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https://www.readbyqxmd.com/read/28081006/latent-feature-representation-with-depth-directional-long-term-recurrent-learning-for-breast-masses-in-digital-breast-tomosynthesis
#1
Dae Hoe Kim, Seong Tae Kim, Jung Min Chang, Yong Man Ro
Characterization of masses in computer-aided detection systems for digital breast tomosynthesis (DBT) is an important step to reduce false positive (FP) rates. To effectively differentiate masses from FPs in DBT, discriminative mass feature representation is required. In this paper, we propose a new latent feature representation boosted by depth directional long-term recurrent learning for characterizing malignant masses. The proposed network is designed to encode mass characteristics in two parts. First, 2D spatial image characteristics of DBT slices are encoded as a slice feature representation by convolutional neural network (CNN)...
January 12, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28053032/a-spiking-working-memory-model-based-on-hebbian-short-term-potentiation
#2
Florian Fiebig, Anders Lansner
: A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations...
January 4, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28045036/speed-hysteresis-and-noise-shaping-of-traveling-fronts-in-neural-fields-role-of-local-circuitry-and-nonlocal-connectivity
#3
Cristiano Capone, Maurizio Mattia
Neural field models are powerful tools to investigate the richness of spatiotemporal activity patterns like waves and bumps, emerging from the cerebral cortex. Understanding how spontaneous and evoked activity is related to the structure of underlying networks is of central interest to unfold how information is processed by these systems. Here we focus on the interplay between local properties like input-output gain function and recurrent synaptic self-excitation of cortical modules, and nonlocal intermodular synaptic couplings yielding to define a multiscale neural field...
January 3, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28040687/de-identification-of-patient-notes-with-recurrent-neural-networks
#4
Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, Peter Szolovits
OBJECTIVE: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of electronic health record databases, the limited number of researchers with access to non-de-identified notes, and the frequent mistakes of human annotators...
December 30, 2016: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28035989/neuroblastoma-a-paradigm-for-big-data-science-in-pediatric-oncology
#5
REVIEW
Brittany M Salazar, Emily A Balczewski, Choong Yong Ung, Shizhen Zhu
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease...
December 27, 2016: International Journal of Molecular Sciences
https://www.readbyqxmd.com/read/28030775/multistability-of-delayed-recurrent-neural-networks-with-mexican-hat-activation-functions
#6
Peng Liu, Zhigang Zeng, Jun Wang
This letter studies the multistability analysis of delayed recurrent neural networks with Mexican hat activation function. Some sufficient conditions are obtained to ensure that an n-dimensional recurrent neural network can have [Formula: see text] equilibrium points with [Formula: see text], and [Formula: see text] of them are locally exponentially stable. Furthermore, the attraction basins of these stable equilibrium points are estimated. We show that the attraction basins of these stable equilibrium points can be larger than their originally partitioned subsets...
December 28, 2016: Neural Computation
https://www.readbyqxmd.com/read/28026786/a-one-layer-recurrent-neural-network-for-constrained-complex-variable-convex-optimization
#7
Sitian Qin, Jiqiang Feng, Jiahui Song, Xingnan Wen, Chen Xu
In this paper, based on CR calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence...
December 22, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28018557/a-hybrid-computer-aided-diagnosis-system-for-prediction-of-breast-cancer-recurrence-hpbcr-using-optimized-ensemble-learning
#8
Mohammad R Mohebian, Hamid R Marateb, Marjan Mansourian, Miguel Angel Mañanas, Fariborz Mokarian
Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT)...
2017: Computational and Structural Biotechnology Journal
https://www.readbyqxmd.com/read/28011771/improving-protein-disorder-prediction-by-deep-bidirectional-long-short-term-memory-recurrent-neural-networks
#9
Jack Hanson, Yuedong Yang, Kuldip Paliwal, Yaoqi Zhou
MOTIVATION: Capturing long-range interactions between structural but not sequence neighbors of proteins is a long-standing challenging problem in bioinformatics. Recently, long short-term memory (LSTM) networks have significantly improved the accuracy of speech and image classification problems by remembering useful past information in long sequential events. Here, we have implemented deep bidirectional LSTM recurrent neural networks in the problem of protein intrinsic disorder prediction...
December 22, 2016: Bioinformatics
https://www.readbyqxmd.com/read/28004040/structured-prediction-models-for-rnn-based-sequence-labeling-in-clinical-text
#10
Abhyuday N Jagannatha, Hong Yu
Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks...
November 2016: Proc Conf Empir Methods Nat Lang Process
https://www.readbyqxmd.com/read/28001002/the-effects-of-guanfacine-and-phenylephrine-on-a-spiking-neuron-model-of-working-memory
#11
Peter Duggins, Terrence C Stewart, Xuan Choo, Chris Eliasmith
We use a spiking neural network model of working memory (WM) capable of performing the spatial delayed response task (DRT) to investigate two drugs that affect WM: guanfacine (GFC) and phenylephrine (PHE). In this model, the loss of information over time results from changes in the spiking neural activity through recurrent connections. We reproduce the standard forgetting curve and then show that this curve changes in the presence of GFC and PHE, whose application is simulated by manipulating functional, neural, and biophysical properties of the model...
