Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing.

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Delis, Ioannis; Dmochowski, Jacek P; Sajda, Paul; Wang, Qi

2018-03-23

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making. Copyright © 2018 Elsevier Inc. All rights reserved.

The neural cell adhesion molecule L1 is distinct from the N-CAM related group of surface antigens BSP-2 and D2

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Faissner, A; Kruse, J; Goridis, C

1984-01-01

The neural cell adhesion molecule L1 and the group of N-CAM related molecules, BSP-2 and D2 antigen, are immunochemically distinct molecular species. The two groups of surface molecules are also functionally distinct entities, since inhibition of Ca2+-independent adhesion among early post-natal m...

Calsyntenins Are Expressed in a Dynamic and Partially Overlapping Manner during Neural Development

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Gemma de Ramon Francàs

2017-08-01

Full Text Available Calsyntenins form a family of linker proteins between distinct populations of vesicles and kinesin motors for axonal transport. They were implicated in synapse formation and synaptic plasticity by findings in worms, mice and humans. These findings were in accordance with the postsynaptic localization of the Calsyntenins in the adult brain. However, they also affect the formation of neural circuits, as loss of Calsyntenin-1 (Clstn1 was shown to interfere with axonal branching and axon guidance. Despite the fact that Calsyntenins were discovered originally in embryonic chicken motoneurons, their distribution in the developing nervous system has not been analyzed in detail so far. Here, we summarize our analysis of the temporal and spatial expression patterns of the cargo-docking proteins Clstn1, Clstn2 and Clstn3 during neural development by comparing the dynamic distribution of their mRNAs by in situ hybridization in the spinal cord, the cerebellum, the retina and the tectum, as well as in the dorsal root ganglia (DRG.

Distinct neural and neuromuscular strategies underlie independent evolution of simplified advertisement calls.

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Leininger, Elizabeth C; Kelley, Darcy B

2013-04-07

Independent or convergent evolution can underlie phenotypic similarity of derived behavioural characters. Determining the underlying neural and neuromuscular mechanisms sheds light on how these characters arose. One example of evolutionarily derived characters is a temporally simple advertisement call of male African clawed frogs (Xenopus) that arose at least twice independently from a more complex ancestral pattern. How did simplification occur in the vocal circuit? To distinguish shared from divergent mechanisms, we examined activity from the calling brain and vocal organ (larynx) in two species that independently evolved simplified calls. We find that each species uses distinct neural and neuromuscular strategies to produce the simplified calls. Isolated Xenopus borealis brains produce fictive vocal patterns that match temporal patterns of actual male calls; the larynx converts nerve activity faithfully into muscle contractions and single clicks. In contrast, fictive patterns from isolated Xenopus boumbaensis brains are short bursts of nerve activity; the isolated larynx requires stimulus bursts to produce a single click of sound. Thus, unlike X. borealis, the output of the X. boumbaensis hindbrain vocal pattern generator is an ancestral burst-type pattern, transformed by the larynx into single clicks. Temporally simple advertisement calls in genetically distant species of Xenopus have thus arisen independently via reconfigurations of central and peripheral vocal neuroeffectors.

Common and distinct neural mechanisms of attentional switching and response conflict.

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Kim, Chobok; Johnson, Nathan F; Gold, Brian T

2012-08-21

The human capacities for overcoming prepotent actions and flexibly switching between tasks represent cornerstones of cognitive control. Functional neuroimaging has implicated a diverse set of brain regions contributing to each of these cognitive control processes. However, the extent to which attentional switching and response conflict draw on shared or distinct neural mechanisms remains unclear. The current study examined the neural correlates of response conflict and attentional switching using event-related functional magnetic resonance imaging (fMRI) and a fully randomized 2×2 design. We manipulated an arrow-word version of the Stroop task to measure conflict and switching in the context of a single task decision, in response to a common set of stimuli. Under these common conditions, both behavioral and imaging data showed significant main effects of conflict and switching but no interaction. However, conjunction analyses identified frontal regions involved in both switching and response conflict, including the dorsal anterior cingulate cortex (dACC) and left inferior frontal junction. In addition, connectivity analyses demonstrated task-dependent functional connectivity patterns between dACC and inferior temporal cortex for attentional switching and between dACC and posterior parietal cortex for response conflict. These results suggest that the brain makes use of shared frontal regions, but can dynamically modulate the connectivity patterns of some of those regions, to deal with attentional switching and response conflict. Copyright © 2012 Elsevier B.V. All rights reserved.

Passivation and control of partially known SISO nonlinear systems via dynamic neural networks

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Reyes-Reyes J.

2000-01-01

Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

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Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-01-01

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

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Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-12-12

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

Distinct neural substrates of visuospatial and verbal-analytic reasoning as assessed by Raven's Advanced Progressive Matrices.

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Chen, Zhencai; De Beuckelaer, Alain; Wang, Xu; Liu, Jia

2017-11-24

Recent studies revealed spontaneous neural activity to be associated with fluid intelligence (gF) which is commonly assessed by Raven's Advanced Progressive Matrices, and embeds two types of reasoning: visuospatial and verbal-analytic reasoning. With resting-state fMRI data, using global brain connectivity (GBC) analysis which averages functional connectivity of a voxel in relation to all other voxels in the brain, distinct neural correlates of these two reasoning types were found. For visuospatial reasoning, negative correlations were observed in both the primary visual cortex (PVC) and the precuneus, and positive correlations were observed in the temporal lobe. For verbal-analytic reasoning, negative correlations were observed in the right inferior frontal gyrus (rIFG), dorsal anterior cingulate cortex and temporoparietal junction, and positive correlations were observed in the angular gyrus. Furthermore, an interaction between GBC value and type of reasoning was found in the PVC, rIFG and the temporal lobe. These findings suggest that visuospatial reasoning benefits more from elaborate perception to stimulus features, whereas verbal-analytic reasoning benefits more from feature integration and hypothesis testing. In sum, the present study offers, for different types of reasoning in gF, first empirical evidence of separate neural substrates in the resting brain.

Partial restoration of mutant enzyme homeostasis in three distinct lysosomal storage disease cell lines by altering calcium homeostasis.

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Ting-Wei Mu

2008-02-01

Full Text Available A lysosomal storage disease (LSD results from deficient lysosomal enzyme activity, thus the substrate of the mutant enzyme accumulates in the lysosome, leading to pathology. In many but not all LSDs, the clinically most important mutations compromise the cellular folding of the enzyme, subjecting it to endoplasmic reticulum-associated degradation instead of proper folding and lysosomal trafficking. A small molecule that restores partial mutant enzyme folding, trafficking, and activity would be highly desirable, particularly if one molecule could ameliorate multiple distinct LSDs by virtue of its mechanism of action. Inhibition of L-type Ca2+ channels, using either diltiazem or verapamil-both US Food and Drug Administration-approved hypertension drugs-partially restores N370S and L444P glucocerebrosidase homeostasis in Gaucher patient-derived fibroblasts; the latter mutation is associated with refractory neuropathic disease. Diltiazem structure-activity studies suggest that it is its Ca2+ channel blocker activity that enhances the capacity of the endoplasmic reticulum to fold misfolding-prone proteins, likely by modest up-regulation of a subset of molecular chaperones, including BiP and Hsp40. Importantly, diltiazem and verapamil also partially restore mutant enzyme homeostasis in two other distinct LSDs involving enzymes essential for glycoprotein and heparan sulfate degradation, namely alpha-mannosidosis and type IIIA mucopolysaccharidosis, respectively. Manipulation of calcium homeostasis may represent a general strategy to restore protein homeostasis in multiple LSDs. However, further efforts are required to demonstrate clinical utility and safety.

Estimating the Acquisition Price of Enshi Yulu Young Tea Shoots Using Near-Infrared Spectroscopy by the Back Propagation Artificial Neural Network Model in Conjunction with Backward Interval Partial Least Squares Algorithm

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Wang, Sh.-P.; Gong, Z.-M.; Su, X.-Zh.; Liao, J.-Zh.

2017-09-01

Near infrared spectroscopy and the back propagation artificial neural network model in conjunction with backward interval partial least squares algorithm were used to estimate the purchasing price of Enshi yulu young tea shoots. The near-infrared spectra regions most relevant to the tea shoots price model (5700.5-5935.8, 7613.6-7848.9, 8091.8-8327.1, 8331-8566.2, 9287.5-9522.5, and 9526.6-9761.9 cm-1) were selected using backward interval partial least squares algorithm. The first five principal components that explained 99.96% of the variability in those selected spectral data were then used to calibrate the back propagation artificial neural tea shoots purchasing price model. The performance of this model (coefficient of determination for prediction 0.9724; root-mean-square error of prediction 4.727) was superior to those of the back propagation artificial neural model (coefficient of determination for prediction 0.8653, root-mean-square error of prediction 5.125) and the backward interval partial least squares model (coefficient of determination for prediction 0.5932, root-mean-square error of prediction 25.125). The acquisition price model with the combined backward interval partial least squares-back propagation artificial neural network algorithms can evaluate the price of Enshi yulu tea shoots accurately, quickly and objectively.

Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex

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Hao, Yaoyao; Zhang, Qiaosheng; Controzzi, Marco; Cipriani, Christian; Li, Yue; Li, Juncheng; Zhang, Shaomin; Wang, Yiwen; Chen, Weidong; Chiara Carrozza, Maria; Zheng, Xiaoxiang

2014-12-01

Objective. Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear. Approach. To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects. Main results. Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200-300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies. Significance. This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.

Agent-specific learning signals for self-other distinction during mentalising.

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Sam Ereira

2018-04-01

Full Text Available Humans have a remarkable ability to simulate the minds of others. How the brain distinguishes between mental states attributed to self and mental states attributed to someone else is unknown. Here, we investigated how fundamental neural learning signals are selectively attributed to different agents. Specifically, we asked whether learning signals are encoded in agent-specific neural patterns or whether a self-other distinction depends on encoding agent identity separately from this learning signal. To examine this, we tasked subjects to learn continuously 2 models of the same environment, such that one was selectively attributed to self and the other was selectively attributed to another agent. Combining computational modelling with magnetoencephalography (MEG enabled us to track neural representations of prediction errors (PEs and beliefs attributed to self, and of simulated PEs and beliefs attributed to another agent. We found that the representational pattern of a PE reliably predicts the identity of the agent to whom the signal is attributed, consistent with a neural self-other distinction implemented via agent-specific learning signals. Strikingly, subjects exhibiting a weaker neural self-other distinction also had a reduced behavioural capacity for self-other distinction and displayed more marked subclinical psychopathological traits. The neural self-other distinction was also modulated by social context, evidenced in a significantly reduced decoding of agent identity in a nonsocial control task. Thus, we show that self-other distinction is realised through an encoding of agent identity intrinsic to fundamental learning signals. The observation that the fidelity of this encoding predicts psychopathological traits is of interest as a potential neurocomputational psychiatric biomarker.

Distinct functional and temporal requirements for zebrafish Hdac1 during neural crest-derived craniofacial and peripheral neuron development.

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Myron S Ignatius

Full Text Available The regulation of gene expression is accomplished by both genetic and epigenetic means and is required for the precise control of the development of the neural crest. In hdac1(b382 mutants, craniofacial cartilage development is defective in two distinct ways. First, fewer hoxb3a, dlx2 and dlx3-expressing posterior branchial arch precursors are specified and many of those that are consequently undergo apoptosis. Second, in contrast, normal numbers of progenitors are present in the anterior mandibular and hyoid arches, but chondrocyte precursors fail to terminally differentiate. In the peripheral nervous system, there is a disruption of enteric, DRG and sympathetic neuron differentiation in hdac1(b382 mutants compared to wildtype embryos. Specifically, enteric and DRG-precursors differentiate into neurons in the anterior gut and trunk respectively, while enteric and DRG neurons are rarely present in the posterior gut and tail. Sympathetic neuron precursors are specified in hdac1(b382 mutants and they undergo generic neuronal differentiation but fail to undergo noradrenergic differentiation. Using the HDAC inhibitor TSA, we isolated enzyme activity and temporal requirements for HDAC function that reproduce hdac1(b382 defects in craniofacial and sympathetic neuron development. Our study reveals distinct functional and temporal requirements for zebrafish hdac1 during neural crest-derived craniofacial and peripheral neuron development.

Distinct functional and temporal requirements for zebrafish Hdac1 during neural crest-derived craniofacial and peripheral neuron development.

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Ignatius, Myron S; Unal Eroglu, Arife; Malireddy, Smitha; Gallagher, Glen; Nambiar, Roopa M; Henion, Paul D

2013-01-01

The regulation of gene expression is accomplished by both genetic and epigenetic means and is required for the precise control of the development of the neural crest. In hdac1(b382) mutants, craniofacial cartilage development is defective in two distinct ways. First, fewer hoxb3a, dlx2 and dlx3-expressing posterior branchial arch precursors are specified and many of those that are consequently undergo apoptosis. Second, in contrast, normal numbers of progenitors are present in the anterior mandibular and hyoid arches, but chondrocyte precursors fail to terminally differentiate. In the peripheral nervous system, there is a disruption of enteric, DRG and sympathetic neuron differentiation in hdac1(b382) mutants compared to wildtype embryos. Specifically, enteric and DRG-precursors differentiate into neurons in the anterior gut and trunk respectively, while enteric and DRG neurons are rarely present in the posterior gut and tail. Sympathetic neuron precursors are specified in hdac1(b382) mutants and they undergo generic neuronal differentiation but fail to undergo noradrenergic differentiation. Using the HDAC inhibitor TSA, we isolated enzyme activity and temporal requirements for HDAC function that reproduce hdac1(b382) defects in craniofacial and sympathetic neuron development. Our study reveals distinct functional and temporal requirements for zebrafish hdac1 during neural crest-derived craniofacial and peripheral neuron development.

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

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Seungsoo Nam

2018-01-01

Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

On the neural mechanisms subserving consciousness and attention

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Catherine eTallon-Baudry

2012-01-01

Full Text Available Consciousness, as described in the experimental literature, is a multi-faceted phenomenon, that impinges on other well-studied concepts such as attention and control. Do consciousness and attention refer to different aspects of the same core phenomenon, or do they correspond to distinct functions? One possibility to address this question is to examine the neural mechanisms underlying consciousness and attention. If consciousness and attention pertain to the same concept, they should rely on shared neural mechanisms. Conversely, if their underlying mechanisms are distinct, then consciousness and attention should be considered as distinct entities. This paper therefore reviews neurophysiological facts arguing in favor or against a tight relationship between consciousness and attention. Three neural mechanisms that have been associated with both attention and consciousness are examined (neural amplification, involvement of the fronto-parietal network, and oscillatory synchrony, to conclude that the commonalities between attention and consciousness at the neural level may have been overestimated. Last but not least, experiments in which both attention and consciousness were probed at the neural level point toward a dissociation between the two concepts. It therefore appears from this review that consciousness and attention rely on distinct neural properties, although they can interact at the behavioral level. It is proposed that a "cumulative influence model", in which attention and consciousness correspond to distinct neural mechanisms feeding a single decisional process leading to behavior, fits best with available neural and behavioral data. In this view, consciousness should not be considered as a top-level executive function but should rather be defined by its experiential properties.

Nonlinear Inference in Partially Observed Physical Systems and Deep Neural Networks

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Rozdeba, Paul J.

The problem of model state and parameter estimation is a significant challenge in nonlinear systems. Due to practical considerations of experimental design, it is often the case that physical systems are partially observed, meaning that data is only available for a subset of the degrees of freedom required to fully model the observed system's behaviors and, ultimately, predict future observations. Estimation in this context is highly complicated by the presence of chaos, stochasticity, and measurement noise in dynamical systems. One of the aims of this dissertation is to simultaneously analyze state and parameter estimation in as a regularized inverse problem, where the introduction of a model makes it possible to reverse the forward problem of partial, noisy observation; and as a statistical inference problem using data assimilation to transfer information from measurements to the model states and parameters. Ultimately these two formulations achieve the same goal. Similar aspects that appear in both are highlighted as a means for better understanding the structure of the nonlinear inference problem. An alternative approach to data assimilation that uses model reduction is then examined as a way to eliminate unresolved nonlinear gating variables from neuron models. In this formulation, only measured variables enter into the model, and the resulting errors are themselves modeled by nonlinear stochastic processes with memory. Finally, variational annealing, a data assimilation method previously applied to dynamical systems, is introduced as a potentially useful tool for understanding deep neural network training in machine learning by exploiting similarities between the two problems.

Distinct steps of neural induction revealed by Asterix, Obelix and TrkC, genes induced by different signals from the organizer.

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Sonia Pinho

2011-04-01

Full Text Available The amniote organizer (Hensen's node can induce a complete nervous system when grafted into a peripheral region of a host embryo. Although BMP inhibition has been implicated in neural induction, non-neural cells cannot respond to BMP antagonists unless previously exposed to a node graft for at least 5 hours before BMP inhibitors. To define signals and responses during the first 5 hours of node signals, a differential screen was conducted. Here we describe three early response genes: two of them, Asterix and Obelix, encode previously undescribed proteins of unknown function but Obelix appears to be a nuclear RNA-binding protein. The third is TrkC, a neurotrophin receptor. All three genes are induced by a node graft within 4-5 hours but they differ in the extent to which they are inducible by FGF: FGF is both necessary and sufficient to induce Asterix, sufficient but not necessary to induce Obelix and neither sufficient nor necessary for induction of TrkC. These genes are also not induced by retinoic acid, Noggin, Chordin, Dkk1, Cerberus, HGF/SF, Somatostatin or ionomycin-mediated Calcium entry. Comparison of the expression and regulation of these genes with other early neural markers reveals three distinct "epochs", or temporal waves, of gene expression accompanying neural induction by a grafted organizer, which are mirrored by specific stages of normal neural plate development. The results are consistent with neural induction being a cascade of responses elicited by different signals, culminating in the formation of a patterned nervous system.

Partial information decomposition as a unified approach to the specification of neural goal functions.

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Wibral, Michael; Priesemann, Viola; Kay, Jim W; Lizier, Joseph T; Phillips, William A

2017-03-01

In many neural systems anatomical motifs are present repeatedly, but despite their structural similarity they can serve very different tasks. A prime example for such a motif is the canonical microcircuit of six-layered neo-cortex, which is repeated across cortical areas, and is involved in a number of different tasks (e.g. sensory, cognitive, or motor tasks). This observation has spawned interest in finding a common underlying principle, a 'goal function', of information processing implemented in this structure. By definition such a goal function, if universal, cannot be cast in processing-domain specific language (e.g. 'edge filtering', 'working memory'). Thus, to formulate such a principle, we have to use a domain-independent framework. Information theory offers such a framework. However, while the classical framework of information theory focuses on the relation between one input and one output (Shannon's mutual information), we argue that neural information processing crucially depends on the combination of multiple inputs to create the output of a processor. To account for this, we use a very recent extension of Shannon Information theory, called partial information decomposition (PID). PID allows to quantify the information that several inputs provide individually (unique information), redundantly (shared information) or only jointly (synergistic information) about the output. First, we review the framework of PID. Then we apply it to reevaluate and analyze several earlier proposals of information theoretic neural goal functions (predictive coding, infomax and coherent infomax, efficient coding). We find that PID allows to compare these goal functions in a common framework, and also provides a versatile approach to design new goal functions from first principles. Building on this, we design and analyze a novel goal function, called 'coding with synergy', which builds on combining external input and prior knowledge in a synergistic manner. We suggest that

Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

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Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

2012-09-21

The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available. Copyright © 2012 Elsevier B.V. All rights reserved.

Distinct regulatory functions of calpain 1 and 2 during neural stem cell self-renewal and differentiation.

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Daniela M Santos

Full Text Available Calpains are calcium regulated cysteine proteases that have been described in a wide range of cellular processes, including apoptosis, migration and cell cycle regulation. In addition, calpains have been implicated in differentiation, but their impact on neural differentiation requires further investigation. Here, we addressed the role of calpain 1 and calpain 2 in neural stem cell (NSC self-renewal and differentiation. We found that calpain inhibition using either the chemical inhibitor calpeptin or the endogenous calpain inhibitor calpastatin favored differentiation of NSCs. This effect was associated with significant changes in cell cycle-related proteins and may be regulated by calcium. Interestingly, calpain 1 and calpain 2 were found to play distinct roles in NSC fate decision. Calpain 1 expression levels were higher in self-renewing NSC and decreased with differentiation, while calpain 2 increased throughout differentiation. In addition, calpain 1 silencing resulted in increased levels of both neuronal and glial markers, β-III Tubulin and glial fibrillary acidic protein (GFAP. Calpain 2 silencing elicited decreased levels of GFAP. These results support a role for calpain 1 in repressing differentiation, thus maintaining a proliferative NSC pool, and suggest that calpain 2 is involved in glial differentiation.

Functional Connectivity with Distinct Neural Networks Tracks Fluctuations in Gain/Loss Framing Susceptibility

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Smith, David V.; Sip, Kamila E.; Delgado, Mauricio R.

2016-01-01

Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility—indexed as the increase in gambling behavior in loss frames compared to gain frames—was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. PMID:25858445

A sleep state in Drosophila larvae required for neural stem cell proliferation

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Szuperak, Milan; Churgin, Matthew A; Borja, Austin J; Raizen, David M; Fang-Yen, Christopher

2018-01-01

Sleep during development is involved in refining brain circuitry, but a role for sleep in the earliest periods of nervous system elaboration, when neurons are first being born, has not been explored. Here we identify a sleep state in Drosophila larvae that coincides with a major wave of neurogenesis. Mechanisms controlling larval sleep are partially distinct from adult sleep: octopamine, the Drosophila analog of mammalian norepinephrine, is the major arousal neuromodulator in larvae, but dopamine is not required. Using real-time behavioral monitoring in a closed-loop sleep deprivation system, we find that sleep loss in larvae impairs cell division of neural progenitors. This work establishes a system uniquely suited for studying sleep during nascent periods, and demonstrates that sleep in early life regulates neural stem cell proliferation. PMID:29424688

Polarity-specific high-level information propagation in neural networks.

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Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals.

Decision Making under Uncertainty: A Neural Model based on Partially Observable Markov Decision Processes

Directory of Open Access Journals (Sweden)

Rajesh P N Rao

2010-11-01

Full Text Available A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs. Actions are selected based not on a single optimal estimate of state but on the posterior distribution over states (the belief state. We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize

Acute and chronic gregarisation are associated with distinct DNA methylation fingerprints in desert locusts.

Science.gov (United States)

Mallon, Eamonn B; Amarasinghe, Harindra E; Ott, Swidbert R

2016-10-18

Desert locusts (Schistocerca gregaria) show a dramatic form of socially induced phenotypic plasticity known as phase polyphenism. In the absence of conspecifics, locusts occur in a shy and cryptic solitarious phase. Crowding with conspecifics drives a behavioural transformation towards gregariousness that occurs within hours and is followed by changes in physiology, colouration and morphology, resulting in the full gregarious phase syndrome. We analysed methylation-sensitive amplified fragment length polymorphisms (MS-AFLP) to compare the effect of acute and chronic crowding on DNA methylation in the central nervous system. We find that crowd-reared and solitary-reared locusts show markedly different neural MS-AFLP fingerprints. However, crowding for a day resulted in neural MS-AFLP fingerprints that were clearly distinct from both crowd-reared and uncrowded solitary-reared locusts. Our results indicate that changes in DNA methylation associated with behavioural gregarisation proceed through intermediate states that are not simply partial realisations of the endpoint states.

A Mechanistic Neural Field Theory of How Anesthesia Suppresses Consciousness: Synaptic Drive Dynamics, Bifurcations, Attractors, and Partial State Equipartitioning.

Science.gov (United States)

Hou, Saing Paul; Haddad, Wassim M; Meskin, Nader; Bailey, James M

2015-12-01

With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we use dynamical system theory to develop a mechanistic mean field model for neural activity to study the abrupt transition from consciousness to unconsciousness as the concentration of the anesthetic agent increases. The proposed synaptic drive firing-rate model predicts the conscious-unconscious transition as the applied anesthetic concentration increases, where excitatory neural activity is characterized by a Poincaré-Andronov-Hopf bifurcation with the awake state transitioning to a stable limit cycle and then subsequently to an asymptotically stable unconscious equilibrium state. Furthermore, we address the more general question of synchronization and partial state equipartitioning of neural activity without mean field assumptions. This is done by focusing on a postulated subset of inhibitory neurons that are not themselves connected to other inhibitory neurons. Finally, several numerical experiments are presented to illustrate the different aspects of the proposed theory.

The mitochondrial elongation factors MIEF1 and MIEF2 exert partially distinct functions in mitochondrial dynamics

Energy Technology Data Exchange (ETDEWEB)

Liu, Tong; Yu, Rong [Department of Oncology–Pathology, Karolinska Institutet, CCK R8:05, Karolinska University Hospital Solna, SE-171 76 Stockholm (Sweden); Jin, Shao-Bo [Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77 Stockholm (Sweden); Han, Liwei [Department of Oncology–Pathology, Karolinska Institutet, CCK R8:05, Karolinska University Hospital Solna, SE-171 76 Stockholm (Sweden); Lendahl, Urban [Department of Cell and Molecular Biology, Karolinska Institutet, SE-171 77 Stockholm (Sweden); Zhao, Jian, E-mail: Jian.Zhao@ki.se [Department of Oncology–Pathology, Karolinska Institutet, CCK R8:05, Karolinska University Hospital Solna, SE-171 76 Stockholm (Sweden); Nistér, Monica [Department of Oncology–Pathology, Karolinska Institutet, CCK R8:05, Karolinska University Hospital Solna, SE-171 76 Stockholm (Sweden)

2013-11-01

Mitochondria are dynamic organelles whose morphology is regulated by a complex balance of fission and fusion processes, and we still know relatively little about how mitochondrial dynamics is regulated. MIEF1 (also called MiD51) has recently been characterized as a key regulator of mitochondrial dynamics and in this report we explore the functions of its paralog MIEF2 (also called MiD49), to learn to what extent MIEF2 is functionally distinct from MIEF1. We show that MIEF1 and MIEF2 have many functions in common. Both are anchored in the mitochondrial outer membrane, recruit Drp1 from the cytoplasm to the mitochondrial surface and cause mitochondrial fusion, and MIEF2, like MIEF1, can interact with Drp1 and hFis1. MIEF1 and MIEF2, however, also differ in certain aspects. MIEF1 and MIEF2 are differentially expressed in human tissues during development. When overexpressed, MIEF2 exerts a stronger fusion-promoting effect than MIEF1, and in line with this, hFis1 and Mff can only partially revert the MIEF2-induced fusion phenotype, whereas MIEF1-induced fusion is reverted to a larger extent by hFis1 and Mff. MIEF2 forms high molecular weight oligomers, while MIEF1 is largely present as a dimer. Furthermore, MIEF1 and MIEF2 use distinct domains for oligomerization: in MIEF1, the region from amino acid residues 109–154 is required, whereas oligomerization of MIEF2 depends on amino acid residues 1 to 49, i.e. the N-terminal end. We also show that oligomerization of MIEF1 is not required for its mitochondrial localization and interaction with Drp1. In conclusion, our data suggest that the mitochondrial regulators MIEF1 and MIEF2 exert partially distinct functions in mitochondrial dynamics. - Highlights: • MIEF1 and MIEF2 recruit Drp1 to mitochondria and cause mitochondrial fusion. • MIEF2, like MIEF1, can interact with Drp1 and hFis1. • MIEF1 and MIEF2 are differentially expressed in human tissues during development. • MIEF2 exerts a stronger fusion

The mitochondrial elongation factors MIEF1 and MIEF2 exert partially distinct functions in mitochondrial dynamics

International Nuclear Information System (INIS)

Liu, Tong; Yu, Rong; Jin, Shao-Bo; Han, Liwei; Lendahl, Urban; Zhao, Jian; Nistér, Monica

2013-01-01

Mitochondria are dynamic organelles whose morphology is regulated by a complex balance of fission and fusion processes, and we still know relatively little about how mitochondrial dynamics is regulated. MIEF1 (also called MiD51) has recently been characterized as a key regulator of mitochondrial dynamics and in this report we explore the functions of its paralog MIEF2 (also called MiD49), to learn to what extent MIEF2 is functionally distinct from MIEF1. We show that MIEF1 and MIEF2 have many functions in common. Both are anchored in the mitochondrial outer membrane, recruit Drp1 from the cytoplasm to the mitochondrial surface and cause mitochondrial fusion, and MIEF2, like MIEF1, can interact with Drp1 and hFis1. MIEF1 and MIEF2, however, also differ in certain aspects. MIEF1 and MIEF2 are differentially expressed in human tissues during development. When overexpressed, MIEF2 exerts a stronger fusion-promoting effect than MIEF1, and in line with this, hFis1 and Mff can only partially revert the MIEF2-induced fusion phenotype, whereas MIEF1-induced fusion is reverted to a larger extent by hFis1 and Mff. MIEF2 forms high molecular weight oligomers, while MIEF1 is largely present as a dimer. Furthermore, MIEF1 and MIEF2 use distinct domains for oligomerization: in MIEF1, the region from amino acid residues 109–154 is required, whereas oligomerization of MIEF2 depends on amino acid residues 1 to 49, i.e. the N-terminal end. We also show that oligomerization of MIEF1 is not required for its mitochondrial localization and interaction with Drp1. In conclusion, our data suggest that the mitochondrial regulators MIEF1 and MIEF2 exert partially distinct functions in mitochondrial dynamics. - Highlights: • MIEF1 and MIEF2 recruit Drp1 to mitochondria and cause mitochondrial fusion. • MIEF2, like MIEF1, can interact with Drp1 and hFis1. • MIEF1 and MIEF2 are differentially expressed in human tissues during development. • MIEF2 exerts a stronger fusion

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Identification of Non-Linear Structures using Recurrent Neural Networks

DEFF Research Database (Denmark)

Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

1995-01-01

Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

Distinct Neural-Functional Effects of Treatments With Selective Serotonin Reuptake Inhibitors, Electroconvulsive Therapy, and Transcranial Magnetic Stimulation and Their Relations to Regional Brain Function in Major Depression: A Meta-analysis.

Science.gov (United States)

Chau, David T; Fogelman, Phoebe; Nordanskog, Pia; Drevets, Wayne C; Hamilton, J Paul

2017-05-01

Functional neuroimaging studies have examined the neural substrates of treatments for major depressive disorder (MDD). Low sample size and methodological heterogeneity, however, undermine the generalizability of findings from individual studies. We conducted a meta-analysis to identify reliable neural changes resulting from different modes of treatment for MDD and compared them with each other and with reliable neural functional abnormalities observed in depressed versus control samples. We conducted a meta-analysis of studies reporting changes in brain activity (e.g., as indexed by positron emission tomography) following treatments with selective serotonin reuptake inhibitors (SSRIs), electroconvulsive therapy (ECT), or transcranial magnetic stimulation. Additionally, we examined the statistical reliability of overlap among thresholded meta-analytic SSRI, ECT, and transcranial magnetic stimulation maps as well as a map of abnormal neural function in MDD. Our meta-analysis revealed that 1) SSRIs decrease activity in the anterior insula, 2) ECT decreases activity in central nodes of the default mode network, 3) transcranial magnetic stimulation does not result in reliable neural changes, and 4) regional effects of these modes of treatment do not significantly overlap with each other or with regions showing reliable functional abnormality in MDD. SSRIs and ECT produce neurally distinct effects relative to each other and to the functional abnormalities implicated in depression. These treatments therefore may exert antidepressant effects by diminishing neural functions not implicated in depression but that nonetheless impact mood. We discuss how the distinct neural changes resulting from SSRIs and ECT can account for both treatment effects and side effects from these therapies as well as how to individualize these treatments. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Krox20 defines a subpopulation of cardiac neural crest cells contributing to arterial valves and bicuspid aortic valve.

Science.gov (United States)

Odelin, Gaëlle; Faure, Emilie; Coulpier, Fanny; Di Bonito, Maria; Bajolle, Fanny; Studer, Michèle; Avierinos, Jean-François; Charnay, Patrick; Topilko, Piotr; Zaffran, Stéphane

2018-01-03

Although cardiac neural crest cells are required at early stages of arterial valve development, their contribution during valvular leaflet maturation remains poorly understood. Here, we show in mouse that neural crest cells from pre-otic and post-otic regions make distinct contributions to the arterial valve leaflets. Genetic fate-mapping analysis of Krox20-expressing neural crest cells shows a large contribution to the borders and the interleaflet triangles of the arterial valves. Loss of Krox20 function results in hyperplastic aortic valve and partially penetrant bicuspid aortic valve formation. Similar defects are observed in neural crest Krox20 -deficient embryos. Genetic lineage tracing in Krox20 -/- mutant mice shows that endothelial-derived cells are normal, whereas neural crest-derived cells are abnormally increased in number and misplaced in the valve leaflets. In contrast, genetic ablation of Krox20 -expressing cells is not sufficient to cause an aortic valve defect, suggesting that adjacent cells can compensate this depletion. Our findings demonstrate a crucial role for Krox20 in arterial valve development and reveal that an excess of neural crest cells may be associated with bicuspid aortic valve. © 2018. Published by The Company of Biologists Ltd.

Distinction between Neural and Vascular BOLD Oscillations and Intertwined Heart Rate Oscillations at 0.1 Hz in the Resting State and during Movement.

Directory of Open Access Journals (Sweden)

Gert Pfurtscheller

Full Text Available In the resting state, blood oxygen level-dependent (BOLD oscillations with a frequency of about 0.1 Hz are conspicuous. Whether their origin is neural or vascular is not yet fully understood. Furthermore, it is not clear whether these BOLD oscillations interact with slow oscillations in heart rate (HR. To address these two questions, we estimated phase-locking (PL values between precentral gyrus (PCG and insula in 25 scanner-naïve individuals during rest and stimulus-paced finger movements in both hemispheres. PL was quantified in terms of time delay and duration in the frequency band 0.07 to 0.13 Hz. Results revealed both positive and negative time delays. Positive time delays characterize neural BOLD oscillations leading in the PCG, whereas negative time delays represent vascular BOLD oscillations leading in the insula. About 50% of the participants revealed positive time delays distinctive for neural BOLD oscillations, either with short or long unilateral or bilateral phase-locking episodes. An expected preponderance of neural BOLD oscillations was found in the left hemisphere during right-handed movement and unexpectedly in the right hemisphere during rest. Only neural BOLD oscillations were significantly associated with heart rate variability (HRV in the 0.1-Hz range in the first resting state. It is well known that participating in magnetic resonance imaging (MRI studies may be frightening and cause anxiety. In this respect it is important to note that the most significant hemispheric asymmetry (p<0.002 with a right-sided dominance of neural BOLD and a left-sided dominance of vascular BOLD oscillations was found in the first resting session in the scanner-naïve individuals. Whether the enhanced left-sided perfusion (dominance of vascular BOLD or the right-sided dominance of neural BOLD is related to the increased level of anxiety, attention or stress needs further research.

