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Neural networks computing

Minoru Koyama, Avinash Pujala
A series of classical studies in non-human primates has revealed the neuronal activity patterns underlying decision-making. However, the circuit mechanisms for such patterns remain largely unknown. Recent detailed circuit analyses in simpler neural systems have started to reveal the connectivity patterns underlying analogous processes. Here we review a few of these systems that share a particular connectivity pattern, namely mutual inhibition of lateral inhibition. Close examination of these systems suggests that this recurring connectivity pattern ('network motif') is a building block to enforce particular dynamics, which can be used not only for simple behavioral choice but also for more complex choices and other brain functions...
January 16, 2018: Current Opinion in Neurobiology
Eisuke Ito, Takaaki Sato, Daisuke Sano, Etsuko Utagawa, Tsuyoshi Kato
A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps...
January 19, 2018: Food and Environmental Virology
Seyed Abolfazl Hosseini, Iman Esmaili Paeen Afrakoti
The purpose of the present study was to reconstruct the energy spectrum of a poly-energetic neutron source using an algorithm developed based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is a kind of artificial neural network based on the Takagi-Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. The neutron pulse height distributions used as input data in the training procedure for the ANFIS algorithm were obtained from the simulations performed by MCNPX-ESUT computational code (MCNPX-Energy engineering of Sharif University of Technology)...
January 17, 2018: Journal of Radiation Research
Tobias Zimmermann, Bertram Taetz, Gabriele Bleser
Human body motion analysis based on wearable inertial measurement units (IMUs) receives a lot of attention from both the research community and the and industrial community. This is due to the significant role in, for instance, mobile health systems, sports and human computer interaction. In sensor based activity recognition, one of the major issues for obtaining reliable results is the sensor placement/assignment on the body. For inertial motion capture (joint kinematics estimation) and analysis, the IMU-to-segment (I2S) assignment and alignment are central issues to obtain biomechanical joint angles...
January 19, 2018: Sensors
Jose M Esnaola-Acebes, Alex Roxin, Daniele Avitabile, Ernest Montbrió
We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions...
November 2017: Physical Review. E
Nicholas Lubbers, Turab Lookman, Kipton Barros
We apply recent advances in machine learning and computer vision to a central problem in materials informatics: the statistical representation of microstructural images. We use activations in a pretrained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images...
November 2017: Physical Review. E
Zhuocheng Xiao, Jiwei Zhang, Andrew T Sornborger, Louis Tao
Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next...
November 2017: Physical Review. E
Lukas Mosser, Olivier Dubrule, Martin J Blunt
To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets...
October 2017: Physical Review. E
Grégory Dumont, G Bard Ermentrout, Boris Gutkin
The study of brain rhythms is an open-ended, and challenging, subject of interest in neuroscience. One of the best tools for the understanding of oscillations at the single neuron level is the phase-resetting curve (PRC). Synchronization in networks of neurons, effects of noise on the rhythms, effects of transient stimuli on the ongoing rhythmic activity, and many other features can be understood by the PRC. However, most macroscopic brain rhythms are generated by large populations of neurons, and so far it has been unclear how the PRC formulation can be extended to these more common rhythms...
October 2017: Physical Review. E
C Dietz, T Kretz, M H Thoma
Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions...
July 2017: Physical Review. E
Vladimir A Maksimenko, Annika Lüttjohann, Vladimir V Makarov, Mikhail V Goremyko, Alexey A Koronovskii, Vladimir Nedaivozov, Anastasia E Runnova, Gilles van Luijtelaar, Alexander E Hramov, Stefano Boccaletti
We introduce a practical and computationally not demanding technique for inferring interactions at various microscopic levels between the units of a network from the measurements and the processing of macroscopic signals. Starting from a network model of Kuramoto phase oscillators, which evolve adaptively according to homophilic and homeostatic adaptive principles, we give evidence that the increase of synchronization within groups of nodes (and the corresponding formation of synchronous clusters) causes also the defragmentation of the wavelet energy spectrum of the macroscopic signal...
