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neural network

Kasra Esfandiari, Farzaneh Abdollahi, Heidar Ali Talebi
In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Yujian Li, Ting Zhang
The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately...
June 21, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Xianxiu Zhang, Chuandong Li, Tingwen Huang
We discuss the global stability of switching Hopfield neural networks (HNN) with state-dependent impulses using B-equivalence method. Under certain conditions, we show that the state-dependent impulsive switching systems can be reduced to the fixed-time ones, and that the global stability of corresponding comparison system implies the same stability of the considered system. On this basis, a novel stability criterion for the considered HNN is established. Finally, two numerical examples are given to demonstrate the effectiveness of our results...
May 24, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Qawi K Telesford, Arian Ashourvan, Nicholas F Wymbs, Scott T Grafton, Jean M Vettel, Danielle S Bassett
Human behavior is supported by flexible neurophysiological processes that enable the fine-scale manipulation of information across distributed neural circuits. Yet, approaches for understanding the dynamics of these circuit interactions have been limited. One promising avenue for quantifying and describing these dynamics lies in multilayer network models. Here, networks are composed of nodes (which represent brain regions) and time-dependent edges (which represent statistical similarities in activity time series)...
June 24, 2017: Human Brain Mapping
Hongyan Zhu, Bingquan Chu, Chu Zhang, Fei Liu, Linjun Jiang, Yong He
We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380-1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM)...
June 23, 2017: Scientific Reports
Shiliang Liu, Rongjie Yang, Jun Yang, Tongpei Yi, Huixing Song, Mingyan Jiang, Durgesh K Tripathi, Mingdong Ma, Qibing Chen
Fargesia Franchet emend. Yi is closely allied with Thamnocalamus Munro but differs in many major morphological characteristics. Based on traditional morphological characters, it is difficult to differentiate these two genera. The current study measured 19 species in these two genera to determine whether variations in 12 categories of major characters are continuous. In addition, a self-organizing map (SOM) and cluster analysis were used together to reveal whether the known species of Fargesia represent discontinuous sampling of Thamnocalamus...
June 23, 2017: Scientific Reports
Ghislain St-Yves, Thomas Naselaris
We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRFis organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRFmodel is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted...
June 20, 2017: NeuroImage
Ankit N Khambhati, Ann E Sizemore, Richard F Betzel, Danielle S Bassett
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations...
June 20, 2017: NeuroImage
Jessica Wise Younger, Elliot Tucker-Drob, James R Booth
Attempts to characterize the neural differences between individuals with and without dyslexia generally point to reduced activation in and connectivity between brain areas in a reading network composed of the inferior frontal gyrus, the ventral occipito-temporal cortex, and the dorsal temporo-parietal circuit. However, developmental work on brain activity during reading has indicated that some brain areas show developmental decreases in activation with age. Thus, reading network connectivity may also show decreases that are positively associated with increases in reading ability...
June 20, 2017: NeuroImage
Jie Hu, Yue Li, Yunlu Yin, Philip R Blue, Hongbo Yu, Xiaolin Zhou
Altruistic behavior, i.e., promoting the welfare of others at a cost to oneself, is subserved by the integration of various social, affective, and economic factors represented in extensive brain regions. However, it is unclear how different regions interact to process/integrate information regarding the helper's interest and recipient's need when deciding whether to behave altruistically. Here we combined an interactive game with functional Magnetic Resonance Imaging (fMRI) and transcranial direct current stimulation (tDCS) to characterize the neural network underlying the processing/integration of self-interest and other-need...
June 20, 2017: NeuroImage
Tae-Ho Lee, Michelle E Miernicki, Eva H Telzer
Sleep habits developed in adolescence shape long-term trajectories of psychological, educational, and physiological well-being. Adolescents' sleep behaviors are shaped by their parents' sleep at both the behavioral and biological levels. In the current study, we sought to examine how neural concordance in resting-state functional connectivity between parent-child dyads is associated with dyadic concordance in sleep duration and adolescents' sleep quality. To this end, we scanned both parents and their child (N=28 parent-child dyads; parent Mage=42...
June 15, 2017: Developmental Cognitive Neuroscience
Huilin Zhu, Jie Xu, Jiangxue Li, Hongjun Peng, Tingting Cai, Xinge Li, Shijing Wu, Wei Cao, Sailing He
BACKGROUND: Affective disorders (AD) have been conceptualized as neural network-level diseases. In this study, we utilized functional near infrared spectroscopy (fNIRS) to investigate the spontaneous hemodynamic activities in the prefrontal cortex (PFC) of the AD patients with or without medications. METHODS: 42 optical channels were applied to cover the superior frontal gyrus (SFG), middle frontal gyrus (MFG), and inferior frontal gyrus (IFG), which constitute one of the most important affective networks of the brain...
June 17, 2017: Journal of Affective Disorders
Alessandra Ghinato Mainieri, Julio Fernando Prieto Peres, Alexander Moreira-Almeida, Klaus Mathiak, Ute Habel, Nils Kohn
Recent studies indicate high levels of psychotic experiences in the general population. Here, we report a functional imaging study with 8 mentally healthy spiritual mediums and 8 matched controls. The mediums entered a mediumistic-trance state using a standardized manner by closing their eyes and actively seeking to ignore external and internal stimuli to achieve a 'state of emptiness'; in a control condition, they were instructed to re-enact the same mediumistic experience that they had during the mediumistic-trance condition but in a non-trance state (imaginative-trance)...
June 15, 2017: Psychiatry Research
Moritz Augustin, Josef Ladenbauer, Fabian Baumann, Klaus Obermayer
The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated...
June 23, 2017: PLoS Computational Biology
Gabriel Koch Ocker, Krešimir Josić, Eric Shea-Brown, Michael A Buice
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing...
June 23, 2017: PLoS Computational Biology
Yue-Jiao Gong, Jun Zhang, Yicong Zhou
Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning...
June 20, 2017: IEEE Transactions on Neural Networks and Learning Systems
Jiaming Zhu, Zhiqiang Cao, Tianping Zhang, Yuequan Yang, Yang Yi
In this brief, sufficient conditions are proposed for the existence of the compact sets in the neural network controls. First, we point out that the existence of the compact set in a classical neural network control scheme is unsolved and its result is incomplete. Next, as a simple case, we derive the sufficient condition of the existence of the compact set for the neural network control of first-order systems. Finally, we propose the sufficient condition of the existence of the compact set for the neural-network-based backstepping control of high-order nonlinear systems...
June 20, 2017: IEEE Transactions on Neural Networks and Learning Systems
Jiwen Lu, Junlin Hu, Yap-Peng Tan
This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged, respectively...
June 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Nguyen-Quoc-Khanh Le, Quang-Thai Ho, Yu-Yen Ou
In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80...
June 22, 2017: Journal of Computational Chemistry
Jonathan M Grasman, David L Kaplan
Understanding how nerves spontaneously innervate tissues or regenerate small injuries is critical to enhance material-based interventions to regenerate large scale, traumatic injuries. During embryogenesis, neural and vascular tissues form interconnected, complex networks as a result of signaling between these tissue types. Here, we report that human endothelial cells (HUVECs) secrete brain-derived neurotrophic factor (BDNF), which significantly stimulated axonal growth from chicken or rat dorsal root ganglia (DRGs)...
June 22, 2017: Scientific Reports
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