Determining the confidence levels of sensor outputs using neural networks

Energy Technology Data Exchange (ETDEWEB)

Broten, G S; Wood, H C [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Electrical Engineering

1996-12-31

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network`s ability to determine the confidence level is influenced by the complexity of the sensor`s response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

Determining the confidence levels of sensor outputs using neural networks

International Nuclear Information System (INIS)

Broten, G.S.; Wood, H.C.

1995-01-01

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network's ability to determine the confidence level is influenced by the complexity of the sensor's response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

The neural signature of self-concept development in adolescence: The role of domain and valence distinctions

Directory of Open Access Journals (Sweden)

R. van der Cruijsen

2018-04-01

Full Text Available Neuroimaging studies in adults showed that cortical midline regions including medial prefrontal cortex (mPFC and posterior parietal cortex (PPC are important in self-evaluations. The goals of this study were to investigate the contribution of these regions to self-evaluations in late childhood, adolescence, and early adulthood, and to examine whether these differed per domain (academic, physical and prosocial and valence (positive versus negative. Also, we tested whether this activation changes across adolescence. For this purpose, participants between ages 11–21-years (N = 150 evaluated themselves on trait sentences in an fMRI session. Behaviorally, adolescents rated their academic traits less positively than children and young adults. The neural analyses showed that evaluating self-traits versus a control condition was associated with increased activity in mPFC (domain-general effect, and positive traits were associated with increased activity in ventral mPFC (valence effect. Self-related mPFC activation increased linearly with age, but only for evaluating physical traits. Furthermore, an adolescent-specific decrease in striatum activation for positive self traits was found. Finally, we found domain-specific neural activity for evaluating traits in physical (dorsolateral PFC, dorsal mPFC and academic (PPC domains. Together, these results highlight the importance of domain distinctions when studying self-concept development in late childhood, adolescence, and early adulthood. Keywords: Self, fMRI, Adolescence, Development, Medial prefrontal cortex, Self-concept

Recurrent Partial Words

Directory of Open Access Journals (Sweden)

Francine Blanchet-Sadri

2011-08-01

Full Text Available Partial words are sequences over a finite alphabet that may contain wildcard symbols, called holes, which match or are compatible with all letters; partial words without holes are said to be full words (or simply words. Given an infinite partial word w, the number of distinct full words over the alphabet that are compatible with factors of w of length n, called subwords of w, refers to a measure of complexity of infinite partial words so-called subword complexity. This measure is of particular interest because we can construct partial words with subword complexities not achievable by full words. In this paper, we consider the notion of recurrence over infinite partial words, that is, we study whether all of the finite subwords of a given infinite partial word appear infinitely often, and we establish connections between subword complexity and recurrence in this more general framework.

MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

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Artur Popko

2013-06-01

Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

Distinct neural control of intrinsic and extrinsic muscles of the hand during single finger pressing.

Science.gov (United States)

Dupan, Sigrid S G; Stegeman, Dick F; Maas, Huub

2018-06-01

Single finger force tasks lead to unintended activation of the non-instructed fingers, commonly referred to as enslaving. Both neural and mechanical factors have been associated with this absence of finger individuality. This study investigates the amplitude modulation of both intrinsic and extrinsic finger muscles during single finger isometric force tasks. Twelve participants performed single finger flexion presses at 20% of maximum voluntary contraction, while simultaneously the electromyographic activity of several intrinsic and extrinsic muscles associated with all four fingers was recorded using 8 electrode pairs in the hand and two 30-electrode grids on the lower arm. The forces exerted by each of the fingers, in both flexion and extension direction, were recorded with individual force sensors. This study shows distinct activation patterns in intrinsic and extrinsic hand muscles. Intrinsic muscles exhibited individuation, where the agonistic and antagonistic muscles associated with the instructed fingers showed the highest activation. This activation in both agonistic and antagonistic muscles appears to facilitate finger stabilisation during the isometric force task. Extrinsic muscles show an activation independent from instructed finger in both agonistic and antagonistic muscles, which appears to be associated with stabilisation of the wrist, with an additional finger-dependent modulation only present in the agonistic extrinsic muscles. These results indicate distinct muscle patterns in intrinsic and extrinsic hand muscles during single finger isometric force pressing. We conclude that the finger specific activation of intrinsic muscles is not sufficient to fully counteract enslaving caused by the broad activation of the extrinsic muscles. Copyright © 2018 Elsevier B.V. All rights reserved.

Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

Science.gov (United States)

Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

2014-03-01

Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

Distinct Neural Mechanisms Mediate Olfactory Memory Formation at Different Timescales

Science.gov (United States)

McNamara, Ann Marie; Magidson, Phillip D.; Linster, Christiane; Wilson, Donald A.; Cleland, Thomas A.

2008-01-01

Habituation is one of the oldest forms of learning, broadly expressed across sensory systems and taxa. Here, we demonstrate that olfactory habituation induced at different timescales (comprising different odor exposure and intertrial interval durations) is mediated by different neural mechanisms. First, the persistence of habituation memory is…

Distinct neural mechanisms for body form and body motion discriminations

NARCIS (Netherlands)

Vangeneugden, Joris; Peelen, Marius V; Tadin, Duje; Battelli, Lorella

2014-01-01

Actions can be understood based on form cues (e.g., static body posture) as well as motion cues (e.g., gait patterns). A fundamental debate centers on the question of whether the functional and neural mechanisms processing these two types of cues are dissociable. Here, using fMRI, psychophysics, and

Perceptual priming versus explicit memory: dissociable neural correlates at encoding.

Science.gov (United States)

Schott, Björn; Richardson-Klavehn, Alan; Heinze, Hans-Jochen; Düzel, Emrah

2002-05-15

We addressed the hypothesis that perceptual priming and explicit memory have distinct neural correlates at encoding. Event-related potentials (ERPs) were recorded while participants studied visually presented words at deep versus shallow levels of processing (LOPs). The ERPs were sorted by whether or not participants later used studied words as completions to three-letter word stems in an intentional memory test, and by whether or not they indicated that these completions were remembered from the study list. Study trials from which words were later used and not remembered (primed trials) and study trials from which words were later used and remembered (remembered trials) were compared to study trials from which words were later not used (forgotten trials), in order to measure the ERP difference associated with later memory (DM effect). Primed trials involved an early (200-450 msec) centroparietal negative-going DM effect. Remembered trials involved a late (900-1200 msec) right frontal, positive-going DM effect regardless of LOP, as well as an earlier (600-800 msec) central, positive-going DM effect during shallow study processing only. All three DM effects differed topographically, and, in terms of their onset or duration, from the extended (600-1200 msec) fronto-central, positive-going shift for deep compared with shallow study processing. The results provide the first clear evidence that perceptual priming and explicit memory have distinct neural correlates at encoding, consistent with Tulving and Schacter's (1990) distinction between brain systems concerned with perceptual representation versus semantic and episodic memory. They also shed additional light on encoding processes associated with later explicit memory, by suggesting that brain processes influenced by LOP set the stage for other, at least partially separable, brain processes that are more directly related to encoding success.

Artificial intelligence: Deep neural reasoning

Science.gov (United States)

Jaeger, Herbert

2016-10-01

The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

Neural fields theory and applications

CERN Document Server

Graben, Peter; Potthast, Roland; Wright, James

2014-01-01

With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

Central neural pathways for thermoregulation

Science.gov (United States)

Morrison, Shaun F.; Nakamura, Kazuhiro

2010-01-01

Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

Common and distinct neural correlates of emotional processing in Bipolar Disorder and Major Depressive Disorder: A voxel-based meta-analysis of functional magnetic resonance imaging studies

International Nuclear Information System (INIS)

Delvecchio, Giuseppe; Frangou, Sophia; Fossati, Philippe; Boyer, Patrice; Brambilla, Paolo; Falkai, Peter; Gruber, Olivier; Hietala, Jarmo; Lawrie, Stephen M.; Martinot, Jean-Luc; McIntosh, Andrew M.; Meisenzahl, Eva

2012-01-01

Neuroimaging studies have consistently shown functional brain abnormalities in patients with Bipolar Disorder (BD) and Major Depressive Disorder (MDD). However, the extent to which these two disorders are associated with similar or distinct neural changes remains unclear. We conducted a systematic review of functional magnetic resonance imaging studies comparing BD and MDD patients to healthy participants using facial affect processing paradigms. Relevant spatial coordinates from twenty original studies were subjected to quantitative Activation Likelihood Estimation meta-analyses based on 168 BD and 189 MDD patients and 344 healthy controls. We identified common and distinct patterns of neural engagement for BD and MDD within the facial affect processing network. Both disorders were associated with increased engagement of limbic regions. Diagnosis-specific differences were observed in cortical, thalamic and striatal regions. Decreased ventro-lateral prefrontal cortical engagement was associated with BD while relative hypo-activation of the sensorimotor cortices was seen in MDD. Increased responsiveness in the thalamus and basal ganglia were associated with BD. These findings were modulated by stimulus valence. These data suggest that whereas limbic over-activation is reported consistently in patients with mood disorders, future research should consider the relevance of a wider network of regions in formulating conceptual models of BD and MDD. (authors)

Empathy and Stress Related Neural Responses in Maternal Decision Making

Directory of Open Access Journals (Sweden)

S. Shaun Ho

2014-06-01

Full Text Available Mothers need to make caregiving decisions to meet the needs of children, which may or may not result in positive child feedback. Variations in caregivers’ emotional reactivity to unpleasant child-feedback may be partially explained by their dispositional empathy levels. Furthermore, empathic response to the child’s unpleasant feedback likely helps mothers to regulate their own stress. We investigated the relationship between maternal dispositional empathy, stress reactivity, and neural correlates of child feedback to caregiving decisions. In Part 1 of the study, 33 female participants were recruited to undergo a lab-based mild stressor, the Social Evaluation Test (SET, and then in Part 2 of the study, a subset of the participants, fourteen mothers, performed a Parenting Decision Making Task (PDMT in an fMRI setting. Four dimensions of dispositional empathy based on the Interpersonal Reactivity Index were measured in all participants – Personal Distress, Empathic Concern, Perspective Taking, and Fantasy. Overall, we found that the Personal Distress and Perspective Taking were associated with greater and lesser cortisol reactivity, respectively. The four types of empathy were distinctly associated with the negative (versus positive child feedback activation in the brain. Personal Distress was associated with amygdala and hypothalamus activation, Empathic Concern with the left ventral striatum, ventrolateral prefrontal cortex (VLPFC, and supplemental motor area (SMA activation, and Fantasy with the septal area, right SMA and VLPFC activation. Interestingly, hypothalamus-septal coupling during the negative feedback condition was associated with less PDMT-related cortisol reactivity. The roles of distinct forms of dispositional empathy in neural and stress responses are discussed.

Distinct and Shared Endophenotypes of Neural Substrates in Bipolar and Major Depressive Disorders.

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Toshio Matsubara

Full Text Available Little is known about disorder-specific biomarkers of bipolar disorder (BD and major depressive disorder (MDD. Our aim was to determine a neural substrate that could be used to distinguish BD from MDD. Our study included a BD group (10 patients with BD, 10 first-degree relatives (FDRs of individuals with BD, MDD group (17 patients with MDD, 17 FDRs of individuals with MDD, and 27 healthy individuals. Structural and functional brain abnormalities were evaluated by voxel-based morphometry and a trail making test (TMT, respectively. The BD group showed a significant main effect of diagnosis in the gray matter (GM volume of the anterior cingulate cortex (ACC; p = 0.01 and left insula (p < 0.01. FDRs of individuals with BD showed significantly smaller left ACC GM volume than healthy subjects (p < 0.01, and patients with BD showed significantly smaller ACC (p < 0.01 and left insular GM volume (p < 0.01 than healthy subjects. The MDD group showed a tendency toward a main effect of diagnosis in the right and left insular GM volume. The BD group showed a significantly inverse correlation between the left insular GM volume and TMT-A scores (p < 0.05. Our results suggest that the ACC volume could be a distinct endophenotype of BD, while the insular volume could be a shared BD and MDD endophenotype. Moreover, the insula could be associated with cognitive decline and poor outcome in BD.

Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning.

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Zhu, Lusha; Mathewson, Kyle E; Hsu, Ming

2012-01-31

Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents' beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.

Partial monosomy 8q and partial trisomy 9q due to the maternal translocation t(8;9(q24.3;q34.1)

DEFF Research Database (Denmark)

Tos, T; Alp, M Y; Eker, H K

2014-01-01

Partial trisomy 9q34-qter and partial monosomy 8q24.3-qter are very rare chromosomal abnormalities. Characteristic features of partial trisomy 9q34-qter are hypotonia, developmental delay, mild intellectual disability, dolichocephaly, distinct facial phenotype, long and thin fingers, and cardiac...

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network.

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Garagnani, Max; Wennekers, Thomas; Pulvermüller, Friedemann

2009-06-01

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly's halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support.

Pax7 lineage contributions to the mammalian neural crest.

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Barbara Murdoch

Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

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He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Musical and verbal semantic memory: two distinct neural networks?

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Groussard, M; Viader, F; Hubert, V; Landeau, B; Abbas, A; Desgranges, B; Eustache, F; Platel, H

2010-02-01

Semantic memory has been investigated in numerous neuroimaging and clinical studies, most of which have used verbal or visual, but only very seldom, musical material. Clinical studies have suggested that there is a relative neural independence between verbal and musical semantic memory. In the present study, "musical semantic memory" is defined as memory for "well-known" melodies without any knowledge of the spatial or temporal circumstances of learning, while "verbal semantic memory" corresponds to general knowledge about concepts, again without any knowledge of the spatial or temporal circumstances of learning. Our aim was to compare the neural substrates of musical and verbal semantic memory by administering the same type of task in each modality. We used high-resolution PET H(2)O(15) to observe 11 young subjects performing two main tasks: (1) a musical semantic memory task, where the subjects heard the first part of familiar melodies and had to decide whether the second part they heard matched the first, and (2) a verbal semantic memory task with the same design, but where the material consisted of well-known expressions or proverbs. The musical semantic memory condition activated the superior temporal area and inferior and middle frontal areas in the left hemisphere and the inferior frontal area in the right hemisphere. The verbal semantic memory condition activated the middle temporal region in the left hemisphere and the cerebellum in the right hemisphere. We found that the verbal and musical semantic processes activated a common network extending throughout the left temporal neocortex. In addition, there was a material-dependent topographical preference within this network, with predominantly anterior activation during musical tasks and predominantly posterior activation during semantic verbal tasks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Neural Networks in Mobile Robot Motion

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Danica Janglová

2004-03-01

Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

Cognitive deficits caused by prefrontal cortical and hippocampal neural disinhibition.

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Bast, Tobias; Pezze, Marie; McGarrity, Stephanie

2017-10-01

We review recent evidence concerning the significance of inhibitory GABA transmission and of neural disinhibition, that is, deficient GABA transmission, within the prefrontal cortex and the hippocampus, for clinically relevant cognitive functions. Both regions support important cognitive functions, including attention and memory, and their dysfunction has been implicated in cognitive deficits characterizing neuropsychiatric disorders. GABAergic inhibition shapes cortico-hippocampal neural activity, and, recently, prefrontal and hippocampal neural disinhibition has emerged as a pathophysiological feature of major neuropsychiatric disorders, especially schizophrenia and age-related cognitive decline. Regional neural disinhibition, disrupting spatio-temporal control of neural activity and causing aberrant drive of projections, may disrupt processing within the disinhibited region and efferent regions. Recent studies in rats showed that prefrontal and hippocampal neural disinhibition (by local GABA antagonist microinfusion) dysregulates burst firing, which has been associated with important aspects of neural information processing. Using translational tests of clinically relevant cognitive functions, these studies showed that prefrontal and hippocampal neural disinhibition disrupts regional cognitive functions (including prefrontal attention and hippocampal memory function). Moreover, hippocampal neural disinhibition disrupted attentional performance, which does not require the hippocampus but requires prefrontal-striatal circuits modulated by the hippocampus. However, some prefrontal and hippocampal functions (including inhibitory response control) are spared by regional disinhibition. We consider conceptual implications of these findings, regarding the distinct relationships of distinct cognitive functions to prefrontal and hippocampal GABA tone and neural activity. Moreover, the findings support the proposition that prefrontal and hippocampal neural disinhibition

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

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Abdullahi Abubakar Mas’ud

2016-07-01

Full Text Available In order to investigate how artificial neural networks (ANNs have been applied for partial discharge (PD pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1 determining the optimum weights in training the ANN; (2 using PD data captured over long stressing period in training the ANN; (3 ANN recognizing different PD degradation levels; (4 using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5 understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6 developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

cables1 Is Required for Embryonic Neural Development: Molecular, Cellular, and Behavioral Evidence From the Zebrafish

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GROENEWEG, JOLIJN W.; WHITE, YVONNE A.R.; KOKEL, DAVID; PETERSON, RANDALL T.; ZUKERBERG, LAWRENCE R.; BERIN, INNA; RUEDA, BO R.; WOOD, ANTONY W.

2014-01-01

SUMMARY In vitro studies have suggested that the Cables1 gene regulates epithelial cell proliferation, whereas other studies suggest a role in promoting neural differentiation. In efforts to clarify the functions of Cables1 in vivo, we conducted gain- and loss-of-function studies targeting its ortholog (cables1) in the zebrafish embryo. Similar to rodents, zebrafish cables1 mRNA expression is detected most robustly in embryonic neural tissues. Antisense knockdown of cables1 leads to increased numbers of apoptotic cells, particularly in brain tissue, in addition to a distinct behavioral phenotype, characterized by hyperactivity in response to stimulation. Apoptosis and the behavioral abnormality could be rescued by co-expression of a morpholino-resistant cables1 construct. Suppression of p53 expression in cables1 morphants partially rescued both apoptosis and the behavioral phenotype, suggesting that the phenotype of cables1 morphants is due in part to p53-dependent apoptosis. Alterations in the expression patterns of several neural transcription factors were observed in cables1 morphants during early neurulation, suggesting that cables1 is required for early neural differentiation. Ectopic overexpression of cables1 strongly disrupted embryonic morphogenesis, while overexpression of a cables1 mutant lacking the C-terminal cyclin box had little effect, suggesting functional importance of the cyclin box. Lastly, marked reductions in p35, but not Cdk5, were observed in cables1 morphants. Collectively, these data suggest that cables1 is important for neural differentiation during embryogenesis, in a mechanism that likely involves interactions with the Cdk5/p35 kinase pathway. PMID:21268180

Clique-Based Neural Associative Memories with Local Coding and Precoding.

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Mofrad, Asieh Abolpour; Parker, Matthew G; Ferdosi, Zahra; Tadayon, Mohammad H

2016-08-01

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Similar patterns of neural activity predict memory function during encoding and retrieval.

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Kragel, James E; Ezzyat, Youssef; Sperling, Michael R; Gorniak, Richard; Worrell, Gregory A; Berry, Brent M; Inman, Cory; Lin, Jui-Jui; Davis, Kathryn A; Das, Sandhitsu R; Stein, Joel M; Jobst, Barbara C; Zaghloul, Kareem A; Sheth, Sameer A; Rizzuto, Daniel S; Kahana, Michael J

2017-07-15

Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval. Copyright © 2017 Elsevier Inc. All rights reserved.

Learning from large scale neural simulations

DEFF Research Database (Denmark)

Serban, Maria

2017-01-01

Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

Reward-Modulated Hebbian Plasticity as Leverage for Partially Embodied Control in Compliant Robotics

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Burms, Jeroen; Caluwaerts, Ken; Dambre, Joni

2015-01-01

In embodied computation (or morphological computation), part of the complexity of motor control is offloaded to the body dynamics. We demonstrate that a simple Hebbian-like learning rule can be used to train systems with (partial) embodiment, and can be extended outside of the scope of traditional neural networks. To this end, we apply the learning rule to optimize the connection weights of recurrent neural networks with different topologies and for various tasks. We then apply this learning rule to a simulated compliant tensegrity robot by optimizing static feedback controllers that directly exploit the dynamics of the robot body. This leads to partially embodied controllers, i.e., hybrid controllers that naturally integrate the computations that are performed by the robot body into a neural network architecture. Our results demonstrate the universal applicability of reward-modulated Hebbian learning. Furthermore, they demonstrate the robustness of systems trained with the learning rule. This study strengthens our belief that compliant robots should or can be seen as computational units, instead of dumb hardware that needs a complex controller. This link between compliant robotics and neural networks is also the main reason for our search for simple universal learning rules for both neural networks and robotics. PMID:26347645

Neural effects in copper defiient Menkes disease: ATP7A-a distinctive marker

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S K Kanthlal

2016-08-01

Full Text Available Menkes disease, also termed as “Menkes’s syndrome”, is a disastrous infantile neurodegenerative disorder originated by diverse mutations in cupric cation-transport gene called ATP7A. This gene encodes a protein termed as copper transporting P-type ATPase, essential for copper ion transport from intestine to the other parts of our body along with other transporters like copper transporter receptor 1 and divalent metal transporter 1. The copper transportation is vital in the neuronal development and synthesis of various enzymes. It is found to be an appreciated trace element for normal biological functioning but toxic in excess. It is essential for the metallation of cuproenzymes which is responsible for the biosynthesis of neurotransmitters and other vital physiological mechanisms. Copper is also actively involved in the transmission pathway of N-methyl-D-aspartate receptors and its subsequent molecular changes in neural cells. The expression of ATP7A gene in regions of brain depicts the importance of copper in neural development and stabilization. Studies revealed that the mutation of ATP7A gene leads the pathophysiology of various neurodegenerative disorders. This review focused on the normal physiological function of the gene with respect to their harmful outcome of the mutated gene and its associated deficiency which detriments the neural mechanism in Menkes patients.

Briefly Cuing Memories Leads to Suppression of Their Neural Representations

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Norman, Kenneth A.

2014-01-01

Previous studies have linked partial memory activation with impaired subsequent memory retrieval (e.g., Detre et al., 2013) but have not provided an account of this phenomenon at the level of memory representations: How does partial activation change the neural pattern subsequently elicited when the memory is cued? To address this question, we conducted a functional magnetic resonance imaging (fMRI) experiment in which participants studied word-scene paired associates. Later, we weakly reactivated some memories by briefly presenting the cue word during a rapid serial visual presentation (RSVP) task; other memories were more strongly reactivated or not reactivated at all. We tested participants' memory for the paired associates before and after RSVP. Cues that were briefly presented during RSVP triggered reduced levels of scene activity on the post-RSVP memory test, relative to the other conditions. We used pattern similarity analysis to assess how representations changed as a function of the RSVP manipulation. For briefly cued pairs, we found that neural patterns elicited by the same cue on the pre- and post-RSVP tests (preA–postA; preB–postB) were less similar than neural patterns elicited by different cues (preA–postB; preB–postA). These similarity reductions were predicted by neural measures of memory activation during RSVP. Through simulation, we show that our pattern similarity results are consistent with a model in which partial memory activation triggers selective weakening of the strongest parts of the memory. PMID:24899722

Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.

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Wan, Ying; Cao, Jinde; Wen, Guanghui

In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control

Neural activity when people solve verbal problems with insight.

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Mark Jung-Beeman

2004-04-01

Full Text Available People sometimes solve problems with a unique process called insight, accompanied by an "Aha!" experience. It has long been unclear whether different cognitive and neural processes lead to insight versus noninsight solutions, or if solutions differ only in subsequent subjective feeling. Recent behavioral studies indicate distinct patterns of performance and suggest differential hemispheric involvement for insight and noninsight solutions. Subjects solved verbal problems, and after each correct solution indicated whether they solved with or without insight. We observed two objective neural correlates of insight. Functional magnetic resonance imaging (Experiment 1 revealed increased activity in the right hemisphere anterior superior temporal gyrus for insight relative to noninsight solutions. The same region was active during initial solving efforts. Scalp electroencephalogram recordings (Experiment 2 revealed a sudden burst of high-frequency (gamma-band neural activity in the same area beginning 0.3 s prior to insight solutions. This right anterior temporal area is associated with making connections across distantly related information during comprehension. Although all problem solving relies on a largely shared cortical network, the sudden flash of insight occurs when solvers engage distinct neural and cognitive processes that allow them to see connections that previously eluded them.

The Laplacian spectrum of neural networks

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de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

2014-01-01

The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

Optimization of operation schemes in boiling water reactors using neural networks

International Nuclear Information System (INIS)

Ortiz S, J. J.; Castillo M, A.; Pelta, D. A.

2012-10-01

In previous works were presented the results of a recurrent neural network to find the best combination of several groups of fuel cells, fuel load and control bars patterns. These solution groups to each problem of Fuel Management were previously optimized by diverse optimization techniques. The neural network chooses the partial solutions so the combination of them, correspond to a good configuration of the reactor according to a function objective. The values of the involved variables in this objective function are obtained through the simulation of the combination of partial solutions by means of Simulate-3. In the present work, a multilayer neural network that learned how to predict some results of Simulate-3 was used so was possible to substitute it in the objective function for the neural network and to accelerate the response time of the whole system of this way. The preliminary results shown in this work are encouraging to continue carrying out efforts in this sense and to improve the response quality of the system. (Author)

Requirement of mouse BCCIP for neural development and progenitor proliferation.

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Yi-Yuan Huang

Full Text Available Multiple DNA repair pathways are involved in the orderly development of neural systems at distinct stages. The homologous recombination (HR pathway is required to resolve stalled replication forks and critical for the proliferation of progenitor cells during neural development. BCCIP is a BRCA2 and CDKN1A interacting protein implicated in HR and inhibition of DNA replication stress. In this study, we determined the role of BCCIP in neural development using a conditional BCCIP knock-down mouse model. BCCIP deficiency impaired embryonic and postnatal neural development, causing severe ataxia, cerebral and cerebellar defects, and microcephaly. These development defects are associated with spontaneous DNA damage and subsequent cell death in the proliferative cell populations of the neural system during embryogenesis. With in vitro neural spheroid cultures, BCCIP deficiency impaired neural progenitor's self-renewal capability, and spontaneously activated p53. These data suggest that BCCIP and its anti-replication stress functions are essential for normal neural development by maintaining an orderly proliferation of neural progenitors.

The common and distinct neural bases of affect labeling and reappraisal in healthy adults

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Lisa Jane Burklund

2014-03-01

Full Text Available Emotion regulation is commonly characterized as involving conscious and intentional attempts to change felt emotions, such as, for example, through reappraisal whereby one intentionally decreases the intensity of one’s emotional response to a particular stimulus or situation by reinterpreting it in a less threatening way. However, there is growing evidence and appreciation that some types of emotion regulation are unintentional or incidental, meaning that affective modulation is a consequence but not an explicit goal. For example, affect labeling involves simply verbally labeling the emotional content of an external stimulus or one’s own affective responses without an intentional goal of altering emotional responses, yet has been associated with reduced affective responses at the neural and experiential levels. Although both intentional and incidental emotional regulation strategies have been associated with diminished limbic responses and self-reported distress, little previous research has directly compared their underlying neural mechanisms. In this study, we examined the extent to which incidental and intentional emotion regulation, namely, affect labeling and reappraisal, produced common and divergent neural and self-report responses to aversive images relative to an observe-only control condition in a sample of healthy older adults (N=39. Affect labeling and reappraisal produced common activations in several prefrontal regulatory regions, with affect labeling producing stronger responses in direct comparisons. Affect labeling and reappraisal were also associated with similar decreases in amygdala activity. Finally, affect labeling and reappraisal were associated with correlated reductions in self-reported distress. Together these results point to common neurocognitive mechanisms involved in affect labeling and reappraisal, supporting the idea that intentional and incidental emotion regulation may utilize overlapping neural processes.

The neural bases for valuing social equality.

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Aoki, Ryuta; Yomogida, Yukihito; Matsumoto, Kenji

2015-01-01

The neural basis of how humans value and pursue social equality has become a major topic in social neuroscience research. Although recent studies have identified a set of brain regions and possible mechanisms that are involved in the neural processing of equality of outcome between individuals, how the human brain processes equality of opportunity remains unknown. In this review article, first we describe the importance of the distinction between equality of outcome and equality of opportunity, which has been emphasized in philosophy and economics. Next, we discuss possible approaches for empirical characterization of human valuation of equality of opportunity vs. equality of outcome. Understanding how these two concepts are distinct and interact with each other may provide a better explanation of complex human behaviors concerning fairness and social equality. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks.

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Ruiz-Rizzo, Adriana L; Neitzel, Julia; Müller, Hermann J; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's "theory of visual attention" (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

Science.gov (United States)

Ruiz-Rizzo, Adriana L.; Neitzel, Julia; Müller, Hermann J.; Sorg, Christian; Finke, Kathrin

2018-01-01

Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA) allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity) and selectivity functions (i.e., top-down control and spatial laterality). However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower) visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less) efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable visual attention

Distinctive Correspondence Between Separable Visual Attention Functions and Intrinsic Brain Networks

Directory of Open Access Journals (Sweden)

Adriana L. Ruiz-Rizzo

2018-03-01

Full Text Available Separable visual attention functions are assumed to rely on distinct but interacting neural mechanisms. Bundesen's “theory of visual attention” (TVA allows the mathematical estimation of independent parameters that characterize individuals' visual attentional capacity (i.e., visual processing speed and visual short-term memory storage capacity and selectivity functions (i.e., top-down control and spatial laterality. However, it is unclear whether these parameters distinctively map onto different brain networks obtained from intrinsic functional connectivity, which organizes slowly fluctuating ongoing brain activity. In our study, 31 demographically homogeneous healthy young participants performed whole- and partial-report tasks and underwent resting-state functional magnetic resonance imaging (rs-fMRI. Report accuracy was modeled using TVA to estimate, individually, the four TVA parameters. Networks encompassing cortical areas relevant for visual attention were derived from independent component analysis of rs-fMRI data: visual, executive control, right and left frontoparietal, and ventral and dorsal attention networks. Two TVA parameters were mapped on particular functional networks. First, participants with higher (vs. lower visual processing speed showed lower functional connectivity within the ventral attention network. Second, participants with more (vs. less efficient top-down control showed higher functional connectivity within the dorsal attention network and lower functional connectivity within the visual network. Additionally, higher performance was associated with higher functional connectivity between networks: specifically, between the ventral attention and right frontoparietal networks for visual processing speed, and between the visual and executive control networks for top-down control. The higher inter-network functional connectivity was related to lower intra-network connectivity. These results demonstrate that separable

Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model

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Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

2016-01-01

Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction. PMID:27617747

Uncertainty-Dependent Extinction of Fear Memory in an Amygdala-mPFC Neural Circuit Model.

Science.gov (United States)

Li, Yuzhe; Nakae, Ken; Ishii, Shin; Naoki, Honda

2016-09-01

Uncertainty of fear conditioning is crucial for the acquisition and extinction of fear memory. Fear memory acquired through partial pairings of a conditioned stimulus (CS) and an unconditioned stimulus (US) is more resistant to extinction than that acquired through full pairings; this effect is known as the partial reinforcement extinction effect (PREE). Although the PREE has been explained by psychological theories, the neural mechanisms underlying the PREE remain largely unclear. Here, we developed a neural circuit model based on three distinct types of neurons (fear, persistent and extinction neurons) in the amygdala and medial prefrontal cortex (mPFC). In the model, the fear, persistent and extinction neurons encode predictions of net severity, of unconditioned stimulus (US) intensity, and of net safety, respectively. Our simulation successfully reproduces the PREE. We revealed that unpredictability of the US during extinction was represented by the combined responses of the three types of neurons, which are critical for the PREE. In addition, we extended the model to include amygdala subregions and the mPFC to address a recent finding that the ventral mPFC (vmPFC) is required for consolidating extinction memory but not for memory retrieval. Furthermore, model simulations led us to propose a novel procedure to enhance extinction learning through re-conditioning with a stronger US; strengthened fear memory up-regulates the extinction neuron, which, in turn, further inhibits the fear neuron during re-extinction. Thus, our models increased the understanding of the functional roles of the amygdala and vmPFC in the processing of uncertainty in fear conditioning and extinction.

Discrete Neural Correlates for the Recognition of Negative Emotions: Insights from Frontotemporal Dementia

Science.gov (United States)

Kumfor, Fiona; Irish, Muireann; Hodges, John R.; Piguet, Olivier

2013-01-01

Patients with frontotemporal dementia have pervasive changes in emotion recognition and social cognition, yet the neural changes underlying these emotion processing deficits remain unclear. The multimodal system model of emotion proposes that basic emotions are dependent on distinct brain regions, which undergo significant pathological changes in frontotemporal dementia. As such, this syndrome may provide important insight into the impact of neural network degeneration upon the innate ability to recognise emotions. This study used voxel-based morphometry to identify discrete neural correlates involved in the recognition of basic emotions (anger, disgust, fear, sadness, surprise and happiness) in frontotemporal dementia. Forty frontotemporal dementia patients (18 behavioural-variant, 11 semantic dementia, 11 progressive nonfluent aphasia) and 27 healthy controls were tested on two facial emotion recognition tasks: The Ekman 60 and Ekman Caricatures. Although each frontotemporal dementia group showed impaired recognition of negative emotions, distinct associations between emotion-specific task performance and changes in grey matter intensity emerged. Fear recognition was associated with the right amygdala; disgust recognition with the left insula; anger recognition with the left middle and superior temporal gyrus; and sadness recognition with the left subcallosal cingulate, indicating that discrete neural substrates are necessary for emotion recognition in frontotemporal dementia. The erosion of emotion-specific neural networks in neurodegenerative disorders may produce distinct profiles of performance that are relevant to understanding the neurobiological basis of emotion processing. PMID:23805313

Common and distinct brain networks underlying panic and social anxiety disorders.

Science.gov (United States)

Kim, Yong-Ku; Yoon, Ho-Kyoung

2018-01-03

Although panic disorder (PD) and phobic disorders are independent anxiety disorders with distinct sets of diagnostic criteria, there is a high level of overlap between them in terms of pathogenesis and neural underpinnings. Functional connectivity research using resting-state functional magnetic resonance imaging (rsfMRI) shows great potential in identifying the similarities and differences between PD and phobias. Understanding common and distinct networks between PD and phobic disorders is critical for identifying both specific and general neural characteristics of these disorders. We review recent rsfMRI studies and explore the clinical relevance of resting-state functional connectivity (rsFC) in PD and phobias. Although findings differ between studies, there are some meaningful, consistent findings. Social anxiety disorder (SAD) and PD share common default mode network alterations. Alterations within the sensorimotor network are observed primarily in PD. Increased connectivity in the salience network is consistently reported in SAD. This review supports hypotheses that PD and phobic disorders share common rsFC abnormalities and that the different clinical phenotypes between the disorders come from distinct brain functional network alterations. Copyright © 2017 Elsevier Inc. All rights reserved.

Classification of conductance traces with recurrent neural networks

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Lauritzen, Kasper P.; Magyarkuti, András; Balogh, Zoltán; Halbritter, András; Solomon, Gemma C.

