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Neural networks computing

Nian Liu, Junwei Han, Tianming Liu, Xuelong Li
Eye movements in the case of freely viewing natural scenes are believed to be guided by local contrast, global contrast, and top-down visual factors. Although a lot of previous works have explored these three saliency cues for several years, there still exists much room for improvement on how to model them and integrate them effectively. This paper proposes a novel computation model to predict eye fixations, which adopts a multiresolution convolutional neural network (Mr-CNN) to infer these three types of saliency cues from raw image data simultaneously...
November 29, 2016: IEEE Transactions on Neural Networks and Learning Systems
Filipe Alves Neto Verri, Paulo Roberto Urio, Liang Zhao
The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semisupervised learning problems. Three actions govern the particles' dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles...
November 29, 2016: IEEE Transactions on Neural Networks and Learning Systems
Zhenzhen Hu, Yonggang Wen, Jianfeng Wang, Meng Wang, Richang Hong, Shuicheng Yan
Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g. the huge amount of human photos in the social networks. These images may provide no age label, but it is easily to derive the age difference for an image pair of the same person...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Tobias Goehring, Federico Bolner, Jessica J M Monaghan, Bas van Dijk, Andrzej Zarowski, Stefan Bleeck
Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal-to-noise ratio, SNR)...
November 29, 2016: Hearing Research
Ravi K Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A Helvie, Jun Wei, Kenny Cha
PURPOSE: Develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) volume using a deep convolutional neural network (DCNN) with transfer learning from mammograms. METHODS: A data set containing 2282 digitized film and digital mammograms and 324 DBT volumes were collected with IRB approval. The mass of interest on the images was marked by an experienced breast radiologist as reference standard. The data set was partitioned into a training set (2282 mammograms with 2461 masses and 230 DBT views with 228 masses) and an independent test set (94 DBT views with 89 masses)...
December 2016: Medical Physics
Jianfang Cao, Lichao Chen, Min Wang, Hao Shi, Yun Tian
Image classification uses computers to simulate human understanding and cognition of images by automatically categorizing images. This study proposes a faster image classification approach that parallelizes the traditional Adaboost-Backpropagation (BP) neural network using the MapReduce parallel programming model. First, we construct a strong classifier by assembling the outputs of 15 BP neural networks (which are individually regarded as weak classifiers) based on the Adaboost algorithm. Second, we design Map and Reduce tasks for both the parallel Adaboost-BP neural network and the feature extraction algorithm...
December 1, 2016: Scientific Reports
Yousef Rezaei Tabar, Ugur Halici
OBJECTIVE: Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. APPROACH: In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals...
November 30, 2016: Journal of Neural Engineering
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C Nelson, Jessica L Mega, Dale R Webster
Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs...
November 29, 2016: JAMA: the Journal of the American Medical Association
Ke Sun, Zhengjie Wang, Kang Tu, Shaojin Wang, Leiqing Pan
To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models...
November 29, 2016: Scientific Reports
Kun Xie, Grace E Fox, Jun Liu, Cheng Lyu, Jason C Lee, Hui Kuang, Stephanie Jacobs, Meng Li, Tianming Liu, Sen Song, Joe Z Tsien
There is considerable scientific interest in understanding how cell assemblies-the long-presumed computational motif-are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2 (i) -1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information...
2016: Frontiers in Systems Neuroscience
Jia Wang, Lee-Ming Cheng, Tong Su
Designing secure and efficient multivariate public key cryptosystems [multivariate cryptography (MVC)] to strengthen the security of RSA and ECC in conventional and quantum computational environment continues to be a challenging research in recent years. In this paper, we will describe multivariate public key cryptosystems based on extended Clipped Hopfield Neural Network (CHNN) and implement it using the MVC (CHNN-MVC) framework operated in GF(p) space. The Diffie--Hellman key exchange algorithm is extended into the matrix field, which illustrates the feasibility of its new applications in both classic and postquantum cryptography...
November 23, 2016: IEEE Transactions on Neural Networks and Learning Systems
Yuma Miki, Chisako Muramatsu, Tatsuro Hayashi, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita
Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images...
November 12, 2016: Computers in Biology and Medicine
Peijun Hu, Fa Wu, Jialin Peng, Yuanyuan Bao, Feng Chen, Dexing Kong
PURPOSE: Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. METHODS: The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method...
November 24, 2016: International Journal of Computer Assisted Radiology and Surgery
J Leonie Cazemier, Francisco Clascá, Paul H E Tiesinga
Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. "Connectomics" is the emerging subfield of neuroanatomy explicitly aimed at elucidating the wiring of brain networks with cellular resolution and a quantified accuracy. Such data are indispensable for realistic modeling of brain circuitry and function. A connectomic analysis, therefore, needs to identify and measure the soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network...
2016: Frontiers in Neuroanatomy
Baptiste Gauthier, Virginie van Wassenhove
: Humans can consciously project themselves in the future and imagine themselves at different places. Do mental time travel and mental space navigation abilities share common cognitive and neural mechanisms? To test this, we recorded fMRI while participants mentally projected themselves in time or in space (e.g., 9 years ago, in Paris) and ordered historical events from their mental perspective. Behavioral patterns were comparable for mental time and space and shaped by self-projection and by the distance of historical events to the mental position of the self, suggesting the existence of egocentric mapping in both dimensions...
November 23, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Florent Bocquelet, Thomas Hueber, Laurent Girin, Christophe Savariaux, Blaise Yvert
Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech...
November 2016: PLoS Computational Biology
Peijun Hu, Fa Wu, Jialin Peng, Ping Liang, Dexing Kong
The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution...
December 21, 2016: Physics in Medicine and Biology
Jun Haeng Lee, Tobi Delbruck, Michael Pfeiffer
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials...
2016: Frontiers in Neuroscience
Szymon Wichary, Tomasz Smolen
In multi-attribute choice, decision makers use decision strategies to arrive at the final choice. What are the neural mechanisms underlying decision strategy selection? The first goal of this paper is to provide a literature review on the neural underpinnings and cognitive models of decision strategy selection and thus set the stage for a neurocognitive model of this process. The second goal is to outline such a unifying, mechanistic model that can explain the impact of noncognitive factors (e.g., affect, stress) on strategy selection...
2016: Frontiers in Neuroscience
Chentao Wen, Yukiko Ogura, Toshiya Matsushima
To ensure survival, animals must update the internal representations of their environment in a trial-and-error fashion. Psychological studies of associative learning and neurophysiological analyses of dopaminergic neurons have suggested that this updating process involves the temporal-difference (TD) method in the basal ganglia network. However, the way in which the component variables of the TD method are implemented at the neuronal level is unclear. To investigate the underlying neural mechanisms, we trained domestic chicks to associate color cues with food rewards...
2016: Frontiers in Neuroscience
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