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https://www.readbyqxmd.com/read/28938009/altering-neuronal-excitability-to-preserve-network-connectivity-in-a-computational-model-of-alzheimer-s-disease
#1
Willem de Haan, Elisabeth C W van Straaten, Alida A Gouw, Cornelis J Stam
Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer's disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects...
September 22, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28937989/limited-synapse-overproduction-can-speed-development-but-sometimes-with-long-term-energy-and-discrimination-penalties
#2
Harang Ju, Costa M Colbert, William B Levy
Neural circuit development requires that synapses be formed between appropriate neurons. In addition, for a hierarchical network, successful development involves a sequencing of developmental events. It has been suggested that one mechanism that helps speed up development of proper connections is an early overproduction of synapses. Using a computational model of synapse development, such as adaptive synaptogenesis, it is possible to study such overproduction and its role in speeding up development; it is also possible to study other outcomes of synapse overproduction that are seemingly new to the literature...
September 22, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28937298/probing-the-toxicity-of-nanoparticles-a-unified-in-silico-machine-learning-model-based-on-perturbation-theory
#3
Riccardo Concu, Valeria V Kleandrova, Alejandro Speck-Planche, M Natália D S Cordeiro
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs...
September 22, 2017: Nanotoxicology
https://www.readbyqxmd.com/read/28932180/hardware-efficient-on-line-learning-through-pipelined-truncated-error-backpropagation-in-binary-state-networks
#4
Hesham Mostafa, Bruno Pedroni, Sadique Sheik, Gert Cauwenberghs
Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the training phase, however, has attracted relatively little attention. In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28928651/a-velocity-level-bi-criteria-optimization-scheme-for-coordinated-path-tracking-of-dual-robot-manipulators-using-recurrent-neural-network
#5
Lin Xiao, Yongsheng Zhang, Bolin Liao, Zhijun Zhang, Lei Ding, Long Jin
A dual-robot system is a robotic device composed of two robot arms. To eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28926765/texture-and-art-with-deep-neural-networks
#6
REVIEW
Leon A Gatys, Alexander S Ecker, Matthias Bethge
Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they have a long history of informing each other. Recent advances in texture synthesis that were motivated by visual neuroscience have led to a substantial advance in image synthesis and manipulation in computer vision using convolutional neural networks (CNNs). Here, we review these recent advances and discuss how they can in turn inspire new research in visual perception and computational neuroscience...
September 16, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28926475/altered-resting-state-functional-connectivity-of-default-mode-network-and-sensorimotor-network-in-heavy-metal-music-lovers
#7
Yan Sun, Congcong Zhang, Shuxia Duan, Xiaoxia Du, Vince D Calhoun
The aim of this study was to investigate the spontaneous neural activity and functional connectivity (FC) in heavy metal music lovers (HMML) compared with classical music lovers (CML) during resting state. Forty HMML and 31 CML underwent resting-state functional MRI scans. Fractional amplitude of low-frequency fluctuations (fALFF) and seed-based resting-state FC were computed to explore regional activity and functional integration. A voxel-based two-sample t-test was used to test the differences between the two groups...
September 18, 2017: Neuroreport
https://www.readbyqxmd.com/read/28925954/quantitative-structure-activity-relationship-modeling-of-kinase-selectivity-profiles
#8
Sandeepkumar Kothiwale, Corina Borza, Ambra Pozzi, Jens Meiler
The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound...
September 19, 2017: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://www.readbyqxmd.com/read/28924568/convolutional-neural-network-for-high-accuracy-functional-near-infrared-spectroscopy-in-a-brain-computer-interface-three-class-classification-of-rest-right-and-left-hand-motor-execution
#9
Thanawin Trakoolwilaiwan, Bahareh Behboodi, Jaeseok Lee, Kyungsoo Kim, Ji-Woong Choi
The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI...
January 2018: Neurophotonics
https://www.readbyqxmd.com/read/28924506/automated-classification-of-tropical-shrub-species-a-hybrid-of-leaf-shape-and-machine-learning-approach
#10
Miraemiliana Murat, Siow-Wee Chang, Arpah Abu, Hwa Jen Yap, Kien-Thai Yong
Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily...
2017: PeerJ
https://www.readbyqxmd.com/read/28922130/haze-removal-using-radial-basis-function-networks-for-visibility-restoration-applications
#11
Bo-Hao Chen, Shih-Chia Huang, Chian-Ying Li, Sy-Yen Kuo
Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images...
September 15, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28922121/motion-blur-kernel-estimation-via-deep-learning
#12
Xiangyu Xu, Jinshan Pan, Yu-Jin Zhang, Ming-Hsuan Yang
The success of the state-of-the-art deblurring methods mainly depends on restoration of sharp edges in a coarse-tofine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges...