2018-02-01

We present a new automated method for structural classification of the traces obtained in break junction experiments. Using recurrent neural networks trained on the traces of minimal cross-sectional area in molecular dynamics simulations, we successfully separate the traces into two classes: point contact or nanowire. This is done without any assumptions about the expected features of each class. The trained neural network is applied to experimental break junction conductance traces, and it separates the classes as well as the previously used experimental methods. The effect of using partial conductance traces is explored, and we show that the method performs equally well using full or partial traces (as long as the trace just prior to breaking is included). When only the initial part of the trace is included, the results are still better than random chance. Finally, we show that the neural network classification method can be used to classify experimental conductance traces without using simulated results for training, but instead training the network on a few representative experimental traces. This offers a tool to recognize some characteristic motifs of the traces, which can be hard to find by simple data selection algorithms.

Solving of some Problems with On-Line Mode Measurement of Partial Discharges

Directory of Open Access Journals (Sweden)

Karel Zalis

2004-01-01

Full Text Available This paper deals with the problems discussing the transition from off-line diagnostic methods to on-line ones. Based on the experience with commercial partial discharge measuring equipment a new digital system for the evaluation of partial discharge measurement including software and hardware facilities has been developed at the Czech Technical University in Prague. Two expert systems work in this complex evaluating system: a rule-based expert system performing an amplitude analysis of partial discharge impulses for determining the damage of the insulation system, and a neural network which is used for a phase analysis of partial discharge impulses to determine the kind of partial discharge activity. Problem of the elimination of disturbances is also discussed.

NMDA Receptor Signaling Is Important for Neural Tube Formation and for Preventing Antiepileptic Drug-Induced Neural Tube Defects.

Science.gov (United States)

Sequerra, Eduardo B; Goyal, Raman; Castro, Patricio A; Levin, Jacqueline B; Borodinsky, Laura N

2018-05-16

Failure of neural tube closure leads to neural tube defects (NTDs), which can have serious neurological consequences or be lethal. Use of antiepileptic drugs (AEDs) during pregnancy increases the incidence of NTDs in offspring by unknown mechanisms. Here we show that during Xenopus laevis neural tube formation, neural plate cells exhibit spontaneous calcium dynamics that are partially mediated by glutamate signaling. We demonstrate that NMDA receptors are important for the formation of the neural tube and that the loss of their function induces an increase in neural plate cell proliferation and impairs neural cell migration, which result in NTDs. We present evidence that the AED valproic acid perturbs glutamate signaling, leading to NTDs that are rescued with varied efficacy by preventing DNA synthesis, activating NMDA receptors, or recruiting the NMDA receptor target ERK1/2. These findings may prompt mechanistic identification of AEDs that do not interfere with neural tube formation. SIGNIFICANCE STATEMENT Neural tube defects are one of the most common birth defects. Clinical investigations have determined that the use of antiepileptic drugs during pregnancy increases the incidence of these defects in the offspring by unknown mechanisms. This study discovers that glutamate signaling regulates neural plate cell proliferation and oriented migration and is necessary for neural tube formation. We demonstrate that the widely used antiepileptic drug valproic acid interferes with glutamate signaling and consequently induces neural tube defects, challenging the current hypotheses arguing that they are side effects of this antiepileptic drug that cause the increased incidence of these defects. Understanding the mechanisms of neurotransmitter signaling during neural tube formation may contribute to the identification and development of antiepileptic drugs that are safer during pregnancy. Copyright © 2018 the authors 0270-6474/18/384762-12$15.00/0.

Principles of neural information processing

CERN Document Server

Seelen, Werner v

2016-01-01

In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...

Distinct Neural Signatures Detected for ADHD Subtypes After Controlling for Micro-Movements in Resting State Functional Connectivity MRI Data

Directory of Open Access Journals (Sweden)

Damien eFair

2013-02-01

Full Text Available In recent years, there has been growing enthusiasm that functional MRI could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to A examine the impact of emerging techniques for controlling for micro-movements, and B provide novel insights into the neural correlates of ADHD subtypes. Using SVM based MVPA we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C and Inattentive (ADHD-I subtypes demonstrated some overlapping (particularly sensorimotor systems, but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that rs-fcMRI data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical

Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

Science.gov (United States)

Saito, Yoko; Ishii, Kenji; Sakuma, Naoko; Kawasaki, Keiichi; Oda, Keiichi; Mizusawa, Hidehiro

2012-01-01

Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET). We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song), sung lyrics on a single pitch (lyrics), and the sung syllable 'la' on original pitches (melody). The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control) that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control) showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition.

A direct comparison of appetitive and aversive anticipation: Overlapping and distinct neural activation.

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Sege, Christopher T; Bradley, Margaret M; Weymar, Mathias; Lang, Peter J

2017-05-30

fMRI studies of reward find increased neural activity in ventral striatum and medial prefrontal cortex (mPFC), whereas other regions, including the dorsolateral prefrontal cortex (dlPFC), anterior cingulate cortex (ACC), and anterior insula, are activated when anticipating aversive exposure. Although these data suggest differential activation during anticipation of pleasant or of unpleasant exposure, they also arise in the context of different paradigms (e.g., preparation for reward vs. threat of shock) and participants. To determine overlapping and unique regions active during emotional anticipation, we compared neural activity during anticipation of pleasant or unpleasant exposure in the same participants. Cues signalled the upcoming presentation of erotic/romantic, violent, or everyday pictures while BOLD activity during the 9-s anticipatory period was measured using fMRI. Ventral striatum and a ventral mPFC subregion were activated when anticipating pleasant, but not unpleasant or neutral, pictures, whereas activation in other regions was enhanced when anticipating appetitive or aversive scenes. Copyright © 2017 Elsevier B.V. All rights reserved.

Foot Plantar Pressure Estimation Using Artificial Neural Networks

OpenAIRE

Xidias , Elias; Koutkalaki , Zoi; Papagiannis , Panagiotis; Papanikos , Paraskevas; Azariadis , Philip

2015-01-01

Part 1: Smart Products; International audience; In this paper, we present a novel approach to estimate the maximum pressure over the foot plantar surface exerted by a two-layer shoe sole for three distinct phases of the gait cycle. The proposed method is based on Artificial Neural Networks and can be utilized for the determination of the comfort that is related to the sole construction. Input parameters to the proposed neural network are the material properties and the thicknesses of the sole...

Neural networks in continuous optical media

International Nuclear Information System (INIS)

Anderson, D.Z.

1987-01-01

The authors' interest is to see to what extent neural models can be implemented using continuous optical elements. Thus these optical networks represent a continuous distribution of neuronlike processors rather than a discrete collection. Most neural models have three characteristic features: interconnections; adaptivity; and nonlinearity. In their optical representation the interconnections are implemented with linear one- and two-port optical elements such as lenses and holograms. Real-time holographic media allow these interconnections to become adaptive. The nonlinearity is achieved with gain, for example, from two-beam coupling in photorefractive media or a pumped dye medium. Using these basic optical elements one can in principle construct continuous representations of a number of neural network models. The authors demonstrated two devices based on continuous optical elements: an associative memory which recalls an entire object when addressed with a partial object and a tracking novelty filter which identifies time-dependent features in an optical scene. These devices demonstrate the potential of distributed optical elements to implement more formal models of neural networks

Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

Science.gov (United States)

Mundell, Nathan A; Labosky, Patricia A

2011-02-01

Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

How are things adding up? Neural differences between arithmetic operations are due to general problem solving strategies.

Science.gov (United States)

Tschentscher, Nadja; Hauk, Olaf

2014-05-15

A number of previous studies have interpreted differences in brain activation between arithmetic operation types (e.g. addition and multiplication) as evidence in favor of distinct cortical representations, processes or neural systems. It is still not clear how differences in general task complexity contribute to these neural differences. Here, we used a mental arithmetic paradigm to disentangle brain areas related to general problem solving from those involved in operation type specific processes (addition versus multiplication). We orthogonally varied operation type and complexity. Importantly, complexity was defined not only based on surface criteria (for example number size), but also on the basis of individual participants' strategy ratings, which were validated in a detailed behavioral analysis. We replicated previously reported operation type effects in our analyses based on surface criteria. However, these effects vanished when controlling for individual strategies. Instead, procedural strategies contrasted with memory retrieval reliably activated fronto-parietal and motor regions, while retrieval strategies activated parietal cortices. This challenges views that operation types rely on partially different neural systems, and suggests that previously reported differences between operation types may have emerged due to invalid measures of complexity. We conclude that mental arithmetic is a powerful paradigm to study brain networks of abstract problem solving, as long as individual participants' strategies are taken into account. Copyright © 2014 Elsevier Inc. All rights reserved.

Neural Correlates of Visual Aesthetics - Beauty as the Coalescence of Stimulus and Internal State

NARCIS (Netherlands)

Jacobs, Richard H. A. H.; Renken, Remco; Cornelissen, Frans W.

2012-01-01

How do external stimuli and our internal state coalesce to create the distinctive aesthetic pleasures that give vibrance to human experience? Neuroaesthetics has so far focused on the neural correlates of observing beautiful stimuli compared to neutral or ugly stimuli, or on neural correlates of

The Neural Correlates of Moral Thinking: A Meta-Analysis

OpenAIRE

Douglas J. Bryant; Wang F; Kelley Deardeuff; Emily Zoccoli; Chang S. Nam

2016-01-01

We conducted a meta-analysis to evaluate current research that aims to map the neural correlates of two typical conditions of moral judgment: right-wrong moral judgments and decision-making in moral dilemmas. Utilizing the activation likelihood estimation (ALE) method, we conducted a meta-analysis using neuroimaging data obtained from twenty-one previous studies that measured responses in one or the other of these conditions. We found that across the studies (n = 400), distinct neural circuit...

Neural correlates of continuous causal word generation.

Science.gov (United States)

Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

2012-09-01

Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

Learning from neural control.

Science.gov (United States)

Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

Partially flexible MEMS neural probe composed of polyimide and sucrose gel for reducing brain damage during and after implantation

International Nuclear Information System (INIS)

Jeon, Myounggun; Yoon, Eui-Sung; Cho, Il-Joo; Cho, Jeiwon; Jung, Dahee; Kim, Yun Kyung; Shin, Sehyun

2014-01-01

This paper presents a flexible microelectromechanical systems (MEMS) neural probe that minimizes neuron damage and immune response, suitable for chronic recording applications. MEMS neural probes with various features such as high electrode densities have been actively investigated for neuron stimulation and recording to study brain functions. However, successful recording of neural signals in chronic application using rigid silicon probes still remains challenging because of cell death and macrophages accumulated around the electrodes over time from continuous brain movement. Thus, in this paper, we propose a new flexible MEMS neural probe that consists of two segments: a polyimide-based, flexible segment for connection and a rigid segment composed of thin silicon for insertion. While the flexible connection segment is designed to reduce the long-term chronic neuron damage, the thin insertion segment is designed to minimize the brain damage during the insertion process. The proposed flexible neural probe was successfully fabricated using the MEMS process on a silicon on insulator wafer. For a successful insertion, a biodegradable sucrose gel is coated on the flexible segment to temporarily increase the probe stiffness to prevent buckling. After the insertion, the sucrose gel dissolves inside the brain exposing the polyimide probe. By performing an insertion test, we confirm that the flexible probe has enough stiffness. In addition, by monitoring immune responses and brain histology, we successfully demonstrate that the proposed flexible neural probe incurs fivefold less neural damage than that incurred by a conventional silicon neural probe. Therefore, the presented flexible neural probe is a promising candidate for recording stable neural signals for long-time chronic applications. (paper)

Coding of level of ambiguity within neural systems mediating choice.

Science.gov (United States)

Lopez-Paniagua, Dan; Seger, Carol A

2013-01-01

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

Partial Least Squares and Neural Networks for Quantitative Calibration of Laser-induced Breakdown Spectroscopy (LIBs) of Geologic Samples

Science.gov (United States)

Anderson, R. B.; Morris, Richard V.; Clegg, S. M.; Humphries, S. D.; Wiens, R. C.; Bell, J. F., III; Mertzman, S. A.

2010-01-01

The ChemCam instrument [1] on the Mars Science Laboratory (MSL) rover will be used to obtain the chemical composition of surface targets within 7 m of the rover using Laser Induced Breakdown Spectroscopy (LIBS). ChemCam analyzes atomic emission spectra (240-800 nm) from a plasma created by a pulsed Nd:KGW 1067 nm laser. The LIBS spectra can be used in a semiquantitative way to rapidly classify targets (e.g., basalt, andesite, carbonate, sulfate, etc.) and in a quantitative way to estimate their major and minor element chemical compositions. Quantitative chemical analysis from LIBS spectra is complicated by a number of factors, including chemical matrix effects [2]. Recent work has shown promising results using multivariate techniques such as partial least squares (PLS) regression and artificial neural networks (ANN) to predict elemental abundances in samples [e.g. 2-6]. To develop, refine, and evaluate analysis schemes for LIBS spectra of geologic materials, we collected spectra of a diverse set of well-characterized natural geologic samples and are comparing the predictive abilities of PLS, cascade correlation ANN (CC-ANN) and multilayer perceptron ANN (MLP-ANN) analysis procedures.

Isolating Age-Group Differences in Working Memory Load-Related Neural Activity: Assessing the Contribution of Working Memory Capacity Using a Partial-Trial fMRI Method

Science.gov (United States)

Bennett, Ilana J.; Rivera, Hannah G.; Rypma, Bart

2013-01-01

Previous studies examining age-group differences in working memory load-related neural activity have yielded mixed results. When present, age-group differences in working memory capacity are frequently proposed to underlie these neural effects. However, direct relationships between working memory capacity and working memory load-related activity have only been observed in younger adults. These relationships remain untested in healthy aging. Therefore, the present study examined patterns of working memory load-related activity in 22 younger and 20 older adults and assessed the contribution of working memory capacity to these load-related effects. Participants performed a partial-trial delayed response item recognition task during functional magnetic resonance imaging. In this task, participants encoded either 2 or 6 letters, maintained them during a delay, and then indicated whether a probe was present in the memory set. Behavioral results revealed faster and more accurate responses to load 2 versus 6, with age-group differences in this load condition effect for the accuracy measure. Neuroimaging results revealed one region (medial superior frontal gyrus) that showed age-group differences in load-related activity during the retrieval period, with less (greater) neural activity for the low versus high load condition in younger (older) adults. Furthermore, for older adults, load-related activity did not vary as a function of working memory capacity. Thus, working memory-related activity varies with healthy aging, but these patterns are not due solely to working memory capacity. Neurocognitive aging theories that feature capacity will need to account for these results. PMID:23357076

Isolating age-group differences in working memory load-related neural activity: assessing the contribution of working memory capacity using a partial-trial fMRI method.

Science.gov (United States)

Bennett, Ilana J; Rivera, Hannah G; Rypma, Bart

2013-05-15

Previous studies examining age-group differences in working memory load-related neural activity have yielded mixed results. When present, age-group differences in working memory capacity are frequently proposed to underlie these neural effects. However, direct relationships between working memory capacity and working memory load-related activity have only been observed in younger adults. These relationships remain untested in healthy aging. Therefore, the present study examined patterns of working memory load-related activity in 22 younger and 20 older adults and assessed the contribution of working memory capacity to these load-related effects. Participants performed a partial-trial delayed response item recognition task during functional magnetic resonance imaging. In this task, participants encoded either 2 or 6 letters, maintained them during a delay, and then indicated whether a probe was present in the memory set. Behavioral results revealed faster and more accurate responses to load 2 versus 6, with age-group differences in this load condition effect for the accuracy measure. Neuroimaging results revealed one region (medial superior frontal gyrus) that showed age-group differences in load-related activity during the retrieval period, with less (greater) neural activity for the low versus high load condition in younger (older) adults. Furthermore, for older adults, load-related activity did not vary as a function of working memory capacity. Thus, working memory-related activity varies with healthy aging, but these patterns are not due solely to working memory capacity. Neurocognitive aging theories that feature capacity will need to account for these results. Copyright © 2013 Elsevier Inc. All rights reserved.

Application of artificial neural network for heat transfer in porous cone

Science.gov (United States)

Athani, Abdulgaphur; Ahamad, N. Ameer; Badruddin, Irfan Anjum

2018-05-01

Heat transfer in porous medium is one of the classical areas of research that has been active for many decades. The heat transfer in porous medium is generally studied by using numerical methods such as finite element method; finite difference method etc. that solves coupled partial differential equations by converting them into simpler forms. The current work utilizes an alternate method known as artificial neural network that mimics the learning characteristics of neurons. The heat transfer in porous medium fixed in a cone is predicted using backpropagation neural network. The artificial neural network is able to predict this behavior quite accurately.

Autoshaping and Automaintenance: A Neural-Network Approach

Science.gov (United States)

Burgos, Jose E.

2007-01-01

This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an "A-B-A" design…

neural network based load frequency control for restructuring power

African Journals Online (AJOL)

2012-03-01

Mar 1, 2012 ... the system in the back propagation chain used in controller training. For this application, .... The partial derivative of E with respect to ele- ments of Γ, for example W, ... Ki = any non-negative value. Figure 7: Neural Network ...

Task-dependent modulation of oscillatory neural activity during movements

DEFF Research Database (Denmark)

Herz, D. M.; Christensen, M. S.; Reck, C.

2011-01-01

connectivity was strongest between central and cerebellar regions. Our results show that neural coupling within motor networks is modulated in distinct frequency bands depending on the motor task. They provide evidence that dynamic causal modeling in combination with EEG source analysis is a valuable tool......Neural oscillations in different frequency bands have been observed in a range of sensorimotor tasks and have been linked to coupling of spatially distinct neurons. The goal of this study was to detect a general motor network that is activated during phasic and tonic movements and to study the task......-dependent modulation of frequency coupling within this network. To this end we recorded 122-multichannel EEG in 13 healthy subjects while they performed three simple motor tasks. EEG data source modeling using individual MR images was carried out with a multiple source beamformer approach. A bilateral motor network...

Neural substrates for semantic memory of familiar songs: is there an interface between lyrics and melodies?

Directory of Open Access Journals (Sweden)

Yoko Saito

Full Text Available Findings on song perception and song production have increasingly suggested that common but partially distinct neural networks exist for processing lyrics and melody. However, the neural substrates of song recognition remain to be investigated. The purpose of this study was to examine the neural substrates involved in the accessing "song lexicon" as corresponding to a representational system that might provide links between the musical and phonological lexicons using positron emission tomography (PET. We exposed participants to auditory stimuli consisting of familiar and unfamiliar songs presented in three ways: sung lyrics (song, sung lyrics on a single pitch (lyrics, and the sung syllable 'la' on original pitches (melody. The auditory stimuli were designed to have equivalent familiarity to participants, and they were recorded at exactly the same tempo. Eleven right-handed nonmusicians participated in four conditions: three familiarity decision tasks using song, lyrics, and melody and a sound type decision task (control that was designed to engage perceptual and prelexical processing but not lexical processing. The contrasts (familiarity decision tasks versus control showed no common areas of activation between lyrics and melody. This result indicates that essentially separate neural networks exist in semantic memory for the verbal and melodic processing of familiar songs. Verbal lexical processing recruited the left fusiform gyrus and the left inferior occipital gyrus, whereas melodic lexical processing engaged the right middle temporal sulcus and the bilateral temporo-occipital cortices. Moreover, we found that song specifically activated the left posterior inferior temporal cortex, which may serve as an interface between verbal and musical representations in order to facilitate song recognition.

Face recognition: a convolutional neural-network approach.

Science.gov (United States)

Lawrence, S; Giles, C L; Tsoi, A C; Back, A D

1997-01-01

We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

Regional neural tube closure defined by the Grainy head-like transcription factors.

Science.gov (United States)

Rifat, Yeliz; Parekh, Vishwas; Wilanowski, Tomasz; Hislop, Nikki R; Auden, Alana; Ting, Stephen B; Cunningham, John M; Jane, Stephen M

2010-09-15

Primary neurulation in mammals has been defined by distinct anatomical closure sites, at the hindbrain/cervical spine (closure 1), forebrain/midbrain boundary (closure 2), and rostral end of the forebrain (closure 3). Zones of neurulation have also been characterized by morphologic differences in neural fold elevation, with non-neural ectoderm-induced formation of paired dorso-lateral hinge points (DLHP) essential for neural tube closure in the cranial and lower spinal cord regions, and notochord-induced bending at the median hinge point (MHP) sufficient for closure in the upper spinal region. Here we identify a unifying molecular basis for these observations based on the function of the non-neural ectoderm-specific Grainy head-like genes in mice. Using a gene-targeting approach we show that deletion of Grhl2 results in failed closure 3, with mutants exhibiting a split-face malformation and exencephaly, associated with failure of neuro-epithelial folding at the DLHP. Loss of Grhl3 alone defines a distinct lower spinal closure defect, also with defective DLHP formation. The two genes contribute equally to closure 2, where only Grhl gene dosage is limiting. Combined deletion of Grhl2 and Grhl3 induces severe rostral and caudal neural tube defects, but DLHP-independent closure 1 proceeds normally in the upper spinal region. These findings provide a molecular basis for non-neural ectoderm mediated formation of the DLHP that is critical for complete neuraxis closure. (c) 2010 Elsevier Inc. All rights reserved.

The neural basis of financial risk taking.

Science.gov (United States)

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

msh/Msx gene family in neural development.

Science.gov (United States)

Ramos, Casto; Robert, Benoît

2005-11-01

The involvement of Msx homeobox genes in skull and tooth formation has received a great deal of attention. Recent studies also indicate a role for the msh/Msx gene family in development of the nervous system. In this article, we discuss the functions of these transcription factors in neural-tissue organogenesis. We will deal mainly with the interactions of the Drosophila muscle segment homeobox (msh) gene with other homeobox genes and the repressive cascade that leads to neuroectoderm patterning; the role of Msx genes in neural-crest induction, focusing especially on the differences between lower and higher vertebrates; their implication in patterning of the vertebrate neural tube, particularly in diencephalon midline formation. Finally, we will examine the distinct activities of Msx1, Msx2 and Msx3 genes during neurogenesis, taking into account their relationships with signalling molecules such as BMP.

Fractionating the Neural Substrates of Incidental Recognition Memory

Science.gov (United States)

Greene, Ciara M.; Vidaki, Kleio; Soto, David

2015-01-01

Familiar stimuli are typically accompanied by decreases in neural response relative to the presentation of novel items, but these studies often include explicit instructions to discriminate old and new items; this creates difficulties in partialling out the contribution of top-down intentional orientation to the items based on recognition goals.…

Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making

Directory of Open Access Journals (Sweden)

Bryan C. Daniels

2017-06-01

Full Text Available A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a “coding duality” in which there are accumulation and consensus formation processes distinguished by different timescales.

Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making.

Science.gov (United States)

Daniels, Bryan C; Flack, Jessica C; Krakauer, David C

2017-01-01

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.

Abstract and Effector-Selective Decision Signals Exhibit Qualitatively Distinct Dynamics before Delayed Perceptual Reports.

Science.gov (United States)

Twomey, Deirdre M; Kelly, Simon P; O'Connell, Redmond G

2016-07-13

Electrophysiological research has isolated neural signatures of decision formation in a variety of brain regions. Studies in rodents and monkeys have focused primarily on effector-selective signals that translate the emerging decision into a specific motor plan, but, more recently, research on the human brain has identified an abstract signature of evidence accumulation that does not appear to play any direct role in action preparation. The functional dissociations between these distinct signal types have only begun to be characterized, and their dynamics during decisions with deferred actions with or without foreknowledge of stimulus-effector mapping, a commonly studied task scenario in single-unit and functional imaging investigations, have not been established. Here we traced the dynamics of distinct abstract and effector-selective decision signals in the form of the broad-band centro-parietal positivity (CPP) and limb-selective β-band (8-16 and 18-30 Hz) EEG activity, respectively, during delayed-reported motion direction decisions with and without foreknowledge of direction-response mapping. With foreknowledge, the CPP and β-band signals exhibited a similar gradual build-up following evidence onset, but whereas choice-predictive β-band activity persisted up until the delayed response, the CPP dropped toward baseline after peaking. Without foreknowledge, the CPP exhibited identical dynamics, whereas choice-selective β-band activity was eliminated. These findings highlight qualitative functional distinctions between effector-selective and abstract decision signals and are of relevance to the assumptions founding functional neuroimaging investigations of decision-making. Neural signatures of evidence accumulation have been isolated in numerous brain regions. Although animal neurophysiology has largely concentrated on effector-selective decision signals that translate the emerging decision into a specific motor plan, recent research on the human brain has

Chromatin Remodeling BAF (SWI/SNF Complexes in Neural Development and Disorders

Directory of Open Access Journals (Sweden)

Godwin Sokpor

2017-08-01

Full Text Available The ATP-dependent BRG1/BRM associated factor (BAF chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders.

Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders

Science.gov (United States)

Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

2017-01-01

The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders. PMID:28824374

Chromatin Remodeling BAF (SWI/SNF) Complexes in Neural Development and Disorders.

Science.gov (United States)

Sokpor, Godwin; Xie, Yuanbin; Rosenbusch, Joachim; Tuoc, Tran

2017-01-01

The ATP-dependent BRG1/BRM associated factor (BAF) chromatin remodeling complexes are crucial in regulating gene expression by controlling chromatin dynamics. Over the last decade, it has become increasingly clear that during neural development in mammals, distinct ontogenetic stage-specific BAF complexes derived from combinatorial assembly of their subunits are formed in neural progenitors and post-mitotic neural cells. Proper functioning of the BAF complexes plays critical roles in neural development, including the establishment and maintenance of neural fates and functionality. Indeed, recent human exome sequencing and genome-wide association studies have revealed that mutations in BAF complex subunits are linked to neurodevelopmental disorders such as Coffin-Siris syndrome, Nicolaides-Baraitser syndrome, Kleefstra's syndrome spectrum, Hirschsprung's disease, autism spectrum disorder, and schizophrenia. In this review, we focus on the latest insights into the functions of BAF complexes during neural development and the plausible mechanistic basis of how mutations in known BAF subunits are associated with certain neurodevelopmental disorders.

Evaluation of the Legibility of Broken Lines for Partial Sight

OpenAIRE

小林, 秀之

2000-01-01

The present study was designed to investigate the legibility of broken lines for persons with partial sight. The subjects were 10 persons with simulated partial sight, and 4 persons with partial sight. The simulation was obtained using filters and convex lenses. The 30 kind of broken lines was evaluated by the original test that the subjects were read directions of the broken lines in distinction from solid lines. The thickness of lines varied from 0.1mm. to 0.7mm. in 4 steps. The results...

Models of neural networks IV early vision and attention

CERN Document Server

Cowan, Jack; Domany, Eytan

2002-01-01

Close this book for a moment and look around you. You scan the scene by directing your attention, and gaze, at certain specific objects. Despite the background, you discern them. The process is partially intentional and partially preattentive. How all this can be done is described in the fourth volume of Models of Neural Networks devoted to Early Vision and Atten­ tion that you are holding in your hands. Early vision comprises the first stages of visual information processing. It is as such a scientific challenge whose clarification calls for a penetrating review. Here you see the result. The Heraeus Foundation (Hanau) is to be thanked for its support during the initial phase of this project. John Hertz, who has extensive experience in both computational and ex­ perimental neuroscience, provides in "Neurons, Networks, and Cognition" to neural modeling. John Van Opstal explains in a theoretical introduction "The Gaze Control System" how the eye's gaze control is performed and presents a novel theoretical des...

Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability.

Science.gov (United States)

Haar, Shlomi; Donchin, Opher; Dinstein, Ilan

2017-09-13

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction that are thought to be governed by distinct neural processes. Here, we report that individual subjects (males and females) exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects explained their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We therefore propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities. SIGNIFICANCE STATEMENT Neural activity and movement kinematics are remarkably variable. Although intertrial variability is rarely studied, here, we demonstrate that individual human subjects exhibit distinct magnitudes of neural and kinematic variability that are reproducible across movements to different targets and when performing these movements with either arm. Furthermore, when examining the relationship between cortical variability and movement variability, we find that cortical fMRI variability in parietal cortex of individual subjects explained their movement extent variability. This enabled us to explain why some subjects

Solving differential equations with unknown constitutive relations as recurrent neural networks

Energy Technology Data Exchange (ETDEWEB)

Hagge, Tobias J.; Stinis, Panagiotis; Yeung, Enoch H.; Tartakovsky, Alexandre M.

2017-12-08

We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and use a recurrent neural network to “learn” the reaction rate from this data. This is achieved by including discretized ordinary differential equations as part of a recurrent neural network training problem. We extend TensorFlow’s recurrent neural network architecture to create a simple but scalable and effective solver for the unknown functions, and apply it to a fedbatch bioreactor simulation problem. Use of techniques from recent deep learning literature enables training of functions with behavior manifesting over thousands of time steps. Our networks are structurally similar to recurrent neural networks, but differ in purpose, and require modified training strategies.

Probabilistic Wind Power Forecasting with Hybrid Artificial Neural Networks

DEFF Research Database (Denmark)

Wan, Can; Song, Yonghua; Xu, Zhao

2016-01-01

probabilities of prediction errors provide an alternative yet effective solution. This article proposes a hybrid artificial neural network approach to generate prediction intervals of wind power. An extreme learning machine is applied to conduct point prediction of wind power and estimate model uncertainties...... via a bootstrap technique. Subsequently, the maximum likelihood estimation method is employed to construct a distinct neural network to estimate the noise variance of forecasting results. The proposed approach has been tested on multi-step forecasting of high-resolution (10-min) wind power using...... actual wind power data from Denmark. The numerical results demonstrate that the proposed hybrid artificial neural network approach is effective and efficient for probabilistic forecasting of wind power and has high potential in practical applications....

The ctenophore genome and the evolutionary origins of neural systems

NARCIS (Netherlands)

Moroz, Leonid L.; Kocot, Kevin M.; Citarella, Mathew R.; Dosung, Sohn; Norekian, Tigran P.; Povolotskaya, Inna S.; Grigorenko, Anastasia P.; Dailey, Christopher; Berezikov, Eugene; Buckley, Katherine M.; Ptitsyn, Andrey; Reshetov, Denis; Mukherjee, Krishanu; Moroz, Tatiana P.; Bobkova, Yelena; Yu, Fahong; Kapitonov, Vladimir V.; Jurka, Jerzy; Bobkov, Yuri V.; Swore, Joshua J.; Girardo, David O.; Fodor, Alexander; Gusev, Fedor; Sanford, Rachel; Bruders, Rebecca; Kittler, Ellen; Mills, Claudia E.; Rast, Jonathan P.; Derelle, Romain; Solovyev, Victor V.; Kondrashov, Fyodor A.; Swalla, Billie J.; Sweedler, Jonathan V.; Rogaev, Evgeny I.; Halanych, Kenneth M.; Kohn, Andrea B.

2014-01-01

The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores (comb jellies) have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here we

Neural Progenitors Adopt Specific Identities by Directly Repressing All Alternative Progenitor Transcriptional Programs.

Science.gov (United States)

Kutejova, Eva; Sasai, Noriaki; Shah, Ankita; Gouti, Mina; Briscoe, James

2016-03-21

In the vertebrate neural tube, a morphogen-induced transcriptional network produces multiple molecularly distinct progenitor domains, each generating different neuronal subtypes. Using an in vitro differentiation system, we defined gene expression signatures of distinct progenitor populations and identified direct gene-regulatory inputs corresponding to locations of specific transcription factor binding. Combined with targeted perturbations of the network, this revealed a mechanism in which a progenitor identity is installed by active repression of the entire transcriptional programs of other neural progenitor fates. In the ventral neural tube, sonic hedgehog (Shh) signaling, together with broadly expressed transcriptional activators, concurrently activates the gene expression programs of several domains. The specific outcome is selected by repressive input provided by Shh-induced transcription factors that act as the key nodes in the network, enabling progenitors to adopt a single definitive identity from several initially permitted options. Together, the data suggest design principles relevant to many developing tissues. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Visuospatial planning in unmedicated major depressive disorder and bipolar disorder : distinct and common neural correlates

NARCIS (Netherlands)

Rive, M. M.; Koeter, M. W. J.; Veltman, D. J.; Schene, A. H.; Ruhe, H. G.

Background Cognitive impairments are an important feature of both remitted and depressed major depressive disorder (MDD) and bipolar disorder (BD). In particular, deficits in executive functioning may hamper everyday functioning. Identifying the neural substrates of impaired executive functioning

Neural Evidence for a Distinction between Short-Term Memory and the Focus of Attention

Science.gov (United States)

Lewis-Peacock, Jarrod A.; Drysdale, Andrew T.; Oberauer, Klaus; Postle, Bradley R.

2012-01-01

It is widely assumed that the short-term retention of information is accomplished via maintenance of an active neural trace. However, we demonstrate that memory can be preserved across a brief delay despite the apparent loss of sustained representations. Delay period activity may, in fact, reflect the focus of attention, rather than STM. We…

Optimization of operation cycles in BWRs using neural networks

International Nuclear Information System (INIS)

Ortiz S, J. J.; Castillo, A.; Alejandro P, D.

2011-11-01

The first results of a system for the optimization of operation cycles in boiling water reactors by means of a multi state recurrent neural network are present in this work. The neural network finds the best combination of fuel cells; fuel reloads and control bars patterns previously designed, according to an energy function that qualifies the performance of the three partial solutions for the solution of the whole problem. The partial solutions are designed by means of optimization systems non couple among them and that can use any optimization technique. The phase of the fuel axial design is not made and the size of the axial areas is fixed during the optimization process. The methodology was applied to design a balance cycle of 18 months for the reactors of the nuclear power station of Laguna Verde. The results show that is possible to find combinations of partial solutions that in set represent good solutions to the complete design problem of an operation cycle of a nuclear reactor. The results are compared with others obtained previously by other techniques. This system was developed in platform Li nux and programmed in Fortran 95 taking advantage of the 8 nuclei of a work station Dell Precision T7400. (Author)

The neural basis of intuitive and counterintuitive moral judgment

Science.gov (United States)

Wiech, Katja; Shackel, Nicholas; Farias, Miguel; Savulescu, Julian; Tracey, Irene

2012-01-01

Neuroimaging studies on moral decision-making have thus far largely focused on differences between moral judgments with opposing utilitarian (well-being maximizing) and deontological (duty-based) content. However, these studies have investigated moral dilemmas involving extreme situations, and did not control for two distinct dimensions of moral judgment: whether or not it is intuitive (immediately compelling to most people) and whether it is utilitarian or deontological in content. By contrasting dilemmas where utilitarian judgments are counterintuitive with dilemmas in which they are intuitive, we were able to use functional magnetic resonance imaging to identify the neural correlates of intuitive and counterintuitive judgments across a range of moral situations. Irrespective of content (utilitarian/deontological), counterintuitive moral judgments were associated with greater difficulty and with activation in the rostral anterior cingulate cortex, suggesting that such judgments may involve emotional conflict; intuitive judgments were linked to activation in the visual and premotor cortex. In addition, we obtained evidence that neural differences in moral judgment in such dilemmas are largely due to whether they are intuitive and not, as previously assumed, to differences between utilitarian and deontological judgments. Our findings therefore do not support theories that have generally associated utilitarian and deontological judgments with distinct neural systems. PMID:21421730

Differentiation state determines neural effects on microvascular endothelial cells

International Nuclear Information System (INIS)

Muffley, Lara A.; Pan, Shin-Chen; Smith, Andria N.; Ga, Maricar; Hocking, Anne M.; Gibran, Nicole S.

