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Neural architecture human

Gabriele Gratton
Here, I propose a view of the architecture of the human information processing system, and of how it can be adapted to changing task demands (which is the hallmark of cognitive control). This view is informed by an interpretation of brain activity as reflecting the excitability level of neural representations, encoding not only stimuli and temporal contexts, but also action plans and task goals. The proposed cognitive architecture includes three types of circuits: open circuits, involved in feed-forward processing such as that connecting stimuli with responses and characterized by brief, transient brain activity; and two types of closed circuits, positive feedback circuits (characterized by sustained, high-frequency oscillatory activity), which help select and maintain representations, and negative feedback circuits (characterized by brief, low-frequency oscillatory bursts), which are instead associated with changes in representations...
December 11, 2017: Psychophysiology
Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang
BACKGROUND: With the rapid development of deep sequencing techniques in the recent years, enhancers have been systematically identified in such projects as FANTOM and ENCODE, forming genome-wide landscapes in a series of human cell lines. Nevertheless, experimental approaches are still costly and time consuming for large scale identification of enhancers across a variety of tissues under different disease status, making computational identification of enhancers indispensable. RESULTS: To facilitate the identification of enhancers, we propose a computational framework, named DeepEnhancer, to distinguish enhancers from background genomic sequences...
December 1, 2017: BMC Bioinformatics
Lei Xiang, Yu Qiao, Dong Nie, Le An, Qian Wang, Dinggang Shen
Positron emission tomography (PET) is an essential technique in many clinical applications such as tumor detection and brain disorder diagnosis. In order to obtain high-quality PET images, a standard-dose radioactive tracer is needed, which inevitably causes the risk of radiation exposure damage. For reducing the patient's exposure to radiation and maintaining the high quality of PET images, in this paper, we propose a deep learning architecture to estimate the high-quality standard-dose PET (SPET) image from the combination of the low-quality low-dose PET (LPET) image and the accompanying T1-weighted acquisition from magnetic resonance imaging (MRI)...
December 6, 2017: Neurocomputing
Hao Huang, Haihua Xu, Ying Hu, Gang Zhou
Goodness of pronunciation (GOP) is the most widely used method for automatic mispronunciation detection. In this paper, a transfer learning approach to GOP based mispronunciation detection when applying maximum F1-score criterion (MFC) training to deep neural network (DNN)-hidden Markov model based acoustic models is proposed. Rather than train the whole network using MFC, a DNN is used, whose hidden layers are borrowed from native speech recognition with only the softmax layer trained according to the MFC objective function...
November 2017: Journal of the Acoustical Society of America
Bart Liefers, Freerk G Venhuizen, Vivian Schreur, Bram van Ginneken, Carel Hoyng, Sascha Fauser, Thomas Theelen, Clara I Sánchez
We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels...
November 1, 2017: Biomedical Optics Express
Richard F Betzel, Danielle S Bassett
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops...
November 2017: Journal of the Royal Society, Interface
Ritesh Pradhan, Ramazan S Aygun, Manil Maskey, Rahul Ramachandran, Daniel J Cecil
Tropical cyclone intensity estimation is a challenging task as it required domain knowledge while extracting features, significant pre-processing, various sets of parameters obtained from satellites, and human intervention for analysis. The inconsistency of results, significant pre-processing of data, complexity of the problem domain, and problems on generalizability are some of the issues related to intensity estimation. In this study, we design a deep convolutional neural network architecture for categorizing hurricanes based on intensity using graphics processing unit...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Francisco Javier Pérez-Benito, Patricia Villacampa-Fernández, J Alberto Conejero, Juan M García-Gómez, Esperanza Navarro-Pardo
BACKGROUND AND OBJECTIVE: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires...
November 13, 2017: Computer Methods and Programs in Biomedicine
A I Shahin, Yanhui Guo, K M Amin, Amr A Sharawi
BACKGROUND AND OBJECTIVES: White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased...
November 16, 2017: Computer Methods and Programs in Biomedicine
Qiang Luo, Yina Ma, Meghana A Bhatt, P Read Montague, Jianfeng Feng
Impression management, as one of the most essential skills of social function, impacts one's survival and success in human societies. However, the neural architecture underpinning this social skill remains poorly understood. By employing a two-person bargaining game, we exposed three strategies involving distinct cognitive processes for social impression management with different levels of strategic deception. We utilized a novel adaptation of Granger causality accounting for signal-dependent noise (SDN), which captured the directional connectivity underlying the impression management during the bargaining game...
