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Neural Networks: the Official Journal of the International Neural Network Society

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https://www.readbyqxmd.com/read/29763744/characterization-of-electroencephalography-signals-for-estimating-saliency-features-in-videos
#1
Zhen Liang, Yasuyuki Hamada, Shigeyuki Oba, Shin Ishii
Understanding the functions of the visual system has been one of the major targets in neuroscience formany years. However, the relation between spontaneous brain activities and visual saliency in natural stimuli has yet to be elucidated. In this study, we developed an optimized machine learning-based decoding model to explore the possible relationships between the electroencephalography (EEG) characteristics and visual saliency. The optimal features were extracted from the EEG signals and saliency map which was computed according to an unsupervised saliency model ( Tavakoli and Laaksonen, 2017)...
May 12, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29793128/convolutional-neural-networks-for-seizure-prediction-using-intracranial-and-scalp-electroencephalogram
#2
Nhan Duy Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Samuel Ippolito, Omid Kavehei
Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently...
May 7, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29793129/global-mittag-leffler-stability-and-synchronization-analysis-of-fractional-order-quaternion-valued-neural-networks-with-linear-threshold-neurons
#3
Xujun Yang, Chuandong Li, Qiankun Song, Jiyang Chen, Junjian Huang
This paper talks about the stability and synchronization problems of fractional-order quaternion-valued neural networks (FQVNNs) with linear threshold neurons. On account of the non-commutativity of quaternion multiplication resulting from Hamilton rules, the FQVNN models are separated into four real-valued neural network (RVNN) models. Consequently, the dynamic analysis of FQVNNs can be realized by investigating the real-valued ones. Based on the method of M-matrix, the existence and uniqueness of the equilibrium point of the FQVNNs are obtained without detailed proof...
May 4, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29783042/synchronization-of-hybrid-coupled-reaction-diffusion-neural-networks-with-time-delays-via-generalized-intermittent-control-with-spacial-sampled-data
#4
Binglong Lu, Haijun Jiang, Cheng Hu, Abdujelil Abdurahman
The exponential synchronization of hybrid coupled reaction-diffusion neural networks with time delays is discussed in this article. At first, a generalized intermittent control with spacial sampled-data is introduced, which is intermittent in time and data sampling in space. This type of control strategy not only can unify the traditional periodic intermittent control and the aperiodic case, but also can lower the update rate of the controller in both temporal and spatial domains. Next, based on the designed control protocol and the Lyapunov-Krasovskii functional approach, some novel and readily verified criteria are established to guarantee the exponential synchronization of the considered networks...
May 4, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29758462/global-stabilization-analysis-of-inertial-memristive-recurrent-neural-networks-with-discrete-and-distributed-delays
#5
Leimin Wang, Zhigang Zeng, Ming-Feng Ge, Junhao Hu
This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs under proposed feedback control, where the cases with both bounded and unbounded distributed delays are successfully addressed...
May 2, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29763742/period-adding-bifurcation-and-chaos-in-a-hybrid-hindmarsh-rose-model
#6
Yi Yang, Xiaofeng Liao, Tao Dong
Recently, the hybrid neuron models which combine the basic neuron models with impulsive effect(the state reset process) had been proposed, however, the preset value and the reset value of membrane potential were both fixed constants in the known models. In this paper, the Hindmarsh-Rose neuron model with nonlinear reset process is presented where the preset value and the reset value of membrane potential are variable constants. We conduct a qualitative analysis in the vicinity of the equilibrium point or the limit cycle of the proposed system by using the theories of impulsive semi-dynamical systems...
April 27, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29751346/stochastic-exponential-synchronization-of-memristive-neural-networks-with-time-varying-delays-via-quantized-control
#7
Wanli Zhang, Shiju Yang, Chuandong Li, Wei Zhang, Xinsong Yang
This paper focuses on stochastic exponential synchronization of delayed memristive neural networks (MNNs) by the aid of systems with interval parameters which are established by using the concept of Filippov solution. New intermittent controller and adaptive controller with logarithmic quantization are structured to deal with the difficulties induced by time-varying delays, interval parameters as well as stochastic perturbations, simultaneously. Moreover, not only control cost can be reduced but also communication channels and bandwidth are saved by using these controllers...
April 27, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29758461/anti-synchronization-of-complex-valued-memristor-based-delayed-neural-networks
#8
Dan Liu, Song Zhu, Kaili Sun
This paper investigates the anti-synchronization of complex-valued memristor-based neural networks with time delays via designed external controllers. By constructing appropriate Lyapunov functions and using inequality technique, two different types of controllers are derived to guarantee the exponential anti-synchronization of complex-valued memristor-based delayed neural networks. Compared with existing relevant results, the proposed results of this paper are more general and less conservative. In addition, the presented theoretical results are easy to be checked with the parameters of systems themselves...
April 25, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29775850/electrical-resistivity-imaging-inversion-an-isfla-trained-kernel-principal-component-wavelet-neural-network-approach
#9
Feibo Jiang, Li Dong, Qianwei Dai
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN)...
