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

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https://www.readbyqxmd.com/read/28214692/multi-view-clustering-via-multi-manifold-regularized-non-negative-matrix-factorization
#1
Linlin Zong, Xianchao Zhang, Long Zhao, Hong Yu, Qianli Zhao
Non-negative matrix factorization based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, non-negative matrix factorization fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized non-negative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF incorporates consensus manifold and consensus coefficient matrix with multi-manifold regularization to preserve the locally geometrical structure of the multi-view data space...
February 8, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28189840/spike-timing-dependent-plasticity-induces-non-trivial-topology-in-the-brain
#2
R R Borges, F S Borges, E L Lameu, A M Batista, K C Iarosz, I L Caldas, C G Antonopoulos, M S Baptista
We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology evolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous...
January 31, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28192762/neural-mass-models-describing-possible-origin-of-the-excessive-beta-oscillations-correlated-with-parkinsonian-state
#3
Chen Liu, Yulin Zhu, Fei Liu, Jiang Wang, Huiyan Li, Bin Deng, Chris Fietkiewicz, Kenneth A Loparo
In Parkinson's disease, the enhanced beta rhythm is closely associated with akinesia/bradykinesia and rigidity. An increase in beta oscillations (12-35 Hz) within the basal ganglia (BG) nuclei does not proliferate throughout the cortico-basal ganglia loop in uniform fashion; rather it can be subdivided into two distinct frequency bands, i.e. the lower beta (12-20 Hz) and upper beta (21-35 Hz). A computational model of the excitatory and inhibitory neural network that focuses on the population properties is proposed to explore the mechanism underlying the pathological beta oscillations...
January 30, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28189839/stability-analysis-of-delayed-neural-networks-via-a-new-integral-inequality
#4
Bin Yang, Juan Wang, Jun Wang
This paper focuses on stability analysis for neural networks systems with time-varying delays. A more general auxiliary function-based integral inequality is established and some improved delay-dependent stability conditions formulated in terms of linear matrix inequalities (LMIs) are derived by employing a suitable Lyapunov-Krasovskii functional (LKF) and the novel integral inequality. Three well-known application examples are provided to demonstrate the effectiveness and improvements of the proposed method...
January 30, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28189041/biologically-plausible-learning-in-neural-networks-with-modulatory-feedback
#5
W Shane Grant, James Tanner, Laurent Itti
Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model...
January 28, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28161499/orthogonal-self-guided-similarity-preserving-projection-for-classification-and-clustering
#6
Xiaozhao Fang, Yong Xu, Xuelong Li, Zhihui Lai, Shaohua Teng, Lunke Fei
A suitable feature representation can faithfully preserve the intrinsic structure of data. However, traditional dimensionality reduction (DR) methods commonly use the original input features to define the intrinsic structure, which makes the estimated intrinsic structure unreliable since redundant or noisy features may exist in the original input features. Thus a dilemma is that (1) one needs the most suitable feature representation to define the intrinsic structure of data and (2) one should use the proper intrinsic structure of data to perform feature extraction...
January 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28157557/elimination-of-spiral-waves-in-a-locally-connected-chaotic-neural-network-by-a-dynamic-phase-space-constraint
#7
Yang Li, Makito Oku, Guoguang He, Kazuyuki Aihara
In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal is constructed from the feedback internal states of the neurons to detect phase singularities based on their amplitude reduction, before modulating a threshold value to truncate the refractory internal states of the neurons and terminate the spirals. Simulations showed that with appropriate parameter settings, the network was directed from a spiral wave state into either a plane wave (PW) state or a synchronized oscillation (SO) state, where the control vanished automatically and left the original CNN model unaltered...
January 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28157556/self-taught-convolutional-neural-networks-for-short-text-clustering
#8
Jiaming Xu, Bo Xu, Peng Wang, Suncong Zheng, Guanhua Tian, Jun Zhao, Bo Xu
Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC(2)), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method...
January 12, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28119122/constructing-a-meta-tracker-using-dropout-to-imitate-the-behavior-of-an-arbitrary-black-box-tracker
#9
Kourosh Meshgi, Shin-Ichi Maeda, Shigeyuki Oba, Shin Ishii
Imitating the behaviors of an arbitrary visual tracking algorithm enables many higher level tasks such as tracker identification and efficient tracker-fusion. It is also useful for discovering the features essential in a black-box tracker or learning from several trackers to form a super-tracker. In this study, we propose a non-linear feature fusion framework, "MIMIC" that imitates many popular trackers by mixing a pool of heterogeneous features. The MIMIC framework consists of two subtasks, feature selection and feature weight tuning...
January 3, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28152392/global-exponential-stability-and-dissipativity-of-generalized-neural-networks-with-time-varying-delay-signals
#10
R Manivannan, R Samidurai, Jinde Cao, Ahmed Alsaedi, Fuad E Alsaadi
This paper investigates the problems of exponential stability and dissipativity of generalized neural networks (GNNs) with time-varying delay signals. By constructing a novel Lyapunov-Krasovskii functionals (LKFs) with triple integral terms that contain more advantages of the state vectors of the neural networks, and the upper bound on the time-varying delay signals are formulated. We employ a new integral inequality technique (IIT), free-matrix-based (FMB) integral inequality approach, and Wirtinger double integral inequality (WDII) technique together with the reciprocally convex combination (RCC) approach to bound the time derivative of the LKFs...
