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

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https://www.readbyqxmd.com/read/28324758/dynamics-of-complex-valued-fractional-order-neural-networks
#1
Eva Kaslik, Ileana Rodica Rădulescu
The dynamics of complex-valued fractional-order neuronal networks are investigated, focusing on stability, instability and Hopf bifurcations. Sufficient conditions for the asymptotic stability and instability of a steady state of the network are derived, based on the complex system parameters and the fractional order of the system, considering simplified neuronal connectivity structures (hub and ring). In some specific cases, it is possible to identify the critical values of the fractional order for which Hopf bifurcations may occur...
March 6, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28324757/perceptual-category-learning-and-visual-processing-an-exercise-in-computational-cognitive-neuroscience
#2
George Cantwell, Maximilian Riesenhuber, Jessica L Roeder, F Gregory Ashby
The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning...
March 6, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28324759/stability-and-instability-of-a-neuron-network-with-excitatory-and-inhibitory-small-world-connections
#3
Dongyuan Yu, Xu Xu, Jing Zhou, Eric Li
This study considers a delayed neural network with excitatory and inhibitory shortcuts. The global stability of the trivial equilibrium is investigated based on Lyapunov's direct method and the delay-dependent criteria are obtained. It is shown that both the excitatory and inhibitory shortcuts decrease the stability interval, but a time delay can be employed as a global stabilizer. In addition, we analyze the bounds of the eigenvalues of the adjacent matrix using matrix perturbation theory and then obtain the generalized sufficient conditions for local stability...
March 2, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28278430/fractional-order-gradient-descent-learning-of-bp-neural-networks-with-caputo-derivative
#4
Jian Wang, Yanqing Wen, Yida Gou, Zhenyun Ye, Hua Chen
Fractional calculus has been found to be a promising area of research for information processing and modeling of some physical systems. In this paper, we propose a fractional gradient descent method for the backpropagation (BP) training of neural networks. In particular, the Caputo derivative is employed to evaluate the fractional-order gradient of the error defined as the traditional quadratic energy function. The monotonicity and weak (strong) convergence of the proposed approach are proved in detail. Two simulations have been implemented to illustrate the performance of presented fractional-order BP algorithm on three small datasets and one large dataset...
February 22, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28318904/dual-memory-neural-networks-for-modeling-cognitive-activities-of-humans-via-wearable-sensors
#5
Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting...
February 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28342724/a-hypergraph-and-arithmetic-residue-based-probabilistic-neural-network-for-classification-in-intrusion-detection-systems
#6
M R Gauthama Raman, Nivethitha Somu, Kannan Kirthivasan, V S Shankar Sriram
Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS...
February 17, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28254556/a-weighted-information-criterion-for-multiple-minor-components-and-its-adaptive-extraction-algorithms
#7
Yingbin Gao, Xiangyu Kong, Huihui Zhang, Li'an Hou
Minor component (MC) plays an important role in signal processing and data analysis, so it is a valuable work to develop MC extraction algorithms. Based on the concepts of weighted subspace and optimum theory, a weighted information criterion is proposed for searching the optimum solution of a linear neural network. This information criterion exhibits a unique global minimum attained if and only if the state matrix is composed of the desired MCs of an autocorrelation matrix of an input signal. By using gradient ascent method and recursive least square (RLS) method, two algorithms are developed for multiple MCs extraction...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28254237/prediction-of-advertisement-preference-by-fusing-eeg-response-and-sentiment-analysis
#8
Himaanshu Gauba, Pradeep Kumar, Partha Pratim Roy, Priyanka Singh, Debi Prosad Dogra, Balasubramanian Raman
This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video...
February 16, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28318903/understanding-human-intention-by-connecting-perception-and-action-learning-in-artificial-agents
#9
Sangwook Kim, Zhibin Yu, Minho Lee
To develop an advanced human-robot interaction system, it is important to first understand how human beings learn to perceive, think, and act in an ever-changing world. In this paper, we propose an intention understanding system that uses an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN) and demonstrate the effects of perception-action connected learning in an artificial agent, which is inspired by psychological and neurological phenomena in humans. We believe that action and perception are not isolated processes in human mental development, and argue that these psychological and neurological interactions can be replicated in a human-machine scenario...
February 11, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28236678/a-perturbative-approach-for-enhancing-the-performance-of-time-series-forecasting
#10
Paulo S G de Mattos Neto, Tiago A E Ferreira, Aranildo R Lima, Germano C Vasconcelos, George D C Cavalcanti
This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series...
February 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28222299/adaptive-low-rank-subspace-learning-with-online-optimization-for-robust-visual-tracking
#11
Risheng Liu, Di Wang, Yuzhuo Han, Xin Fan, Zhongxuan Luo
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data...
February 10, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28254557/a-new-neural-network-model-for-solving-random-interval-linear-programming-problems
#12
Ziba Arjmandzadeh, Mohammadreza Safi, Alireza Nazemi
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem...
February 9, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28214692/multi-view-clustering-via-multi-manifold-regularized-non-negative-matrix-factorization
#13
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/28223007/global-dissipativity-analysis-for-delayed-quaternion-valued-neural-networks
#14
Zhengwen Tu, Jinde Cao, Ahmed Alsaedi, Tasawar Hayat
The problem of global dissipativity analysis for quaternion-valued neural networks (QVNNs) with time-varying delays is firstly investigated in this paper. The QVNN is studied as a single entirety without any decomposition. Several algebraic conditions ensuring the global dissipativity and globally exponential dissipativity for QVNNs are derived by employing Lyapunov theory and some analytic techniques. Furthermore, the positive invariant sets, globally attractive sets and globally exponentially attractive sets are figured out as well...
February 4, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28254393/global-dissipativity-of-memristor-based-neutral-type-inertial-neural-networks
#15
Zhengwen Tu, Jinde Cao, Ahmed Alsaedi, Fuad Alsaadi
The problem of global dissipativity for memristor-based inertial networks with time-varying delay of neutral type is investigated in this paper. Based on a proper variable substitution, the inertial system is transformed into a conventional system. Some sufficient criteria are established to ascertain the global dissipativity for the aforementioned inertial neural networks by employing analytical techniques and Lyapunov method. Meanwhile, the globally exponentially attractive sets and positive invariant sets are also presented here...
February 3, 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
#16
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/28232260/object-class-segmentation-of-rgb-d-video-using-recurrent-convolutional-neural-networks
#17
Mircea Serban Pavel, Hannes Schulz, Sven Behnke
Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity...
January 30, 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
#18
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
#19
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
#20
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
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