April 24, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29753177/quasi-projective-synchronization-of-fractional-order-complex-valued-recurrent-neural-networks
#10
Shuai Yang, Juan Yu, Cheng Hu, Haijun Jiang
In this paper, without separating the complex-valued neural networks into two real-valued systems, the quasi-projective synchronization of fractional-order complex-valued neural networks is investigated. First, two new fractional-order inequalities are established by using the theory of complex functions, Laplace transform and Mittag-Leffler functions, which generalize traditional inequalities with the first-order derivative in the real domain. Additionally, different from hybrid control schemes given in the previous work concerning the projective synchronization, a simple and linear control strategy is designed in this paper and several criteria are derived to ensure quasi-projective synchronization of the complex-valued neural networks with fractional-order based on the established fractional-order inequalities and the theory of complex functions...
April 23, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29747058/generalized-pinning-synchronization-of-delayed-cohen-grossberg-neural-networks-with-discontinuous-activations
#11
Dongshu Wang, Lihong Huang, Longkun Tang, Jinsen Zhuang
In this article, generalized pinning synchronization problem is investigated for a class of Cohen-Grossberg neural networks with discontinuous neuron activations and mixed delays. By designing generalized pinning state-feedback and adaptive controllers, several criteria for global exponential synchronization and global asymptotical synchronization of the drive-response based system are obtained in view of non-smooth analysis theory with generalized Lyapunov functional method, in which first pinning the neurons with very small self-inhibition and small amplification functions is pointed out...
April 21, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29705669/using-a-model-of-human-visual-perception-to-improve-deep-learning
#12
Michael Stettler, Gregory Francis
Deep learning algorithms achieve human-level (or better) performance on many tasks, but there still remain situations where humans learn better or faster. With regard to classification of images, we argue that some of those situations are because the human visual system represents information in a format that promotes good training and classification. To demonstrate this idea, we show how occluding objects can impair performance of a deep learning system that is trained to classify digits in the MNIST database...
April 17, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29705670/effect-of-dilution-in-asymmetric-recurrent-neural-networks
#13
Viola Folli, Giorgio Gosti, Marco Leonetti, Giancarlo Ruocco
We study with numerical simulation the possible limit behaviors of synchronous discrete-time deterministic recurrent neural networks composed of N binary neurons as a function of a network's level of dilution and asymmetry. The network dilution measures the fraction of neuron couples that are connected, and the network asymmetry measures to what extent the underlying connectivity matrix is asymmetric. For each given neural network, we study the dynamical evolution of all the different initial conditions, thus characterizing the full dynamical landscape without imposing any learning rule...
April 16, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29705668/a-frequency-domain-approach-to-improve-anns-generalization-quality-via-proper-initialization
#14
Majdi Chaari, Afef Fekih, Abdennour C Seibi, Jalel Ben Hmida
The ability to train a network without memorizing the input/output data, thereby allowing a good predictive performance when applied to unseen data, is paramount in ANN applications. In this paper, we propose a frequency-domain approach to evaluate the network initialization in terms of quality of training, i.e., generalization capabilities. As an alternative to the conventional time-domain methods, the proposed approach eliminates the approximate nature of network validation using an excess of unseen data...
April 16, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29702424/bio-inspired-spiking-neural-network-for-nonlinear-systems-control
#15
Javier PĂ©rez, Juan A Cabrera, Juan J Castillo, Juan M Velasco
Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement...
April 12, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29715684/biased-dropout-and-crossmap-dropout-learning-towards-effective-dropout-regularization-in-convolutional-neural-network
#16
Alvin Poernomo, Dae-Ki Kang
Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has shown remarkable results on many deep neural network cases, its actual effect on CNN has not been thoroughly explored. Moreover, training a Dropout model will significantly increase the training time as it takes longer time to converge than a non-Dropout model with the same architecture...
April 9, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29689457/design-of-double-fuzzy-clustering-driven-context-neural-networks
#17
Eun-Hu Kim, Sung-Kwun Oh, Witold Pedrycz
In this study, we introduce a novel category of double fuzzy clustering-driven context neural networks (DFCCNNs). The study is focused on the development of advanced design methodologies for redesigning the structure of conventional fuzzy clustering-based neural networks. The conventional fuzzy clustering-based neural networks typically focus on dividing the input space into several local spaces (implied by clusters). In contrast, the proposed DFCCNNs take into account two distinct local spaces called context and cluster spaces, respectively...
April 9, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29674234/spiking-neural-networks-for-handwritten-digit-recognition-supervised-learning-and-network-optimization
#18
Shruti R Kulkarni, Bipin Rajendran
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy...
April 6, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29674233/the-growing-curvilinear-component-analysis-gcca-neural-network
#19
Giansalvo Cirrincione, Vincenzo Randazzo, Eros Pasero
Big high dimensional data is becoming a challenging field of research. There exist a lot of techniques which infer information. However, because of the curse of dimensionality, a necessary step is the dimensionality reduction (DR) of the information. DR can be performed by linear and nonlinear algorithms. In general, linear algorithms are faster because of less computational burden. A related problem is dealing with time-varying high dimensional data, where the time dependence is due to nonstationary data distribution...
April 6, 2018: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29665538/personalized-response-generation-by-dual-learning-based-domain-adaptation
#20
Min Yang, Wenting Tu, Qiang Qu, Zhou Zhao, Xiaojun Chen, Jia Zhu
Open-domain conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. The goal of this paper is to further improve the response generation, using personalization criteria. We propose a novel method called PRGDDA (Personalized Response Generation by Dual-learning based Domain Adaptation) which is a personalized response generation model based on theories of domain adaptation and dual learning...
April 5, 2018: Neural Networks: the Official Journal of the International Neural Network Society
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