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https://www.readbyqxmd.com/read/28645844/modelling-and-interpreting-mesoscale-network-dynamics
#1
REVIEW
Ankit N Khambhati, Ann E Sizemore, Richard F Betzel, Danielle S Bassett
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations...
June 20, 2017: NeuroImage
https://www.readbyqxmd.com/read/28644841/low-dimensional-spike-rate-models-derived-from-networks-of-adaptive-integrate-and-fire-neurons-comparison-and-implementation
#2
Moritz Augustin, Josef Ladenbauer, Fabian Baumann, Klaus Obermayer
The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated...
June 23, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28644840/linking-structure-and-activity-in-nonlinear-spiking-networks
#3
Gabriel Koch Ocker, Krešimir Josić, Eric Shea-Brown, Michael A Buice
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing...
June 23, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28641890/a-comparative-research-of-different-ensemble-surrogate-models-based-on-set-pair-analysis-for-the-dnapl-contaminated-aquifer-remediation-strategy-optimization
#4
Zeyu Hou, Wenxi Lu, Haibo Xue, Jin Lin
Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods...
June 15, 2017: Journal of Contaminant Hydrology
https://www.readbyqxmd.com/read/28641250/deep-convolutional-neural-network-for-inverse-problems-in-imaging
#5
Kyong Hwan Jin, Michael T McCann, Emmanuel Froustey, Michael Unser
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise nonlinearity) when the normal operator ( H*H where H* is the adjoint of the forward imaging operator, H ) of the forward model is a convolution...
June 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28640825/olfactory-learning-without-the-mushroom-bodies-spiking-neural-network-models-of-the-honeybee-lateral-antennal-lobe-tract-reveal-its-capacities-in-odour-memory-tasks-of-varied-complexities
#6
HaDi MaBouDi, Hideaki Shimazaki, Martin Giurfa, Lars Chittka
The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn...
June 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28640401/two-stage-approach-for-risk-estimation-of-fetal-trisomy-21-and-other-aneuploidies-using-computational-intelligence-systems
#7
A C Neocleous, A Syngelaki, K H Nicolaides, C N Schizas
OBJECTIVE: To estimate the risk for fetal trisomy 21 (T21) and other chromosomal abnormalities at 11-13 week's gestation using computational intelligence classification methods. METHODS: As a first step, we train the artificial neural networks with 72054 euploid pregnancies, 295 cases of T21 and 305 of other chromosomal abnormalities (OCA). Then, we sort the cases into two categories of "no-risk" and "risk". The cases of "no-risk" are no further examined, while the cases with "risk" are forwarded in Stage 2 for further examination where we classify them in three types of risk, namely "no-risk", "moderate-risk" and "high-risk"...
June 22, 2017: Ultrasound in Obstetrics & Gynecology
https://www.readbyqxmd.com/read/28633164/ann-prediction-of-ligament-stiffnesses-for-the-enhanced-predictive-ability-of-a-patient-specific-computational-foot-ankle-model
#8
Ruchi Chande, Jennifer S Wayne
Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data is not always available for all soft tissues nor is it known for patient specific work...
June 20, 2017: Journal of Biomechanical Engineering
https://www.readbyqxmd.com/read/28631404/structural-connectivity-differences-in-motor-network-between-tremor-dominant-and-nontremor-parkinson-s-disease
#9
Gaetano Barbagallo, Maria Eugenia Caligiuri, Gennarina Arabia, Andrea Cherubini, Angela Lupo, Rita Nisticò, Maria Salsone, Fabiana Novellino, Maurizio Morelli, Giuseppe Lucio Cascini, Domenico Galea, Aldo Quattrone
Motor phenotypes of Parkinson's disease (PD) are recognized to have different prognosis and therapeutic response, but the neural basis for this clinical heterogeneity remains largely unknown. The main aim of this study was to compare differences in structural connectivity metrics of the main motor network between tremor-dominant and nontremor PD phenotypes (TD-PD and NT-PD, respectively) using probabilistic tractography-based network analysis. A total of 63 PD patients (35 TD-PD patients and 28 NT-PD patients) and 30 healthy controls underwent a 3 T MRI...
June 20, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28630458/the-value-of-instability-an-investigation-of-intra-subject-variability-in-brain-activity-among-obese-adolescent-girls
#10
L O Bauer, R J Houston
BACKGROUND: The present study investigated the value of intra-subject variability (ISV) as a metric for revealing differences in cognition and brain activation associated with an obese versus lean body mass. METHODS: Ninety-six adolescents with a lean body mass (BMI %-ile=5-85), and 92 adolescents with an obese body mass (BMI %-ile >=95), performed two tasks (Stroop and Go/NoGo) challenging response inhibition skills. The standard deviations and averages of their reaction time and P300 electroencephalographic responses to task stimuli were computed across trials...
June 20, 2017: International Journal of Obesity: Journal of the International Association for the Study of Obesity
https://www.readbyqxmd.com/read/28628860/in-silico-modeling-on-adme-properties-of-natural-products-classification-models-for-blood-brain-barrier-permeability-its-application-to-traditional-chinese-medicine-and-in-vitro-experimental-validation
#11
Xiuqing Zhang, Ting Liu, Xiaohui Fan, Ni Ai
In silico modeling of blood-brain barrier (BBB) permeability plays an important role in early discovery of central nervous system (CNS) drugs due to its high-throughput and cost-effectiveness. Natural products (NP) have demonstrated considerable therapeutic efficacy against several CNS diseases. However, BBB permeation property of NP is scarcely evaluated both experimentally and computationally. It is well accepted that significant difference in chemical spaces exists between NP and synthetic drugs, which calls into doubt on suitability of available synthetic chemical based BBB permeability models for the evaluation of NP...