2012-01-01

Growing evidence indicates that nerves and capillaries interact paracrinely in uninjured skin and cutaneous wounds. Although mature neurons are the predominant neural cell in the skin, neural progenitor cells have also been detected in uninjured adult skin. The aim of this study was to characterize differential paracrine effects of neural progenitor cells and mature sensory neurons on dermal microvascular endothelial cells. Our results suggest that neural progenitor cells and mature sensory neurons have unique secretory profiles and distinct effects on dermal microvascular endothelial cell proliferation, migration, and nitric oxide production. Neural progenitor cells and dorsal root ganglion neurons secrete different proteins related to angiogenesis. Specific to neural progenitor cells were dipeptidyl peptidase-4, IGFBP-2, pentraxin-3, serpin f1, TIMP-1, TIMP-4 and VEGF. In contrast, endostatin, FGF-1, MCP-1 and thrombospondin-2 were specific to dorsal root ganglion neurons. Microvascular endothelial cell proliferation was inhibited by dorsal root ganglion neurons but unaffected by neural progenitor cells. In contrast, microvascular endothelial cell migration in a scratch wound assay was inhibited by neural progenitor cells and unaffected by dorsal root ganglion neurons. In addition, nitric oxide production by microvascular endothelial cells was increased by dorsal root ganglion neurons but unaffected by neural progenitor cells. -- Highlights: ► Dorsal root ganglion neurons, not neural progenitor cells, regulate microvascular endothelial cell proliferation. ► Neural progenitor cells, not dorsal root ganglion neurons, regulate microvascular endothelial cell migration. ► Neural progenitor cells and dorsal root ganglion neurons do not effect microvascular endothelial tube formation. ► Dorsal root ganglion neurons, not neural progenitor cells, regulate microvascular endothelial cell production of nitric oxide. ► Neural progenitor cells and dorsal root

Chitosan derived co-spheroids of neural stem cells and mesenchymal stem cells for neural regeneration.

Science.gov (United States)

Han, Hao-Wei; Hsu, Shan-Hui

2017-10-01

Chitosan has been considered as candidate biomaterials for neural applications. The effective treatment of neurodegeneration or injury to the central nervous system (CNS) is still in lack nowadays. Adult neural stem cells (NSCs) represents a promising cell source to treat the CNS diseases but they are limited in number. Here, we developed the core-shell spheroids of NSCs (shell) and mesenchymal stem cells (MSCs, core) by co-culturing cells on the chitosan surface. The NSCs in chitosan derived co-spheroids displayed a higher survival rate than those in NSC homo-spheroids. The direct interaction of NSCs with MSCs in the co-spheroids increased the Notch activity and differentiation tendency of NSCs. Meanwhile, the differentiation potential of MSCs in chitosan derived co-spheroids was significantly enhanced toward neural lineages. Furthermore, NSC homo-spheroids and NSC/MSC co-spheroids derived on chitosan were evaluated for their in vivo efficacy by the embryonic and adult zebrafish brain injury models. The locomotion activity of zebrafish receiving chitosan derived NSC homo-spheroids or NSC/MSC co-spheroids was partially rescued in both models. Meanwhile, the higher survival rate was observed in the group of adult zebrafish implanted with chitosan derived NSC/MSC co-spheroids as compared to NSC homo-spheroids. These evidences indicate that chitosan may provide an extracellular matrix-like environment to drive the interaction and the morphological assembly between NSCs and MSCs and promote their neural differentiation capacities, which can be used for neural regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Degree of handedness and priming: Further evidence for a distinction between production and identification priming mechanisms.

Directory of Open Access Journals (Sweden)

Donna J. LaVoie

2015-02-01

Full Text Available The distinction between implicit and explicit forms of memory retrieval is long-standing, and important to the extent it reveals how different neural architecture supports different aspects of memory function. Similarly, distinctions have been made between kinds of repetition priming, a form of implicit memory retrieval. This study focuses on the production-identification priming distinction, which delineates priming tasks involving verification of stimulus features as compared to priming tasks that require use of a cue to guide response retrieval. Studies investigating this dissociation in dementia or similar patient populations indicate that these forms of priming may differ in their neural bases. The current study looks at degree of handedness as a way of investigating inferred neural architecture supporting these two forms of priming. A growing body of research indicates that degree of handedness (consistent, or CH, versus inconsistent, or ICH is associated with greater interhemispheric interaction and functional access to right hemisphere processing in ICH, with superior performance seen in ICH on memory tasks reliant on this processing. Arguments about the theoretical mechanisms underlying identification and production forms of perceptual priming tasks suggest that performance on these tasks will differ as a function of degree of handedness. We tested this question in a group of CH and ICH young adults, who were asked to study lists of words prior to performing a production priming task (word stem completion, a perceptual word identification task, and a word stem cued recall task. While both handedness groups exhibited reliable priming across tasks, word stem completion priming was greater in ICH than CH participants, with identification priming not differing between groups. This dissociation supports the argument that production and identification forms of priming have different underlying neural bases.

Distinct neural control of intrinsic and extrinsic muscles of the hand during single finger pressing

NARCIS (Netherlands)

Dupan, Sigrid S.G.; Stegeman, Dick F.; Maas, Huub

2018-01-01

Single finger force tasks lead to unintended activation of the non-instructed fingers, commonly referred to as enslaving. Both neural and mechanical factors have been associated with this absence of finger individuality. This study investigates the amplitude modulation of both intrinsic and

Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

NARCIS (Netherlands)

Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

2016-01-01

Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

Discrete Neural Signatures of Basic Emotions.

Science.gov (United States)

Saarimäki, Heini; Gotsopoulos, Athanasios; Jääskeläinen, Iiro P; Lampinen, Jouko; Vuilleumier, Patrik; Hari, Riitta; Sams, Mikko; Nummenmaa, Lauri

2016-06-01

Categorical models of emotions posit neurally and physiologically distinct human basic emotions. We tested this assumption by using multivariate pattern analysis (MVPA) to classify brain activity patterns of 6 basic emotions (disgust, fear, happiness, sadness, anger, and surprise) in 3 experiments. Emotions were induced with short movies or mental imagery during functional magnetic resonance imaging. MVPA accurately classified emotions induced by both methods, and the classification generalized from one induction condition to another and across individuals. Brain regions contributing most to the classification accuracy included medial and inferior lateral prefrontal cortices, frontal pole, precentral and postcentral gyri, precuneus, and posterior cingulate cortex. Thus, specific neural signatures across these regions hold representations of different emotional states in multimodal fashion, independently of how the emotions are induced. Similarity of subjective experiences between emotions was associated with similarity of neural patterns for the same emotions, suggesting a direct link between activity in these brain regions and the subjective emotional experience. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The partially filled viscous ring damper.

Science.gov (United States)

Alfriend, K. T.

1973-01-01

The problem of a spinning satellite with a partially filled viscous ring damper is investigated. It is shown that there are two distinct modes of motion, the nutation-synchronous mode and spin-synchronous mode. From an approximate solution of the equations of motion a time constant is obtained for each mode. From a consideration of the fluid dynamics several methods are developed for determining the damping constant.

A Neural Network Model of the Visual Short-Term Memory

DEFF Research Database (Denmark)

Petersen, Anders; Kyllingsbæk, Søren; Hansen, Lars Kai

2009-01-01

In this paper a neural network model of Visual Short-Term Memory (VSTM) is presented. The model links closely with Bundesen’s (1990) well-established mathematical theory of visual attention. We evaluate the model’s ability to fit experimental data from a classical whole and partial report study...

Commentary: Elucidating the Neural Correlates of Early Childhood Memory

Science.gov (United States)

Mullally, Sinead L.

2015-01-01

Both episodic memory and the key neural structure believed to support it, namely the hippocampus, are believed to undergo protracted periods of postnatal developmental. Critically however, the hippocampus is comprised of distinct subfields and circuits, and these circuits appear to mature at different rates (Lavenex and Banta Lavenex, 2013).…

Shindigs, brunches, and rodeos: the neural basis of event words.

Science.gov (United States)

Bedny, Marina; Dravida, Swethasri; Saxe, Rebecca

2014-09-01

Events (e.g., "running" or "eating") constitute a basic type within human cognition and human language. We asked whether thinking about events, as compared to other conceptual categories, depends on partially independent neural circuits. Indirect evidence for this hypothesis comes from previous studies showing elevated posterior temporal responses to verbs, which typically label events. Neural responses to verbs could, however, be driven either by their grammatical or by their semantic properties. In the present experiment, we separated the effects of grammatical class (verb vs. noun) and semantic category (event vs. object) by measuring neural responses to event nouns (e.g., "the hurricane"). Participants rated the semantic relatedness of event nouns, as well as of two categories of object nouns-animals (e.g., "the alligator") and plants (e.g., "the acorn")-and three categories of verbs-manner of motion (e.g., "to roll"), emission (e.g., "to sparkle"), and perception (e.g., "to gaze"). As has previously been observed, we found larger responses to verbs than to object nouns in the left posterior middle (LMTG) and superior (LSTG) temporal gyri. Crucially, we also found that the LMTG responds more to event than to object nouns. These data suggest that part of the posterior lateral temporal response to verbs is driven by their semantic properties. By contrast, a more superior region, at the junction of the temporal and parietal cortices, responded more to verbs than to all nouns, irrespective of their semantic category. We concluded that the neural mechanisms engaged when thinking about event and object categories are partially dissociable.

Is "Learning" episodic memory? Distinct cognitive and neuroanatomic correlates of immediate recall during learning trials in neurologically normal aging and neurodegenerative cohorts.

Science.gov (United States)

Casaletto, K B; Marx, G; Dutt, S; Neuhaus, J; Saloner, R; Kritikos, L; Miller, B; Kramer, J H

2017-07-28

Although commonly interpreted as a marker of episodic memory during neuropsychological exams, relatively little is known regarding the neurobehavior of "total learning" immediate recall scores. Medial temporal lobes are clearly associated with delayed recall performances, yet immediate recall may necessitate networks beyond traditional episodic memory. We aimed to operationalize cognitive and neuroanatomic correlates of total immediate recall in several aging syndromes. Demographically-matched neurologically normal adults (n=91), individuals with Alzheimer's disease (n=566), logopenic variant primary progressive aphasia (PPA) (n=34), behavioral variant frontotemporal dementia (n=97), semantic variant PPA (n=71), or nonfluent/agrammatic variant PPA (n=39) completed a neurocognitive battery, including the CVLT-Short Form trials 1-4 Total Immediate Recall; a majority subset also completed a brain MRI. Regressions covaried for age and sex, and MMSE in cognitive and total intracranial volume in neuroanatomic models. Neurologically normal adults demonstrated a heterogeneous pattern of cognitive associations with total immediate recall (executive, speed, delayed recall), such that no singular cognitive or neuroanatomic correlate uniquely predicted performance. Within the clinical cohorts, there were syndrome-specific cognitive and neural associations with total immediate recall; e.g., semantic processing was the strongest cognitive correlate in svPPA (partial r=0.41), while frontal volumes was the only meaningful neural correlate in bvFTD (partial r=0.20). Medial temporal lobes were not independently associated with total immediate recall in any group (ps>0.05). Multiple neurobehavioral systems are associated with "total learning" immediate recall scores that importantly differ across distinct clinical syndromes. Conventional memory networks may not be sufficient or even importantly contribute to total immediate recall in many syndromes. Interpreting learning scores as

The LILARTI neural network system

Energy Technology Data Exchange (ETDEWEB)

Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

1992-10-01

The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

Neural representations of emotion are organized around abstract event features.

Science.gov (United States)

Skerry, Amy E; Saxe, Rebecca

2015-08-03

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neural signal processing for identifying failed fuel rods in nuclear reactors

International Nuclear Information System (INIS)

Seixas, Jose M. de; Soares Filho, William; Pereira, Wagner C.A.; Teles, Claudio C.B.

2002-01-01

Ultrasonic pulses were used for automatic detection of failed nuclear fuel rods. For experimental tests of the proposed method, an assembly prototype of 16 x 16 rods was built by using genuine rods but without fuel inside (just air). Some rods were partially filled with water to simulate cracked rods. Using neural signal processing on the received echoes of the emitted ultrasonic pulses, a detection efficiency of 97% was obtained. Neural detection is shown to outperform other classical discriminating methods and can also reveal important features of the signal structure of the received echoes. (author)

Neural complexity: A graph theoretic interpretation

Science.gov (United States)

Barnett, L.; Buckley, C. L.; Bullock, S.

2011-04-01

One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

Function of FEZF1 during early neural differentiation of human embryonic stem cells.

Science.gov (United States)

Liu, Xin; Su, Pei; Lu, Lisha; Feng, Zicen; Wang, Hongtao; Zhou, Jiaxi

2018-01-01

The understanding of the mechanism underlying human neural development has been hampered due to lack of a cellular system and complicated ethical issues. Human embryonic stem cells (hESCs) provide an invaluable model for dissecting human development because of unlimited self-renewal and the capacity to differentiate into nearly all cell types in the human body. In this study, using a chemical defined neural induction protocol and molecular profiling, we identified Fez family zinc finger 1 (FEZF1) as a potential regulator of early human neural development. FEZF1 is rapidly up-regulated during neural differentiation in hESCs and expressed before PAX6, a well-established marker of early human neural induction. We generated FEZF1-knockout H1 hESC lines using CRISPR-CAS9 technology and found that depletion of FEZF1 abrogates neural differentiation of hESCs. Moreover, loss of FEZF1 impairs the pluripotency exit of hESCs during neural specification, which partially explains the neural induction defect caused by FEZF1 deletion. However, enforced expression of FEZF1 itself fails to drive neural differentiation in hESCs, suggesting that FEZF1 is necessary but not sufficient for neural differentiation from hESCs. Taken together, our findings identify one of the earliest regulators expressed upon neural induction and provide insight into early neural development in human.

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

Science.gov (United States)

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

Bilingual Lexical Interactions in an Unsupervised Neural Network Model

Science.gov (United States)

Zhao, Xiaowei; Li, Ping

2010-01-01

In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network

International Nuclear Information System (INIS)

Konoz, Elahe; Golmohammadi, Hassan

2008-01-01

An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are: R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately

The response of early neural genes to FGF signaling or inhibition of BMP indicate the absence of a conserved neural induction module

Directory of Open Access Journals (Sweden)

Rogers Crystal D

2011-12-01

Full Text Available Abstract Background The molecular mechanism that initiates the formation of the vertebrate central nervous system has long been debated. Studies in Xenopus and mouse demonstrate that inhibition of BMP signaling is sufficient to induce neural tissue in explants or ES cells respectively, whereas studies in chick argue that instructive FGF signaling is also required for the expression of neural genes. Although additional signals may be involved in neural induction and patterning, here we focus on the roles of BMP inhibition and FGF8a. Results To address the question of necessity and sufficiency of BMP inhibition and FGF signaling, we compared the temporal expression of the five earliest genes expressed in the neuroectoderm and determined their requirements for induction at the onset of neural plate formation in Xenopus. Our results demonstrate that the onset and peak of expression of the genes vary and that they have different regulatory requirements and are therefore unlikely to share a conserved neural induction regulatory module. Even though all require inhibition of BMP for expression, some also require FGF signaling; expression of the early-onset pan-neural genes sox2 and foxd5α requires FGF signaling while other early genes, sox3, geminin and zicr1 are induced by BMP inhibition alone. Conclusions We demonstrate that BMP inhibition and FGF signaling induce neural genes independently of each other. Together our data indicate that although the spatiotemporal expression patterns of early neural genes are similar, the mechanisms involved in their expression are distinct and there are different signaling requirements for the expression of each gene.

Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks

CSIR Research Space (South Africa)

Gerrand, Jonathan D

2017-07-01

Full Text Available of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5...

Neural network application to diesel generator diagnostics

International Nuclear Information System (INIS)

Logan, K.P.

1990-01-01

Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

Neural basis of social status hierarchy across species.

Science.gov (United States)

Chiao, Joan Y

2010-12-01

Social status hierarchy is a ubiquitous principle of social organization across the animal kingdom. Recent findings in social neuroscience reveal distinct neural networks associated with the recognition and experience of social hierarchy in humans, as well as modulation of these networks by personality and culture. Additionally, allelic variation in the serotonin transporter gene is associated with prevalence of social hierarchy across species and cultures, suggesting the importance of the study of genetic factors underlying social hierarchy. Future studies are needed to determine how genetic and environmental factors shape neural systems involved in the production and maintenance of social hierarchy across ontogeny and phylogeny. Copyright © 2010 Elsevier Ltd. All rights reserved.

Three Pillars for the Neural Control of Appetite.

Science.gov (United States)

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

The role of BAF (mSWI/SNF) complexes in mammalian neural development.

Science.gov (United States)

Son, Esther Y; Crabtree, Gerald R

2014-09-01

The BAF (mammalian SWI/SNF) complexes are a family of multi-subunit ATP-dependent chromatin remodelers that use ATP hydrolysis to alter chromatin structure. Distinct BAF complex compositions are possible through combinatorial assembly of homologous subunit families and can serve non-redundant functions. In mammalian neural development, developmental stage-specific BAF assemblies are found in embryonic stem cells, neural progenitors and postmitotic neurons. In particular, the neural progenitor-specific BAF complexes are essential for controlling the kinetics and mode of neural progenitor cell division, while neuronal BAF function is necessary for the maturation of postmitotic neuronal phenotypes as well as long-term memory formation. The microRNA-mediated mechanism for transitioning from npBAF to nBAF complexes is instructive for the neuronal fate and can even convert fibroblasts into neurons. The high frequency of BAF subunit mutations in neurological disorders underscores the rate-determining role of BAF complexes in neural development, homeostasis, and plasticity. © 2014 Wiley Periodicals, Inc.

Neural representations of social status hierarchy in human inferior parietal cortex.

Science.gov (United States)

Chiao, Joan Y; Harada, Tokiko; Oby, Emily R; Li, Zhang; Parrish, Todd; Bridge, Donna J

2009-01-01

Mental representations of social status hierarchy share properties with that of numbers. Previous neuroimaging studies have shown that the neural representation of numerical magnitude lies within a network of regions within inferior parietal cortex. However the neural basis of social status hierarchy remains unknown. Using fMRI, we studied subjects while they compared social status magnitude of people, objects and symbols, as well as numerical magnitude. Both social status and number comparisons recruited bilateral intraparietal sulci. We also observed a semantic distance effect whereby neural activity within bilateral intraparietal sulci increased for semantically close relative to far numerical and social status comparisons. These results demonstrate that social status and number comparisons recruit distinct and overlapping neuronal representations within human inferior parietal cortex.

The neural bases of orthographic working memory

Directory of Open Access Journals (Sweden)

Jeremy Purcell

2014-04-01

First, these results reveal a neurotopography of OWM lesion sites that is well-aligned with results from neuroimaging of orthographic working memory in neurally intact participants (Rapp & Dufor, 2011. Second, the dorsal neurotopography of the OWM lesion overlap is clearly distinct from what has been reported for lesions associated with either lexical or sublexical deficits (e.g., Henry, Beeson, Stark, & Rapcsak, 2007; Rapcsak & Beeson, 2004; these have, respectively, been identified with the inferior occipital/temporal and superior temporal/inferior parietal regions. These neurotopographic distinctions support the claims of the computational distinctiveness of long-term vs. working memory operations. The specific lesion loci raise a number of questions to be discussed regarding: (a the selectivity of these regions and associated deficits to orthographic working memory vs. working memory more generally (b the possibility that different lesion sub-regions may correspond to different components of the OWM system.

Soft computing integrating evolutionary, neural, and fuzzy systems

CERN Document Server

Tettamanzi, Andrea

2001-01-01

Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

Neural evidence for a distinction between short-term memory and the focus of attention

OpenAIRE

Lewis-Peacock, Jarrod A; Drysdale, Andrew T; Oberauer, Klaus; Postle, Bradley R

2012-01-01

It is widely assumed that the short-term retention of information is accomplished via maintenance of an active neural trace. However, we demonstrate that memory can be preserved across a brief delay despite the apparent loss of sustained representations. Delay-period activity may in fact reflect the focus of attention, rather than short-term memory. We unconfounded attention and memory by causing external and internal shifts of attention away from items that were being actively retained. Mult...

Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

Science.gov (United States)

Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

2013-10-02

Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

Character Recognition Using Genetically Trained Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

A Distinct Population of Microglia Supports Adult Neurogenesis in the Subventricular Zone

DEFF Research Database (Denmark)

Ribeiro Xavier, Anna L.; Kress, Benjamin T.; Goldman, Steven A.

2015-01-01

found that microglia residing in the SVZ and adjacent rostral migratory stream (RMS) comprise a morphologically and antigenically distinct phenotype of immune effectors. Whereas exhibiting characteristics of alternatively activated microglia, the SVZ/RMS microglia were clearly distinguished by their low...... STATEMENT: Microglial cells are a specialized population of macrophages in the CNS, playing key roles as immune mediators. As integral components in the CNS, the microglia stand out for using the same mechanisms, phagocytosis and cytochemokine release, to promote homeostasis, synaptic pruning, and neural...... toward olfactory bulb layers. In addition to other unique populations residing in the SVZ niche, microglia display distinct morphofunctional properties that boost neuronal progenitor survival and migration in the mammalian brain....

Spreading paths in partially observed social networks

Science.gov (United States)

Onnela, Jukka-Pekka; Christakis, Nicholas A.

2012-03-01

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.

Spreading paths in partially observed social networks.

Science.gov (United States)

Onnela, Jukka-Pekka; Christakis, Nicholas A

2012-03-01

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.

Distinct neural substrates of visuospatial and verbal-analytic reasoning as assessed by Raven's Advanced Progressive Matrices

NARCIS (Netherlands)

Chen, Zhencai; De Beuckelaer, A.; Wang, Xu; Liu, Jia

2017-01-01

Recent studies revealed spontaneous neural activity to be associated with fluid intelligence (gF) which is commonly assessed by Raven’s Advanced Progressive Matrices, and embeds two types of reasoning: visuospatial and verbal-analytic reasoning. With resting-state fMRI data, using global brain

SORN: a self-organizing recurrent neural network

Directory of Open Access Journals (Sweden)

Andreea Lazar

2009-10-01

Full Text Available Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success.

Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

Science.gov (United States)

Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

2014-03-01

Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

PLZF regulates fibroblast growth factor responsiveness and maintenance of neural progenitors.

Science.gov (United States)

Gaber, Zachary B; Butler, Samantha J; Novitch, Bennett G

2013-10-01

Distinct classes of neurons and glial cells in the developing spinal cord arise at specific times and in specific quantities from spatially discrete neural progenitor domains. Thus, adjacent domains can exhibit marked differences in their proliferative potential and timing of differentiation. However, remarkably little is known about the mechanisms that account for this regional control. Here, we show that the transcription factor Promyelocytic Leukemia Zinc Finger (PLZF) plays a critical role shaping patterns of neuronal differentiation by gating the expression of Fibroblast Growth Factor (FGF) Receptor 3 and responsiveness of progenitors to FGFs. PLZF elevation increases FGFR3 expression and STAT3 pathway activity, suppresses neurogenesis, and biases progenitors towards glial cell production. In contrast, PLZF loss reduces FGFR3 levels, leading to premature neuronal differentiation. Together, these findings reveal a novel transcriptional strategy for spatially tuning the responsiveness of distinct neural progenitor groups to broadly distributed mitogenic signals in the embryonic environment.

Partial duplication of head--a rare congenital anomaly.

Science.gov (United States)

Hemachandran, Manikkapurath; Radotra, Bishan Dass

2004-10-01

Duplication of notochord results in rare congenital anomalies like double headed monsters, with or without trunk/limb duplication, depending upon the extent of notochordal abnormality. Here we describe the morphological abnormalities in a case of partial duplication of cranial structures with fusion of the two. Autopsy findings suggest that the bifurcation of the neural tube took place around 4th to 6th week of gestation. There are only few reports in English literature describing the autopsy findings of such an anomaly, which is termed as Diprosopus triophthalmus in the modern literature.

Identification and integration of sensory modalities: Neural basis and relation to consciousness

NARCIS (Netherlands)

Pennartz, C.M.A.

2009-01-01

A key question in studying consciousness is how neural operations in the brain can identify streams of sensory input as belonging to distinct modalities, which contributes to the representation of qualitatively different experiences. The basis for identification of modalities is proposed to be

Neural markers of individual and age differences in TVA attention capacity parameters

DEFF Research Database (Denmark)

Wiegand, Iris

2013-01-01

The ‘Theory of Visual Attention’ quantifies an interindividual’s capacity of attentional resources in parameters visual processing speed C and vSTM storage capacity K. Distinct neural markers of interindividual differences in these functions were identified by combining TVA-based assessment...

The neural basis of body form and body action agnosia.

Science.gov (United States)

Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria

2008-10-23

Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.

Autoshaping and Automaintenance: A Neural-Network Approach

OpenAIRE

Burgos, José E

2007-01-01

This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an A-B-A design and four instances of feedfoward architectures. In A, networks received a positive contingency between inputs that simulated a conditioned stimulus (C...

Approaching the Distinction between Intuition and Insight.

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Zhang, Zhonglu; Lei, Yi; Li, Hong

2016-01-01

Intuition and insight share similar cognitive and neural basis. Though, there are still some essential differences between the two. Here in this short review, we discriminated between intuition, and insight in two aspects. First, intuition, and insight are toward different aspects of information processing. Whereas intuition involves judgment about "yes or no," insight is related to "what" is the solution. Second, tacit knowledge play different roles in between intuition and insight. On the one hand, tacit knowledge is conducive to intuitive judgment. On the other hand, tacit knowledge may first impede but later facilitate insight occurrence. Furthermore, we share theoretical, and methodological views on how to access the distinction between intuition and insight.

Neural Correlates of Irritability in Disruptive Mood Dysregulation and Bipolar Disorders.

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Wiggins, Jillian Lee; Brotman, Melissa A; Adleman, Nancy E; Kim, Pilyoung; Oakes, Allison H; Reynolds, Richard C; Chen, Gang; Pine, Daniel S; Leibenluft, Ellen

2016-07-01

Bipolar disorder and disruptive mood dysregulation disorder (DMDD) are clinically and pathophysiologically distinct, yet irritability can be a clinical feature of both illnesses. The authors examine whether the neural mechanisms mediating irritability differ between bipolar disorder and DMDD, using a face emotion labeling paradigm because such labeling is deficient in both patient groups. The authors hypothesized that during face emotion labeling, irritability would be associated with dysfunctional activation in the amygdala and other temporal and prefrontal regions in both disorders, but that the nature of these associations would differ between DMDD and bipolar disorder. During functional MRI acquisition, 71 youths (25 with DMDD, 24 with bipolar disorder, and 22 healthy youths) performed a labeling task with happy, fearful, and angry faces of varying emotional intensity. Participants with DMDD and bipolar disorder showed similar levels of irritability and did not differ from each other or from healthy youths in face emotion labeling accuracy. Irritability correlated with amygdala activity across all intensities for all emotions in the DMDD group; such correlation was present in the bipolar disorder group only for fearful faces. In the ventral visual stream, associations between neural activity and irritability were found more consistently in the DMDD group than in the bipolar disorder group, especially in response to ambiguous angry faces. These results suggest diagnostic specificity in the neural correlates of irritability, a symptom of both DMDD and bipolar disorder. Such evidence of distinct neural correlates suggests the need to evaluate different approaches to treating irritability in the two disorders.

Does friendship give us non-derivative partial reasons ?

Directory of Open Access Journals (Sweden)

Andrew Reisner

2008-02-01

Full Text Available One way to approach the question of whether there are non-derivative partial reasons of any kind is to give an account of what partial reasons are, and then to consider whether there are such reasons. If there are, then it is at least possible that there are partial reasons of friendship. It is this approach that will be taken here, and it produces several interesting results. The first is a point about the structure of partial reasons. It is at least a necessary condition of a reason’s being partial that it has an explicit relational component. This component, technically, is a relatum in the reason relation that itself is a relation between the person to whom the reason applies and the person whom the action for which there is a reason concerns. The second conclusion of the paper is that this relational component is also required for a number of types of putatively impartial reasons. In order to avoid trivialising the distinction between partial and impartial reasons, some further sufficient condition must be applied. Finally, there is some prospect for a way of distinguishing between impartial reasons that contain a relational component and partial reasons, but that this approach suggests that the question of whether ethics is partial or impartial will be settled at the level of normative ethical discourse, or at least not at the level of discourse about the nature of reasons for action.

Neural mechanisms underlying human consensus decision-making.

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Suzuki, Shinsuke; Adachi, Ryo; Dunne, Simon; Bossaerts, Peter; O'Doherty, John P

2015-04-22

Consensus building in a group is a hallmark of animal societies, yet little is known about its underlying computational and neural mechanisms. Here, we applied a computational framework to behavioral and fMRI data from human participants performing a consensus decision-making task with up to five other participants. We found that participants reached consensus decisions through integrating their own preferences with information about the majority group members' prior choices, as well as inferences about how much each option was stuck to by the other people. These distinct decision variables were separately encoded in distinct brain areas-the ventromedial prefrontal cortex, posterior superior temporal sulcus/temporoparietal junction, and intraparietal sulcus-and were integrated in the dorsal anterior cingulate cortex. Our findings provide support for a theoretical account in which collective decisions are made through integrating multiple types of inference about oneself, others, and environments, processed in distinct brain modules. Copyright © 2015 Elsevier Inc. All rights reserved.

Biologically inspired EM image alignment and neural reconstruction.

Science.gov (United States)

Knowles-Barley, Seymour; Butcher, Nancy J; Meinertzhagen, Ian A; Armstrong, J Douglas

2011-08-15

Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. An open-source reference implementation is available in the Supplementary information. seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk Supplementary data are available at Bioinformatics online.

Modulation of neural activity during object naming: Effects of time and practice

NARCIS (Netherlands)

Turennout, M.I. van; Bielamowicz, L.; Martin, A.

2003-01-01

Repeated exposure to objects improves our ability to identify and name them, even after a long delay. Previous brain imaging studies have demonstrated that this experience-related facilitation of object naming is associated with neural changes in distinct brain regions. We used event-related

Neural basis of limb ownership in individuals with body integrity identity disorder

NARCIS (Netherlands)

van Dijk, Milenna T.; van Wingen, Guido A.; van Lammeren, Anouk; Blom, Rianne M.; de Kwaasteniet, Bart P.; Scholte, H. Steven; Denys, Damiaan

2013-01-01

Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID) lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis

Neural coding in graphs of bidirectional associative memories.

Science.gov (United States)

Bouchain, A David; Palm, Günther

2012-01-24

In the last years we have developed large neural network models for the realization of complex cognitive tasks in a neural network architecture that resembles the network of the cerebral cortex. We have used networks of several cortical modules that contain two populations of neurons (one excitatory, one inhibitory). The excitatory populations in these so-called "cortical networks" are organized as a graph of Bidirectional Associative Memories (BAMs), where edges of the graph correspond to BAMs connecting two neural modules and nodes of the graph correspond to excitatory populations with associative feedback connections (and inhibitory interneurons). The neural code in each of these modules consists essentially of the firing pattern of the excitatory population, where mainly it is the subset of active neurons that codes the contents to be represented. The overall activity can be used to distinguish different properties of the patterns that are represented which we need to distinguish and control when performing complex tasks like language understanding with these cortical networks. The most important pattern properties or situations are: exactly fitting or matching input, incomplete information or partially matching pattern, superposition of several patterns, conflicting information, and new information that is to be learned. We show simple simulations of these situations in one area or module and discuss how to distinguish these situations based on the overall internal activation of the module. This article is part of a Special Issue entitled "Neural Coding". Copyright © 2011 Elsevier B.V. All rights reserved.

Detecting Math Anxiety with a Mixture Partial Credit Model

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Ölmez, Ibrahim Burak; Cohen, Allan S.

2017-01-01

The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math…

Neural evidence that human emotions share core affective properties.

Science.gov (United States)

Wilson-Mendenhall, Christine D; Barrett, Lisa Feldman; Barsalou, Lawrence W

2013-06-01

Research on the "emotional brain" remains centered around the idea that emotions like fear, happiness, and sadness result from specialized and distinct neural circuitry. Accumulating behavioral and physiological evidence suggests, instead, that emotions are grounded in core affect--a person's fluctuating level of pleasant or unpleasant arousal. A neuroimaging study revealed that participants' subjective ratings of valence (i.e., pleasure/displeasure) and of arousal evoked by various fear, happiness, and sadness experiences correlated with neural activity in specific brain regions (orbitofrontal cortex and amygdala, respectively). We observed these correlations across diverse instances within each emotion category, as well as across instances from all three categories. Consistent with a psychological construction approach to emotion, the results suggest that neural circuitry realizes more basic processes across discrete emotions. The implicated brain regions regulate the body to deal with the world, producing the affective changes at the core of emotions and many other psychological phenomena.

Context-Dependent Neural Modulations in the Perception of Duration.

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Murai, Yuki; Yotsumoto, Yuko

2016-01-01

Recent neuroimaging studies have revealed that distinct brain networks are recruited in the perception of sub- and supra-second timescales, whereas psychophysical studies have suggested that there are common or continuous mechanisms for perceiving these two durations. The present study aimed to elucidate the neural implementation of such continuity by examining the neural correlates of peri-second timing. We measured neural activity during a duration reproduction task using functional magnetic resonance imaging. Our results replicate the findings of previous studies in showing that separate neural networks are recruited for sub-versus supra-second time perception: motor systems including the motor cortex and the supplementary motor area for sub-second perception, and the frontal, parietal, and auditory cortical areas for supra-second perception. We further found that the peri-second perception activated both the sub- and supra-second networks, and that the timing system that processed duration perception in previous trials was more involved in subsequent peri-second processing. These results indicate that the sub- and supra-second timing systems overlap at around 1 s, and cooperate to optimally encode duration based on the hysteresis of previous trials.

Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging.

Science.gov (United States)

Truong, Trong-Kha; Song, Allen W; Chen, Nan-Kuei

2015-01-01

In most diffusion tensor imaging (DTI) studies, images are acquired with either a partial-Fourier or a parallel partial-Fourier echo-planar imaging (EPI) sequence, in order to shorten the echo time and increase the signal-to-noise ratio (SNR). However, eddy currents induced by the diffusion-sensitizing gradients can often lead to a shift of the echo in k-space, resulting in three distinct types of artifacts in partial-Fourier DTI. Here, we present an improved DTI acquisition and reconstruction scheme, capable of generating high-quality and high-SNR DTI data without eddy current-induced artifacts. This new scheme consists of three components, respectively, addressing the three distinct types of artifacts. First, a k-space energy-anchored DTI sequence is designed to recover eddy current-induced signal loss (i.e., Type 1 artifact). Second, a multischeme partial-Fourier reconstruction is used to eliminate artificial signal elevation (i.e., Type 2 artifact) associated with the conventional partial-Fourier reconstruction. Third, a signal intensity correction is applied to remove artificial signal modulations due to eddy current-induced erroneous T2(∗) -weighting (i.e., Type 3 artifact). These systematic improvements will greatly increase the consistency and accuracy of DTI measurements, expanding the utility of DTI in translational applications where quantitative robustness is much needed.