December 23, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28110107/controller-design-for-global-fixed-time-synchronization-of-delayed-neural-networks-with-discontinuous-activations
#11
Leimin Wang, Zhigang Zeng, Junhao Hu, Xiaoping Wang
This paper addresses the controller design problem for global fixed-time synchronization of delayed neural networks (DNNs) with discontinuous activations. To solve this problem, adaptive control and state feedback control laws are designed. Then based on the two controllers and two lemmas, the error system is proved to be globally asymptotically stable and even fixed-time stable. Moreover, some sufficient and easy checked conditions are derived to guarantee the global synchronization of drive and response systems in fixed time...
December 23, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28039780/an-empirical-model-of-activity-in-macaque-inferior-temporal-cortex
#12
Salman Khan, Bryan Tripp
There are compelling computational models of many properties of the primate ventral visual stream, but a gap remains between the models and the physiology. To facilitate ongoing refinement of these models, we have compiled diverse information from the electrophysiology literature into a statistical model of inferotemporal (IT) cortex responses. This is a purely descriptive model, so it has little explanatory power. However it is able to directly incorporate a rich and extensible set of tuning properties. So far, we have approximated tuning curves and statistics of tuning diversity for occlusion, clutter, size, orientation, position, and object selectivity in early versus late response phases...
December 13, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28110106/fast-learning-method-for-convolutional-neural-networks-using-extreme-learning-machine-and-its-application-to-lane-detection
#13
Jihun Kim, Jonghong Kim, Gil-Jin Jang, Minho Lee
Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection...
December 10, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28039779/reaction-times-in-visual-search-can-be-explained-by-a-simple-model-of-neural-synchronization
#14
Yakov Kazanovich, Roman Borisyuk
We present an oscillatory neural network model that can account for reaction times in visual search experiments. The model consists of a central oscillator that represents the central executive of the attention system and a number of peripheral oscillators that represent objects in the display. The oscillators are described as generalized Kuramoto type oscillators with adapted parameters. An object is considered as being included in the focus of attention if the oscillator associated with this object is in-phase with the central oscillator...
December 10, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28064015/cognitive-memory-and-mapping-in-a-brain-like-system-for-robotic-navigation
#15
Huajin Tang, Weiwei Huang, Aditya Narayanamoorthy, Rui Yan
Electrophysiological studies in animals may provide a great insight into developing brain-like models of spatial cognition for robots. These studies suggest that the spatial ability of animals requires proper functioning of the hippocampus and the entorhinal cortex (EC). The involvement of the hippocampus in spatial cognition has been extensively studied, both in animal as well as in theoretical studies, such as in the brain-based models by Edelman and colleagues. In this work, we extend these earlier models, with a particular focus on the spatial coding properties of the EC and how it functions as an interface between the hippocampus and the neocortex, as proposed by previous work...
December 7, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28088645/towards-solving-the-hard-problem-of-consciousness-the-varieties-of-brain-resonances-and-the-conscious-experiences-that-they-support
#16
REVIEW
Stephen Grossberg
The hard problem of consciousness is the problem of explaining how we experience qualia or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they are. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how emergent properties of several brain mechanisms interacting together embody detailed properties of individual conscious psychological experiences. This article summarizes evidence that Adaptive Resonance Theory, or ART, accomplishes this goal...
December 6, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28040585/on-global-exponential-stability-of-positive-neural-networks-with-time-varying-delay
#17
Le Van Hien
This paper presents a new result on the existence, uniqueness and global exponential stability of a positive equilibrium of positive neural networks in the presence of bounded time-varying delay. Based on some novel comparison techniques, a testable condition is derived to ensure that all the state trajectories of the system converge exponentially to a unique positive equilibrium. The effectiveness of the obtained results is illustrated by a numerical example.
December 1, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27955819/decentralized-event-triggered-synchronization-of-uncertain-markovian-jumping-neutral-type-neural-networks-with-mixed-delays
#18
Sibel Senan, M Syed Ali, R Vadivel, Sabri Arik
In this study, we present an approach for the decentralized event-triggered synchronization of Markovian jumping neutral-type neural networks with mixed delays. We present a method for designing decentralized event-triggered synchronization, which only utilizes locally available information, in order to determine the time instants for transmission from sensors to a central controller. By applying a novel Lyapunov-Krasovskii functional, as well as using the reciprocal convex combination method and some inequality techniques such as Jensen's inequality, we obtain several sufficient conditions in terms of a set of linear matrix inequalities (LMIs) under which the delayed neural networks are stochastically stable in terms of the error systems...
February 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27955818/adaptive-exponential-synchronization-of-complex-valued-cohen-grossberg-neural-networks-with-known-and-unknown-parameters
#19
Jin Hu, Chunna Zeng
The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks...
February 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27939066/dissipativity-and-stability-analysis-of-fractional-order-complex-valued-neural-networks-with-time-delay
#20
G Velmurugan, R Rakkiyappan, V Vembarasan, Jinde Cao, Ahmed Alsaedi
As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay...
February 2017: Neural Networks: the Official Journal of the International Neural Network Society
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