2017: Frontiers in Human Neuroscience
Chi-Hua Chen, Yunpeng Wang, Min-Tzu Lo, Andrew Schork, Chun-Chieh Fan, Dominic Holland, Karolina Kauppi, Olav B Smeland, Srdjan Djurovic, Nilotpal Sanyal, Derrek P Hibar, Paul M Thompson, Wesley K Thompson, Ole A Andreassen, Anders M Dale
Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5'UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs...
November 16, 2017: Scientific Reports
Raffaele Pugliese, Federico Fontana, Amanda Marchini, Fabrizio Gelain
Self-assembling peptides (SAP) have drawn an increasing interest in the tissue engineering community. They display unquestionable biomimetic properties, tailorability and promising biocompatibility. However their use has been hampered by poor mechanical properties making them fragile soft scaffolds. To increase SAP hydrogel stiffness we introduced a novel strategy based on multiple ramifications of (LDLK)3, a well-known linear SAP, connected with one or multiple "lysine knots". Differently branched SAPs were tested by increasing the number of (LDLK)3-like branches and by adding the neuro-regenerative functional motif BMHP1 as a single branch...
November 8, 2017: Acta Biomaterialia
Zhiwei Wang, Kristina Zeljic, Qinying Jiang, Yong Gu, Wei Wang, Zheng Wang
Ubiquitous variability between individuals in visual perception is difficult to standardize and has thus essentially been ignored. Here we construct a quantitative psychophysical measure of illusory rotary motion based on the Pinna-Brelstaff figure (PBF) in 73 healthy volunteers and investigate the neural circuit mechanisms underlying perceptual variation using functional magnetic resonance imaging (fMRI). We acquired fMRI data from a subset of 42 subjects during spontaneous and 3 stimulus conditions: expanding PBF, expanding modified-PBF (illusion-free) and expanding modified-PBF with physical rotation...
November 22, 2016: Cerebral Cortex
Abdulmajid Murad, Jae-Young Pyun
Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data...
November 6, 2017: Sensors
Yusuke Fujiwara, Jongho Lee, Takahiro Ishikawa, Shinji Kakei, Jun Izawa
The visuomotor transformation during a goal-directed movement may involve a coordinate transformation from visual 'extrinsic' to muscle-like 'intrinsic' coordinate frames, which might be processed via a multilayer network architecture composed of neural basis functions. This theory suggests that the postural change during a goal-directed movement task alters activity patterns of the neurons in the intermediate layer of the visuomotor transformation that recieves both visual and proprioceptive inputs, and thus influence the multi-voxel pattern of the blood oxygenation level dependent signal...
November 2, 2017: Scientific Reports
Evelyn Tang, Chad Giusti, Graham L Baum, Shi Gu, Eli Pollock, Ari E Kahn, David R Roalf, Tyler M Moore, Kosha Ruparel, Ruben C Gur, Raquel E Gur, Theodore D Satterthwaite, Danielle S Bassett
As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. Here we use a network representation of diffusion imaging data from 882 youth ages 8-22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development. Notably, stable controllers in subcortical areas are negatively related to cognitive performance. Investigating structural mechanisms supporting these changes, we simulate network evolution with a set of growth rules...
November 1, 2017: Nature Communications
Clint J Perry, Luigi Baciadonna
Until recently, whether invertebrates might exhibit emotions was unknown. This possibility has traditionally been dismissed by many as emotions are frequently defined with reference to human subjective experience, and invertebrates are often not considered to have the neural requirements for such sophisticated abilities. However, emotions are understood in humans and other vertebrates to be multifaceted brain states, comprising dissociable subjective, cognitive, behavioural and physiological components. In addition, accumulating literature is providing evidence of the impressive cognitive capacities and behavioural flexibility of invertebrates...
November 1, 2017: Journal of Experimental Biology
Eva Bystrenova, Zuzana Bednarikova, Marianna Barbalinardo, Francesco Valle, Zuzana Gazova, Fabio Biscarini
Peptide aggregation into oligomers and fibrillar architectures is a hallmark of severe neurodegenerative pathologies, diabetes mellitus or systemic amyloidoses. The polymorphism of amyloid forms and their distribution are both effectors that potentially modulate the disease, thus it is important to understand the molecular basis of protein amyloid disorders through the interaction of the different amyloid forms with neural cells and tissues. Here we explore the effect of amyloid fibrils on the human neuroblastoma (SH-SY5Y) cell line in vitro...
October 18, 2017: Colloids and Surfaces. B, Biointerfaces
Jaron T Colas
In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems...
2017: PloS One
Stanislas Dehaene, Hakwan Lau, Sid Kouider
The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word "consciousness" conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense)...
October 27, 2017: Science
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