keyword
MENU ▼
Read by QxMD icon Read
search

Neural networks computing

keyword
https://www.readbyqxmd.com/read/28103186/what-the-success-of-brain-imaging-implies-about-the-neural-code
#1
Olivia Guest, Bradley C Love
The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity...
January 19, 2017: ELife
https://www.readbyqxmd.com/read/28102689/high-dimensional-atomistic-neural-network-potentials-for-molecule-surface-interactions-hcl-scattering-from-au-111
#2
Brian Kolb, Xuan Luo, Xueyao Zhou, Bin Jiang, Hua Guo
Ab initio molecular dynamics (AIMD) simulations of molecule-surface scattering allow first-principles characterization of the dynamics. However, the large number of density functional theory calculations along the trajectories are very costly, limiting simulations of long-time events. To avoid this computational bottleneck, we report here the development of a high-dimensional molecule-surface interaction potential energy surface (PES) with movable surface atoms, using a machine learning approach. With 60 degrees of freedom, this PES allows energy transfer between the impinging molecule and surface atoms...
January 19, 2017: Journal of Physical Chemistry Letters
https://www.readbyqxmd.com/read/28102460/neural-mass-models-as-a-tool-to-investigate-neural-dynamics-during-seizures
#3
Tatiana Kameneva, Tianlin Ying, Ben Guo, Dean R Freestone
Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value...
January 19, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/28101760/a-cad-of-fully-automated-colonic-polyp-detection-for-contrasted-and-non-contrasted-ct-scans
#4
Gökalp Tulum, Bülent Bolat, Onur Osman
PURPOSE: Computer-aided detection (CAD) systems are developed to help radiologists detect colonic polyps over CT scans. It is possible to reduce the detection time and increase the detection accuracy rates by using CAD systems. In this paper, we aimed to develop a fully integrated CAD system for automated detection of polyps that yields a high polyp detection rate with a reasonable number of false positives. METHODS: The proposed CAD system is a multistage implementation whose main components are: automatic colon segmentation, candidate detection, feature extraction and classification...
January 18, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28100828/an-integrated-modelling-framework-for-neural-circuits-with-multiple-neuromodulators
#5
Alok Joshi, Vahab Youssofzadeh, Vinith Vemana, T M McGinnity, Girijesh Prasad, KongFatt Wong-Lin
Neuromodulators are endogenous neurochemicals that regulate biophysical and biochemical processes, which control brain function and behaviour, and are often the targets of neuropharmacological drugs. Neuromodulator effects are generally complex partly owing to the involvement of broad innervation, co-release of neuromodulators, complex intra- and extrasynaptic mechanism, existence of multiple receptor subtypes and high interconnectivity within the brain. In this work, we propose an efficient yet sufficiently realistic computational neural modelling framework to study some of these complex behaviours...
January 2017: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/28098774/learning-to-diagnose-cirrhosis-with-liver-capsule-guided-ultrasound-image-classification
#6
Xiang Liu, Jia Lin Song, Shuo Hong Wang, Jing Wen Zhao, Yan Qiu Chen
This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images...
January 13, 2017: Sensors
https://www.readbyqxmd.com/read/28095421/approximate-inference-for-time-varying-interactions-and-macroscopic-dynamics-of-neural-populations
#7
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons...
January 17, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28095201/multisensory-bayesian-inference-depends-on-synapse-maturation-during-training-theoretical-analysis-and-neural-modeling-implementation
#8
Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference...
January 17, 2017: Neural Computation
https://www.readbyqxmd.com/read/28094850/discriminating-solitary-cysts-from-soft-tissue-lesions-in-mammography-using-a-pretrained-deep-convolutional-neural-network
#9
Thijs Kooi, Bram van Ginneken, Nico Karssemeijer, Ard den Heeten
PURPOSE: It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. We develop a method to discriminate benign solitary cysts from malignant masses in digital mammography. We think a system like this can have merit in the clinic as a decision aid or complementary to specialised modalities. METHODS: We employ a deep Convolutional Neural Network (CNN) to classify cyst and mass patches...
January 17, 2017: Medical Physics
https://www.readbyqxmd.com/read/28093472/social-status-dependent-shift-in-neural-circuit-activation-affects-decision-making
#10
Thomas Miller, Katie Clements, Sungwoo Ahn, Choongseok Park, Eoon Hye Ji, Fadi A Issa
: In a social group, animals make behavioral decisions that fit their social ranks. These behavioral choices are dependent on the various social cues experienced during social interactions. In vertebrates, little is known of how social status affects the underlying neural mechanisms regulating decision-making circuits that drive competing behaviors. Here, we demonstrate that social status in zebrafish (Danio rerio) influences behavioral decisions by shifting the balance in neural circuit activation between two competing networks (escape and swim)...
January 16, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28092580/determination-of-the-edge-of-criticality-in-echo-state-networks-through-fisher-information-maximization
#11
Lorenzo Livi, Filippo Maria Bianchi, Cesare Alippi
It is a widely accepted fact that the computational capability of recurrent neural networks (RNNs) is maximized on the so-called "edge of criticality." Once the network operates in this configuration, it performs efficiently on a specific application both in terms of: 1) low prediction error and 2) high short-term memory capacity. Since the behavior of recurrent networks is strongly influenced by the particular input signal driving the dynamics, a universal, application-independent method for determining the edge of criticality is still missing...
