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https://www.readbyqxmd.com/read/28444633/distributed-representations-of-action-sequences-in-anterior-cingulate-cortex-a-recurrent-neural-network-approach
#1
Danesh Shahnazian, Clay B Holroyd
Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks...
April 25, 2017: Psychonomic Bulletin & Review
https://www.readbyqxmd.com/read/28441867/accurate-neural-network-description-of-surface-phonons-in-reactive-gas-surface-dynamics-n2-ru-0001
#2
Khosrow Shakouri, Jörg Behler, Joerg Meyer, Geert-Jan Kroes
Ab initio molecular dynamics (AIMD) simulations enable the accurate description of reactive molecule-surface scattering especially if energy transfer involving surface phonons is important. However, presently the computational expense of AIMD rules out its application to systems where reaction probabilities are smaller than about 1 percent. Here we show that this problem can be overcome by a high-dimensional neural network fit of the molecule-surface interaction potential, which also incorporates the dependence on phonons by taking into account all degrees of freedom of the surface explicitly...
April 25, 2017: Journal of Physical Chemistry Letters
https://www.readbyqxmd.com/read/28441114/neural-circuitry-of-reward-prediction-error
#3
Mitsuko Watabe-Uchida, Neir Eshel, Naoshige Uchida
Dopamine neurons facilitate learning by calculating reward prediction error, or the difference between expected and actual reward. Despite two decades of research, it remains unclear how dopamine neurons make this calculation. Here we review studies that tackle this problem from a diverse set of approaches, from anatomy to electrophysiology to computational modeling and behavior. Several patterns emerge from this synthesis: that dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from upstream regions; that they combine multiple separate and redundant inputs, which are themselves interconnected in a dense recurrent network; and that despite the complexity of inputs, the output from dopamine neurons is remarkably homogeneous and robust...
April 24, 2017: Annual Review of Neuroscience
https://www.readbyqxmd.com/read/28439524/brainsegnet-a-convolutional-neural-network-architecture-for-automated-segmentation-of-human-brain-structures
#4
Raghav Mehta, Aabhas Majumdar, Jayanthi Sivaswamy
Automated segmentation of cortical and noncortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume...
April 2017: Journal of Medical Imaging
https://www.readbyqxmd.com/read/28436897/tensor-factorized-neural-networks
#5
Jen-Tzung Chien, Yi-Ting Bao
The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, because the temporal or spatial information in neighboring ways is disregarded. More parameters are required to learn the complicated data structure...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436888/neuromorphic-hardware-architecture-using-the-neural-engineering-framework-for-pattern-recognition
#6
Runchun Wang, Chetan Singh Thakur, Gregory Cohen, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach...
April 24, 2017: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/28436847/learning-trans-dimensional-random-fields-with-applications-to-language-modeling
#7
Bin Wang, Zhijian Ou, Zhiqiang Tan
To describe trans-dimensional observations in sample spaces of different dimensions, we propose a probabilistic model, called the trans-dimensional random field (TRF) by explicitly mixing a collection of random fields. In the framework of stochastic approximation (SA), we develop an effective training algorithm, called augmented SA, which jointly estimates the model parameters and normalizing constants while using trans-dimensional mixture sampling to generate observations of different dimensions. Furthermore, we introduce several statistical and computational techniques to improve the convergence of the training algorithm and reduce computational cost, which together enable us to successfully train TRF models on large datasets...
April 24, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28434616/impaired-tuning-of-neural-ensembles-and-the-pathophysiology-of-schizophrenia-a-translational-and-computational-neuroscience-perspective
#8
REVIEW
John H Krystal, Alan Anticevic, Genevieve J Yang, George Dragoi, Naomi R Driesen, Xiao-Jing Wang, John D Murray
The functional optimization of neural ensembles is central to human higher cognitive functions. When the functions through which neural activity is tuned fail to develop or break down, symptoms and cognitive impairments arise. This review considers ways in which disturbances in the balance of excitation and inhibition might develop and be expressed in cortical networks in association with schizophrenia. This presentation is framed within a developmental perspective that begins with disturbances in glutamate synaptic development in utero...
May 15, 2017: Biological Psychiatry
https://www.readbyqxmd.com/read/28434615/searching-for-cross-diagnostic-convergence-neural-mechanisms-governing-excitation-and-inhibition-balance-in-schizophrenia-and-autism-spectrum-disorders
#9
REVIEW
Jennifer H Foss-Feig, Brendan D Adkinson, Jie Lisa Ji, Genevieve Yang, Vinod H Srihari, James C McPartland, John H Krystal, John D Murray, Alan Anticevic
Recent theoretical accounts have proposed excitation and inhibition (E/I) imbalance as a possible mechanistic, network-level hypothesis underlying neural and behavioral dysfunction across neurodevelopmental disorders, particularly autism spectrum disorder (ASD) and schizophrenia (SCZ). These two disorders share some overlap in their clinical presentation as well as convergence in their underlying genes and neurobiology. However, there are also clear points of dissociation in terms of phenotypes and putatively affected neural circuitry...
