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Neural networks computing

Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, István Csabai
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions...
March 15, 2018: Scientific Reports
James P Roach, Aleksandra Pidde, Eitan Katz, Jiaxing Wu, Nicolette Ognjanovski, Sara J Aton, Michal R Zochowski
Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections...
March 15, 2018: Proceedings of the National Academy of Sciences of the United States of America
Shuchao Pang, Mehmet A Orgun, Zhezhou Yu
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images...
May 2018: Computer Methods and Programs in Biomedicine
Mürsel Karadas, Adam M Wojciechowski, Alexander Huck, Nils Ole Dalby, Ulrik Lund Andersen, Axel Thielscher
We suggest a novel approach for wide-field imaging of the neural network dynamics of brain slices that uses highly sensitivity magnetometry based on nitrogen-vacancy (NV) centers in diamond. In-vitro recordings in brain slices is a proven method for the characterization of electrical neural activity and has strongly contributed to our understanding of the mechanisms that govern neural information processing. However, this traditional approach only acquires signals from a few positions, which severely limits its ability to characterize the dynamics of the underlying neural networks...
March 14, 2018: Scientific Reports
Samuel J Yang, Marc Berndl, D Michael Ando, Mariya Barch, Arunachalam Narayanaswamy, Eric Christiansen, Stephan Hoyer, Chris Roat, Jane Hung, Curtis T Rueden, Asim Shankar, Steven Finkbeiner, Philip Nelson
BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality...
March 15, 2018: BMC Bioinformatics
Ilia Korvigo, Andrey Afanasyev, Nikolay Romashchenko, Mikhail Skoblov
Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data...
2018: PloS One
Jean-Paul Noel, Olaf Blanke, Elisa Magosso, Andrea Serino
Interactions between the body and the environment occur within the Peri-Personal Space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS...
March 14, 2018: Journal of Neurophysiology
Bei-Bei Wu, Ye Ma, Lei Xie, Jin-Zhuang Huang, Zong-Bo Sun, Zhi-Duo Hou, Rui-Wei Guo, Zhi-Rong Lin, Shou-Xing Duan, Shan-Shan Zhao, Yao-Xie, Dan-Miao Sun, Chun-Min Zhu, Shu-Hua Ma
BACKGROUND: Systemic lupus erythematosus (SLE) is associated with cognitive deficit but the exact neural mechanisms remain unclear. PURPOSE: To explore sequential brain activities using functional magnetic resonance imaging (fMRI) during the performance of a decision-making task, and to determine whether serum or clinical markers can reflect the involvement of the brain in SLE. SUBJECTS: Sixteen female SLE patients without overt clinical neuropsychiatric symptoms and 16 healthy controls were included...
March 14, 2018: Journal of Magnetic Resonance Imaging: JMRI
Martin Lamos, Radek Marecek, Tomáš Slavíček, Michal Mikl, Ivan Rektor, Jiri Jan
Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity.
 Approach. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity...
March 14, 2018: Journal of Neural Engineering
Maurizio Giordano, Kumar Parijat Tripathi, Mario Rosario Guarracino
BACKGROUND: System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models...
March 8, 2018: BMC Bioinformatics
Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A Gutman, Jill S Barnholtz-Sloan, José E Velázquez Vega, Daniel J Brat, Lee A D Cooper
Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
Nataliya Kraynyukova, Tatjana Tchumatchenko
A hallmark of cortical circuits is their versatility. They can perform multiple fundamental computations such as normalization, memory storage, and rhythm generation. Yet it is far from clear how such versatility can be achieved in a single circuit, given that specialized models are often needed to replicate each computation. Here, we show that the stabilized supralinear network (SSN) model, which was originally proposed for sensory integration phenomena such as contrast invariance, normalization, and surround suppression, can give rise to dynamic cortical features of working memory, persistent activity, and rhythm generation...
March 12, 2018: Proceedings of the National Academy of Sciences of the United States of America
Nick Wasylyshyn, Brett Hemenway Falk, Javier O Garcia, Christopher N Cascio, Matthew Brook O'Donnell, C Raymond Bingham, Bruce Simons-Morton, Jean M Vettel, Emily B Falk
Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms...
February 1, 2018: Social Cognitive and Affective Neuroscience
Y Wang, X Mi, G J M Rosa, Z Chen, P Lin, S Wang, Z Bao
Neural networks (NN) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypical outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that could find an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L1-norm of the parameters (weights and biases), therefore solve the problem of over-parameterization in NN...
February 24, 2018: Journal of Animal Science
Paul Spiering, Joerg Meyer
At present, molecular dynamics with electronic friction (MDEF) is the workhorse model to go beyond the Born-Oppenheimer approximation in modeling dynamics of molecules at metal surfaces. Concomitant friction coefficients can be calculated with either the local density friction approximation (LDFA) or orbital-dependent friction (ODF), which -- unlike LDFA -- accounts for anisotropy while relying on other approximations. Statistically converged observables for diatomics on static surfaces including all degrees of freedom have hitherto only been calculated with LDFA due to the computational cost of ODF...
March 12, 2018: Journal of Physical Chemistry Letters
Alexander Tack, Anirban Mukhopadhyay, Stefan Zachow
OBJECTIVE: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. METHOD: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain...
March 8, 2018: Osteoarthritis and Cartilage
Ding Wang, Derong Liu
The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition...
February 21, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Christiane Linster
Norepinephrine (NE), a central neuromodulator, influences sensory processing at the behavioral and physiological levels, potentially by regulating signal-to-noise ratio in sensory neural networks. To understand neuromodulatory function at the perceptual level, one must understand the neural mechanisms underlying these effects. In this review, we illustrate this process using the olfactory system as a model system. We discuss basic computations in the olfactory system and their modulation by NE. We review known cellular effects of NE in the olfactory bulb and cortex and how these effects modulate olfactory computation...
March 7, 2018: Brain Research
Luis A Camuñas-Mesa, Yaisel L Domínguez-Cordero, Alejandro Linares-Barranco, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco
Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli...
2018: Frontiers in Neuroscience
Camila Viana Vieira Farhate, Zigomar Menezes de Souza, Stanley Robson de Medeiros Oliveira, Rose Luiza Moraes Tavares, João Luís Nunes Carvalho
Soil CO2 emissions are regarded as one of the largest flows of the global carbon cycle and small changes in their magnitude can have a large effect on the CO2 concentration in the atmosphere. Thus, a better understanding of this attribute would enable the identification of promoters and the development of strategies to mitigate the risks of climate change. Therefore, our study aimed at using data mining techniques to predict the soil CO2 emission induced by crop management in sugarcane areas in Brazil. To do so, we used different variable selection methods (correlation, chi-square, wrapper) and classification (Decision tree, Bayesian models, neural networks, support vector machine, bagging with logistic regression), and finally we tested the efficiency of different approaches through the Receiver Operating Characteristic (ROC) curve...
2018: PloS One
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