December 21, 2016: Topics in Cognitive Science
https://www.readbyqxmd.com/read/27991805/understanding-the-neural-basis-of-cognitive-bias-modification-as-a-clinical-treatment-for-depression
#12
Akihiro Eguchi, Daniel Walters, Nele Peerenboom, Hannah Dury, Elaine Fox, Simon Stringer
OBJECTIVE: Cognitive bias modification (CBM) eliminates cognitive biases toward negative information and is efficacious in reducing depression recurrence, but the mechanisms behind the bias elimination are not fully understood. The present study investigated, through computer simulation of neural network models, the neural dynamics underlying the use of CBM in eliminating the negative biases in the way that depressed patients evaluate facial expressions. METHOD: We investigated 2 new CBM methodologies using biologically plausible synaptic learning mechanisms-continuous transformation learning and trace learning-which guide learning by exploiting either the spatial or temporal continuity between visual stimuli presented during training...
December 19, 2016: Journal of Consulting and Clinical Psychology
https://www.readbyqxmd.com/read/27990266/breeding-novel-solutions-in-the-brain-a-model-of-darwinian-neurodynamics
#13
András Szilágyi, István Zachar, Anna Fedor, Harold P de Vladar, Eörs Szathmáry
Background: The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain. Methods: We combine known components of the brain - recurrent neural networks (acting as attractors), the action selection loop and implicit working memory - to provide the appropriate Darwinian architecture...
2016: F1000Research
https://www.readbyqxmd.com/read/27974160/capturing-and-manipulating-activated-neuronal-ensembles-with-cane-delineates-a-hypothalamic-social-fear-circuit
#14
Katsuyasu Sakurai, Shengli Zhao, Jun Takatoh, Erica Rodriguez, Jinghao Lu, Andrew D Leavitt, Min Fu, Bao-Xia Han, Fan Wang
We developed a technology (capturing activated neuronal ensembles [CANE]) to label, manipulate, and transsynaptically trace neural circuits that are transiently activated in behavioral contexts with high efficiency and temporal precision. CANE consists of a knockin mouse and engineered viruses designed to specifically infect activated neurons. Using CANE, we selectively labeled neurons that were activated by either fearful or aggressive social encounters in a hypothalamic subnucleus previously known as a locus for aggression, and discovered that social-fear and aggression neurons are intermixed but largely distinct...
November 23, 2016: Neuron
https://www.readbyqxmd.com/read/27973557/encoding-in-balanced-networks-revisiting-spike-patterns-and-chaos-in-stimulus-driven-systems
#15
Guillaume Lajoie, Kevin K Lin, Jean-Philippe Thivierge, Eric Shea-Brown
Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences of chaos for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be room for fine stimulus features to be precisely resolved...
December 2016: PLoS Computational Biology
https://www.readbyqxmd.com/read/27959927/ridge-polynomial-neural-network-with-error-feedback-for-time-series-forecasting
#16
Waddah Waheeb, Rozaida Ghazali, Tutut Herawan
Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback...
2016: PloS One
https://www.readbyqxmd.com/read/27958268/making-brain-machine-interfaces-robust-to-future-neural-variability
#17
David Sussillo, Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets...
December 13, 2016: Nature Communications
https://www.readbyqxmd.com/read/27942426/urdu-nasta-liq-text-recognition-using-implicit-segmentation-based-on-multi-dimensional-long-short-term-memory-neural-networks
#18
Saeeda Naz, Arif Iqbal Umar, Riaz Ahmed, Muhammad Imran Razzak, Sheikh Faisal Rashid, Faisal Shafait
The recognition of Arabic script and its derivatives such as Urdu, Persian, Pashto etc. is a difficult task due to complexity of this script. Particularly, Urdu text recognition is more difficult due to its Nasta'liq writing style. Nasta'liq writing style inherits complex calligraphic nature, which presents major issues to recognition of Urdu text owing to diagonality in writing, high cursiveness, context sensitivity and overlapping of characters. Therefore, the work done for recognition of Arabic script cannot be directly applied to Urdu recognition...
2016: SpringerPlus
https://www.readbyqxmd.com/read/27932957/emergence-of-selectivity-to-looming-stimuli-in-a-spiking-network-model-of-the-optic-tectum
#19
Eric V Jang, Carolina Ramirez-Vizcarrondo, Carlos D Aizenman, Arseny S Khakhalin
The neural circuits in the optic tectum of Xenopus tadpoles are selectively responsive to looming visual stimuli that resemble objects approaching the animal at a collision trajectory. This selectivity is required for adaptive collision avoidance behavior in this species, but its underlying mechanisms are not known. In particular, it is still unclear how the balance between the recurrent spontaneous network activity and the newly arriving sensory flow is set in this structure, and to what degree this balance is important for collision detection...
2016: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/27918886/computational-principles-and-models-of-multisensory-integration
#20
REVIEW
Chandramouli Chandrasekaran
Combining information from multiple senses creates robust percepts, speeds up responses, enhances learning, and improves detection, discrimination, and recognition. In this review, I discuss computational models and principles that provide insight into how this process of multisensory integration occurs at the behavioral and neural level. My initial focus is on drift-diffusion and Bayesian models that can predict behavior in multisensory contexts. I then highlight how recent neurophysiological and perturbation experiments provide evidence for a distributed redundant network for multisensory integration...
December 2, 2016: Current Opinion in Neurobiology
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