May 15, 2017: Biological Psychiatry
https://www.readbyqxmd.com/read/28432822/focal-liver-lesions-segmentation-and-classification-in-nonenhanced-t2-weighted-mri
#10
Ilias Gatos, Stavros Tsantis, Maria Karamesini, Stavros Spiliopoulos, Dimitris Karnabatidis, John D Hazle, George C Kagadis
PURPOSE: To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. METHODS: 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction...
April 22, 2017: Medical Physics
https://www.readbyqxmd.com/read/28429195/computer-assisted-diagnosis-system-for-breast-cancer-in-computed-tomography-laser-mammography-ctlm
#11
Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M Iqbal Saripan, Abdul Rahman Ramli
Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633-643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images...
April 20, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28427143/activity-induced-spontaneous-spikes-in-gabaergic-neurons-suppress-seizure-discharges-an-implication-of-computational-modeling
#12
Wei Lu, Jing Feng, Bo Wen, Kewei Wang, Jin-Hui Wang
BACKGROUND: Epilepsy, a prevalent neurological disorder, appears self-termination. The endogenous mechanism for seizure self-termination remains to be addressed in order to develop new strategies for epilepsy treatment. We aim to examine the role of activity-induced spontaneous spikes at GABAergic neurons as an endogenous mechanism in the seizure self-termination. METHODS AND RESULTS: Neuronal spikes were induced by depolarization pulses at cortical GABAergic neurons from temporal lobe epilepsy patients and mice, in which some of these neurons fired activity-induced spontaneous spikes...
February 23, 2017: Oncotarget
https://www.readbyqxmd.com/read/28425947/deep-count-fruit-counting-based-on-deep-simulated-learning
#13
Maryam Rahnemoonfar, Clay Sheppard
Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force...
April 20, 2017: Sensors
https://www.readbyqxmd.com/read/28425500/structure-shapes-dynamics-and-directionality-in-diverse-brain-networks-mathematical-principles-and-empirical-confirmation-in-three-species
#14
Joon-Young Moon, Junhyeok Kim, Tae-Wook Ko, Minkyung Kim, Yasser Iturria-Medina, Jee-Hyun Choi, Joseph Lee, George A Mashour, UnCheol Lee
Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks...
April 20, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28424430/exploring-non-stationarity-patterns-in-schizophrenia-neural-reorganization-abnormalities-in-the-alpha-band
#15
Pablo Núñez, Jesus Poza, Alejandro Bachiller, Javier Gomez-Pilar, Alba Lubeiro, Vicente Molina, Roberto Hornero
OBJECTIVE: The aim of this paper was to characterize brain non-stationarity during an auditory oddball task in schizophrenia (SCH). The level of non-stationarity was measured in the baseline and response windows of relevant tones in SCH patients and healthy controls. APPROACH: Event-related potentials were recorded from 28 SCH patients and 51 controls. Non-stationarity was estimated in the conventional electroencephalography frequency bands by means of Kullback-Leibler divergence (KLD)...
April 20, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28422957/towards-a-theory-of-cortical-columns-from-spiking-neurons-to-interacting-neural-populations-of-finite-size
#16
Tilo Schwalger, Moritz Deger, Wulfram Gerstner
Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50-2000 neurons of the same type but different populations account for different neuron types...
April 19, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28422668/sensitivity-analysis-for-probabilistic-neural-network-structure-reduction
#17
Piotr A Kowalski, Maciej Kusy
In this paper, we propose the use of local sensitivity analysis (LSA) for the structure simplification of the probabilistic neural network (PNN). Three algorithms are introduced. The first algorithm applies LSA to the PNN input layer reduction by selecting significant features of input patterns. The second algorithm utilizes LSA to remove redundant pattern neurons of the network. The third algorithm combines the proposed two and constitutes the solution of how they can work together. PNN with a product kernel estimator is used, where each multiplicand computes a one-dimensional Cauchy function...
April 12, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28422080/study-on-temperature-and-synthetic-compensation-of-piezo-resistive-differential-pressure-sensors-by-coupled-simulated-annealing-and-simplex-optimized-kernel-extreme-learning-machine
#18
Ji Li, Guoqing Hu, Yonghong Zhou, Chong Zou, Wei Peng, Jahangir Alam Sm
As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task...
April 19, 2017: Sensors
https://www.readbyqxmd.com/read/28422047/template-for-the-neural-control-of-directed-stepping-generalized-to-all-legs-of-mantisbot
#19
Nicholas Stephen Szczecinski, Roger Quinn
We previously developed a neural controller for one leg of our six-legged robot, MantisBot, that could direct locomotion toward a goal by modulating leg-local reflexes with simple descending commands from a head sensor. In this work, we successfully apply an automated method to tune the control network for all three pairs of legs of our hexapod robot MantisBot in only 90 seconds with a desktop computer. Each foot's motion changes appropriately as the body's intended direction of travel changes. In addition, several results from studies of walking insects are captured by this model...
April 19, 2017: Bioinspiration & Biomimetics
https://www.readbyqxmd.com/read/28420678/automated-analysis-of-high-content-microscopy-data-with-deep-learning
#20
Oren Z Kraus, Ben T Grys, Jimmy Ba, Yolanda Chong, Brendan J Frey, Charles Boone, Brenda J Andrews
Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization...
April 18, 2017: Molecular Systems Biology
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