January 16, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28092321/dexmedetomidine-disrupts-the-local-and-global-efficiencies-of-large-scale-brain-networks
#12
Javeria A Hashmi, Marco L Loggia, Sheraz Khan, Lei Gao, Jieun Kim, Vitaly Napadow, Emery N Brown, Oluwaseun Akeju
BACKGROUND: A clear understanding of the neural basis of consciousness is fundamental to research in clinical and basic neuroscience disciplines and anesthesia. Recently, decreased efficiency of information integration was suggested as a core network feature of propofol-induced unconsciousness. However, it is unclear whether this finding can be generalized to dexmedetomidine, which has a different molecular target. METHODS: Dexmedetomidine was administered as a 1-μg/kg bolus over 10 min, followed by a 0...
January 16, 2017: Anesthesiology
https://www.readbyqxmd.com/read/28088140/ring-polymer-molecular-dynamics-studies-on-the-rate-coefficient-of-the-abstraction-channel-of-hydrogen-plus-ethane-propane-and-dimethyl-ether
#13
Qingyong Meng, Jun Chen
To accurately compute the rates of the abstraction channels of hydrogen plus ethane (Et), propane (Pr), and dimethyl ether (DME), ring-polymer molecular dynamics (RPMD) method is used in conjunction with the recently constructed local permutation invariant polynomial neural-networks potential energy surface of the parent H + CH4 system [Q. Meng et al., J. Chem. Phys. 144, 154312 (2016)]. For H + Et, one of the H atoms in CH4 of the parent system is replaced by a methyl group, while for the H + DME reaction, it is replaced by the methoxyl group...
January 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28087242/brains-for-birds-and-babies-neural-parallels-between-birdsong-and-speech-acquisition
#14
REVIEW
Jonathan Prather, Kazuo Okanoya, Johan J Bolhuis
Language as a computational cognitive mechanism appears to be unique to the human species. However, there are remarkable behavioral similarities between song learning in songbirds and speech acquisition in human infants that are absent in non-human primates. Here we review important neural parallels between birdsong and speech. In both cases there are separate but continually interacting neural networks that underlie vocal production, sensorimotor learning, and auditory perception and memory. As in the case of human speech, neural activity related to birdsong learning is lateralized, and mirror neurons linking perception and performance may contribute to sensorimotor learning...
January 10, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28086889/hybrid-brain-computer-interface-for-biomedical-cyber-physical-system-application-using-wireless-embedded-eeg-systems
#15
Rifai Chai, Ganesh R Naik, Sai Ho Ling, Hung T Nguyen
BACKGROUND: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. METHODS: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels...
January 7, 2017: Biomedical Engineering Online
https://www.readbyqxmd.com/read/28081006/latent-feature-representation-with-depth-directional-long-term-recurrent-learning-for-breast-masses-in-digital-breast-tomosynthesis
#16
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)...
February 7, 2017: Physics in Medicine and Biology
https://www.readbyqxmd.com/read/28080124/statistical-learning-of-parts-and-wholes-a-neural-network-approach
#17
David C Plaut, Anna K Vande Velde
Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contribute systematically to their meanings. Studies of incidental auditory or visual statistical learning suggest that, as participants learn about wholes they become insensitive to parts embedded within them, but this seems difficult to reconcile with a broad range of findings in which parts and wholes work together to contribute to behavior...
January 12, 2017: Journal of Experimental Psychology. General
https://www.readbyqxmd.com/read/28079471/pre-implant-modeling-of-depth-lead-placement-in-white-matter-for-maximizing-the-extent-of-cortical-activation-during-direct-neurostimulation-therapy
#18
Leopoldo Cendejas Zaragoza, Richard W Byrne, Marvin A Rossi
INTRODUCTION: The objective of this work was to predict preoperatively the maximum extent to which direct stimulation therapy can propagate through an epileptic circuit for stabilizing refractory focal-onset epilepsy. A pre-surgical workflow is presented which comprises a computationally intensive process for calculating the volume of cortical activation (VOCA) surrounding cylindrical depth contacts virtually placed in white matter. The process employs an activation function (AF) derived from cable modeling of an axon...
January 12, 2017: Neurological Research
https://www.readbyqxmd.com/read/28079187/a-spiking-neural-network-model-of-3d-perception-for-event-based-neuromorphic-stereo-vision-systems
#19
Marc Osswald, Sio-Hoi Ieng, Ryad Benosman, Giacomo Indiveri
Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain...
January 12, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28076681/computational-modeling-of-neurotransmitter-release-evoked-by-electrical-stimulation-non-linear-approaches-to-predicting-stimulation-evoked-dopamine-release
#20
James K Trevathan, Ali Yousefi, Hyung Ook Park, John J Bartoletta, Kip A Ludwig, Kendall H Lee, J Luis Lujan
Neurochemical changes evoked by electrical stimulation of the nervous system have been linked to both therapeutic and undesired effects of neuromodulation therapies used to treat obsessive-compulsive disorder, depression, epilepsy, Parkinson's disease, stroke, hypertension, tinnitus, and many other indications. In fact, interest in better understanding the role of neurochemical signaling in neuromodulation therapies has been a focus of recent government- and industry-sponsored programs whose ultimate goal is to usher in an era of personalized medicine by creating neuromodulation therapies that respond to real-time changes in patient status...
January 11, 2017: ACS Chemical Neuroscience
keyword
keyword
101202
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"