" of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions...
February 14, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30880526/compact-hardware-synthesis-of-stochastic-spiking-neural-networks
#4
Fabio Galán-Prado, Alejandro Morán, Joan Font, Miquel Roca, Josep L Rosselló
Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biologically-inspired SNN is currently a complex challenge of high scientific and technical interest. In this work, we propose a compact digital design for the implementation of high-volume SNN that considers the intrinsic stochastic processes present in biological neurons and enables high-density hardware implementation...
February 8, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30854906/monitor-based-spiking-recurrent-network-for-the-representation-of-complex-dynamic-patterns
#5
Ruihan Hu, Qijun Huang, Hao Wang, Jin He, Sheng Chang
Neural networks are powerful computation tools for mimicking the human brain to solve realistic problems. Since spiking neural networks are a type of brain-inspired network, called the novel spiking system, Monitor-based Spiking Recurrent network (MbSRN), is derived to learn and represent patterns in this paper. This network provides a computational framework for memorizing the targets using a simple dynamic model that maintains biological plasticity. Based on a recurrent reservoir, the MbSRN presents a mechanism called a 'monitor' to track the components of the state space in the training stage online and to self-sustain the complex dynamics in the testing stage...
February 8, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30859856/enhanced-data-covariance-estimation-using-weighted-combination-of-multiple-gaussian-kernels-for-improved-m-eeg-source-localization
#6
J D Martinez-Vargas, L Duque-Muñoz, F Vargas-Bonilla, J D Lopez, G Castellanos-Dominguez
In the recent past, estimating brain activity with magneto/electroencephalography (M/EEG) has been increasingly employed as a noninvasive technique for understanding the brain functions and neural dynamics. However, one of the main open problems when dealing with M/EEG data is its non-Gaussian and nonstationary structure. In this paper, we introduce a methodology for enhancing the data covariance estimation using a weighted combination of multiple Gaussian kernels, termed WM-MK, that relies on the Kullback-Leibler divergence for associating each kernel weight to its relevance...
January 22, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30880525/regularized-group-sparse-discriminant-analysis-for-p300-based-brain-computer-interface
#7
Qiang Wu, Yu Zhang, Ju Liu, Jiande Sun, Andrzej Cichocki, Feng Gao
Event-related potentials (ERPs) especially P300 are popular effective features for brain-computer interface (BCI) systems based on electroencephalography (EEG). Traditional ERP-based BCI systems may perform poorly for small training samples, i.e. the undersampling problem. In this study, the ERP classification problem was investigated, in particular, the ERP classification in the high-dimensional setting with the number of features larger than the number of samples was studied. A flexible group sparse discriminative analysis algorithm based on Moreau-Yosida regularization was proposed for alleviating the undersampling problem...
January 14, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30841769/how-far-can-neural-correlations-reduce-uncertainty-comparison-of-information-transmission-rates-for-markov-and-bernoulli-processes
#8
Agnieszka Pregowska, Ehud Kaplan, Janusz Szczepanski
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 definition of information is to combine information with uncertainty: the higher the uncertainty of a given event, the more information is conveyed by that event. Correlations can reduce uncertainty or the amount of information, but by how much? In this paper we address this question by a direct comparison of the information per symbol conveyed by the words coming from a binary Markov source (temporal code) with the information per symbol coming from the corresponding Bernoulli source (uncorrelated, rate code)...
January 14, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30776988/alternative-diagnosis-of-epilepsy-in-children-without-epileptiform-discharges-using-deep-convolutional-neural-networks
#9
Lung-Chang Lin, Chen-Sen Ouyang, Rong-Ching Wu, Rei-Cheng Yang, Ching-Tai Chiang
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 (ED) specifically indicate epilepsy, only approximately 50% of patients with epilepsy have ED in their first EEG. In this study, we developed a deep convolutional neural network (ConvNet)-based classifier to distinguish EEG between patients with epilepsy without ED and controls...
January 8, 2019: International Journal of Neural Systems
https://read.qxmd.com/read/30776985/an-attention-based-spiking-neural-network-for-unsupervised-spike-sorting
#10
Marie Bernert, Blaise Yvert
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 each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience...
December 27, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30587047/introduction
#11
Piotr Suffczynski
No abstract text is available yet for this article.
December 26, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30563385/introduction
#12
José Manuel Ferrández, Diego Andina, Eduardo Fernández
No abstract text is available yet for this article.
December 19, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30782022/a-machine-learning-approach-to-reveal-the-neurophenotypes-of-autisms
#13
Juan M Górriz, Javier Ramírez, F Segovia, Francisco J Martínez, Meng-Chuan Lai, Michael V Lombardo, Simon Baron-Cohen, John Suckling
Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants...
December 13, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30776987/proprioceptive-recognition-with-artificial-neural-networks-based-on-organizations-of-spinocerebellar-tract-and-cerebellum
#14
Hui Guang, Linhong Ji
Muscle kinematics and kinetics are nonlinearly encoded by proprioceptors, and the changes in muscle length and velocity are integrated into Ia afferent. Besides, proprioceptive signals from multiple muscles are probably mixed in afferent pathways, which all lead to difficulties in proprioceptive recognition for the cerebellum. In this study, the artificial neural networks, whose organizations are biologically based on the spinocerebellar tract and cerebellum, are utilized to decode the proprioceptive signals...
December 9, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30776986/multiplex-limited-penetrable-horizontal-visibility-graph-from-eeg-signals-for-driver-fatigue-detection
#15
Qing Cai, Zhong-Ke Gao, Yu-Xuan Yang, Wei-Dong Dang, Celso Grebogi
Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior...
December 9, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30556459/a-3d-convolutional-neural-network-to-model-retinal-ganglion-cell-s-responses-to-light-patterns-in-mice
#16
Antonio Lozano, Cristina Soto-Sánchez, Javier Garrigós, J Javier Martínez, J Manuel Ferrández, Eduardo Fernández
Deep Learning offers flexible powerful tools that have advanced our understanding of the neural coding of neurosensory systems. In this work, a 3D Convolutional Neural Network (3D CNN) is used to mimic the behavior of a population of mice retinal ganglion cells in response to different light patterns. For this purpose, we projected homogeneous RGB flashes and checkerboards stimuli with variable luminances and wavelength spectrum to mimic a more naturalistic stimuli environment onto the mouse retina. We also used white moving bars in order to localize the spatial position of the recorded cells...
December 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30556458/author-index-volume-28-2018
#17
(no author information available yet)
No abstract text is available yet for this article.
December 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30646793/a-method-for-suppressing-electrical-stimulation-artifacts-from-electromyography
#18
Yurong Li, Jun Chen, Yuan Yang
When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities...
November 14, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30638083/cortical-thickness-and-surface-area-networks-in-healthy-aging-alzheimer-s-disease-and-behavioral-variant-fronto-temporal-dementia
#19
Vesna Vuksanović, Roger T Staff, Trevor Ahearn, Alison D Murray, Claude M Wischik
Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer's disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort...
November 14, 2018: International Journal of Neural Systems
https://read.qxmd.com/read/30614325/perceptual-generalization-and-context-in-a-network-memory-inspired-long-term-memory-for-artificial-cognition
#20
Richard J Duro, Jose A Becerra, Juan Monroy, Francisco Bellas
In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what different authors call "automation" of what is learnt, as a complementary system to more common prospective functions. The LTM proposed here provides for a relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that is representative of the contexts in which they are relevant in a configural associative structure...
November 14, 2018: International Journal of Neural Systems
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