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Neural networks computing

Yoonsik Shim, Andrew Philippides, Kevin Staras, Phil Husbands
We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events...
October 2016: PLoS Computational Biology
Patricia Melin, German Prado-Arechiga, Martha Pulido, Ivette Miramontes
OBJECTIVE: The development of an artificial modular neural network (MNN) method for diagnosing and classification of arterial Hypertension based on the level of the blood pressure (BP) of a patient is presented. The main goal is to diagnose the degree of hypertension based on the BP values using MNN applying response integration via a gating network approach. DESIGN AND METHOD: This study was performed with 28 patients to classify the BP levels, based on the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC) Guidelines of Hypertension...
September 2016: Journal of Hypertension
Yi Yang, Juan Wen, Liqiang Guo, Xiang Wan, Peifu Du, Ping Feng, Yi Shi, Qing Wan
Emulating neural behaviors at the synaptic level is of great significance for building neuromorphic computational systems and realizing artificial intelligence. Here, oxide-based electric-double-layer (EDL) thin-film transistors were fabricated by using 3-triethoxysilylpropylamine modified graphene oxide (KH550-GO) electrolyte as the gate dielectrics. Resulting from the EDL effect and electrochemical doping between mobile protons and the indium-zinc-oxide channel layer, long-term synaptic plasticity was emulated in our devices...
October 17, 2016: ACS Applied Materials & Interfaces
Akihiro Eguchi, Simon M Stringer
As Rubin's famous vase demonstrates, our visual perception tends to assign luminance contrast borders to one or other of the adjacent image regions. Experimental evidence for the neuronal coding of such border-ownership in the primate visual system has been reported in neurophysiology. We have investigated exactly how such neural circuits may develop through visually-guided learning. More specifically, we have investigated through computer simulation how top-down connections may play a fundamental role in the development of border ownership representations in the early cortical visual layers V1/V2...
October 13, 2016: Neurobiology of Learning and Memory
Douglas McLelland, Rufin VanRullen
Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles...
October 2016: PLoS Computational Biology
Guoxing Wen, C L Philip Chen, Yan-Jun Liu, Zhi Liu
Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the state delays are compensated by designing a Lyapunov-Krasovskii functional. Finally, based on Lyapunov stability theory, it is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals...
October 11, 2016: IEEE Transactions on Cybernetics
F Vallone, E Vannini, A Cintio, M Caleo, A Di Garbo
Epilepsy is characterized by substantial network rearrangements leading to spontaneous seizures and little is known on how an epileptogenic focus impacts on neural activity in the contralateral hemisphere. Here, we used a model of unilateral epilepsy induced by injection of the synaptic blocker tetanus neurotoxin (TeNT) in the mouse primary visual cortex (V1). Local field potential (LFP) signals were simultaneously recorded from both hemispheres of each mouse in acute phase (peak of toxin action) and chronic condition (completion of TeNT effects)...
September 2016: Physical Review. E
P Kumudha, R Venkatesan
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers...
2016: TheScientificWorldJournal
Yu-Wen Wang, Chin-Shun Wu, Guo-Yi Zhang, Chih-Han Chang, Kuo-Sheng Cheng, Wei-Jen Yao, Yu-Kang Chang, Tsair-Wei Chien, Li-Ching Lin, Keng-Ren Lin
PURPOSE: Minimal axial diameter (MIAD) in magnetic resonance imaging (MRI) was recognized as the most useful parameter in diagnosing lateral retropharyngeal lymph (LRPL) nodes in nasopharyngeal carcinoma (NPC). This study aims to explore the additional nodal parameters in MRI and positron emission tomography-computed tomography for increasing the prediction accuracy. MATERIALS AND METHODS: A total of 663 LRPL nodes were retrospectively collected from 335 patients with NPC...
