Neural Networks (Computer)

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  Subject Areas on Research

  • A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks. 
  • A comparison of neural network models for the prediction of the cost of care for acute coronary syndrome patients. 
  • A comparison of three techniques for rapid model development: an application in patient risk-stratification. 
  • A constrained optimization approach to preserving prior knowledge during incremental training. 
  • A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system. 
  • A framework for integrating the songbird brain. 
  • A model of visually-guided smooth pursuit eye movements based on behavioral observations. 
  • A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. 
  • A neural network model to predict lung radiation-induced pneumonitis. 
  • A neural network to predict symptomatic lung injury. 
  • A neural network-based method for spectral distortion correction in photon counting x-ray CT. 
  • A novel memristive multilayer feedforward small-world neural network with its applications in PID control. 
  • A robust framework to predict mercury speciation in combustion flue gases. 
  • A strategy for the rapid identification of phosphorylation sites in the phosphoproteome. 
  • A systematic study of the class imbalance problem in convolutional neural networks. 
  • Acute pulmonary embolism: artificial neural network approach for diagnosis. 
  • Acute pulmonary embolism: cost-effectiveness analysis of the effect of artificial neural networks on patient care. 
  • Adaptive feedback control by constrained approximate dynamic programming. 
  • Adaptive prediction of internal target motion using external marker motion: a technical study. 
  • An artificial neural network approach to quantitative single photon emission computed tomographic reconstruction with collimator, attenuation, and scatter compensation. 
  • An artificial neural network for estimating scatter exposures in portable chest radiography. 
  • An artificial neural network for lesion detection on single-photon emission computed tomographic images. 
  • Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis. 
  • Analyzing animal behavior via classifying each video frame using convolutional neural networks. 
  • Application of artificial neural networks to a study of nursing burnout. 
  • Applications of neural networks in urologic oncology. 
  • Artificial neural network for diagnosis of acute pulmonary embolism: effect of case and observer selection. 
  • Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. 
  • Artificial neural network-based glaucoma diagnosis using retinal nerve fiber layer analysis. 
  • Artificial neural network: improving the quality of breast biopsy recommendations. 
  • Artificial neural networks for single photon emission computed tomography. A study of cold lesion detection and localization. 
  • Artificial neural networks improve the accuracy of cancer survival prediction. 
  • Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. 
  • Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning. 
  • Available space and extracellular transport of macromolecules: effects of pore size and connectedness. 
  • Beam-orientation customization using an artificial neural network. 
  • Big data: More than big data sets. 
  • Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. 
  • Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors. 
  • Building knowledge in a complex preterm birth problem domain. 
  • Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. 
  • Cerebellar learning using perturbations. 
  • Circuit design and exponential stabilization of memristive neural networks. 
  • Classification of crystallization outcomes using deep convolutional neural networks. 
  • Combination of multiple serum markers using an artificial neural network to improve specificity in discriminating malignant from benign pelvic masses. 
  • Combining location and expression data for principled discovery of genetic regulatory network models. 
  • Combining multiple models to generate consensus: application to radiation-induced pneumonitis prediction. 
  • Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer. 
  • Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. 
  • Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data. 
  • Computation in neural systems. Editorial. 
  • Computer simulations of morphologically reconstructed CA3 hippocampal neurons. 
  • Computer-aided diagnosis in chest radiography: results of large-scale observer tests at the 1996-2001 RSNA scientific assemblies. 
  • Computer-aided diagnosis in chest radiology. 
  • Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. 
  • Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification. 
  • Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges. 
  • Converting neural signals from place codes to rate codes. 
  • Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks. 
  • Cross-institutional evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer. 
  • Daniel Amit (1938-2007). 
  • Data mining methods find demographic predictors of preterm birth. 
  • Deep Learning to Classify Radiology Free-Text Reports. 
  • Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. 
  • Differences between computer-aided diagnosis of breast masses and that of calcifications. 
  • Dynamics and plasticity of stimulus-selective persistent activity in cortical network models. 
  • Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks. 
  • Effects of synaptic connectivity on liquid state machine performance. 
  • Effects of wide band inhibitors in the dorsal cochlear nucleus. II. Model calculations of the responses to complex sounds. 
  • Efficient supervised learning in networks with binary synapses. 
  • Evaluation of deep convolutional neural networks for glaucoma detection. 
  • Exact inferences in a neural implementation of a hidden Markov model. 
  • Familiarity Detection is an Intrinsic Property of Cortical Microcircuits with Bidirectional Synaptic Plasticity. 
  • Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. 
  • Fibroblast growth factor23 is associated with axonal integrity and neural network architecture in the human frontal lobes. 
  • Genetic variants improve breast cancer risk prediction on mammograms. 
  • Geometric diffusions for the analysis of data from sensor networks. 
  • Glaucoma detection based on deep convolutional neural network. 
  • Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays. 
  • How connectivity, background activity, and synaptic properties shape the cross-correlation between spike trains. 
  • How noise affects the synchronization properties of recurrent networks of inhibitory neurons. 
  • Impact of missing data in evaluating artificial neural networks trained on complete data. 
  • Improved noninvasive diagnosis of acute pulmonary embolism with optimally selected clinical and chest radiographic findings. 
  • Independent component analyses for quantifying neuronal ensemble interactions. 
  • Insights from a simple expression for linear fisher information in a recurrently connected population of spiking neurons. 
  • Interpreting spatial and temporal neural activity through a recurrent neural network brain-machine interface. 
  • Is cortical connectivity optimized for storing information? 
  • Joint modeling of DNA sequence and physical properties to improve eukaryotic promoter recognition. 
  • Learning receptive fields using predictive feedback. 
  • Lesion size quantification in SPECT using an artificial neural network classification approach. 
  • MChip, a low density microarray, differentiates among seasonal human H1N1, North American swine H1N1, and the 2009 pandemic H1N1. 
  • Memristor crossbar-based neuromorphic computing system: a case study. 
  • Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. 
  • Multifractal texture analysis of perfusion lung scans as a potential diagnostic tool for acute pulmonary embolism. 
  • Multiscale approximation with hierarchical radial basis functions networks. 
  • Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds. 
  • Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer. 
  • Neural network classification of nerve activity recorded in a mixed nerve. 
  • Nonlinear feedforward networks with stochastic outputs: infomax implies redundancy reduction. 
  • On Deep Learning for Medical Image Analysis. 
  • On the maximization of information flow between spiking neurons. 
  • On the origin of chaos in autonomous Boolean networks. 
  • Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks. 
  • Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell. 
  • Optimal properties of analog perceptrons with excitatory weights. 
  • Outcome analysis of patients with acute pancreatitis by using an artificial neural network. 
  • PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe. 
  • Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms. 
  • Perceptron error surface analysis: a case study in breast cancer diagnosis. 
  • Perceptual learning as improved probabilistic inference in early sensory areas. 
  • Plasticity of directional place fields in a model of rodent CA3. 
  • Practical outcomes of applying ensemble machine learning classifiers to High-Throughput Screening (HTS) data analysis and screening. 
  • Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. 
  • Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. 
  • Prediction of breast cancer malignancy using an artificial neural network. 
  • Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks. 
  • Predictive model for the diagnosis of intraabdominal abscess. 
  • Probabilistic population codes and the exponential family of distributions. 
  • Probabilistic population codes for Bayesian decision making. 
  • Proper staging techniques in testicular cancer patients. 
  • Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. 
  • QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. 
  • Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study. 
  • Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network. 
  • Retrospective and prospective persistent activity induced by Hebbian learning in a recurrent cortical network. 
  • Robustness of a neural network model for differencing. 
  • Screening of hyaluronic acid-poly(ethylene glycol) composite hydrogels to support intervertebral disc cell biosynthesis using artificial neural network analysis. 
  • Self-organizing map for cluster analysis of a breast cancer database. 
  • Semantic priming in a cortical network model. 
  • Sleep-wake classification via quantifying heart rate variability by convolutional neural network. 
  • Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. 
  • Smooth function approximation using neural networks. 
  • Some features of the spread of epidemics and information on a random graph. 
  • Storing structured sparse memories in a multi-modular cortical network model. 
  • The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. 
  • The interaction of bayesian priors and sensory data and its neural circuit implementation in visually guided movement. 
  • The neural substrates of super memory. 
  • The statistics of repeating patterns of cortical activity can be reproduced by a model network of stochastic binary neurons. 
  • Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network. 
  • Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. 
  • Two types of afferent terminals innervate cochlear inner hair cells in C57BL/6J mice. 
  • Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach. 
  • Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model. 
  • Use of MindMapper software for research domain mapping. 
  • Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses. 
  • Using neural networks as an aid in the determination of disease status: comparison of clinical diagnosis to neural-network predictions in a pedigree with autosomal dominant limb-girdle muscular dystrophy. 
  • Wagner's canalization model. 
  • What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. 
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  Keywords of People

  • Berchuck, Andrew, James M. Ingram Professor of Gynecologic Oncology, Obstetrics and Gynecology, Gynecologic Oncology
  • Craig, Stephen L, William T. Miller Professor of Chemistry, Duke University Energy Initiative
  • Farsiu, Sina, Paul Ruffin Scarborough Associate Professor of Engineering, Electrical and Computer Engineering
  • Hartemink, Alexander J., Professor in the Department of Computer Science, Biology
  • Hsu-Kim, Heileen, Associate Professor in the Department of Civil and Environmental Engineering, Civil and Environmental Engineering
  • Nicolelis, Miguel Angelo L., Duke School of Medicine Professor in Neuroscience, Psychology and Neuroscience
  • Sullivan, Daniel Carl, Professor Emeritus of Radiology, Radiology
  • West, Vivian L, Clinical Associate in the School of Nursing, School of Nursing