Differentiating neural reward responsiveness in autism versus ADHD

Directory of Open Access Journals (Sweden)

Gregor Kohls

2014-10-01

Full Text Available Although attention deficit hyperactivity disorders (ADHD and autism spectrum disorders (ASD share certain neurocognitive characteristics, it has been hypothesized to differentiate the two disorders based on their brain's reward responsiveness to either social or monetary reward. Thus, the present fMRI study investigated neural activation in response to both reward types in age and IQ-matched boys with ADHD versus ASD relative to typically controls (TDC. A significant group by reward type interaction effect emerged in the ventral striatum with greater activation to monetary versus social reward only in TDC, whereas subjects with ADHD responded equally strong to both reward types, and subjects with ASD showed low striatal reactivity across both reward conditions. Moreover, disorder-specific neural abnormalities were revealed, including medial prefrontal hyperactivation in response to social reward in ADHD versus ventral striatal hypoactivation in response to monetary reward in ASD. Shared dysfunction was characterized by fronto-striato-parietal hypoactivation in both clinical groups when money was at stake. Interestingly, lower neural activation within parietal circuitry was associated with higher autistic traits across the entire study sample. In sum, the present findings concur with the assumption that both ASD and ADHD display distinct and shared neural dysfunction in response to reward.

Stage-specific control of neural crest stem cell proliferation by the small rho GTPases Cdc42 and Rac1

DEFF Research Database (Denmark)

Fuchs, Sebastian; Herzog, Dominik; Sumara, Grzegorz

2009-01-01

-renewal and proliferation of later stage, but not early migratory NCSCs. This stage-specific requirement for small Rho GTPases is due to changes in NCSCs that, during development, acquire responsiveness to mitogenic EGF acting upstream of both Cdc42 and Rac1. Thus, our data reveal distinct mechanisms for growth control......The neural crest (NC) generates a variety of neural and non-neural tissues during vertebrate development. Both migratory NC cells and their target structures contain cells with stem cell features. Here we show that these populations of neural crest-derived stem cells (NCSCs) are differentially...

Application of a neural network to control a pressurized water reactor

International Nuclear Information System (INIS)

Lin, C.; Ku, C.C.; Lee, C.S.

1993-01-01

A neural network has been trained to control a pressurized water reactor. The inputs of the training pattern are the plant signals, and the outputs are the control rod actions. The training patterns are some kind of lookup table of control action. The table is designed by the heuristic method, which is based on the designer's knowledge of the controlled system and the operation experience. This method has two advantages: The controller's performance does not depend on the mathematical model of the plant, and the controller could be a nonlinear one. The advantages of using neural networks to implement the controller are to save computing time and overcome partial hardware failure

The role of stochasticity in an information-optimal neural population code

International Nuclear Information System (INIS)

Stocks, N G; Nikitin, A P; McDonnell, M D; Morse, R P

2009-01-01

In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

Evaluation of the cranial base in amnion rupture sequence involving the anterior neural tube: implications regarding recurrence risk.

Science.gov (United States)

Jones, Kenneth Lyons; Robinson, Luther K; Benirschke, Kurt

2006-09-01

Amniotic bands can cause disruption of the cranial end of the developing fetus, leading in some cases to a neural tube closure defect. Although recurrence for unaffected parents of an affected child with a defect in which the neural tube closed normally but was subsequently disrupted by amniotic bands is negligible; for a primary defect in closure of the neural tube to which amnion has subsequently adhered, recurrence risk is 1.7%. In that primary defects of neural tube closure are characterized by typical abnormalities of the base of the skull, evaluation of the cranial base in such fetuses provides an approach for making a distinction between these 2 mechanisms. This distinction has implications regarding recurrence risk. The skull base of 2 fetuses with amnion rupture sequence involving the cranial end of the neural tube were compared to that of 1 fetus with anencephaly as well as that of a structurally normal fetus. The skulls were cleaned, fixed in 10% formalin, recleaned, and then exposed to 10% KOH solution. After washing and recleaning, the skulls were exposed to hydrogen peroxide for bleaching and photography. Despite involvement of the anterior neural tube in both fetuses with amnion rupture sequence, in Case 3 the cranial base was normal while in Case 4 the cranial base was similar to that seen in anencephaly. This technique provides a method for determining the developmental pathogenesis of anterior neural tube defects in cases of amnion rupture sequence. As such, it provides information that can be used to counsel parents of affected children with respect to recurrence risk.

Retinoic acid temporally orchestrates colonization of the gut by vagal neural crest cells.

Science.gov (United States)

Uribe, Rosa A; Hong, Stephanie S; Bronner, Marianne E

2018-01-01

The enteric nervous system arises from neural crest cells that migrate as chains into and along the primitive gut, subsequently differentiating into enteric neurons and glia. Little is known about the mechanisms governing neural crest migration en route to and along the gut in vivo. Here, we report that Retinoic Acid (RA) temporally controls zebrafish enteric neural crest cell chain migration. In vivo imaging reveals that RA loss severely compromises the integrity and migration of the chain of neural crest cells during the window of time window when they are moving along the foregut. After loss of RA, enteric progenitors accumulate in the foregut and differentiate into enteric neurons, but subsequently undergo apoptosis resulting in a striking neuronal deficit. Moreover, ectopic expression of the transcription factor meis3 and/or the receptor ret, partially rescues enteric neuron colonization after RA attenuation. Collectively, our findings suggest that retinoic acid plays a critical temporal role in promoting enteric neural crest chain migration and neuronal survival upstream of Meis3 and RET in vivo. Copyright © 2017 Elsevier Inc. All rights reserved.

Neural correlates of affective influence on choice.

Science.gov (United States)

Piech, Richard M; Lewis, Jade; Parkinson, Caroline H; Owen, Adrian M; Roberts, Angela C; Downing, Paul E; Parkinson, John A

2010-03-01

Making the right choice depends crucially on the accurate valuation of the available options in the light of current needs and goals of an individual. Thus, the valuation of identical options can vary considerably with motivational context. The present study investigated the neural structures underlying context dependent evaluation. We instructed participants to choose from food menu items based on different criteria: on their anticipated taste or on ease of preparation. The aim of the manipulation was to assess which neural sites were activated during choice guided by incentive value, and which during choice based on a value-irrelevant criterion. To assess the impact of increased motivation, affect-guided choice and cognition-guided choice was compared during the sated and hungry states. During affective choice, we identified increased activity in structures representing primarily valuation and taste (medial prefrontal cortex, insula). During cognitive choice, structures showing increased activity included those implicated in suppression and conflict monitoring (lateral orbitofrontal cortex, anterior cingulate). Hunger influenced choice-related activity in the ventrolateral prefrontal cortex. Our results show that choice is associated with the use of distinct neural structures for the pursuit of different goals. Published by Elsevier Inc.

Distinct neural correlates of emotional and cognitive empathy in older adults.

Science.gov (United States)

Moore, Raeanne C; Dev, Sheena I; Jeste, Dilip V; Dziobek, Isabel; Eyler, Lisa T

2015-04-30

Empathy is thought to be a mechanism underlying prosocial behavior across the lifespan, yet little is known about how levels of empathy relate to individual differences in brain functioning among older adults. In this exploratory study, we examined the neural correlates of affective and cognitive empathy in older adults. Thirty older adults (M=79 years) underwent fMRI scanning and neuropsychological testing and completed a test of affective and cognitive empathy. Brain response during processing of cognitive and emotional stimuli was measured by fMRI in a priori and task-related regions and was correlated with levels of empathy. Older adults with higher levels of affective empathy showed more deactivation in the amygdala and insula during a working memory task, whereas those with higher cognitive empathy showed greater insula activation during a response inhibition task. Our preliminary findings suggest that brain systems linked to emotional and social processing respond differently among older adults with more or less affective and cognitive empathy. That these relationships can be seen both during affective and non-emotional tasks of "cold" cognitive abilities suggests that empathy may impact social behavior through both emotional and cognitive mechanisms. Published by Elsevier Ireland Ltd.

Inter-progenitor pool wiring: An evolutionarily conserved strategy that expands neural circuit diversity.

Science.gov (United States)

Suzuki, Takumi; Sato, Makoto

2017-11-15

Diversification of neuronal types is key to establishing functional variations in neural circuits. The first critical step to generate neuronal diversity is to organize the compartmental domains of developing brains into spatially distinct neural progenitor pools. Neural progenitors in each pool then generate a unique set of diverse neurons through specific spatiotemporal specification processes. In this review article, we focus on an additional mechanism, 'inter-progenitor pool wiring', that further expands the diversity of neural circuits. After diverse types of neurons are generated in one progenitor pool, a fraction of these neurons start migrating toward a remote brain region containing neurons that originate from another progenitor pool. Finally, neurons of different origins are intermingled and eventually form complex but precise neural circuits. The developing cerebral cortex of mammalian brains is one of the best examples of inter-progenitor pool wiring. However, Drosophila visual system development has revealed similar mechanisms in invertebrate brains, suggesting that inter-progenitor pool wiring is an evolutionarily conserved strategy that expands neural circuit diversity. Here, we will discuss how inter-progenitor pool wiring is accomplished in mammalian and fly brain systems. Copyright © 2017 Elsevier Inc. All rights reserved.

Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks

Directory of Open Access Journals (Sweden)

Minoru Sasaki

2012-01-01

Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.

Speaking in Multiple Languages: Neural Correlates of Language Proficiency in Multilingual Word Production

Science.gov (United States)

Videsott, Gerda; Herrnberger, Barbel; Hoenig, Klaus; Schilly, Edgar; Grothe, Jo; Wiater, Werner; Spitzer, Manfred; Kiefer, Markus

2010-01-01

The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a…

Banknote recognition: investigating processing and cognition framework using competitive neural network.

Science.gov (United States)

Oyedotun, Oyebade K; Khashman, Adnan

2017-02-01

Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

Science.gov (United States)

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

An approach to the interpretation of backpropagation neural network models in QSAR studies.

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Baskin, I I; Ait, A O; Halberstam, N M; Palyulin, V A; Zefirov, N S

2002-03-01

An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes.

Psychological and neural mechanisms of experimental extinction: a selective review.

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Delamater, Andrew R; Westbrook, R Frederick

2014-02-01

The present review examines key psychological concepts in the study of experimental extinction and implications these have for an understanding of the underlying neurobiology of extinction learning. We suggest that many of the signature characteristics of extinction learning (spontaneous recovery, renewal, reinstatement, rapid reacquisition) can be accommodated by the standard associative learning theory assumption that extinction results in partial erasure of the original learning together with new inhibitory learning. Moreover, we consider recent behavioral and neural evidence that supports the partial erasure view of extinction, but also note shortcomings in our understanding of extinction circuits as these relate to the negative prediction error concept. Recent work suggests that common prediction error and stimulus-specific prediction error terms both may be required to explain neural plasticity both in acquisition and extinction learning. In addition, we suggest that many issues in the content of extinction learning have not been fully addressed in current research, but that neurobiological approaches should be especially helpful in addressing such issues. These include questions about the nature of extinction learning (excitatory CS-No US, inhibitory CS-US learning, occasion setting processes), especially as this relates to studies of the micro-circuitry of extinction, as well as its representational content (sensory, motivational, response). An additional understudied problem in extinction research is the role played by attention processes and their underlying neural networks, although some research and theory converge on the idea that extinction is accompanied by attention decrements (i.e., habituation-like processes). Copyright © 2013 Elsevier Inc. All rights reserved.

Neuroprotective effects of ginsenoside Rg1-induced neural stem cell transplantation on hypoxic-ischemic encephalopathy

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Ying-bo Li

2015-01-01

Full Text Available Ginsenoside Rg1 is the major pharmacologically active component of ginseng, and is reported to have various therapeutic actions. To determine whether it induces the differentiation of neural stem cells, and whether neural stem cell transplantation after induction has therapeutic effects on hypoxic-ischemic encephalopathy, we cultured neural stem cells in 10-80 µM ginsenoside Rg1. Immunohistochemistry revealed that of the concentrations tested, 20 mM ginsenoside Rg1 had the greatest differentiation-inducing effect and was the concentration used for subsequent experiments. Whole-cell patch clamp showed that neural stem cells induced by 20 µM ginsenoside Rg1 were more mature than non-induced cells. We then established neonatal rat models of hypoxic-ischemic encephalopathy using the suture method, and ginsenoside Rg1-induced neural stem cells were transplanted via intracerebroventricular injection. These tests confirmed that neural stem cells induced by ginsenoside had fewer pathological lesions and had a significantly better behavioral capacity than model rats that received saline. Transplanted neural stem cells expressed neuron-specific enolase, and were mainly distributed in the hippocampus and cerebral cortex. The present data suggest that ginsenoside Rg1-induced neural stem cells can promote the partial recovery of complicated brain functions in models of hypoxic-ischemic encephalopathy.

Neural imaginaries and clinical epistemology: Rhetorically mapping the adolescent brain in the clinical encounter.

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Buchbinder, Mara

2015-10-01

The social work of brain images has taken center stage in recent theorizing of the intersections between neuroscience and society. However, neuroimaging is only one of the discursive modes through which public representations of neurobiology travel. This article adopts an expanded view toward the social implications of neuroscientific thinking to examine how neural imaginaries are constructed in the absence of visual evidence. Drawing on ethnographic fieldwork conducted over 18 months (2008-2009) in a United States multidisciplinary pediatric pain clinic, I examine the pragmatic clinical work undertaken to represent ambiguous symptoms in neurobiological form. Focusing on one physician, I illustrate how, by rhetorically mapping the brain as a therapeutic tool, she engaged in a distinctive form of representation that I call neural imagining. In shifting my focus away from the purely material dimensions of brain images, I juxtapose the cultural work of brain scanning technologies with clinical neural imaginaries in which the teenage brain becomes a space of possibility, not to map things as they are, but rather, things as we hope they might be. These neural imaginaries rely upon a distinctive clinical epistemology that privileges the creative work of the imagination over visualization technologies in revealing the truths of the body. By creating a therapeutic space for adolescents to exercise their imaginative faculties and a discursive template for doing so, neural imagining relocates adolescents' agency with respect to epistemologies of bodily knowledge and the role of visualization practices therein. In doing so, it provides a more hopeful alternative to the dominant popular and scientific representations of the teenage brain that view it primarily through the lens of pathology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Why Overlearned Sequences are Special: Distinct Neural Networks for Ordinal Sequences

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Vani ePariyadath

2012-12-01

Full Text Available Several observations suggest that overlearned ordinal categories (e.g., letters, numbers, weekdays, months are processed differently than non-ordinal categories in the brain. In synesthesia, for example, anomalous perceptual experiences are most often triggered by members of ordinal categories (Rich et al., 2005; Eagleman, 2009. In semantic dementia, the processing of ordinal stimuli appears to be preserved relative to non-ordinal ones (Cappelletti et al., 2001. Moreover, ordinal stimuli often map onto unconscious spatial representations, as observed in the SNARC effect (Dehaene et al, 1993; Fias, 1996. At present, little is known about the neural representation of ordinal categories. Using functional neuroimaging, we show that words in ordinal categories are processed in a fronto-temporo-parietal network biased toward the right hemisphere. This differs from words in non-ordinal categories (such as names of furniture, animals, cars and fruit, which show an expected bias toward the left hemisphere. Further, we find that increased predictability of stimulus order correlates with smaller regions of BOLD activation, a phenomenon we term prediction suppression. Our results provide new insights into the processing of ordinal stimuli, and suggest a new anatomical framework for understanding the patterns seen in synesthesia, unconscious spatial representation, and semantic dementia.

Comparative Analysis of Maximum Power Point Tracking Controllers under Partial Shaded Conditions in a Photovoltaic System

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R. Ramaprabha

2015-06-01

Full Text Available Mismatching effects due to partial shaded conditions are the major drawbacks existing in today’s photovoltaic (PV systems. These mismatch effects are greatly reduced in distributed PV system architecture where each panel is effectively decoupled from its neighboring panel. To obtain the optimal operation of the PV panels, maximum power point tracking (MPPT techniques are used. In partial shaded conditions, detecting the maximum operating point is difficult as the characteristic curves are complex with multiple peaks. In this paper, a neural network control technique is employed for MPPT. Detailed analyses were carried out on MPPT controllers in centralized and distributed architecture under partial shaded environments. The efficiency of the MPPT controllers and the effectiveness of the proposed control technique under partial shaded environments was examined using MATLAB software. The results were validated through experimentation.

Sense and antisense transcripts of the developmentally regulated murine hsp70.2 gene are expressed in distinct and only partially overlapping areas in the adult brain

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Murashov, A. K.; Wolgemuth, D. J.

1996-01-01

We have examined the spatial pattern of expression of a member of the hsp70 gene family, hsp70.2, in the mouse central nervous system. Surprisingly, RNA blot analysis and in situ hybridization revealed abundant expression of an 'antisense' hsp70.2 transcript in several areas of adult mouse brain. Two different transcripts recognized by sense and antisense riboprobes for the hsp70.2 gene were expressed in distinct and only partially overlapping neuronal populations. RNA blot analysis revealed low levels of the 2.7 kb transcript of hsp70.2 in several areas of the brain, with highest signal in the hippocampus. Abundant expression of a slightly larger (approximately 2.8 kb) 'antisense' transcript was detected in several brain regions, notably in the brainstem, cerebellum, mesencephalic tectum, thalamus, cortex, and hippocampus. In situ hybridization revealed that the sense and antisense transcripts were both predominantly neuronal and localized to the same cell types in the granular layer of the cerebellum, trapezoid nucleus of the superior olivary complex, locus coeruleus and hippocampus. The hsp70.2 antisense transcripts were particularly abundant in the frontal cortex, dentate gyrus, subthalamic nucleus, zona incerta, superior and inferior colliculi, central gray, brainstem, and cerebellar Purkinje cells. Our findings have revealed a distinct cellular and spatial localization of both sense and antisense transcripts, demonstrating a new level of complexity in the function of the heat shock genes.

Neural correlates of sad feelings in healthy girls.

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Lévesque, J; Joanette, Y; Mensour, B; Beaudoin, G; Leroux, J-M; Bourgouin, P; Beauregard, M

2003-01-01

Emotional development is indisputably one of the cornerstones of personality development during infancy. According to the differential emotions theory (DET), primary emotions are constituted of three distinct components: the neural-evaluative, the expressive, and the experiential. The DET further assumes that these three components are biologically based and functional nearly from birth. Such a view entails that the neural substrate of primary emotions must be similar in children and adults. Guided by this assumption of the DET, the present functional magnetic resonance imaging study was conducted to identify the neural correlates of sad feelings in healthy children. Fourteen healthy girls (aged 8-10) were scanned while they watched sad film excerpts aimed at externally inducing a transient state of sadness (activation task). Emotionally neutral film excerpts were also presented to the subjects (reference task). The subtraction of the brain activity measured during the viewing of the emotionally neutral film excerpts from that noted during the viewing of the sad film excerpts revealed that sad feelings were associated with significant bilateral activations of the midbrain, the medial prefrontal cortex (Brodmann area [BA] 10), and the anterior temporal pole (BA 21). A significant locus of activation was also noted in the right ventrolateral prefrontal cortex (BA 47). These results are compatible with those of previous functional neuroimaging studies of sadness in adults. They suggest that the neural substrate underlying the subjective experience of sadness is comparable in children and adults. Such a similitude provides empirical support to the DET assumption that the neural substrate of primary emotions is biologically based.

Distinctive genomic signature of neural and intestinal organoids from familial Parkinson's disease patient-derived induced pluripotent stem cells.

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Son, M-Y; Sim, H; Son, Y S; Jung, K B; Lee, M-O; Oh, J-H; Chung, S-K; Jung, C-R; Kim, J

2017-12-01

The leucine-rich repeat kinase 2 (LRRK2) G2019S mutation is the most common genetic cause of Parkinson's disease (PD). There is compelling evidence that PD is not only a brain disease but also a gastrointestinal disorder; nonetheless, its pathogenesis remains unclear. We aimed to develop human neural and intestinal tissue models of PD patients harbouring an LRRK2 mutation to understand the link between LRRK2 and PD pathology by investigating the gene expression signature. We generated PD patient-specific induced pluripotent stem cells (iPSCs) carrying an LRRK2 G2019S mutation (LK2GS) and then differentiated into three-dimensional (3D) human neuroectodermal spheres (hNESs) and human intestinal organoids (hIOs). To unravel the gene and signalling networks associated with LK2GS, we analysed differentially expressed genes in the microarray data by functional clustering, gene ontology (GO) and pathway analyses. The expression profiles of LK2GS were distinct from those of wild-type controls in hNESs and hIOs. The most represented GO biological process in hNESs and hIOs was synaptic transmission, specifically synaptic vesicle trafficking, some defects of which are known to be related to PD. The results were further validated in four independent PD-specific hNESs and hIOs by microarray and qRT-PCR analysis. We provide the first evidence that LK2GS also causes significant changes in gene expression in the intestinal cells. These hNES and hIO models from the same genetic background of PD patients could be invaluable resources for understanding PD pathophysiology and for advancing the complexity of in vitro models with 3D expandable organoids. © 2017 British Neuropathological Society.

Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

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S. Venkatesh

2012-01-01

Full Text Available Partial discharge (PD is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization and unlabelled (K-means versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.

The role of stochasticity in an information-optimal neural population code

Energy Technology Data Exchange (ETDEWEB)

Stocks, N G; Nikitin, A P [School of Engineering, University of Warwick, Coventry CV4 7AL (United Kingdom); McDonnell, M D [Institute for Telecommunications Research, University of South Australia, SA 5095 (Australia); Morse, R P, E-mail: n.g.stocks@warwick.ac.u [School of Life and Health Sciences, Aston University, Birmingham B4 7ET (United Kingdom)

2009-12-01

In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

Quantitative Analysis of Human Pluripotency and Neural Specification by In-Depth (PhosphoProteomic Profiling

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Ilyas Singec

2016-09-01

Full Text Available Controlled differentiation of human embryonic stem cells (hESCs can be utilized for precise analysis of cell type identities during early development. We established a highly efficient neural induction strategy and an improved analytical platform, and determined proteomic and phosphoproteomic profiles of hESCs and their specified multipotent neural stem cell derivatives (hNSCs. This quantitative dataset (nearly 13,000 proteins and 60,000 phosphorylation sites provides unique molecular insights into pluripotency and neural lineage entry. Systems-level comparative analysis of proteins (e.g., transcription factors, epigenetic regulators, kinase families, phosphorylation sites, and numerous biological pathways allowed the identification of distinct signatures in pluripotent and multipotent cells. Furthermore, as predicted by the dataset, we functionally validated an autocrine/paracrine mechanism by demonstrating that the secreted protein midkine is a regulator of neural specification. This resource is freely available to the scientific community, including a searchable website, PluriProt.

Neural Global Pattern Similarity Underlies True and False Memories.

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Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

Spectral analysis of stellar light curves by means of neural networks

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Tagliaferri, R.; Ciaramella, A.; Milano, L.; Barone, F.; Longo, G.

1999-06-01

Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to solve the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise and works well also with few points in the sequence. We benchmark the system on synthetic and real signals with the Periodogram and with the Cramer-Rao lower bound. This work was been partially supported by IIASS, by MURST 40\\% and by the Italian Space Agency.

Global Asymptotic Stability of Impulsive CNNs with Proportional Delays and Partially Lipschitz Activation Functions

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Xueli Song

2014-01-01

Full Text Available This paper researches global asymptotic stability of impulsive cellular neural networks with proportional delays and partially Lipschitz activation functions. Firstly, by means of the transformation vi(t=ui(et, the impulsive cellular neural networks with proportional delays are transformed into impulsive cellular neural networks with the variable coefficients and constant delays. Secondly, we provide novel criteria for the uniqueness and exponential stability of the equilibrium point of the latter by relative nonlinear measure and prove that the exponential stability of equilibrium point of the latter implies the asymptotic stability of one of the former. We furthermore obtain a sufficient condition to the uniqueness and global asymptotic stability of the equilibrium point of the former. Our method does not require conventional assumptions on global Lipschitz continuity, boundedness, and monotonicity of activation functions. Our results are generalizations and improvements of some existing ones. Finally, an example and its simulations are provided to illustrate the correctness of our analysis.

Epigenetic profiles in children with a neural tube defect; a case-control study in two populations

NARCIS (Netherlands)

L. Stolk (Lisette); M.I. Both (Marieke); N.H. van Mill (Nina); M.M.P.J. Verbiest (Michael); P.H.C. Eilers (Paul); H. Zhu (Huiping); L. Suarez (Lucina); A.G. Uitterlinden (André); R.P.M. Steegers-Theunissen (Régine)

2013-01-01

textabstractFolate deficiency is implicated in the causation of neural tube defects (NTDs). The preventive effect of periconceptional folic acid supplement use is partially explained by the treatment of a deranged folate-dependent one carbon metabolism, which provides methyl groups for

Anisotropy of ongoing neural activity in the primate visual cortex

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Maier A

2014-09-01

Full Text Available Alexander Maier,1 Michele A Cox,1 Kacie Dougherty,1 Brandon Moore,1 David A Leopold2 1Department of Psychology, College of Arts and Science, Vanderbilt University, Nashville, TN, USA; 2Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, National Institute of Health, Bethesda, MD, USA Abstract: The mammalian neocortex features distinct anatomical variation in its tangential and radial extents. This review consolidates previously published findings from our group in order to compare and contrast the spatial profile of neural activity coherence across these distinct cortical dimensions. We focus on studies of ongoing local field potential (LFP data obtained simultaneously from multiple sites in the primary visual cortex in two types of experiments in which electrode contacts were spaced either along the cortical surface or at different laminar positions. These studies demonstrate that across both dimensions the coherence of ongoing LFP fluctuations diminishes as a function of interelectrode distance, although the nature and spatial scale of this falloff is very different. Along the cortical surface, the overall LFP coherence declines gradually and continuously away from a given position. In contrast, across the cortical layers, LFP coherence is discontinuous and compartmentalized as a function of depth. Specifically, regions of high LFP coherence fall into discrete superficial and deep laminar zones, with an abrupt discontinuity between the granular and infragranular layers. This spatial pattern of ongoing LFP coherence is similar when animals are at rest and when they are engaged in a behavioral task. These results point to the existence of partially segregated laminar zones of cortical processing that extend tangentially within the laminar compartments and are thus oriented orthogonal to the cortical columns. We interpret these electrophysiological observations in light of the known anatomical organization of

Artificial neural Network-Based modeling and monitoring of photovoltaic generator

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H. MEKKI

2015-03-01

Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

Leptin reverses corticosterone-induced inhibition of neural stem cell proliferation through activating the NR2B subunits of NMDA receptors

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Shi, Wen-Zhu [Anesthesia and Operation Center, Hainan Branch of Chinese PLA General Hospital, Hainan 572013 (China); Anesthesia and Operation Center, Chinese PLA General Hospital, Beijing 100853 (China); Miao, Yu-Liang [Department of Anesthesiology, PLA No. 306 Hospital, Beijing 100101 (China); Guo, Wen-Zhi [Department of Anesthesiology, Beijing Military General Hospital of Chinese People’s Liberation Army, Beijing 100700 (China); Wu, Wei, E-mail: wwzwgk@163.com [Department of Head and Neck Surgery of Otolaryngology, PLA No. 306 Hospital, Beijing 100101 (China); Li, Bao-Wei [Department of Head and Neck Surgery of Otolaryngology, PLA No. 306 Hospital, Beijing 100101 (China); An, Li-Na [Department of Anesthesiology, Armed Police General Hospital, Beijing 100039 (China); Fang, Wei-Wu [Department of Anesthesiology, PLA No. 306 Hospital, Beijing 100101 (China); Mi, Wei-Dong, E-mail: elite2005gg@163.com [Anesthesia and Operation Center, Chinese PLA General Hospital, Beijing 100853 (China)

2014-04-25

Highlights: • Leptin promotes the proliferation of neural stem cells isolated from embryonic mouse hippocampus. • Leptin reverses corticosterone-induced inhibition of neural stem cell proliferation. • The effects of leptin are partially mediated by upregulating NR2B subunits. - Abstract: Corticosterone inhibits the proliferation of hippocampal neural stem cells (NSCs). The removal of corticosterone-induced inhibition of NSCs proliferation has been reported to contribute to neural regeneration. Leptin has been shown to regulate brain development, improve angiogenesis, and promote neural regeneration; however, its effects on corticosterone-induced inhibition of NSCs proliferation remain unclear. Here we reported that leptin significantly promoted the proliferation of hippocampal NSCs in a concentration-dependent pattern. Also, leptin efficiently reversed the inhibition of NSCs proliferation induced by corticosterone. Interestingly, pre-treatment with non-specific NMDA antagonist MK-801, specific NR2B antagonist Ro 25-6981, or small interfering RNA (siRNA) targeting NR2B, significantly blocked the effect of leptin on corticosterone-induced inhibition of NSCs proliferation. Furthermore, corticosterone significantly reduced the protein expression of NR2B, whereas pre-treatment with leptin greatly reversed the attenuation of NR2B expression caused by corticosterone in cultured hippocampal NSCs. Our findings demonstrate that leptin reverses the corticosterone-induced inhibition of NSCs proliferation. This process is, at least partially mediated by increased expression of NR2B subunits of NMDA receptors.

Specific neural traces for intonational discourse categories as revealed by human-evoked potentials.

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Borràs-Comes, Joan; Costa-Faidella, Jordi; Prieto, Pilar; Escera, Carles

2012-04-01

The neural representation of segmental and tonal phonological distinctions has been shown by means of the MMN ERP, yet this is not the case for intonational discourse contrasts. In Catalan, a rising-falling intonational sequence can be perceived as a statement or as a counterexpectational question, depending exclusively on the size of the pitch range interval of the rising movement. We tested here, using the MMN, whether such categorical distinctions elicited distinct neurophysiological patterns of activity, supporting their specific neural representation. From a behavioral identification experiment, we set the boundary between the two categories and defined four stimuli across the continuum. Although the physical distance between each pair of stimuli was kept constant, the central pair represented an across-category contrast, whereas the other pairs represented within-category contrasts. These four auditory stimuli were contrasted by pairs in three different oddball blocks. The mean amplitude of the MMN was larger for the across-category contrast, suggesting that intonational contrasts in the target language can be encoded automatically in the auditory cortex. These results are in line with recent findings in other fields of linguistics, showing that, when a boundary between categories is crossed, the MMN response is not just larger but rather includes a separate subcomponent.

Neural activation toward erotic stimuli in homosexual and heterosexual males.

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Kagerer, Sabine; Klucken, Tim; Wehrum, Sina; Zimmermann, Mark; Schienle, Anne; Walter, Bertram; Vaitl, Dieter; Stark, Rudolf

2011-11-01

Studies investigating sexual arousal exist, yet there are diverging findings on the underlying neural mechanisms with regard to sexual orientation. Moreover, sexual arousal effects have often been confounded with general arousal effects. Hence, it is still unclear which structures underlie the sexual arousal response in homosexual and heterosexual men. Neural activity and subjective responses were investigated in order to disentangle sexual from general arousal. Considering sexual orientation, differential and conjoint neural activations were of interest. The functional magnetic resonance imaging (fMRI) study focused on the neural networks involved in the processing of sexual stimuli in 21 male participants (11 homosexual, 10 heterosexual). Both groups viewed pictures with erotic content as well as aversive and neutral stimuli. The erotic pictures were subdivided into three categories (most sexually arousing, least sexually arousing, and rest) based on the individual subjective ratings of each participant. Blood oxygen level-dependent responses measured by fMRI and subjective ratings. A conjunction analysis revealed conjoint neural activation related to sexual arousal in thalamus, hypothalamus, occipital cortex, and nucleus accumbens. Increased insula, amygdala, and anterior cingulate gyrus activation could be linked to general arousal. Group differences emerged neither when viewing the most sexually arousing pictures compared with highly arousing aversive pictures nor compared with neutral pictures. Results suggest that a widespread neural network is activated by highly sexually arousing visual stimuli. A partly distinct network of structures underlies sexual and general arousal effects. The processing of preferred, highly sexually arousing stimuli recruited similar structures in homosexual and heterosexual males. © 2011 International Society for Sexual Medicine.

Classification of intelligence quotient via brainwave sub-band power ratio features and artificial neural network.

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Jahidin, A H; Megat Ali, M S A; Taib, M N; Tahir, N Md; Yassin, I M; Lias, S

2014-04-01

This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network. Subsequently, the brain behaviour model has been developed using an artificial neural network that is trained with optimized learning rate, momentum constant and hidden nodes. Findings indicate that the distinct intelligence quotient groups can be classified from the brainwave sub-band power ratios with 100% training and 88.89% testing accuracies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Computation of partially invariant solutions for the Einstein Walker manifolds' identifying equations

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Nadjafikhah, Mehdi; Jafari, Mehdi

2013-12-01

In this paper, partially invariant solutions (PISs) method is applied in order to obtain new four-dimensional Einstein Walker manifolds. This method is based on subgroup classification for the symmetry group of partial differential equations (PDEs) and can be regarded as the generalization of the similarity reduction method. For this purpose, those cases of PISs which have the defect structure δ=1 and are resulted from two-dimensional subalgebras are considered in the present paper. Also it is shown that the obtained PISs are distinct from the invariant solutions that obtained by similarity reduction method.

Distinct medial temporal networks encode surprise during motivation by reward versus punishment

Science.gov (United States)

Murty, Vishnu P.; LaBar, Kevin S.; Adcock, R. Alison

2016-01-01

Adaptive motivated behavior requires predictive internal representations of the environment, and surprising events are indications for encoding new representations of the environment. The medial temporal lobe memory system, including the hippocampus and surrounding cortex, encodes surprising events and is influenced by motivational state. Because behavior reflects the goals of an individual, we investigated whether motivational valence (i.e., pursuing rewards versus avoiding punishments) also impacts neural and mnemonic encoding of surprising events. During functional magnetic resonance imaging (fMRI), participants encountered perceptually unexpected events either during the pursuit of rewards or avoidance of punishments. Despite similar levels of motivation across groups, reward and punishment facilitated the processing of surprising events in different medial temporal lobe regions. Whereas during reward motivation, perceptual surprises enhanced activation in the hippocampus, during punishment motivation surprises instead enhanced activation in parahippocampal cortex. Further, we found that reward motivation facilitated hippocampal coupling with ventromedial PFC, whereas punishment motivation facilitated parahippocampal cortical coupling with orbitofrontal cortex. Behaviorally, post-scan testing revealed that reward, but not punishment, motivation resulted in greater memory selectivity for surprising events encountered during goal pursuit. Together these findings demonstrate that neuromodulatory systems engaged by anticipation of reward and punishment target separate components of the medial temporal lobe, modulating medial temporal lobe sensitivity and connectivity. Thus, reward and punishment motivation yield distinct neural contexts for learning, with distinct consequences for how surprises are incorporated into predictive mnemonic models of the environment. PMID:26854903

Distinct medial temporal networks encode surprise during motivation by reward versus punishment.