September 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28922116/iterative-low-dose-ct-reconstruction-with-priors-trained-by-artificial-neural-network
#13
Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li
Dose reduction in computed tomography (CT) is essential for decreasing radiation risk in clinical applications. Iterative reconstruction algorithms are one of the most promising way to compensate for the increased noise due to reduction of photon flux. Most iterative reconstruction algorithms incorporate manually designed prior functions of the reconstructed image to suppress noises while maintaining structures of the image. These priors basically rely on smoothness constraints and cannot exploit more complex features of the image...
September 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28920911/efficient-online-learning-algorithms-based-on-lstm-neural-networks
#14
Tolga Ergen, Suleyman Serdar Kozat
We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions...
September 13, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28919043/neocortical-dynamics-during-whisker-based-sensory-discrimination-in-head-restrained-mice
#15
Fritjof Helmchen, Ariel Gilad, Jerry L Chen
A fundamental task frequently encountered by brains is to rapidly and reliably discriminate between sensory stimuli of the same modality, be it distinct auditory sounds, odors, visual patterns, or tactile textures. A key mammalian brain structure involved in discrimination behavior is the neocortex. Sensory processing not only involves the respective primary sensory area, which is crucial for perceptual detection, but additionally relies on cortico-cortical communication among several regions including higher-order sensory areas as well as frontal cortical areas...
September 14, 2017: Neuroscience
https://www.readbyqxmd.com/read/28912551/the-antipsychotic-drugs-olanzapine-and-haloperidol-modify-network-connectivity-and-spontaneous-activity-of-neural-networks-in-vitro
#16
Egor Dzyubenko, Georg Juckel, Andreas Faissner
Impaired neural synchronization is a hallmark of psychotic conditions such as schizophrenia. It has been proposed that schizophrenia-related cognitive deficits are caused by an unbalance of reciprocal inhibitory and stimulatory signaling. This supposedly leads to decreased power of induced gamma oscillations during the performance of cognitive tasks. In light of this hypothesis an efficient antipsychotic treatment should modify the connectivity and synchronization of local neural circuits. To address this issue, we investigated a model of hippocampal neuronal networks in vitro...
September 14, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28911111/combinatorial-ensemble-mirna-target-prediction-of-co-regulation-networks-with-non-prediction-data
#17
Jason A Davis, Sita J Saunders, Martin Mann, Rolf Backofen
MicroRNAs (miRNAs) are key regulators of cell-fate decisions in development and disease with a vast array of target interactions that can be investigated using computational approaches. For this study, we developed metaMIR, a combinatorial approach to identify miRNAs that co-regulate identified subsets of genes from a user-supplied list. We based metaMIR predictions on an improved dataset of human miRNA-target interactions, compiled using a machine-learning-based meta-analysis of established algorithms. Simultaneously, the inverse dataset of negative interactions not likely to occur was extracted to increase classifier performance, as measured using an expansive set of experimentally validated interactions from a variety of sources...
September 6, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28911049/aberrant-hyperconnectivity-in-the-motor-system-at-rest-is-linked-to-motor-abnormalities-in-schizophrenia-spectrum-disorders
#18
Sebastian Walther, Katharina Stegmayer, Andrea Federspiel, Stephan Bohlhalter, Roland Wiest, Petra V Viher
Motor abnormalities are frequently observed in schizophrenia and structural alterations of the motor system have been reported. The association of aberrant motor network function, however, has not been tested. We hypothesized that abnormal functional connectivity would be related to the degree of motor abnormalities in schizophrenia. In 90 subjects (46 patients) we obtained resting stated functional magnetic resonance imaging (fMRI) for 8 minutes 40 seconds at 3T. Participants further completed a motor battery on the scanning day...
September 1, 2017: Schizophrenia Bulletin
https://www.readbyqxmd.com/read/28910352/deep-learning-approach-to-bacterial-colony-classification
#19
Bartosz Zieliński, Anna Plichta, Krzysztof Misztal, Przemysław Spurek, Monika Brzychczy-Włoch, Dorota Ochońska
In microbiology it is diagnostically useful to recognize various genera and species of bacteria. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such methods can minimize the risk of incorrect recognition. In this article, we apply the state of the art method for texture analysis to classify genera and species of bacteria...
2017: PloS One
https://www.readbyqxmd.com/read/28906450/obstacle-recognition-based-on-machine-learning-for-on-chip-lidar-sensors-in-a-cyber-physical-system
#20
Fernando Castaño, Gerardo Beruvides, Rodolfo E Haber, Antonio Artuñedo
Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink...
September 14, 2017: Sensors
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