July 2017: Physical Review. E
Herng-Hua Chang, Cheng-Yuan Li, Audrey Haihong Gallogly
OBJECTIVE: Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. METHODS: Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components...
February 2018: IEEE Transactions on Bio-medical Engineering
Johannes Leugering, Gordon Pipa
A neuronal population is a computational unit that receives a multivariate, time-varying input signal and creates a related multivariate output. These neural signals are modeled as stochastic processes that transmit information in real time, subject to stochastic noise. In a stationary environment, where the input signals can be characterized by constant statistical properties, the systematic relationship between its input and output processes determines the computation carried out by a population. When these statistical characteristics unexpectedly change, the population needs to adapt to its new environment if it is to maintain stable operation...
January 17, 2018: Neural Computation
Christopher J Hillar, Ngoc M Tran
The Hopfield recurrent neural network is a classical auto-associative model of memory, in which collections of symmetrically coupled McCulloch-Pitts binary neurons interact to perform emergent computation. Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministic dynamics, a basic open problem is to design a family of Hopfield networks with a number of noise-tolerant memories that grows exponentially with neural population size...
January 16, 2018: Journal of Mathematical Neuroscience
Mehmet Ufuk Dalmış, Suzan Vreemann, Thijs Kooi, Ritse M Mann, Nico Karssemeijer, Albert Gubern-Mérida
Current computer-aided detection (CADe) systems for contrast-enhanced breast MRI rely on both spatial information obtained from the early-phase and temporal information obtained from the late-phase of the contrast enhancement. However, late-phase information might not be available in a screening setting, such as in abbreviated MRI protocols, where acquisition is limited to early-phase scans. We used deep learning to develop a CADe system that exploits the spatial information obtained from the early-phase scans...
January 2018: Journal of Medical Imaging
Quynh C Nguyen, Mehdi Sajjadi, Matt McCullough, Minh Pham, Thu T Nguyen, Weijun Yu, Hsien-Wen Meng, Ming Wen, Feifei Li, Ken R Smith, Kim Brunisholz, Tolga Tasdizen
BACKGROUND: Neighbourhood quality has been connected with an array of health issues, but neighbourhood research has been limited by the lack of methods to characterise large geographical areas. This study uses innovative computer vision methods and a new big data source of street view images to automatically characterise neighbourhood built environments. METHODS: A total of 430 000 images were obtained using Google's Street View Image API for Salt Lake City, Chicago and Charleston...
January 15, 2018: Journal of Epidemiology and Community Health
Juhua Zhang, Wenbo Peng, Lei Wang
Motivation: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets of genome-wide nucleosome occupancy profiles and computational tools to the analysis on these data, the complex language of eukaryotic Nucleosome positioning remains incompletely understood. Results: Here, we address this challenge using an approach based on a state-of-the-art machine learning method...
January 10, 2018: Bioinformatics
Paolo Napoletano, Flavio Piccoli, Raimondo Schettini
Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set...
January 12, 2018: Sensors
Jonay Toledo, Jose D Piñeiro, Rafael Arnay, Daniel Acosta, Leopoldo Acosta
In this paper, a study of the odometric system for the autonomous cart Verdino, which is an electric vehicle based on a golf cart, is presented. A mathematical model of the odometric system is derived from cart movement equations, and is used to compute the vehicle position and orientation. The inputs of the system are the odometry encoders, and the model uses the wheels diameter and distance between wheels as parameters. With this model, a least square minimization is made in order to get the nominal best parameters...
January 12, 2018: Sensors
Debarghya Sarkar, Jun Tao, Wei Wang, Qingfeng Lin, Matthew Yeung, Chenhao Ren, Rehan Kapadia
Neuromorphic or "brain-like" computation is a leading candidate for efficient, fault-tolerant processing of large-scale data, as well as real-time sensing and transduction of complex multivariate systems and networks such as self-driving vehicles or Internet of Things applications. In biology, the synapse serves as an active memory unit in the neural system, and is the component responsible for learning and memory. Electronically emulating this element via a compact, scalable technology which can be integrated in a 3-D architecture is critical for future implementations of neuromorphic processors...
January 12, 2018: ACS Nano
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