Science.gov (United States)

Murty, Vishnu P; LaBar, Kevin S; Adcock, R Alison

2016-10-01

Adaptive motivated behavior requires predictive internal representations of the environment, and surprising events are indications for encoding new representations of the environment. The medial temporal lobe memory system, including the hippocampus and surrounding cortex, encodes surprising events and is influenced by motivational state. Because behavior reflects the goals of an individual, we investigated whether motivational valence (i.e., pursuing rewards versus avoiding punishments) also impacts neural and mnemonic encoding of surprising events. During functional magnetic resonance imaging (fMRI), participants encountered perceptually unexpected events either during the pursuit of rewards or avoidance of punishments. Despite similar levels of motivation across groups, reward and punishment facilitated the processing of surprising events in different medial temporal lobe regions. Whereas during reward motivation, perceptual surprises enhanced activation in the hippocampus, during punishment motivation surprises instead enhanced activation in parahippocampal cortex. Further, we found that reward motivation facilitated hippocampal coupling with ventromedial PFC, whereas punishment motivation facilitated parahippocampal cortical coupling with orbitofrontal cortex. Behaviorally, post-scan testing revealed that reward, but not punishment, motivation resulted in greater memory selectivity for surprising events encountered during goal pursuit. Together these findings demonstrate that neuromodulatory systems engaged by anticipation of reward and punishment target separate components of the medial temporal lobe, modulating medial temporal lobe sensitivity and connectivity. Thus, reward and punishment motivation yield distinct neural contexts for learning, with distinct consequences for how surprises are incorporated into predictive mnemonic models of the environment. Copyright © 2016 Elsevier Inc. All rights reserved.

Terrain Mapping and Classification in Outdoor Environments Using Neural Networks

OpenAIRE

Alberto Yukinobu Hata; Denis Fernando Wolf; Gustavo Pessin; Fernando Osório

2009-01-01

This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstra...

Task-dependent neural bases of perceiving emotionally expressive targets

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Jamil eZaki

2012-08-01

Full Text Available Social cognition is fundamentally interpersonal: individuals’ behavior and dispositions critically affect their interaction partners’ information processing. However, cognitive neuroscience studies, partially because of methodological constraints, have remained largely perceiver-centric: focusing on the abilities, motivations, and goals of social perceivers while largely ignoring interpersonal effects. Here, we address this knowledge gap by examining the neural bases of perceiving emotionally expressive and inexpressive social targets. Sixteen perceivers were scanned using fMRI while they watched targets discussing emotional autobiographical events. Perceivers continuously rated each target’s emotional state or eye-gaze direction. The effects of targets’ emotional expressivity on perceiver’s brain activity depended on task set: when perceivers explicitly attended to targets’ emotions, expressivity predicted activity in neural structures—including medial prefrontal and posterior cingulate cortex—associated with drawing inferences about mental states. When perceivers instead attended to targets’ eye-gaze, target expressivity predicted activity in regions—including somatosensory cortex, fusiform gyrus, and motor cortex—associated with monitoring sensorimotor states and biological motion. These findings suggest that expressive targets affect information processing in manner that depends on perceivers’ goals. More broadly, these data provide an early step towards understanding the neural bases of interpersonal social cognition.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

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Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Two cognitive and neural systems for endogenous and exogenous spatial attention.

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Chica, Ana B; Bartolomeo, Paolo; Lupiáñez, Juan

2013-01-15

different behavioral effects, and partially distinct neural substrates. Copyright © 2012 Elsevier B.V. All rights reserved.

Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

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I. Topalova

2005-03-01

Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

Directory of Open Access Journals (Sweden)

I. Topalova

2008-11-01

Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

Science.gov (United States)

Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

Readout from iconic memory and selective spatial attention involve similar neural processes.

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Ruff, Christian C; Kristjánsson, Arni; Driver, Jon

2007-10-01

Iconic memory and spatial attention are often considered separately, but they may have functional similarities. Here we provide functional magnetic resonance imaging evidence for some common underlying neural effects. Subjects judged three visual stimuli in one hemifield of a bilateral array comprising six stimuli. The relevant hemifield for partial report was indicated by an auditory cue, administered either before the visual array (precue, spatial attention) or shortly after the array (postcue, iconic memory). Pre- and postcues led to similar activity modulations in lateral occipital cortex contralateral to the cued side. This finding indicates that readout from iconic memory can have some neural effects similar to those of spatial attention. We also found common bilateral activation of a fronto-parietal network for postcue and precue trials. These neuroimaging data suggest that some common neural mechanisms underlie selective spatial attention and readout from iconic memory. Some differences were also found; compared with precues, postcues led to higher activity in the right middle frontal gyrus.

A developmental perspective on the neural bases of human empathy.

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Tousignant, Béatrice; Eugène, Fanny; Jackson, Philip L

2017-08-01

While empathy has been widely studied in philosophical and psychological literatures, recent advances in social neuroscience have shed light on the neural correlates of this complex interpersonal phenomenon. In this review, we provide an overview of brain imaging studies that have investigated the neural substrates of human empathy. Based on existing models of the functional architecture of empathy, we review evidence of the neural underpinnings of each main component, as well as their development from infancy. Although early precursors of affective sharing and self-other distinction appear to be present from birth, recent findings also suggest that even higher-order components of empathy such as perspective-taking and emotion regulation demonstrate signs of development during infancy. This merging of developmental and social neuroscience literature thus supports the view that ontogenic development of empathy is rooted in early infancy, well before the emergence of verbal abilities. With age, the refinement of top-down mechanisms may foster more appropriate empathic responses, thus promoting greater altruistic motivation and prosocial behaviors. Copyright © 2016 Elsevier Inc. All rights reserved.

Training feed-forward neural networks with gain constraints

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Hartman

2000-04-01

Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

A universal multilingual weightless neural network tagger via quantitative linguistics.

Science.gov (United States)

Carneiro, Hugo C C; Pedreira, Carlos E; França, Felipe M G; Lima, Priscila M V

2017-07-01

In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks. However, mWANN-Tagger needed to be tuned for every new corpus, since each one required a different parameter configuration. For mWANN-Tagger to be truly multilingual, it should be usable for any new language with no need of parameter tuning. This article proposes a study that aims to find a relation between the lexical diversity of a language and the parameter configuration that would produce the best performing mWANN-Tagger instance. Preliminary analyses suggested that a single parameter configuration may be applied to the eight aforementioned languages. The mWANN-Tagger instance produced by this configuration was as accurate as the language-dependent ones obtained through tuning. Afterwards, the weightless neural tagger was further subjected to new corpora in languages that range from very isolating to polysynthetic ones. The best performing instances of mWANN-Tagger are again the ones produced by the universal parameter configuration. Hence, mWANN-Tagger can be applied to new corpora with no need of parameter tuning, making it a universal multilingual part-of-speech tagger. Further experiments with Universal Dependencies treebanks reveal that mWANN-Tagger may be extended and that it has potential to outperform most state-of-the-art part-of-speech taggers if better word representations are provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimization of operation schemes in boiling water reactors using neural networks; Optimizacion de esquemas de operacion en reactores de agua en ebullicion usando redes neuronales

Energy Technology Data Exchange (ETDEWEB)

Ortiz S, J. J.; Castillo M, A. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Pelta, D. A., E-mail: juanjose.ortiz@inin.gob.mx [Universidad de Granada, Escuela Superior de Ingenierias, Informatica y Telecomunicacion, C/Daniel Saucedo Aranda s/n, 18071 Granada (Spain)

2012-10-15

In previous works were presented the results of a recurrent neural network to find the best combination of several groups of fuel cells, fuel load and control bars patterns. These solution groups to each problem of Fuel Management were previously optimized by diverse optimization techniques. The neural network chooses the partial solutions so the combination of them, correspond to a good configuration of the reactor according to a function objective. The values of the involved variables in this objective function are obtained through the simulation of the combination of partial solutions by means of Simulate-3. In the present work, a multilayer neural network that learned how to predict some results of Simulate-3 was used so was possible to substitute it in the objective function for the neural network and to accelerate the response time of the whole system of this way. The preliminary results shown in this work are encouraging to continue carrying out efforts in this sense and to improve the response quality of the system. (Author)

hESC Differentiation toward an Autonomic Neuronal Cell Fate Depends on Distinct Cues from the Co-Patterning Vasculature

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Lisette M. Acevedo

2015-06-01

Full Text Available To gain insight into the cellular and molecular cues that promote neurovascular co-patterning at the earliest stages of human embryogenesis, we developed a human embryonic stem cell model to mimic the developing epiblast. Contact of ectoderm-derived neural cells with mesoderm-derived vasculature is initiated via the neural crest (NC, not the neural tube (NT. Neurovascular co-patterning then ensues with specification of NC toward an autonomic fate requiring vascular endothelial cell (EC-secreted nitric oxide (NO and direct contact with vascular smooth muscle cells (VSMCs via T-cadherin-mediated homotypic interactions. Once a neurovascular template has been established, NT-derived central neurons then align themselves with the vasculature. Our findings reveal that, in early human development, the autonomic nervous system forms in response to distinct molecular cues from VSMCs and ECs, providing a model for how other developing lineages might coordinate their co-patterning.

Neural bases of imitation and pantomime in acute stroke patients: distinct streams for praxis.

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Hoeren, Markus; Kümmerer, Dorothee; Bormann, Tobias; Beume, Lena; Ludwig, Vera M; Vry, Magnus-Sebastian; Mader, Irina; Rijntjes, Michel; Kaller, Christoph P; Weiller, Cornelius

2014-10-01

Apraxia is a cognitive disorder of skilled movements that characteristically affects the ability to imitate meaningless gestures, or to pantomime the use of tools. Despite substantial research, the neural underpinnings of imitation and pantomime have remained debated. An influential model states that higher motor functions are supported by different processing streams. A dorso-dorsal stream may mediate movements based on physical object properties, like reaching or grasping, whereas skilled tool use or pantomime rely on action representations stored within a ventro-dorsal stream. However, given variable results of past studies, the role of the two streams for imitation of meaningless gestures has remained uncertain, and the importance of the ventro-dorsal stream for pantomime of tool use has been questioned. To clarify the involvement of ventral and dorsal streams in imitation and pantomime, we performed voxel-based lesion-symptom mapping in a sample of 96 consecutive left-hemisphere stroke patients (mean age ± SD, 63.4 ± 14.8 years, 56 male). Patients were examined in the acute phase after ischaemic stroke (after a mean of 5.3, maximum 10 days) to avoid interference of brain reorganization with a reliable lesion-symptom mapping as best as possible. Patients were asked to imitate 20 meaningless hand and finger postures, and to pantomime the use of 14 common tools depicted as line drawings. Following the distinction between movement engrams and action semantics, pantomime errors were characterized as either movement or content errors, respectively. Whereas movement errors referred to incorrect spatio-temporal features of overall recognizable movements, content errors reflected an inability to associate tools with their prototypical actions. Both imitation and pantomime deficits were associated with lesions within the lateral occipitotemporal cortex, posterior inferior parietal lobule, posterior intraparietal sulcus and superior parietal lobule. However, the areas

Higher-Order Neural Networks Applied to 2D and 3D Object Recognition

Science.gov (United States)

Spirkovska, Lilly; Reid, Max B.

1994-01-01

A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.

Synthesis on accumulation of putative neurotransmitters by cultured neural crest cells

International Nuclear Information System (INIS)

Maxwell, G.D.; Sietz, P.D.; Rafford, C.E.

1982-01-01

The events mediating the differentiation of embryonic neural crest cells into several types of neurons are incompletely understood. In order to probe one aspect of this differentiation, we have examined the capacity of cultured quail trunk neural crest cells to synthesize, from radioactive precursors, and store several putative neurotransmitter compounds. These neural crest cultures develop the capacity to synthesize and accumulate acetylcholine and the catecholamines norepinephrine and dopamine. In contrast, detectable but relatively little synthesis and accumulation of 5-hydroxytryptamine gamma-aminobutyric acid, or octopamine from the appropriate radiolabeled precursors were observed. The capacity for synthesis and accumulation of radiolabeled acetylcholine and catecholamines is very low or absent at 2 days in vitro. Between 3 and 7 days in vitro, there is a marked rise in both catecholamine and acetylcholine accumulation in the cultures. These findings suggest that, under the particular conditions used in these experiments, the development of neurotransmitter biosynthesis in trunk neural crest cells ijs restricted and resembles, at least partially, the pattern observed in vivo. The development of this capacity to synthesize and store radiolabeled acetylcholine and catecholamines from the appropriate radioactive precursors coincides closely with the development of the activities of the synthetic enzymes choline acetyltransferase and dopamine beta-hydroxylase reported by others

Neural systems for preparatory control of imitation.

Science.gov (United States)

Cross, Katy A; Iacoboni, Marco

2014-01-01

Humans have an automatic tendency to imitate others. Previous studies on how we control these tendencies have focused on reactive mechanisms, where inhibition of imitation is implemented after seeing an action. This work suggests that reactive control of imitation draws on at least partially specialized mechanisms. Here, we examine preparatory imitation control, where advance information allows control processes to be employed before an action is observed. Drawing on dual route models from the spatial compatibility literature, we compare control processes using biological and non-biological stimuli to determine whether preparatory imitation control recruits specialized neural systems that are similar to those observed in reactive imitation control. Results indicate that preparatory control involves anterior prefrontal, dorsolateral prefrontal, posterior parietal and early visual cortices regardless of whether automatic responses are evoked by biological (imitative) or non-biological stimuli. These results indicate both that preparatory control of imitation uses general mechanisms, and that preparatory control of imitation draws on different neural systems from reactive imitation control. Based on the regions involved, we hypothesize that preparatory control is implemented through top-down attentional biasing of visual processing.

Three-Dimensional-Bioprinted Dopamine-Based Matrix for Promoting Neural Regeneration.

Science.gov (United States)

Zhou, Xuan; Cui, Haitao; Nowicki, Margaret; Miao, Shida; Lee, Se-Jun; Masood, Fahed; Harris, Brent T; Zhang, Lijie Grace

2018-03-14

Central nerve repair and regeneration remain challenging problems worldwide, largely because of the extremely weak inherent regenerative capacity and accompanying fibrosis of native nerves. Inadequate solutions to the unmet needs for clinical therapeutics encourage the development of novel strategies to promote nerve regeneration. Recently, 3D bioprinting techniques, as one of a set of valuable tissue engineering technologies, have shown great promise toward fabricating complex and customizable artificial tissue scaffolds. Gelatin methacrylate (GelMA) possesses excellent biocompatible and biodegradable properties because it contains many arginine-glycine-aspartic acids (RGD) and matrix metalloproteinase sequences. Dopamine (DA), as an essential neurotransmitter, has proven effective in regulating neuronal development and enhancing neurite outgrowth. In this study, GelMA-DA neural scaffolds with hierarchical structures were 3D-fabricated using our custom-designed stereolithography-based printer. DA was functionalized on GelMA to synthesize a biocompatible printable ink (GelMA-DA) for improving neural differentiation. Additionally, neural stem cells (NSCs) were employed as the primary cell source for these scaffolds because of their ability to terminally differentiate into a variety of cell types including neurons, astrocytes, and oligodendrocytes. The resultant GelMA-DA scaffolds exhibited a highly porous and interconnected 3D environment, which is favorable for supporting NSC growth. Confocal microscopy analysis of neural differentiation demonstrated that a distinct neural network was formed on the GelMA-DA scaffolds. In particular, the most significant improvements were the enhanced neuron gene expression of TUJ1 and MAP2. Overall, our results demonstrated that 3D-printed customizable GelMA-DA scaffolds have a positive role in promoting neural differentiation, which is promising for advancing nerve repair and regeneration in the future.

Zebrafish msxB, msxC and msxE function together to refine the neural-nonneural border and regulate cranial placodes and neural crest development.

Science.gov (United States)

Phillips, Bryan T; Kwon, Hye-Joo; Melton, Colt; Houghtaling, Paul; Fritz, Andreas; Riley, Bruce B

2006-06-15

The zebrafish muscle segment homeobox genes msxB, msxC and msxE are expressed in partially overlapping domains in the neural crest and preplacodal ectoderm. We examined the roles of these msx genes in early development. Disrupting individual msx genes causes modest variable defects, whereas disrupting all three produces a reproducible severe phenotype, suggesting functional redundancy. Neural crest differentiation is blocked at an early stage. Preplacodal development begins normally, but placodes arising from the msx expression domain later show elevated apoptosis and are reduced in size. Cell proliferation is normal in these tissues. Unexpectedly, Msx-deficient embryos become ventralized by late gastrulation whereas misexpression of msxB dorsalizes the embryo. These effects appear to involve Distal-less (Dlx) protein activity, as loss of dlx3b and dlx4b suppresses ventralization in Msx-depleted embryos. At the same time, Msx-depletion restores normal preplacodal gene expression to dlx3b-dlx4b mutants. These data suggest that mutual antagonism between Msx and Dlx proteins achieves a balance of function required for normal preplacodal differentiation and placement of the neural-nonneural border.

The neural mechanisms of semantic and response conflicts: an fMRI study of practice-related effects in the Stroop task.

Science.gov (United States)

Chen, Zhencai; Lei, Xu; Ding, Cody; Li, Hong; Chen, Antao

2013-02-01

Previous studies have demonstrated that there are separate neural mechanisms underlying semantic and response conflicts in the Stroop task. However, the practice effects of these conflicts need to be elucidated and the possible involvements of common neural mechanisms are yet to be established. We employed functional magnetic resonance imaging (fMRI) in a 4-2 mapping practice-related Stroop task to determine the neural substrates under these conflicts. Results showed that different patterns of brain activations are associated with practice in the attentional networks (e.g., dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), and posterior parietal cortex (PPC)) for both conflicts, response control regions (e.g., inferior frontal junction (IFJ), inferior frontal gyrus (IFG)/insula, and pre-supplementary motor areas (pre-SMA)) for semantic conflict, and posterior cortex for response conflict. We also found areas of common activation in the left hemisphere within the attentional networks, for the early practice stage in semantic conflict and the late stage in "pure" response conflict using conjunction analysis. The different practice effects indicate that there are distinct mechanisms underlying these two conflict types: semantic conflict practice effects are attributable to the automation of stimulus processing, conflict and response control; response conflict practice effects are attributable to the proportional increase of conflict-related cognitive resources. In addition, the areas of common activation suggest that the semantic conflict effect may contain a partial response conflict effect, particularly at the beginning of the task. These findings indicate that there are two kinds of response conflicts contained in the key-pressing Stroop task: the vocal-level (mainly in the early stage) and key-pressing (mainly in the late stage) response conflicts; thus, the use of the subtraction method for the exploration of semantic and response conflicts

Homology and homoplasy of swimming behaviors and neural circuits in the Nudipleura (Mollusca, Gastropoda, Opisthobranchia)

Science.gov (United States)

Newcomb, James M.; Sakurai, Akira; Lillvis, Joshua L.; Gunaratne, Charuni A.; Katz, Paul S.

2012-01-01

How neural circuit evolution relates to behavioral evolution is not well understood. Here the relationship between neural circuits and behavior is explored with respect to the swimming behaviors of the Nudipleura (Mollusca, Gastropoda, Opithobranchia). Nudipleura is a diverse monophyletic clade of sea slugs among which only a small percentage of species can swim. Swimming falls into a limited number of categories, the most prevalent of which are rhythmic left–right body flexions (LR) and rhythmic dorsal–ventral body flexions (DV). The phylogenetic distribution of these behaviors suggests a high degree of homoplasy. The central pattern generator (CPG) underlying DV swimming has been well characterized in Tritonia diomedea and in Pleurobranchaea californica. The CPG for LR swimming has been elucidated in Melibe leonina and Dendronotus iris, which are more closely related. The CPGs for the categorically distinct DV and LR swimming behaviors consist of nonoverlapping sets of homologous identified neurons, whereas the categorically similar behaviors share some homologous identified neurons, although the exact composition of neurons and synapses in the neural circuits differ. The roles played by homologous identified neurons in categorically distinct behaviors differ. However, homologous identified neurons also play different roles even in the swim CPGs of the two LR swimming species. Individual neurons can be multifunctional within a species. Some of those functions are shared across species, whereas others are not. The pattern of use and reuse of homologous neurons in various forms of swimming and other behaviors further demonstrates that the composition of neural circuits influences the evolution of behaviors. PMID:22723353

The neural basis of emotions varies over time: different regions go with onset- and offset-bound processes underlying emotion intensity.

Science.gov (United States)

Résibois, Maxime; Verduyn, Philippe; Delaveau, Pauline; Rotgé, Jean-Yves; Kuppens, Peter; Van Mechelen, Iven; Fossati, Philippe

2017-08-01

According to theories of emotion dynamics, emotions unfold across two phases in which different types of processes come to the fore: emotion onset and emotion offset. Differences in onset-bound processes are reflected by the degree of explosiveness or steepness of the response at onset, and differences in offset-bound processes by the degree of accumulation or intensification of the subsequent response. Whether onset- and offset-bound processes have distinctive neural correlates and, hence, whether the neural basis of emotions varies over time, still remains unknown. In the present fMRI study, we address this question using a recently developed paradigm that allows to disentangle explosiveness and accumulation. Thirty-one participants were exposed to neutral and negative social feedback, and asked to reflect on its contents. Emotional intensity while reading and thinking about the feedback was measured with an intensity profile tracking approach. Using non-negative matrix factorization, the resulting profile data were decomposed in explosiveness and accumulation components, which were subsequently entered as continuous regressors of the BOLD response. It was found that the neural basis of emotion intensity shifts as emotions unfold over time with emotion explosiveness and accumulation having distinctive neural correlates. © The Author (2017). Published by Oxford University Press.

Development of test algorithm for semiconductor package with defects by using probabilistic neural network

International Nuclear Information System (INIS)

Kim, Jae Yeol; Sim, Jae Gi; Ko, Myoung Soo; Kim, Chang Hyun; Kim, Hun Cho

2001-01-01

In this study, researchers developing the estimative algorithm for artificial defects in semiconductor packages and performing it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Probabilistic Neural Network. Self-Organizing Map and Probabilistic Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages. This study presumes probability density function from a sample of learning and present which is automatically determine method. PNN can distinguish flaws very difficult distinction as well as. This can do parallel process to stand in a row we confirm that is very efficiently classifier if we applied many data real the process.

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Directory of Open Access Journals (Sweden)

Min Wang

2017-01-01

Full Text Available A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF neural network (NN approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

Methods for discriminating gas-liquid two phase flow patterns based on gray neural networks and SVM

International Nuclear Information System (INIS)

Li Jingjing; Zhou Tao; Duan Jun; Zhang Lei

2013-01-01

Background: The flow patterns of two phase flow will directly influence the heat transfer and mass transfer of the flow. Purpose: By wavelet analysis of the pressure drop experimental data, the wavelet coefficients of different frequency can be obtained. Methods: Get the wavelet energy and then train them in the model of BP neural network to distinguish the flow patterns. Introduced the implant gray neural networks model and use it for the two phase flow for the first time. At the same time, set up the method of training the pressure data and wavelet energy data in the support vector machine. Results: Through treatment of the gray layer, the result of the neural network is more accuracy. It can obviously reduce the effect of data marginalization. The accuracy of the pressure drop Lib-SVM method is 95.2%. Conclusions: The results show that these three methods can make a distinction among the different flow patterns and the Lib-SVM method gets the best result, then the gray neural networks, and at last the BP neural networks. (authors)

Neural correlates of face processing in etiologically-distinct 12-month-old infants at high-risk of autism spectrum disorder

Directory of Open Access Journals (Sweden)

Maggie W. Guy

2018-01-01

Full Text Available Neural correlates of face processing were examined in 12-month-olds at high-risk for autism spectrum disorder (ASD, including 21 siblings of children with ASD (ASIBs and 15 infants with fragile X syndrome (FXS, as well as 21 low-risk (LR controls. Event-related potentials were recorded to familiar and novel face and toy stimuli. All infants demonstrated greater N290 amplitude to faces than toys. At the Nc component, LR infants showed greater amplitude to novel stimuli than to their mother’s face and own toy, whereas infants with FXS showed the opposite pattern of responses and ASIBs did not differentiate based on familiarity. These results reflect developing face specialization across high- and low-risk infants and reveal neural patterns that distinguish between groups at high-risk for ASD. Keywords: Event-related potentials, Infancy, Face processing, Autism spectrum disorders

Involvement of Atm and Trp53 in neural cell loss due to Terf2 inactivation during mouse brain development.

Science.gov (United States)

Kim, Jusik; Choi, Inseo; Lee, Youngsoo

2017-11-01

Maintenance of genomic integrity is one of the critical features for proper neurodevelopment and inhibition of neurological diseases. The signals from both ATM and ATR to TP53 are well-known mechanisms to remove neural cells with DNA damage during neurogenesis. Here we examined the involvement of Atm and Atr in genomic instability due to Terf2 inactivation during mouse brain development. Selective inactivation of Terf2 in neural progenitors induced apoptosis, resulting in a complete loss of the brain structure. This neural loss was rescued partially in both Atm and Trp53 deficiency, but not in an Atr-deficient background in the mouse. Atm inactivation resulted in incomplete brain structures, whereas p53 deficiency led to the formation of multinucleated giant neural cells and the disruption of the brain structure. These giant neural cells disappeared in Lig4 deficiency. These data demonstrate ATM and TP53 are important for the maintenance of telomere homeostasis and the surveillance of telomere dysfunction during neurogenesis.

Neural activity and emotional processing following military deployment: Effects of mild traumatic brain injury and posttraumatic stress disorder.

Science.gov (United States)

Zuj, Daniel V; Felmingham, Kim L; Palmer, Matthew A; Lawrence-Wood, Ellie; Van Hooff, Miranda; Lawrence, Andrew J; Bryant, Richard A; McFarlane, Alexander C

2017-11-01

Posttraumatic Stress Disorder (PTSD) and mild traumatic brain injury (mTBI) are common comorbidities during military deployment that affect emotional brain processing, yet few studies have examined the independent effects of mTBI and PTSD. The purpose of this study was to examine distinct differences in neural responses to emotional faces in mTBI and PTSD. Twenty-one soldiers reporting high PTSD symptoms were compared to 21 soldiers with low symptoms, and 16 soldiers who reported mTBI-consistent injury and symptoms were compared with 16 soldiers who did not sustain an mTBI. Participants viewed emotional face expressions while their neural activity was recorded (via event-related potentials) prior to and following deployment. The high-PTSD group displayed increased P1 and P2 amplitudes to threatening faces at post-deployment compared to the low-PTSD group. In contrast, the mTBI group displayed reduced face-specific processing (N170 amplitude) to all facial expressions compared to the no-mTBI group. Here, we identified distinctive neural patterns of emotional face processing, with attentional biases towards threatening faces in PTSD, and reduced emotional face processing in mTBI. Copyright © 2017 Elsevier Inc. All rights reserved.

Common and distinct neural correlates of facial emotion processing in social anxiety disorder and Williams syndrome: A systematic review and voxel-based meta-analysis of functional resonance imaging studies.

Science.gov (United States)

Binelli, C; Subirà, S; Batalla, A; Muñiz, A; Sugranyés, G; Crippa, J A; Farré, M; Pérez-Jurado, L; Martín-Santos, R

2014-11-01

Social Anxiety Disorder (SAD) and Williams-Beuren Syndrome (WS) are two conditions which seem to be at opposite ends in the continuum of social fear but show compromised abilities in some overlapping areas, including some social interactions, gaze contact and processing of facial emotional cues. The increase in the number of neuroimaging studies has greatly expanded our knowledge of the neural bases of facial emotion processing in both conditions. However, to date, SAD and WS have not been compared. We conducted a systematic review of functional magnetic resonance imaging (fMRI) studies comparing SAD and WS cases to healthy control participants (HC) using facial emotion processing paradigms. Two researchers conducted comprehensive PubMed/Medline searches to identify all fMRI studies of facial emotion processing in SAD and WS. The following search key-words were used: "emotion processing"; "facial emotion"; "social anxiety"; "social phobia"; "Williams syndrome"; "neuroimaging"; "functional magnetic resonance"; "fMRI" and their combinations, as well as terms specifying individual facial emotions. We extracted spatial coordinates from each study and conducted two separate voxel-wise activation likelihood estimation meta-analyses, one for SAD and one for WS. Twenty-two studies met the inclusion criteria: 17 studies of SAD and five of WS. We found evidence for both common and distinct patterns of neural activation. Limbic engagement was common to SAD and WS during facial emotion processing, although we observed opposite patterns of activation for each disorder. Compared to HC, SAD cases showed hyperactivation of the amygdala, the parahippocampal gyrus and the globus pallidus. Compared to controls, participants with WS showed hypoactivation of these regions. Differential activation in a number of regions specific to either condition was also identified: SAD cases exhibited greater activation of the insula, putamen, the superior temporal gyrus, medial frontal regions and

BWR fuel cycle optimization using neural networks

International Nuclear Information System (INIS)

Ortiz-Servin, Juan Jose; Castillo, Jose Alejandro; Pelta, David Alejandro

2011-01-01

Highlights: → OCONN a new system to optimize all nuclear fuel management steps in a coupled way. → OCON is based on an artificial recurrent neural network to find the best combination of partial solutions to each fuel management step. → OCONN works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. → Results show OCONN is able to find good combinations according the global objective function. - Abstract: In nuclear fuel management activities for BWRs, four combinatorial optimization problems are solved: fuel lattice design, axial fuel bundle design, fuel reload design and control rod patterns design. Traditionally, these problems have been solved in separated ways due to their complexity and the required computational resources. In the specialized literature there are some attempts to solve fuel reloads and control rod patterns design or fuel lattice and axial fuel bundle design in a coupled way. In this paper, the system OCONN to solve all of these problems in a coupled way is shown. This system is based on an artificial recurrent neural network to find the best combination of partial solutions to each problem, in order to maximize a global objective function. The new system works with a fuel lattices' stock, a fuel reloads' stock and a control rod patterns' stock, previously obtained with different heuristic techniques. The system was tested to design an equilibrium cycle with a cycle length of 18 months. Results show that the new system is able to find good combinations. Cycle length is reached and safety parameters are fulfilled.

Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex

Science.gov (United States)

Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

2017-10-01

All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

Science.gov (United States)

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Enhanced growth of neural networks on conductive cellulose-derived nanofibrous scaffolds

International Nuclear Information System (INIS)

Kuzmenko, Volodymyr; Kalogeropoulos, Theodoros; Thunberg, Johannes; Johannesson, Sara; Hägg, Daniel; Enoksson, Peter; Gatenholm, Paul

2016-01-01

The problem of recovery from neurodegeneration needs new effective solutions. Tissue engineering is viewed as a prospective approach for solving this problem since it can help to develop healthy neural tissue using supportive scaffolds. This study presents effective and sustainable tissue engineering methods for creating biomaterials from cellulose that can be used either as scaffolds for the growth of neural tissue in vitro or as drug screening models. To reach this goal, nanofibrous electrospun cellulose mats were made conductive via two different procedures: carbonization and addition of multi-walled carbon nanotubes. The resulting scaffolds were much more conductive than untreated cellulose material and were used to support growth and differentiation of SH-SY5Y neuroblastoma cells. The cells were evaluated by scanning electron microscopy and confocal microscopy methods over a period of 15 days at different time points. The results showed that the cellulose-derived conductive scaffolds can provide support for good cell attachment, growth and differentiation. The formation of a neural network occurred within 10 days of differentiation, which is a promising length of time for SH-SY5Y neuroblastoma cells. - Highlights: • The conductive scaffolds for neural tissue engineering are derived from cellulose. • The scaffolds are used to support growth and differentiation of SH-SY5Y cells. • Distinctive cell differentiation occurs within 10 days on conductive scaffolds. • Electrical conductivity and nanotopography improve neural network formation.

Bioinformatics approach of three partial polyprenol reductase genes in Kandelia obovata

Science.gov (United States)

Basyuni, M.; Wati, R.; Sagami, H.; Oku, H.; Baba, S.

2018-03-01

This present study describesthe bioinformatics approach to analyze three partial polyprenol reductase genes from mangrove plant, Kandeliaobovataas well aspredictedphysical and chemical properties, potential peptide, subcellular localization, and phylogenetic. The diversity was noted in the physical and chemical properties of three partial polyprenol reductase genes. The values of chloroplast were relatively high, showed that chloroplast transit peptide occurred in mangrove polyprenol reductase. The target peptide value of mitochondria varied from 0.088 to 0.198 indicated it was possible to be present. These results suggested the importance of understanding the diversity of physicochemical properties of the different amino acids in polyprenol reductase. The subcellular localization of two partial genes located in the plasma membrane. To confirm the homology among the polyprenol reductase in the database, a dendrogram was drawn. The phylogenetic tree depicts that there are three clusters, the partial genes of K. obovata joined the largest one: C23157 was close to Ricinus communis polyprenol reductase. Whereas, C23901 and C24171 were grouped with Ipomoea nil polyprenol reductase, suggested that these polyprenol reductase genes form distinct separation into tropical habitat plants.

Evolvable synthetic neural system

Science.gov (United States)

Curtis, Steven A. (Inventor)

2009-01-01

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Neural Correlates of Sexual Orientation in Heterosexual, Bisexual, and Homosexual Men

Science.gov (United States)

Safron, Adam; Sylva, David; Klimaj, Victoria; Rosenthal, A. M.; Li, Meng; Walter, Martin; Bailey, J. Michael

2017-01-01

Studies of subjective and genital sexual arousal in monosexual (i.e. heterosexual and homosexual) men have repeatedly found that erotic stimuli depicting men’s preferred sex produce strong responses, whereas erotic stimuli depicting the other sex produce much weaker responses. Inconsistent results have previously been obtained in bisexual men, who have sometimes demonstrated distinctly bisexual responses, but other times demonstrated patterns more similar to those observed in monosexual men. We used fMRI to investigate neural correlates of responses to erotic pictures and videos in heterosexual, bisexual, and homosexual men, ages 25–50. Sixty participants were included in video analyses, and 62 were included in picture analyses. We focused on the ventral striatum (VS), due to its association with incentive motivation. Patterns were consistent with sexual orientation, with heterosexual and homosexual men showing female-favoring and male-favoring responses, respectively. Bisexual men tended to show less differentiation between male and female stimuli. Consistent patterns were observed in the whole brain, including the VS, and also in additional regions such as occipitotemporal, anterior cingulate, and orbitofrontal cortices. This study extends previous findings of gender-specific neural responses in monosexual men, and provides initial evidence for distinct brain activity patterns in bisexual men. PMID:28145518

Young Adults with Autism Spectrum Disorder Show Early Atypical Neural Activity during Emotional Face Processing

Directory of Open Access Journals (Sweden)

Rachel C. Leung

2018-02-01

Full Text Available Social cognition is impaired in autism spectrum disorder (ASD. The ability to perceive and interpret affect is integral to successful social functioning and has an extended developmental course. However, the neural mechanisms underlying emotional face processing in ASD are unclear. Using magnetoencephalography (MEG, the present study explored neural activation during implicit emotional face processing in young adults with and without ASD. Twenty-six young adults with ASD and 26 healthy controls were recruited. Participants indicated the location of a scrambled pattern (target that was presented alongside a happy or angry face. Emotion-related activation sources for each emotion were estimated using the Empirical Bayes Beamformer (pcorr ≤ 0.001 in Statistical Parametric Mapping 12 (SPM12. Emotional faces elicited elevated fusiform, amygdala and anterior insula and reduced anterior cingulate cortex (ACC activity in adults with ASD relative to controls. Within group comparisons revealed that angry vs. happy faces elicited distinct neural activity in typically developing adults; there was no distinction in young adults with ASD. Our data suggest difficulties in affect processing in ASD reflect atypical recruitment of traditional emotional processing areas. These early differences may contribute to difficulties in deriving social reward from faces, ascribing salience to faces, and an immature threat processing system, which collectively could result in deficits in emotional face processing.