June 7, 2017: Journal of Molecular Graphics & Modelling
https://www.readbyqxmd.com/read/28627811/energy-based-neural-networks-as-a-tool-for-harmony-based-virtual-screening
#12
Nelly I Zhokhova, Igor I Baskin
In Energy-Based Neural Networks (EBNNs), relationships between variables are captured by means of a scalar function conventionally called "energy". In this article, we introduce a procedure of "harmony search", which looks for compounds providing the lowest energies for the EBNNs trained on active compounds. It can be considered as a special kind of similarity search that takes into account regularities in the structures of active compounds. In this paper, we show that harmony search can be used for performing virtual screening...
June 19, 2017: Molecular Informatics
https://www.readbyqxmd.com/read/28624435/revisiting-the-flip-side-long-term-depression-of-synaptic-efficacy-in-the-hippocampus
#13
REVIEW
Cristina Pinar, Christine J Fontaine, Juan Triviño-Paredes, Carina P Lottenberg, Joana Gil-Mohapel, Brian R Christie
Synaptic plasticity is widely regarded as a putative biological substrate for learning and memory processes. While both decreases and increases in synaptic strength are seen as playing a role in learning and memory, long-term depression (LTD) of synaptic efficacy has received far less attention than its counterpart long-term potentiation (LTP). Never-the-less, LTD at synapses can play an important role in increasing computational flexibility in neural networks. In addition, like learning and memory processes, the magnitude of LTD can be modulated by factors that include stress and sex hormones, neurotrophic support, learning environments, and age...
June 14, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28623886/epsilon-cp-using-deep-learning-to-combine-information-from-multiple-sources-for-protein-contact-prediction
#14
Kolja Stahl, Michael Schneider, Oliver Brock
BACKGROUND: Accurately predicted contacts allow to compute the 3D structure of a protein. Since the solution space of native residue-residue contact pairs is very large, it is necessary to leverage information to identify relevant regions of the solution space, i.e. correct contacts. Every additional source of information can contribute to narrowing down candidate regions. Therefore, recent methods combined evolutionary and sequence-based information as well as evolutionary and physicochemical information...
June 17, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28620273/state-dependent-decoding-algorithms-improve-the-performance-of-a-bidirectional-bmi-in-anesthetized-rats
#15
Vito De Feo, Fabio Boi, Houman Safaai, Arno Onken, Stefano Panzeri, Alessandro Vato
Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of information that can be decoded from neural activity recorded form the brain. We have recently proposed that such decoding performance may be improved when using state-dependent decoding algorithms that predict and discount the large component of the trial-to-trial variability of neural activity which is due to the dependence of neural responses on the network's current internal state...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28615793/classification-of-clinical-significance-of-mri-prostate-findings-using-3d-convolutional-neural-networks
#16
Alireza Mehrtash, Alireza Sedghi, Mohsen Ghafoorian, Mehdi Taghipour, Clare M Tempany, William M Wells, Tina Kapur, Parvin Mousavi, Purang Abolmaesumi, Andriy Fedorov
Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge...
February 11, 2017: Proceedings of SPIE
https://www.readbyqxmd.com/read/28615003/3d-deep-convolutional-neural-networks-for-amino-acid-environment-similarity-analysis
#17
Wen Torng, Russ B Altman
BACKGROUND: Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures...
June 14, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28613186/distribution-preserving-stratified-sampling-for-learning-problems
#18
Cristiano Cervellera, Danilo Maccio
The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data...
June 9, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28613185/improving-cnn-performance-accuracies-with-min-max-objective
#19
Weiwei Shi, Yihong Gong, Xiaoyu Tao, Jinjun Wang, Nanning Zheng
We propose a novel method for improving performance accuracies of convolutional neural network (CNN) without the need to increase the network complexity. We accomplish the goal by applying the proposed Min-Max objective to a layer below the output layer of a CNN model in the course of training. The Min-Max objective explicitly ensures that the feature maps learned by a CNN model have the minimum within-manifold distance for each object manifold and the maximum between-manifold distances among different object manifolds...
June 9, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28612121/estimation-of-respiratory-volume-from-thoracoabdominal-breathing-distances-comparison-of-two-models-of-machine-learning
#20
Rémy Dumond, Steven Gastinger, Hala Abdul Rahman, Alexis Le Faucheur, Patrice Quinton, Haitao Kang, Jacques Prioux
PURPOSE: The purposes of this study were to both improve the accuracy of respiratory volume (V) estimates using the respiratory magnetometer plethysmography (RMP) technique and facilitate the use of this technique. METHOD: We compared two models of machine learning (ML) for estimating [Formula: see text]: a linear model (multiple linear regression-MLR) and a nonlinear model (artificial neural network-ANN), and we used cross-validation to validate these models. Fourteen healthy adults, aged [Formula: see text] years participated in the present study...
June 13, 2017: European Journal of Applied Physiology
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