2016: PloS One
Guillaume Marrelec, Arnaud Messé, Alain Giron, David Rudrauf
Brain computation relies on effective interactions between ensembles of neurons. In neuroimaging, measures of functional connectivity (FC) aim at statistically quantifying such interactions, often to study normal or pathological cognition. Their capacity to reflect a meaningful variety of patterns as expected from neural computation in relation to cognitive processes remains debated. The relative weights of time-varying local neurophysiological dynamics versus static structural connectivity (SC) in the generation of FC as measured remains unsettled...
October 2016: PLoS Computational Biology
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis
Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer...
October 12, 2016: Nature
Alvin Rajkomar, Sneha Lingam, Andrew G Taylor, Michael Blum, John Mongan
The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Yi C Zhang, Alexander C Kagen
TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Dongdong Bai, Chaoqun Wang, Bo Zhang, Xiaodong Yi, Yuhua Tang
The loop closure detection (LCD) is an essential part of visual simultaneous localization and mapping systems (SLAM). LCD is capable of identifying and compensating the accumulation drift of localization algorithms to produce an consistent map if the loops are checked correctly. Deep convolutional neural networks (CNNs) have outperformed state-of-the-art solutions that use traditional hand-crafted features in many computer vision and pattern recognition applications. After the great success of CNNs, there has been much interest in applying CNNs features to robotic fields such as visual LCD...
2016: Robotics and Biomimetics
Peter B M Thomas, Tadas Baltrušaitis, Peter Robinson, Anthony J Vivian
PURPOSE: We validate a video-based method of head posture measurement. METHODS: The Cambridge Face Tracker uses neural networks (constrained local neural fields) to recognize facial features in video. The relative position of these facial features is used to calculate head posture. First, we assess the accuracy of this approach against videos in three research databases where each frame is tagged with a precisely measured head posture. Second, we compare our method to a commercially available mechanical device, the Cervical Range of Motion device: four subjects each adopted 43 distinct head postures that were measured using both methods...
September 2016: Translational Vision Science & Technology
Bernhard U Seeber, Ian C Bruce
This special issue of Network: Computation in Neural Systems on the topic of "Computational models of the electrically stimulated auditory system" incorporates review articles spanning a wide range of approaches to modeling cochlear implant stimulation of the auditory system. The purpose of this overview paper is to provide a historical context for the different modeling endeavors and to point toward how computational modeling could play a key role in the understanding, evaluation, and improvement of cochlear implants in the future...
2016: Network: Computation in Neural Systems
Brandon S Coventry, Aravindakshan Parthasarathy, Alexandra L Sommer, Edward L Bartlett
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons...
October 10, 2016: Journal of Computational Neuroscience
Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Keum-Shik Hong
We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks...
2016: Computational Intelligence and Neuroscience
Kadir Sabanci, Ahmet Kayabasi, Abdurrahim Toktas
BACKGROUND: A simplified computer vision-based application using artificial neural network (ANN) depending on multilayer perceptron (MLP) for accurately classifying the wheat grains into bread or durum is presented. The images of 100 bread wheat grains and 100 durum wheat grains are taken via a high resolution camera and they are subjected to a pre-processing. The main visual features of 4 dimensions, 3 colours and 5 texture are acquired using image processing techniques (IPTs). A total number of 21 visual features are reproduced from the 12 main features to diversify the input population for training and testing the ANN model...
October 8, 2016: Journal of the Science of Food and Agriculture
Guangming Zhang, James J Xia, Michael Liebschner, Xiaoyan Zhang, Daeseung Kim, Xiaobo Zhou
In craniomaxillofacial (CMF) surgery, a reliable way of simulating the soft tissue deformation resulted from skeletal reconstruction is vitally important for preventing the risks of facial distortion postoperatively. However, it is difficult to simulate the soft tissue behaviors affected by different types of CMF surgery. This study presents an integrated bio-mechanical and statistical learning model to improve accuracy and reliability of predictions on soft facial tissue behavior. The Rubin-Bodner (RB) model is initially used to describe the biomechanical behavior of the soft facial tissue...
October 4, 2016: Medical Engineering & Physics
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