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior.

Science.gov (United States)

Groen, Iris Ia; Greene, Michelle R; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M; Baker, Chris I

2018-03-07

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

Alteration of neural action potential patterns by axonal stimulation: the importance of stimulus location.

Science.gov (United States)

Crago, Patrick E; Makowski, Nathaniel S

2014-10-01

Stimulation of peripheral nerves is often superimposed on ongoing motor and sensory activity in the same axons, without a quantitative model of the net action potential train at the axon endpoint. We develop a model of action potential patterns elicited by superimposing constant frequency axonal stimulation on the action potentials arriving from a physiologically activated neural source. The model includes interactions due to collision block, resetting of the neural impulse generator, and the refractory period of the axon at the point of stimulation. Both the mean endpoint firing rate and the probability distribution of the action potential firing periods depend strongly on the relative firing rates of the two sources and the intersite conduction time between them. When the stimulus rate exceeds the neural rate, neural action potentials do not reach the endpoint and the rate of endpoint action potentials is the same as the stimulus rate, regardless of the intersite conduction time. However, when the stimulus rate is less than the neural rate, and the intersite conduction time is short, the two rates partially sum. Increases in stimulus rate produce non-monotonic increases in endpoint rate and continuously increasing block of neurally generated action potentials. Rate summation is reduced and more neural action potentials are blocked as the intersite conduction time increases. At long intersite conduction times, the endpoint rate simplifies to being the maximum of either the neural or the stimulus rate. This study highlights the potential of increasing the endpoint action potential rate and preserving neural information transmission by low rate stimulation with short intersite conduction times. Intersite conduction times can be decreased with proximal stimulation sites for muscles and distal stimulation sites for sensory endings. The model provides a basis for optimizing experiments and designing neuroprosthetic interventions involving motor or sensory stimulation.

Overlapping neural response to the pain or harm of people, animals, and nature.

Science.gov (United States)

Mathur, Vani A; Cheon, Bobby K; Harada, Tokiko; Scimeca, Jason M; Chiao, Joan Y

2016-01-29

Interpersonal pain perception is a fundamental and evolutionarily beneficial social process. While critical for navigating the social world, whether or not people rely on similar processes to perceive and respond to the harm of the non-human biological world remains largely unknown. Here we investigate whether neural reactivity toward the suffering of other people is distinct from or overlapping with the neural response to pain and harm inflicted upon non-human entities, specifically animals and nature. We used fMRI to measure neural activity while participants (n=15) perceived and reported how badly they felt for the pain or harm of humans, animals, and nature, relative to neutral situations. Neural regions associated with perceiving the pain of other people (e.g. dorsal anterior cingulate cortex, bilateral anterior insula) were similarly recruited when perceiving and responding to painful scenes across people, animals, and nature. These results suggest that similar brain responses are relied upon when perceiving the harm of social and non-social biological entities, broadly construed, and that activity within the dorsal anterior cingulate cortex and bilateral anterior insula in response to pain-relevant stimuli is not uniquely specific to humans. Copyright © 2016 Elsevier Ltd. All rights reserved.

Outbreak tracking of Aleutian mink disease virus (AMDV) using partial NS1 gene sequencing

DEFF Research Database (Denmark)

Ryt-Hansen, Pia; Hjulsager, Charlotte Kristiane; Hagberg, E. E.

2017-01-01

. However, in 2015, several outbreaks of AMDV occurred at mink farms throughout Denmark, and the sources of these outbreaks were not known. Partial NS1 gene sequencing, phylogenetic analyses data were utilized along with epidemiological to determine the origin of the outbreaks. The phylogenetic analyses...... not be excluded. This study confirmed that partial NS1 sequencing can be used in outbreak tracking to determine major viral clusters of AMDV. Using this method, two new distinct AMDV clusters with low intra-cluster sequence diversity were identified, and epidemiological data helped to reveal possible ways...

Canonical Wnt signaling transiently stimulates proliferation and enhances neurogenesis in neonatal neural progenitor cultures

International Nuclear Information System (INIS)

Hirsch, Cordula; Campano, Louise M.; Woehrle, Simon; Hecht, Andreas

2007-01-01

Canonical Wnt signaling triggers the formation of heterodimeric transcription factor complexes consisting of β-catenin and T cell factors, and thereby controls the execution of specific genetic programs. During the expansion and neurogenic phases of embryonic neural development canonical Wnt signaling initially controls proliferation of neural progenitor cells, and later neuronal differentiation. Whether Wnt growth factors affect neural progenitor cells postnatally is not known. Therefore, we have analyzed the impact of Wnt signaling on neural progenitors isolated from cerebral cortices of newborn mice. Expression profiling of pathway components revealed that these cells are fully equipped to respond to Wnt signals. However, Wnt pathway activation affected only a subset of neonatal progenitors and elicited a limited increase in proliferation and neuronal differentiation in distinct subsets of cells. Moreover, Wnt pathway activation only transiently stimulated S-phase entry but did not support long-term proliferation of progenitor cultures. The dampened nature of the Wnt response correlates with the predominant expression of inhibitory pathway components and the rapid actuation of negative feedback mechanisms. Interestingly, in differentiating cell cultures activation of canonical Wnt signaling reduced Hes1 and Hes5 expression suggesting that during postnatal neural development, Wnt/β-catenin signaling enhances neurogenesis from progenitor cells by interfering with Notch pathway activity

Development switch in neural circuitry underlying odor-malaise learning.

Science.gov (United States)

Shionoya, Kiseko; Moriceau, Stephanie; Lunday, Lauren; Miner, Cathrine; Roth, Tania L; Sullivan, Regina M

2006-01-01

Fetal and infant rats can learn to avoid odors paired with illness before development of brain areas supporting this learning in adults, suggesting an alternate learning circuit. Here we begin to document the transition from the infant to adult neural circuit underlying odor-malaise avoidance learning using LiCl (0.3 M; 1% of body weight, ip) and a 30-min peppermint-odor exposure. Conditioning groups included: Paired odor-LiCl, Paired odor-LiCl-Nursing, LiCl, and odor-saline. Results showed that Paired LiCl-odor conditioning induced a learned odor aversion in postnatal day (PN) 7, 12, and 23 pups. Odor-LiCl Paired Nursing induced a learned odor preference in PN7 and PN12 pups but blocked learning in PN23 pups. 14C 2-deoxyglucose (2-DG) autoradiography indicated enhanced olfactory bulb activity in PN7 and PN12 pups with odor preference and avoidance learning. The odor aversion in weanling aged (PN23) pups resulted in enhanced amygdala activity in Paired odor-LiCl pups, but not if they were nursing. Thus, the neural circuit supporting malaise-induced aversions changes over development, indicating that similar infant and adult-learned behaviors may have distinct neural circuits.

Mediator Med23 deficiency enhances neural differentiation of murine embryonic stem cells through modulating BMP signaling.

Science.gov (United States)

Zhu, Wanqu; Yao, Xiao; Liang, Yan; Liang, Dan; Song, Lu; Jing, Naihe; Li, Jinsong; Wang, Gang

2015-02-01

Unraveling the mechanisms underlying early neural differentiation of embryonic stem cells (ESCs) is crucial to developing cell-based therapies of neurodegenerative diseases. Neural fate acquisition is proposed to be controlled by a 'default' mechanism, for which the molecular regulation is not well understood. In this study, we investigated the functional roles of Mediator Med23 in pluripotency and lineage commitment of murine ESCs. Unexpectedly, we found that, despite the largely unchanged pluripotency and self-renewal of ESCs, Med23 depletion rendered the cells prone to neural differentiation in different differentiation assays. Knockdown of two other Mediator subunits, Med1 and Med15, did not alter the neural differentiation of ESCs. Med15 knockdown selectively inhibited endoderm differentiation, suggesting the specificity of cell fate control by distinctive Mediator subunits. Gene profiling revealed that Med23 depletion attenuated BMP signaling in ESCs. Mechanistically, MED23 modulated Bmp4 expression by controlling the activity of ETS1, which is involved in Bmp4 promoter-enhancer communication. Interestingly, med23 knockdown in zebrafish embryos also enhanced neural development at early embryogenesis, which could be reversed by co-injection of bmp4 mRNA. Taken together, our study reveals an intrinsic, restrictive role of MED23 in early neural development, thus providing new molecular insights for neural fate determination. © 2015. Published by The Company of Biologists Ltd.

Neural Mechanisms Underlying Risk and Ambiguity Attitudes.

Science.gov (United States)

Blankenstein, Neeltje E; Peper, Jiska S; Crone, Eveline A; van Duijvenvoorde, Anna C K

2017-11-01

Individual differences in attitudes to risk (a taste for risk, known probabilities) and ambiguity (a tolerance for uncertainty, unknown probabilities) differentially influence risky decision-making. However, it is not well understood whether risk and ambiguity are coded differently within individuals. Here, we tested whether individual differences in risk and ambiguity attitudes were reflected in distinct neural correlates during choice and outcome processing of risky and ambiguous gambles. To these ends, we developed a neuroimaging task in which participants ( n = 50) chose between a sure gain and a gamble, which was either risky or ambiguous, and presented decision outcomes (gains, no gains). From a separate task in which the amount, probability, and ambiguity level were varied, we estimated individuals' risk and ambiguity attitudes. Although there was pronounced neural overlap between risky and ambiguous gambling in a network typically related to decision-making under uncertainty, relatively more risk-seeking attitudes were associated with increased activation in valuation regions of the brain (medial and lateral OFC), whereas relatively more ambiguity-seeking attitudes were related to temporal cortex activation. In addition, although striatum activation was observed during reward processing irrespective of a prior risky or ambiguous gamble, reward processing after an ambiguous gamble resulted in enhanced dorsomedial PFC activation, possibly functioning as a general signal of uncertainty coding. These findings suggest that different neural mechanisms reflect individual differences in risk and ambiguity attitudes and that risk and ambiguity may impact overt risk-taking behavior in different ways.

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

Science.gov (United States)

Korfiatis, Panagiotis; Kline, Timothy L; Lachance, Daniel H; Parney, Ian F; Buckner, Jan C; Erickson, Bradley J

2017-10-01

Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p deep neural architectures can be used to predict molecular biomarkers from routine medical images.

Combinatorial neural codes from a mathematical coding theory perspective.

Science.gov (United States)

Curto, Carina; Itskov, Vladimir; Morrison, Katherine; Roth, Zachary; Walker, Judy L

2013-07-01

Shannon's seminal 1948 work gave rise to two distinct areas of research: information theory and mathematical coding theory. While information theory has had a strong influence on theoretical neuroscience, ideas from mathematical coding theory have received considerably less attention. Here we take a new look at combinatorial neural codes from a mathematical coding theory perspective, examining the error correction capabilities of familiar receptive field codes (RF codes). We find, perhaps surprisingly, that the high levels of redundancy present in these codes do not support accurate error correction, although the error-correcting performance of receptive field codes catches up to that of random comparison codes when a small tolerance to error is introduced. However, receptive field codes are good at reflecting distances between represented stimuli, while the random comparison codes are not. We suggest that a compromise in error-correcting capability may be a necessary price to pay for a neural code whose structure serves not only error correction, but must also reflect relationships between stimuli.

Neural organization of linguistic short-term memory is sensory modality-dependent: evidence from signed and spoken language.

Science.gov (United States)

Pa, Judy; Wilson, Stephen M; Pickell, Herbert; Bellugi, Ursula; Hickok, Gregory

2008-12-01

Despite decades of research, there is still disagreement regarding the nature of the information that is maintained in linguistic short-term memory (STM). Some authors argue for abstract phonological codes, whereas others argue for more general sensory traces. We assess these possibilities by investigating linguistic STM in two distinct sensory-motor modalities, spoken and signed language. Hearing bilingual participants (native in English and American Sign Language) performed equivalent STM tasks in both languages during functional magnetic resonance imaging. Distinct, sensory-specific activations were seen during the maintenance phase of the task for spoken versus signed language. These regions have been previously shown to respond to nonlinguistic sensory stimulation, suggesting that linguistic STM tasks recruit sensory-specific networks. However, maintenance-phase activations common to the two languages were also observed, implying some form of common process. We conclude that linguistic STM involves sensory-dependent neural networks, but suggest that sensory-independent neural networks may also exist.

Morphological neural networks

Energy Technology Data Exchange (ETDEWEB)

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

Human neural progenitors express functional lysophospholipid receptors that regulate cell growth and morphology

Directory of Open Access Journals (Sweden)

Callihan Phillip

2008-12-01

Full Text Available Abstract Background Lysophospholipids regulate the morphology and growth of neurons, neural cell lines, and neural progenitors. A stable human neural progenitor cell line is not currently available in which to study the role of lysophospholipids in human neural development. We recently established a stable, adherent human embryonic stem cell-derived neuroepithelial (hES-NEP cell line which recapitulates morphological and phenotypic features of neural progenitor cells isolated from fetal tissue. The goal of this study was to determine if hES-NEP cells express functional lysophospholipid receptors, and if activation of these receptors mediates cellular responses critical for neural development. Results Our results demonstrate that Lysophosphatidic Acid (LPA and Sphingosine-1-phosphate (S1P receptors are functionally expressed in hES-NEP cells and are coupled to multiple cellular signaling pathways. We have shown that transcript levels for S1P1 receptor increased significantly in the transition from embryonic stem cell to hES-NEP. hES-NEP cells express LPA and S1P receptors coupled to Gi/o G-proteins that inhibit adenylyl cyclase and to Gq-like phospholipase C activity. LPA and S1P also induce p44/42 ERK MAP kinase phosphorylation in these cells and stimulate cell proliferation via Gi/o coupled receptors in an Epidermal Growth Factor Receptor (EGFR- and ERK-dependent pathway. In contrast, LPA and S1P stimulate transient cell rounding and aggregation that is independent of EGFR and ERK, but dependent on the Rho effector p160 ROCK. Conclusion Thus, lysophospholipids regulate neural progenitor growth and morphology through distinct mechanisms. These findings establish human ES cell-derived NEP cells as a model system for studying the role of lysophospholipids in neural progenitors.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

Science.gov (United States)

Arcos-García, Álvaro; Álvarez-García, Juan A; Soria-Morillo, Luis M

2018-03-01

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. Copyright © 2018 Elsevier Ltd. All rights reserved.

Molecular Characterization of Down Syndrome Embryonic Stem Cells Reveals a Role for RUNX1 in Neural Differentiation

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Tomer Halevy

2016-10-01

Full Text Available Down syndrome (DS is the leading genetic cause of mental retardation and is caused by a third copy of human chromosome 21. The different pathologies of DS involve many tissues with a distinct array of neural phenotypes. Here we characterize embryonic stem cell lines with DS (DS-ESCs, and focus on the neural aspects of the disease. Our results show that neural progenitor cells (NPCs differentiated from five independent DS-ESC lines display increased apoptosis and downregulation of forehead developmental genes. Analysis of differentially expressed genes suggested RUNX1 as a key transcription regulator in DS-NPCs. Using genome editing we were able to disrupt all three copies of RUNX1 in DS-ESCs, leading to downregulation of several RUNX1 target developmental genes accompanied by reduced apoptosis and neuron migration. Our work sheds light on the role of RUNX1 and the importance of dosage balance in the development of neural phenotypes in DS.

Heparan Sulfate Proteoglycans as Drivers of Neural Progenitors Derived From Human Mesenchymal Stem Cells.

Science.gov (United States)

Okolicsanyi, Rachel K; Oikari, Lotta E; Yu, Chieh; Griffiths, Lyn R; Haupt, Larisa M

2018-01-01

Background: Due to their relative ease of isolation and their high ex vivo and in vitro expansive potential, human mesenchymal stem cells (hMSCs) are an attractive candidate for therapeutic applications in the treatment of brain injury and neurological diseases. Heparan sulfate proteoglycans (HSPGs) are a family of ubiquitous proteins involved in a number of vital cellular processes including proliferation and stem cell lineage differentiation. Methods: Following the determination that hMSCs maintain neural potential throughout extended in vitro expansion, we examined the role of HSPGs in mediating the neural potential of hMSCs. hMSCs cultured in basal conditions (undifferentiated monolayer cultures) were found to co-express neural markers and HSPGs throughout expansion with modulation of the in vitro niche through the addition of exogenous HS influencing cellular HSPG and neural marker expression. Results: Conversion of hMSCs into hMSC Induced Neurospheres (hMSC IN) identified distinctly localized HSPG staining within the spheres along with altered gene expression of HSPG core protein and biosynthetic enzymes when compared to undifferentiated hMSCs. Conclusion: Comparison of markers of pluripotency, neural self-renewal and neural lineage specification between hMSC IN, hMSC and human neural stem cell (hNSC H9) cultures suggest that in vitro generated hMSC IN may represent an intermediary neurogenic cell type, similar to a common neural progenitor cell. In addition, this data demonstrates HSPGs and their biosynthesis machinery, are associated with hMSC IN formation. The identification of specific HSPGs driving hMSC lineage-specification will likely provide new markers to allow better use of hMSCs in therapeutic applications and improve our understanding of human neurogenesis.

Combining BMI stimulation and mathematical modeling for acute stroke recovery and neural repair

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Sara L Gonzalez Andino

2011-07-01

Full Text Available Rehabilitation is a neural plasticity-exploiting approach that forces undamaged neural circuits to undertake the functionality of other circuits damaged by stroke. It aims to partial restoration of the neural functions by circuit remodeling rather than by the regeneration of damaged circuits. The core hypothesis of the present paper is that - in stroke - Brain Machine Interfaces can be designed to target neural repair instead of rehabilitation. To support this hypothesis we first review existing evidence on the role of endogenous or externally applied electric fields on all processes involved in CNS repair. We then describe our own results to illustrate the neuroprotective and neuroregenerative effects of BMI- electrical stimulation on sensory deprivation-related degenerative processes of the CNS. Finally, we discuss three of the crucial issues involved in the design of neural repair-oriented BMIs: when to stimulate, where to stimulate and - the particularly important but unsolved issue of - how to stimulate. We argue that optimal parameters for the electrical stimulation can be determined from studying and modeling the dynamics of the electric fields that naturally emerge at the central and peripheral nervous system during spontaneous healing in both, experimental animals and human patients. We conclude that a closed-loop BMI that defines the optimal stimulation parameters from a priori developed experimental models of the dynamics of spontaneous repair and the on-line monitoring of neural activity might place BMIs as an alternative or complement to stem-cell transplantation or pharmacological approaches, intensively pursued nowadays.

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications

OpenAIRE

Pasquier, Claude; Promponas, Vasilis; Hamodrakas, Stavros

2009-01-01

International audience; A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the av...

Neural Decoding of Bistable Sounds Reveals an Effect of Intention on Perceptual Organization.

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Billig, Alexander J; Davis, Matthew H; Carlyon, Robert P

2018-03-14

Auditory signals arrive at the ear as a mixture that the brain must decompose into distinct sources based to a large extent on acoustic properties of the sounds. An important question concerns whether listeners have voluntary control over how many sources they perceive. This has been studied using pure high (H) and low (L) tones presented in the repeating pattern HLH-HLH-, which can form a bistable percept heard either as an integrated whole (HLH-) or as segregated into high (H-H-) and low (-L-) sequences. Although instructing listeners to try to integrate or segregate sounds affects reports of what they hear, this could reflect a response bias rather than a perceptual effect. We had human listeners (15 males, 12 females) continuously report their perception of such sequences and recorded neural activity using MEG. During neutral listening, a classifier trained on patterns of neural activity distinguished between periods of integrated and segregated perception. In other conditions, participants tried to influence their perception by allocating attention either to the whole sequence or to a subset of the sounds. They reported hearing the desired percept for a greater proportion of time than when listening neutrally. Critically, neural activity supported these reports; stimulus-locked brain responses in auditory cortex were more likely to resemble the signature of segregation when participants tried to hear segregation than when attempting to perceive integration. These results indicate that listeners can influence how many sound sources they perceive, as reflected in neural responses that track both the input and its perceptual organization. SIGNIFICANCE STATEMENT Can we consciously influence our perception of the external world? We address this question using sound sequences that can be heard either as coming from a single source or as two distinct auditory streams. Listeners reported spontaneous changes in their perception between these two interpretations while

Time- and task-dependent non-neural effects of real and sham TMS.

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Felix Duecker

Full Text Available Transcranial magnetic stimulation (TMS is widely used in experimental brain research to manipulate brain activity in humans. Next to the intended neural effects, every TMS pulse produces a distinct clicking sound and sensation on the head which can also influence task performance. This necessitates careful consideration of control conditions in order to ensure that behavioral effects of interest can be attributed to the neural consequences of TMS and not to non-neural effects of a TMS pulse. Surprisingly, even though these non-neural effects of TMS are largely unknown, they are often assumed to be unspecific, i.e. not dependent on TMS parameters. This assumption is inherent to many control strategies in TMS research but has recently been challenged on empirical grounds. Here, we further develop the empirical basis of control strategies in TMS research. We investigated the time-dependence and task-dependence of the non-neural effects of TMS and compared real and sham TMS over vertex. Critically, we show that non-neural TMS effects depend on a complex interplay of these factors. Although TMS had no direct neural effects, both pre- and post-stimulus TMS time windows modulated task performance on both a sensory detection task and a cognitive angle judgment task. For the most part, these effects were quantitatively similar across tasks but effect sizes were clearly different. Moreover, the effects of real and sham TMS were almost identical with interesting exceptions that shed light on the relative contribution of auditory and somato-sensory aspects of a TMS pulse. Knowledge of such effects is of critical importance for the interpretation of TMS experiments and helps deciding what constitutes an appropriate control condition. Our results broaden the empirical basis of control strategies in TMS research and point at potential pitfalls that should be avoided.

Nonstimulated human uncommitted mesenchymal stem cells express cell markers of mesenchymal and neural lineages.

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Minguell, José J; Fierro, Fernando A; Epuñan, María J; Erices, Alejandro A; Sierralta, Walter D

2005-08-01

Ex vivo cultures of human bone marrow-derived mesenchymal stem cells (MSCs) contain subsets of progenitors exhibiting dissimilar properties. One of these subsets comprises uncommitted progenitors displaying distinctive features, such as morphology, a quiescent condition, growth factor production, and restricted tissue biodistribution after transplantation. In this study, we assessed the competence of these cells to express, in the absence of differentiation stimuli, markers of mesoderm and ectodermic (neural) cell lineages. Fluorescence microscopy analysis showed a unique pattern of expression of osteogenic, chondrogenic, muscle, and neural markers. The depicted "molecular signature" of these early uncommitted progenitors, in the absence of differentiation stimuli, is consistent with their multipotentiality and plasticity as suggested by several in vitro and in vivo studies.

Selective attention on representations in working memory: cognitive and neural mechanisms.

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Ku, Yixuan

2018-01-01

Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

Origin-Dependent Neural Cell Identities in Differentiated Human iPSCs In Vitro and after Transplantation into the Mouse Brain

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Gunnar Hargus

2014-09-01

Full Text Available The differentiation capability of induced pluripotent stem cells (iPSCs toward certain cell types for disease modeling and drug screening assays might be influenced by their somatic cell of origin. Here, we have compared the neural induction of human iPSCs generated from fetal neural stem cells (fNSCs, dermal fibroblasts, or cord blood CD34+ hematopoietic progenitor cells. Neural progenitor cells (NPCs and neurons could be generated at similar efficiencies from all iPSCs. Transcriptomics analysis of the whole genome and of neural genes revealed a separation of neuroectoderm-derived iPSC-NPCs from mesoderm-derived iPSC-NPCs. Furthermore, we found genes that were similarly expressed in fNSCs and neuroectoderm, but not in mesoderm-derived iPSC-NPCs. Notably, these neural signatures were retained after transplantation into the cortex of mice and paralleled with increased survival of neuroectoderm-derived cells in vivo. These results indicate distinct origin-dependent neural cell identities in differentiated human iPSCs both in vitro and in vivo.

Remembering the past and imagining the future: common and distinct neural substrates during event construction and elaboration

OpenAIRE

Addis, Donna Rose; Wong, Alana T.; Schacter, Daniel L.

2006-01-01

People can consciously re-experience past events and pre-experience possible future events. This fMRI study examined the neural regions mediating the construction and elaboration of past and future events. Participants were cued with a noun for 20 seconds and instructed to construct a past or future event within a specified time period (week, year, 5–20 years). Once participants had the event in mind, they made a button press and for the remainder of the 20 seconds elaborated on the event. Im...

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Two Distinct Scene-Processing Networks Connecting Vision and Memory.

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Baldassano, Christopher; Esteva, Andre; Fei-Fei, Li; Beck, Diane M

2016-01-01

A number of regions in the human brain are known to be involved in processing natural scenes, but the field has lacked a unifying framework for understanding how these different regions are organized and interact. We provide evidence from functional connectivity and meta-analyses for a new organizational principle, in which scene processing relies upon two distinct networks that split the classically defined parahippocampal place area (PPA). The first network of strongly connected regions consists of the occipital place area/transverse occipital sulcus and posterior PPA, which contain retinotopic maps and are not strongly coupled to the hippocampus at rest. The second network consists of the caudal inferior parietal lobule, retrosplenial complex, and anterior PPA, which connect to the hippocampus (especially anterior hippocampus), and are implicated in both visual and nonvisual tasks, including episodic memory and navigation. We propose that these two distinct networks capture the primary functional division among scene-processing regions, between those that process visual features from the current view of a scene and those that connect information from a current scene view with a much broader temporal and spatial context. This new framework for understanding the neural substrates of scene-processing bridges results from many lines of research, and makes specific functional predictions.

In defense of the personal/impersonal distinction in moral psychology research: Cross-cultural validation of the dual process model of moral judgment

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Adam B. Moore

2011-04-01

Full Text Available The dual process model of moral judgment (DPM; Greene et al., 2004 argues that such judgments are influenced by both emotion-laden intuition and controlled reasoning. These influences are associated with distinct neural circuitries and different response tendencies. After reanalyzing data from an earlier study, McGuire et al. (2009 questioned the level of support for the dual process model and asserted that the distinction between emotion evoking moral dilemmas (personal dilemmas and those that do not trigger such intuitions (impersonal dilemmas is spurious. Using similar reanalysis methods on data reported by Moore, Clark, and Kane (2008, we show that the personal/impersonal distinction is reliable. Furthermore, new data show that this distinction is fundamental to moral judgment across widely different cultures (U.S. and China and supports claims made by the DPM.

Patterns of work attitudes: A neural network approach

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Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.

2000-05-01

In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

CERN Document Server

Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

Common and distinct neural features of social and non-social reward processing in autism and social anxiety disorder.

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Richey, John A; Rittenberg, Alison; Hughes, Lauren; Damiano, Cara R; Sabatino, Antoinette; Miller, Stephanie; Hanna, Eleanor; Bodfish, James W; Dichter, Gabriel S

2014-03-01

Autism spectrum disorders (ASDs) and social anxiety disorder (SAD) are both characterized by social dysfunction, but no study to date has compared neural responses to social rewards in ASDs and SAD. Neural responses during social and non-social reward anticipation and outcomes were examined in individuals with ASD (n = 16), SAD (n = 15) and a control group (n = 19) via functional magnetic resonance imaging. Analyses modeling all three groups revealed increased nucleus accumbens (NAc) activation in SAD relative to ASD during monetary reward anticipation, whereas both the SAD and ASD group demonstrated decreased bilateral NAc activation relative to the control group during social reward anticipation. During reward outcomes, the SAD group did not differ significantly from the other two groups in ventromedial prefrontal cortex activation to either reward type. Analyses comparing only the ASD and SAD groups revealed greater bilateral amygdala activation to social rewards in SAD relative to ASD during both anticipation and outcome phases, and the magnitude of left amygdala hyperactivation in the SAD group during social reward anticipation was significantly correlated with the severity of trait anxiety symptoms. Results suggest reward network dysfunction to both monetary and social rewards in SAD and ASD during reward anticipation and outcomes, but that NAc hypoactivation during monetary reward anticipation differentiates ASD from SAD.

Continuous partial status cause of hyperintensity in cerebral cortex in magnetic resonance imaging

International Nuclear Information System (INIS)

Lopez L, Ingeborg; Gonzalez L, Daniela; Quezada R, Patricio; Cartier R, Luis

2012-01-01

Magnetic Resonance Imaging has demonstrated functional changes of the cerebral cortex in relation to status epilepticus, which can eventually localize the origin of the crisis. The purpose of this presentation is relevant to this condition and pretends to highlight the action of incidental situations that can modify it. We present a 29 year old woman with a neurosurgical intervention for a neuroblastoma irradiated fifteen years ago, which incidentally starts a continuous partial status epilepticus, expressed by clonies of the face and left limbs associated with functional impotence, resistant to oral therapy. Faced with the suspicion of recurrence of the tumor, a brain MRI is performed, showing hyperintensity of all neural areas the right hemisphere, with no evidence of tumor recurrence. Once submitted the status epilepticus, the hyperintensity disappeared in the hemisphere. This extensive reaction of the neural structures might be related to a permanent effect of radiation, which may have caused a mismatch functional glia, of the blood-brain barrier and interneural network

Neural Control of a Tracking Task via Attention-Gated Reinforcement Learning for Brain-Machine Interfaces.

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Wang, Yiwen; Wang, Fang; Xu, Kai; Zhang, Qiaosheng; Zhang, Shaomin; Zheng, Xiaoxiang

2015-05-01

Reinforcement learning (RL)-based brain machine interfaces (BMIs) enable the user to learn from the environment through interactions to complete the task without desired signals, which is promising for clinical applications. Previous studies exploited Q-learning techniques to discriminate neural states into simple directional actions providing the trial initial timing. However, the movements in BMI applications can be quite complicated, and the action timing explicitly shows the intention when to move. The rich actions and the corresponding neural states form a large state-action space, imposing generalization difficulty on Q-learning. In this paper, we propose to adopt attention-gated reinforcement learning (AGREL) as a new learning scheme for BMIs to adaptively decode high-dimensional neural activities into seven distinct movements (directional moves, holdings and resting) due to the efficient weight-updating. We apply AGREL on neural data recorded from M1 of a monkey to directly predict a seven-action set in a time sequence to reconstruct the trajectory of a center-out task. Compared to Q-learning techniques, AGREL could improve the target acquisition rate to 90.16% in average with faster convergence and more stability to follow neural activity over multiple days, indicating the potential to achieve better online decoding performance for more complicated BMI tasks.

Intrusive images in psychological disorders: characteristics, neural mechanisms, and treatment implications.

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Brewin, Chris R; Gregory, James D; Lipton, Michelle; Burgess, Neil

2010-01-01

Involuntary images and visual memories are prominent in many types of psychopathology. Patients with posttraumatic stress disorder, other anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions corresponding to a small number of real or imaginary events, usually extremely vivid, detailed, and with highly distressing content. Both memory and imagery appear to rely on common networks involving medial prefrontal regions, posterior regions in the medial and lateral parietal cortices, the lateral temporal cortex, and the medial temporal lobe. Evidence from cognitive psychology and neuroscience implies distinct neural bases to abstract, flexible, contextualized representations (C-reps) and to inflexible, sensory-bound representations (S-reps). We revise our previous dual representation theory of posttraumatic stress disorder to place it within a neural systems model of healthy memory and imagery. The revised model is used to explain how the different types of distressing visual intrusions associated with clinical disorders arise, in terms of the need for correct interaction between the neural systems supporting S-reps and C-reps via visuospatial working memory. Finally, we discuss the treatment implications of the new model and relate it to existing forms of psychological therapy.

The neural basis of unconditional love.

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Beauregard, Mario; Courtemanche, Jérôme; Paquette, Vincent; St-Pierre, Evelyne Landry

2009-05-15

Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.

Genetically distinct isolates of Spirocerca sp. from a naturally infected red fox (Vulpes vulpes) from Denmark

DEFF Research Database (Denmark)

Al-Sabi, Mohammad Nafi Solaiman; Hansen, Mette Sif; Chriél, Mariann

2014-01-01

sugar-salt solu-tion, and sieving failed to detect eggs of Spirocerca sp. in feces collected from the colon.This is the first report of spirocercosis in Denmark, and may have been caused by a recentintroduction by migrating paratenic or definitive host. Analysis of two overlapping par-tial sequences...... of the cox1 gene, from individual worms, revealed distinct genetic variation(7–9%) between the Danish worms and isolates of S. lupi from Europe, Asia and Africa.This was confirmed by phylogenetic analysis that clearly separated the Danish worms fromother isolates of S. lupi. The distinct genetic differences...

Intermittent reductions in respiratory neural activity elicit spinal TNF-α-independent, atypical PKC-dependent inactivity-induced phrenic motor facilitation.

Science.gov (United States)

Baertsch, Nathan A; Baker-Herman, Tracy L

2015-04-15

In many neural networks, mechanisms of compensatory plasticity respond to prolonged reductions in neural activity by increasing cellular excitability or synaptic strength. In the respiratory control system, a prolonged reduction in synaptic inputs to the phrenic motor pool elicits a TNF-α- and atypical PKC-dependent form of spinal plasticity known as inactivity-induced phrenic motor facilitation (iPMF). Although iPMF may be elicited by a prolonged reduction in respiratory neural activity, iPMF is more efficiently induced when reduced respiratory neural activity (neural apnea) occurs intermittently. Mechanisms giving rise to iPMF following intermittent neural apnea are unknown. The purpose of this study was to test the hypothesis that iPMF following intermittent reductions in respiratory neural activity requires spinal TNF-α and aPKC. Phrenic motor output was recorded in anesthetized and ventilated rats exposed to brief intermittent (5, ∼1.25 min), brief sustained (∼6.25 min), or prolonged sustained (30 min) neural apnea. iPMF was elicited following brief intermittent and prolonged sustained neural apnea, but not following brief sustained neural apnea. Unlike iPMF following prolonged neural apnea, spinal TNF-α was not required to initiate iPMF during intermittent neural apnea; however, aPKC was still required for its stabilization. These results suggest that different patterns of respiratory neural activity induce iPMF through distinct cellular mechanisms but ultimately converge on a similar downstream pathway. Understanding the diverse cellular mechanisms that give rise to inactivity-induced respiratory plasticity may lead to development of novel therapeutic strategies to treat devastating respiratory control disorders when endogenous compensatory mechanisms fail. Copyright © 2015 the American Physiological Society.

Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder

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Walshe Muriel

2011-01-01

Full Text Available Abstract Background Impairments in executive function and language processing are characteristic of both schizophrenia and bipolar disorder. Their functional neuroanatomy demonstrate features that are shared as well as specific to each disorder. Determining the distinct pattern of neural responses in schizophrenia and bipolar disorder may provide biomarkers for their diagnoses. Methods 104 participants underwent functional magnetic resonance imaging (fMRI scans while performing a phonological verbal fluency task. Subjects were 32 patients with schizophrenia in remission, 32 patients with bipolar disorder in an euthymic state, and 40 healthy volunteers. Neural responses to verbal fluency were examined in each group, and the diagnostic potential of the pattern of the neural responses was assessed with machine learning analysis. Results During the verbal fluency task, both patient groups showed increased activation in the anterior cingulate, left dorsolateral prefrontal cortex and right putamen as compared to healthy controls, as well as reduced deactivation of precuneus and posterior cingulate. The magnitude of activation was greatest in patients with schizophrenia, followed by patients with bipolar disorder and then healthy individuals. Additional recruitment in the right inferior frontal and right dorsolateral prefrontal cortices was observed in schizophrenia relative to both bipolar disorder and healthy subjects. The pattern of neural responses correctly identified individual patients with schizophrenia with an accuracy of 92%, and those with bipolar disorder with an accuracy of 79% in which mis-classification was typically of bipolar subjects as healthy controls. Conclusions In summary, both schizophrenia and bipolar disorder are associated with altered function in prefrontal, striatal and default mode networks, but the magnitude of this dysfunction is particularly marked in schizophrenia. The pattern of response to verbal fluency is highly

Antagonism between the transcription factors NANOG and OTX2 specifies rostral or caudal cell fate during neural patterning transition.

Science.gov (United States)

Su, Zhenghui; Zhang, Yanqi; Liao, Baojian; Zhong, Xiaofen; Chen, Xin; Wang, Haitao; Guo, Yiping; Shan, Yongli; Wang, Lihui; Pan, Guangjin

2018-03-23

During neurogenesis, neural patterning is a critical step during which neural progenitor cells differentiate into neurons with distinct functions. However, the molecular determinants that regulate neural patterning remain poorly understood. Here we optimized the "dual SMAD inhibition" method to specifically promote differentiation of human pluripotent stem cells (hPSCs) into forebrain and hindbrain neural progenitor cells along the rostral-caudal axis. We report that neural patterning determination occurs at the very early stage in this differentiation. Undifferentiated hPSCs expressed basal levels of the transcription factor orthodenticle homeobox 2 (OTX2) that dominantly drove hPSCs into the "default" rostral fate at the beginning of differentiation. Inhibition of glycogen synthase kinase 3β (GSK3β) through CHIR99021 application sustained transient expression of the transcription factor NANOG at early differentiation stages through Wnt signaling. Wnt signaling and NANOG antagonized OTX2 and, in the later stages of differentiation, switched the default rostral cell fate to the caudal one. Our findings have uncovered a mutual antagonism between NANOG and OTX2 underlying cell fate decisions during neural patterning, critical for the regulation of early neural development in humans. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

Science.gov (United States)

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

Distinct aspects of frontal lobe structure mediate age-related differences in fluid intelligence and multitasking

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Kievit, Rogier A.; Davis, Simon W.; Mitchell, Daniel J.; Taylor, Jason R.; Duncan, John; Tyler, Lorraine K.; Brayne, Carol; Bullmore, Ed; Calder, Andrew; Cusack, Rhodri; Dalgleish, Tim; Matthews, Fiona; Marslen-Wilson, William; Rowe, James; Shafto, Meredith; Campbell, Karen; Cheung, Teresa; Geerligs, Linda; McCarrey, Anna; Tsvetanov, Kamen; Williams, Nitin; Bates, Lauren; Emery, Tina; Erzinçlioglu, Sharon; Gadie, Andrew; Gerbase, Sofia; Georgieva, Stanimira; Hanley, Claire; Parkin, Beth; Troy, David; Allen, Jodie; Amery, Gillian; Amunts, Liana; Barcroft, Anne; Castle, Amanda; Dias, Cheryl; Dowrick, Jonathan; Fair, Melissa; Fisher, Hayley; Goulding, Anna; Grewal, Adarsh; Hale, Geoff; Hilton, Andrew; Johnson, Frances; Johnston, Patricia; Kavanagh-Williamson, Thea; Kwasniewska, Magdalena; McMinn, Alison; Norman, Kim; Penrose, Jessica; Roby, Fiona; Rowland, Diane; Sargeant, John; Squire, Maggie; Stevens, Beth; Stoddart, Aldabra; Stone, Cheryl; Thompson, Tracy; Yazlik, Ozlem; Barnes, Dan; Dixon, Marie; Hillman, Jaya; Mitchell, Joanne; Villis, Laura; Henson, Richard N.A.

2014-01-01

Ageing is characterized by declines on a variety of cognitive measures. These declines are often attributed to a general, unitary underlying cause, such as a reduction in executive function owing to atrophy of the prefrontal cortex. However, age-related changes are likely multifactorial, and the relationship between neural changes and cognitive measures is not well-understood. Here we address this in a large (N=567), population-based sample drawn from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. We relate fluid intelligence and multitasking to multiple brain measures, including grey matter in various prefrontal regions and white matter integrity connecting those regions. We show that multitasking and fluid intelligence are separable cognitive abilities, with differential sensitivities to age, which are mediated by distinct neural subsystems that show different prediction in older versus younger individuals. These results suggest that prefrontal ageing is a manifold process demanding multifaceted models of neurocognitive ageing. PMID:25519467

The COP9 signalosome converts temporal hormone signaling to spatial restriction on neural competence.

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Yi-Chun Huang

2014-11-01

Full Text Available During development, neural competence is conferred and maintained by integrating spatial and temporal regulations. The Drosophila sensory bristles that detect mechanical and chemical stimulations are arranged in stereotypical positions. The anterior wing margin (AWM is arrayed with neuron-innervated sensory bristles, while posterior wing margin (PWM bristles are non-innervated. We found that the COP9 signalosome (CSN suppresses the neural competence of non-innervated bristles at the PWM. In CSN mutants, PWM bristles are transformed into neuron-innervated, which is attributed to sustained expression of the neural-determining factor Senseless (Sens. The CSN suppresses Sens through repression of the ecdysone signaling target gene broad (br that encodes the BR-Z1 transcription factor to activate sens expression. Strikingly, CSN suppression of BR-Z1 is initiated at the prepupa-to-pupa transition, leading to Sens downregulation, and termination of the neural competence of PWM bristles. The role of ecdysone signaling to repress br after the prepupa-to-pupa transition is distinct from its conventional role in activation, and requires CSN deneddylating activity and multiple cullins, the major substrates of deneddylation. Several CSN subunits physically associate with ecdysone receptors to represses br at the transcriptional level. We propose a model in which nuclear hormone receptors cooperate with the deneddylation machinery to temporally shutdown downstream target gene expression, conferring a spatial restriction on neural competence at the PWM.

Neural Oscillations and Synchrony in Brain Dysfunction and Neuropsychiatric Disorders: It's About Time.

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Mathalon, Daniel H; Sohal, Vikaas S

2015-08-01

Neural oscillations are rhythmic fluctuations over time in the activity or excitability of single neurons, local neuronal populations or "assemblies," and/or multiple regionally distributed neuronal assemblies. Synchronized oscillations among large numbers of neurons are evident in electrocorticographic, electroencephalographic, magnetoencephalographic, and local field potential recordings and are generally understood to depend on inhibition that paces assemblies of excitatory neurons to produce alternating temporal windows of reduced and increased excitability. Synchronization of neural oscillations is supported by the extensive networks of local and long-range feedforward and feedback bidirectional connections between neurons. Here, we review some of the major methods and measures used to characterize neural oscillations, with a focus on gamma oscillations. Distinctions are drawn between stimulus-independent oscillations recorded during resting states or intervals between task events, stimulus-induced oscillations that are time locked but not phase locked to stimuli, and stimulus-evoked oscillations that are both time and phase locked to stimuli. Synchrony of oscillations between recording sites, and between the amplitudes and phases of oscillations of different frequencies (cross-frequency coupling), is described and illustrated. Molecular mechanisms underlying gamma oscillations are also reviewed. Ultimately, understanding the temporal organization of neuronal network activity, including interactions between neural oscillations, is critical for elucidating brain dysfunction in neuropsychiatric disorders.

A common neural code for perceived and inferred emotion.

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Skerry, Amy E; Saxe, Rebecca

2014-11-26

Although the emotions of other people can often be perceived from overt reactions (e.g., facial or vocal expressions), they can also be inferred from situational information in the absence of observable expressions. How does the human brain make use of these diverse forms of evidence to generate a common representation of a target's emotional state? In the present research, we identify neural patterns that correspond to emotions inferred from contextual information and find that these patterns generalize across different cues from which an emotion can be attributed. Specifically, we use functional neuroimaging to measure neural responses to dynamic facial expressions with positive and negative valence and to short animations in which the valence of a character's emotion could be identified only from the situation. Using multivoxel pattern analysis, we test for regions that contain information about the target's emotional state, identifying representations specific to a single stimulus type and representations that generalize across stimulus types. In regions of medial prefrontal cortex (MPFC), a classifier trained to discriminate emotional valence for one stimulus (e.g., animated situations) could successfully discriminate valence for the remaining stimulus (e.g., facial expressions), indicating a representation of valence that abstracts away from perceptual features and generalizes across different forms of evidence. Moreover, in a subregion of MPFC, this neural representation generalized to trials involving subjectively experienced emotional events, suggesting partial overlap in neural responses to attributed and experienced emotions. These data provide a step toward understanding how the brain transforms stimulus-bound inputs into abstract representations of emotion. Copyright © 2014 the authors 0270-6474/14/3315997-12$15.00/0.

Affective traits link to reliable neural markers of incentive anticipation.

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Wu, Charlene C; Samanez-Larkin, Gregory R; Katovich, Kiefer; Knutson, Brian

2014-01-01

While theorists have speculated that different affective traits are linked to reliable brain activity during anticipation of gains and losses, few have directly tested this prediction. We examined these associations in a community sample of healthy human adults (n=52) as they played a Monetary Incentive Delay task while undergoing functional magnetic resonance imaging (FMRI). Factor analysis of personality measures revealed that subjects independently varied in trait Positive Arousal and trait Negative Arousal. In a subsample (n=14) retested over 2.5years later, left nucleus accumbens (NAcc) activity during anticipation of large gains (+$5.00) and right anterior insula activity during anticipation of large losses (-$5.00) showed significant test-retest reliability (intraclass correlations>0.50, p'santicipation of large gains, while trait Negative Arousal correlated with individual differences in right anterior insula activity during anticipation of large losses. Associations of affective traits with neural activity were not attributable to the influence of other potential confounds (including sex, age, wealth, and motion). Together, these results demonstrate selective links between distinct affective traits and reliably-elicited activity in neural circuits associated with anticipation of gain versus loss. The findings thus reveal neural markers for affective dimensions of healthy personality, and potentially for related psychiatric symptoms. © 2013. Published by Elsevier Inc. All rights reserved.

Neural networks

International Nuclear Information System (INIS)

Denby, Bruce; Lindsey, Clark; Lyons, Louis

1992-01-01

The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

Recurrent Neural Networks to Correct Satellite Image Classification Maps

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Maggiori, Emmanuel; Charpiat, Guillaume; Tarabalka, Yuliya; Alliez, Pierre

2017-09-01

While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps.

Dissecting neural pathways for forgetting in Drosophila olfactory aversive memory.

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Shuai, Yichun; Hirokawa, Areekul; Ai, Yulian; Zhang, Min; Li, Wanhe; Zhong, Yi

2015-12-01

Recent studies have identified molecular pathways driving forgetting and supported the notion that forgetting is a biologically active process. The circuit mechanisms of forgetting, however, remain largely unknown. Here we report two sets of Drosophila neurons that account for the rapid forgetting of early olfactory aversive memory. We show that inactivating these neurons inhibits memory decay without altering learning, whereas activating them promotes forgetting. These neurons, including a cluster of dopaminergic neurons (PAM-β'1) and a pair of glutamatergic neurons (MBON-γ4>γ1γ2), terminate in distinct subdomains in the mushroom body and represent parallel neural pathways for regulating forgetting. Interestingly, although activity of these neurons is required for memory decay over time, they are not required for acute forgetting during reversal learning. Our results thus not only establish the presence of multiple neural pathways for forgetting in Drosophila but also suggest the existence of diverse circuit mechanisms of forgetting in different contexts.

Cognitive and Neural Effects of Vision-Based Speed-of-Processing Training in Older Adults with Amnestic Mild Cognitive Impairment: A Pilot Study.

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Lin, Feng; Heffner, Kathi L; Ren, Ping; Tivarus, Madalina E; Brasch, Judith; Chen, Ding-Geng; Mapstone, Mark; Porsteinsson, Anton P; Tadin, Duje

2016-06-01

To examine the cognitive and neural effects of vision-based speed-of-processing (VSOP) training in older adults with amnestic mild cognitive impairment (aMCI) and contrast those effects with an active control (mental leisure activities (MLA)). Randomized single-blind controlled pilot trial. Academic medical center. Individuals with aMCI (N = 21). Six-week computerized VSOP training. Multiple cognitive processing measures, instrumental activities of daily living (IADLs), and two resting state neural networks regulating cognitive processing: central executive network (CEN) and default mode network (DMN). VSOP training led to significantly greater improvements in trained (processing speed and attention: F1,19  = 6.61, partial η(2)  = 0.26, P = .02) and untrained (working memory: F1,19  = 7.33, partial η(2)  = 0.28, P = .01; IADLs: F1,19  = 5.16, partial η(2)  = 0.21, P = .03) cognitive domains than MLA and protective maintenance in DMN (F1, 9  = 14.63, partial η(2)  = 0.62, P = .004). VSOP training, but not MLA, resulted in a significant improvement in CEN connectivity (Z = -2.37, P = .02). Target and transfer effects of VSOP training were identified, and links between VSOP training and two neural networks associated with aMCI were found. These findings highlight the potential of VSOP training to slow cognitive decline in individuals with aMCI. Further delineation of mechanisms underlying VSOP-induced plasticity is necessary to understand in which populations and under what conditions such training may be most effective. © 2016, Copyright the Authors Journal compilation © 2016, The American Geriatrics Society.

Probabilistic Teleportation via Quantum Channel with Partial Information

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Desheng Liu

2015-06-01

Full Text Available Two novel schemes are proposed to teleport an unknown two-level quantum state probabilistically when the sender and the receiver only have partial information about the quantum channel, respectively. This is distinct from the fact that either the sender or the receiver has entire information about the quantum channel in previous schemes for probabilistic teleportation. Theoretical analysis proves that these schemes are straightforward, efficient and cost-saving. The concrete realization procedures of our schemes are presented in detail, and the result shows that our proposals could extend the application range of probabilistic teleportation.

Optimization of operation cycles in BWRs using neural networks; Optimizacion de ciclos de operacion en BWRs usando redes neuronales

Energy Technology Data Exchange (ETDEWEB)

Ortiz S, J. J.; Castillo, A. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Alejandro P, D., E-mail: juanjose.ortiz@inin.gob.mx [Universidad de Granada, ETS de Ingenierias, Informatica y de Telecomunicacion, C/Daniel Saucedo Aranda s/n, 18071 Granada (Spain)

2011-11-15

The first results of a system for the optimization of operation cycles in boiling water reactors by means of a multi state recurrent neural network are present in this work. The neural network finds the best combination of fuel cells; fuel reloads and control bars patterns previously designed, according to an energy function that qualifies the performance of the three partial solutions for the solution of the whole problem. The partial solutions are designed by means of optimization systems non couple among them and that can use any optimization technique. The phase of the fuel axial design is not made and the size of the axial areas is fixed during the optimization process. The methodology was applied to design a balance cycle of 18 months for the reactors of the nuclear power station of Laguna Verde. The results show that is possible to find combinations of partial solutions that in set represent good solutions to the complete design problem of an operation cycle of a nuclear reactor. The results are compared with others obtained previously by other techniques. This system was developed in platform Li nux and programmed in Fortran 95 taking advantage of the 8 nuclei of a work station Dell Precision T7400. (Author)

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

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Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

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Kai Olav Ellefsen

2015-04-01

Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Neural basis of three dimensions of agitated behaviors in patients with Alzheimer disease

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Banno K

2014-02-01

Full Text Available Koichi Banno,1 Shutaro Nakaaki,2 Junko Sato,1 Katsuyoshi Torii,1 Jin Narumoto,3 Jun Miyata,4 Nobutsugu Hirono,5 Toshi A Furukawa,6 Masaru Mimura,2 Tatsuo Akechi1 1Department of Psychiatry and Cognitive-Behavioral Medicine, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan; 2Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; 3Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 4Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan; 5Department of Psychology, Kobe Gakuin University; Hyogo, Japan; 6Departments of Health Promotion and Human Behavior and of Clinical Epidemiology, Graduate School of Medicine/School of Public Health, Kyoto University, Kyoto, Japan Background: Agitated behaviors are frequently observed in patients with Alzheimer disease (AD. The neural substrate underlying the agitated behaviors in dementia is unclear. We hypothesized that different dimensions of agitated behaviors are mediated by distinct neural systems. Methods: All the patients (n=32 underwent single photon emission computed tomography (SPECT. Using the Agitated Behavior in Dementia scale, we identified the relationships between regional cerebral blood flow (rCBF patterns and the presence of each of three dimensions of agitated behavior (physically agitated behavior, verbally agitated behavior, and psychosis symptoms in AD patients. Statistical parametric mapping (SPM software was used to explore these neural correlations. Results: Physically agitated behavior was significantly correlated with lower rCBF values in the right superior temporal gyrus (Brodmann 22 and the right inferior frontal gyrus (Brodmann 47. Verbally agitated behavior was significantly associated with lower rCBF values in the left inferior frontal gyrus (Brodmann 46, 44 and the left insula (Brodmann 13. The psychosis symptoms were significantly correlated

Analysis of Insulating Material of XLPE Cables considering Innovative Patterns of Partial Discharges

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Fernando Figueroa Godoy

2017-01-01

Full Text Available This paper aims to analyze the quality of insulation in high voltage underground cables XLPE using a prototype which classifies the following usual types of patterns of partial discharge (PD: (1 internal PD, (2 superficial PD, (3 corona discharge in air, and (4 corona discharge in oil, in addition to considering two new PD patterns: (1 false contact and (2 floating ground. The tests and measurements to obtain the patterns and study cases of partial discharges were performed at the Testing Laboratory Equipment and Materials (LEPEM of the Federal Electricity Commission of Mexico (CFE using a measuring equipment LDIC and norm IEC60270. To classify the six patterns of partial discharges mentioned above a Probabilistic Neural Network Bayesian Modified (PNNBM method having the feature of using a large amount of data will be used and it is not saturated. In addition, PNN converges, always finding a solution in a short period of time with low computational cost. The insulation of two high voltage cables with different characteristics was analyzed. The test results allow us to conclude which wire has better insulation.

Neural theory for the perception of causal actions.

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Fleischer, Falk; Christensen, Andrea; Caggiano, Vittorio; Thier, Peter; Giese, Martin A

2012-07-01

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.

Larger Neural Responses Produce BOLD Signals That Begin Earlier in Time

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Serena eThompson

2014-06-01

Full Text Available Functional MRI analyses commonly rely on the assumption that the temporal dynamics of hemodynamic response functions (HRFs are independent of the amplitude of the neural signals that give rise to them. The validity of this assumption is particularly important for techniques that use fMRI to resolve sub-second timing distinctions between responses, in order to make inferences about the ordering of neural processes. Whether or not the detailed shape of the HRF is independent of neural response amplitude remains an open question, however. We performed experiments in which we measured responses in primary visual cortex (V1 to large, contrast-reversing checkerboards at a range of contrast levels, which should produce varying amounts of neural activity. Ten subjects (ages 22-52 were studied in each of two experiments using 3 Tesla scanners. We used rapid, 250 msec, temporal sampling (repetition time, or TR and both short and long inter-stimulus interval (ISI stimulus presentations. We tested for a systematic relationship between the onset of the HRF and its amplitude across conditions, and found a strong negative correlation between the two measures when stimuli were separated in time (long- and medium-ISI experiments, but not the short-ISI experiment. Thus, stimuli that produce larger neural responses, as indexed by HRF amplitude, also produced HRFs with shorter onsets. The relationship between amplitude and latency was strongest in voxels with lowest mean-normalized variance (i.e., parenchymal voxels. The onset differences observed in the longer-ISI experiments are likely attributable to mechanisms of neurovascular coupling, since they are substantially larger than reported differences in the onset of action potentials in V1 as a function of response amplitude.

Effects of alexithymia and empathy on the neural processing of social and monetary rewards.

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Goerlich, Katharina Sophia; Votinov, Mikhail; Lammertz, Sarah E; Winkler, Lina; Spreckelmeyer, Katja N; Habel, Ute; Gründer, Gerhard; Gossen, Anna

2017-07-01

Empathy has been found to affect the neural processing of social and monetary rewards. Alexithymia, a subclinical condition showing a close inverse relationship with empathy is linked to dysfunctions of socio-emotional processing in the brain. Whether alexithymia alters the neural processing of rewards, which is currently unknown. Here, we investigated the influence of both alexithymia and empathy on reward processing using a social incentive delay (SID) task and a monetary incentive delay (MID) task in 45 healthy men undergoing functional magnetic resonance imaging. Controlling for temperament-character dimensions and rejection sensitivity, the relationship of alexithymia and empathy with neural activity in several a priori regions of interest (ROIs) was examined by means of partial correlations, while participants anticipated and received social and monetary rewards. Results were considered significant if they survived Holm-Bonferroni correction for multiple comparisons. Alexithymia modulated neural activity in several ROIs of the emotion and reward network, both during the anticipation of social and monetary rewards and in response to the receipt of monetary rewards. In contrast, empathy did not affect reward anticipation and modulated ROI activity only in response to the receipt of social rewards. These results indicate a significant influence of alexithymia on the processing of social and monetary rewards in the healthy brain.

Critical features of coupling parameter in synchronization of small world neural networks

International Nuclear Information System (INIS)

Li Yanlong; Ma Jun; Xu Wenke; Li Hongbo; Wu Min

2008-01-01

The critical features of a coupling parameter in the synchronization of small world neural networks are investigated. A power law decay form is observed in this spatially extended system, the larger linked degree becomes, the larger critical coupling intensity. There exists maximal and minimal critical coupling intensity for synchronization in the extended system. An approximate synchronization diagram has been constructed. In the case of partial coupling, a primary result is presented about the critical coupling fraction for various linked degree of networks

Shared neural coding for social hierarchy and reward value in primate amygdala.

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Munuera, Jérôme; Rigotti, Mattia; Salzman, C Daniel

2018-03-01

The social brain hypothesis posits that dedicated neural systems process social information. In support of this, neurophysiological data have shown that some brain regions are specialized for representing faces. It remains unknown, however, whether distinct anatomical substrates also represent more complex social variables, such as the hierarchical rank of individuals within a social group. Here we show that the primate amygdala encodes the hierarchical rank of individuals in the same neuronal ensembles that encode the rewards associated with nonsocial stimuli. By contrast, orbitofrontal and anterior cingulate cortices lack strong representations of hierarchical rank while still representing reward values. These results challenge the conventional view that dedicated neural systems process social information. Instead, information about hierarchical rank-which contributes to the assessment of the social value of individuals within a group-is linked in the amygdala to representations of rewards associated with nonsocial stimuli.

Learning language with the wrong neural scaffolding: The cost of neural commitment to sounds.

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Amy Sue Finn

2013-11-01

Full Text Available Does tuning to one’s native language explain the sensitive period for language learning? We explore the idea that tuning to (or becoming more selective for the properties of one’s native-language could result in being less open (or plastic for tuning to the properties of a new language. To explore how this might lead to the sensitive period for grammar learning, we ask if tuning to an earlier-learned aspect of language (sound structure has an impact on the neural representation of a later-learned aspect (grammar. English-speaking adults learned one of two miniature artificial languages over 4 days in the lab. Compared to English, both languages had novel grammar, but only one was comprised of novel sounds. After learning a language, participants were scanned while judging the grammaticality of sentences. Judgments were performed for the newly learned language and English. Learners of the similar-sounds language recruited regions that overlapped more with English. Learners of the distinct-sounds language, however, recruited the Superior Temporal Gyrus (STG to a greater extent, which was coactive with the Inferior Frontal Gyrus (IFG. Across learners, recruitment of IFG (but not STG predicted both learning success in tests conducted prior to the scan and grammatical judgment ability during the scan. Data suggest that adults’ difficulty learning language, especially grammar, could be due, at least in part, to the neural commitments they have made to the lower level linguistic components of their native language.

Learning language with the wrong neural scaffolding: the cost of neural commitment to sounds

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Finn, Amy S.; Hudson Kam, Carla L.; Ettlinger, Marc; Vytlacil, Jason; D'Esposito, Mark

2013-01-01

Does tuning to one's native language explain the “sensitive period” for language learning? We explore the idea that tuning to (or becoming more selective for) the properties of one's native-language could result in being less open (or plastic) for tuning to the properties of a new language. To explore how this might lead to the sensitive period for grammar learning, we ask if tuning to an earlier-learned aspect of language (sound structure) has an impact on the neural representation of a later-learned aspect (grammar). English-speaking adults learned one of two miniature artificial languages (MALs) over 4 days in the lab. Compared to English, both languages had novel grammar, but only one was comprised of novel sounds. After learning a language, participants were scanned while judging the grammaticality of sentences. Judgments were performed for the newly learned language and English. Learners of the similar-sounds language recruited regions that overlapped more with English. Learners of the distinct-sounds language, however, recruited the Superior Temporal Gyrus (STG) to a greater extent, which was coactive with the Inferior Frontal Gyrus (IFG). Across learners, recruitment of IFG (but not STG) predicted both learning success in tests conducted prior to the scan and grammatical judgment ability during the scan. Data suggest that adults' difficulty learning language, especially grammar, could be due, at least in part, to the neural commitments they have made to the lower level linguistic components of their native language. PMID:24273497

Neural Basis of Limb Ownership in Individuals with Body Integrity Identity Disorder

OpenAIRE

van Dijk, Milenna T.; van Wingen, Guido A.; van Lammeren, Anouk; Blom, Rianne M.; de Kwaasteniet, Bart P.; Scholte, H. Steven; Denys, Damiaan

2013-01-01

Our body feels like it is ours. However, individuals with body integrity identity disorder (BIID) lack this feeling of ownership for distinct limbs and desire amputation of perfectly healthy body parts. This extremely rare condition provides us with an opportunity to study the neural basis underlying the feeling of limb ownership, since these individuals have a feeling of disownership for a limb in the absence of apparent brain damage. Here we directly compared brain activation between limbs ...

Fast analysis of spectral data using neural networks

International Nuclear Information System (INIS)

Roach, C.M.

1992-01-01

Fast analysis techniques are highly desirable in experiments where measurements are recorded at high rates. In fusion experiments the processing required to obtain plasma parameters is usually orders of magnitude slower than the data acquisition. Spectroscopic diagnostics suffer greatly from this problem. The extraction of plasma parameters from a measured spectrum typically corresponds to a nonlinear mapping between distinct multi-dimensional spaces. Where no analytic expression for the mapping exists, conventional analysis methods (e.g. least squares) are usually iterative and therefore slow. With this concern in mind a fast spectral analysis method involving neural networks has been investigated. (author) 6 refs., 3 figs

The effect of visual parameters on neural activation during nonsymbolic number comparison and its relation to math competency.

Science.gov (United States)

Wilkey, Eric D; Barone, Jordan C; Mazzocco, Michèle M M; Vogel, Stephan E; Price, Gavin R

2017-10-01

Nonsymbolic numerical comparison task performance (whereby a participant judges which of two groups of objects is numerically larger) is thought to index the efficiency of neural systems supporting numerical magnitude perception, and performance on such tasks has been related to individual differences in math competency. However, a growing body of research suggests task performance is heavily influenced by visual parameters of the stimuli (e.g. surface area and dot size of object sets) such that the correlation with math is driven by performance on trials in which number is incongruent with visual cues. Almost nothing is currently known about whether the neural correlates of nonsymbolic magnitude comparison are also affected by visual congruency. To investigate this issue, we used functional magnetic resonance imaging (fMRI) to analyze neural activity during a nonsymbolic comparison task as a function of visual congruency in a sample of typically developing high school students (n = 36). Further, we investigated the relation to math competency as measured by the preliminary scholastic aptitude test (PSAT) in 10th grade. Our results indicate that neural activity was modulated by the ratio of the dot sets being compared in brain regions previously shown to exhibit an effect of ratio (i.e. left anterior cingulate, left precentral gyrus, left intraparietal sulcus, and right superior parietal lobe) when calculated from the average of congruent and incongruent trials, as it is in most studies, and that the effect of ratio within those regions did not differ as a function of congruency condition. However, there were significant differences in other regions in overall task-related activation, as opposed to the neural ratio effect, when congruent and incongruent conditions were contrasted at the whole-brain level. Math competency negatively correlated with ratio-dependent neural response in the left insula across congruency conditions and showed distinct correlations when

Selective attention on representations in working memory: cognitive and neural mechanisms

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Yixuan Ku

2018-04-01

Full Text Available Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

Exploring the role of neural mirroring in children with autism spectrum disorder.

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Ruysschaert, Lieselot; Warreyn, Petra; Wiersema, Jan R; Oostra, Ann; Roeyers, Herbert

2014-04-01

Investigating the underlying neural mechanisms of autism spectrum disorder (ASD) has recently been influenced by the discovery of mirror neurons. These neurons, active during both observation and execution of actions, are thought to play a crucial role in imitation and other social-communicative skills that are often impaired in ASD. In the current electroencephalographic study, we investigated mu suppression, indicating neural mirroring in children with ASD between the ages of 24 and 48 months and age-matched typically developing children, during observation of goal-directed actions and non-goal-directed mimicked hand movements, as well as during action execution. Results revealed no significant group differences with significant central mu suppression in the ASD children and control children during both execution and observation of goal-directed actions and during observation of hand movements. Furthermore, no significant correlations between mu suppression on one hand and quality of imitation, age, and social communication questionnaire scores on the other hand were found. These findings challenge the "broken mirror" hypothesis of ASD, suggesting that impaired neural mirroring is not a distinctive feature of ASD. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.

Cast Partial Denture versus Acrylic Partial Denture for Replacement of Missing Teeth in Partially Edentulous Patients

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Pramita Suwal

2017-03-01

Full Text Available Aim: To compare the effects of cast partial denture with conventional all acrylic denture in respect to retention, stability, masticatory efficiency, comfort and periodontal health of abutments. Methods: 50 adult partially edentulous patient seeking for replacement of missing teeth having Kennedy class I and II arches with or without modification areas were selected for the study. Group-A was treated with cast partial denture and Group-B with acrylic partial denture. Data collected during follow-up visit of 3 months, 6 months, and 1 year by evaluating retention, stability, masticatory efficiency, comfort, periodontal health of abutment. Results: Chi-square test was applied to find out differences between the groups at 95% confidence interval where p = 0.05. One year comparison shows that cast partial denture maintained retention and stability better than acrylic partial denture (p< 0.05. The masticatory efficiency was significantly compromising from 3rd month to 1 year in all acrylic partial denture groups (p< 0.05. The comfort of patient with cast partial denture was maintained better during the observation period (p< 0.05. Periodontal health of abutment was gradually deteriorated in all acrylic denture group (p

Frontal Underactivation During Working Memory Processing in Adults With Acute Partial Sleep Deprivation: A Near-Infrared Spectroscopy Study

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Michael K. Yeung

2018-05-01

Full Text Available Individuals with partial sleep deprivation may have working memory (WM impairment, but the underlying neural mechanism of this phenomenon is relatively unknown. The present study examined neural processing during WM performance in individuals with and without partial sleep deprivation using near-infrared spectroscopy (NIRS. Forty college students (10 males were equally split into Sufficient Sleep (SS and Insufficient Sleep (IS groups based on self-reports of previous night's sleep duration. Participants in the SS group obtained the recommended amounts of sleep according to various sleep organizations (i.e., >7.0 h, whereas those in the IS group obtained amounts of sleep no greater than the lower limit of the recommendation (i.e., ≤7.0 h. All participants underwent an n-back paradigm with a WM load (i.e., 3-back and a control condition (i.e., 0-back while their prefrontal hemodynamics were recorded by NIRS. The IS and SS groups performed the tasks comparably well. However, unlike the SS group, which exhibited bilateral frontal activation indicated by increased oxyhemoglobin concentration and decreased deoxyhemoglobin concentration during WM processing (i.e., 3-back > 0-back, the IS group did not exhibit such activation. In addition, levels of WM-related frontal activation, especially those on the left side, correlated with sleep duration the night before, even when habitual sleep duration was controlled for. The findings suggest the presence of frontal lobe dysfunction in the absence of evident WM difficulties in individuals with acute partial sleep deprivation. They also highlight the importance of a good night's sleep to brain health.

Evolvable Neural Software System

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Curtis, Steven A.

2009-01-01

The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

Neural processing of amplitude and formant rise time in dyslexia.

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Peter, Varghese; Kalashnikova, Marina; Burnham, Denis

2016-06-01

This study aimed to investigate how children with dyslexia weight amplitude rise time (ART) and formant rise time (FRT) cues in phonetic discrimination. Passive mismatch responses (MMR) were recorded for a/ba/-/wa/contrast in a multiple deviant odd-ball paradigm to identify the neural response to cue weighting in 17 children with dyslexia and 17 age-matched control children. The deviant stimuli had either partial or full ART or FRT cues. The results showed that ART did not generate an MMR in either group, whereas both partial and full FRT cues generated MMR in control children while only full FRT cues generated MMR in children with dyslexia. These findings suggest that children, both controls and those with dyslexia, discriminate speech based on FRT cues and not ART cues. However, control children have greater sensitivity to FRT cues in speech compared to children with dyslexia. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Microglia modulate hippocampal neural precursor activity in response to exercise and aging.

Science.gov (United States)

Vukovic, Jana; Colditz, Michael J; Blackmore, Daniel G; Ruitenberg, Marc J; Bartlett, Perry F

2012-05-09

Exercise has been shown to positively augment adult hippocampal neurogenesis; however, the cellular and molecular pathways mediating this effect remain largely unknown. Previous studies have suggested that microglia may have the ability to differentially instruct neurogenesis in the adult brain. Here, we used transgenic Csf1r-GFP mice to investigate whether hippocampal microglia directly influence the activation of neural precursor cells. Our results revealed that an exercise-induced increase in neural precursor cell activity was mediated via endogenous microglia and abolished when these cells were selectively removed from hippocampal cultures. Conversely, microglia from the hippocampi of animals that had exercised were able to activate latent neural precursor cells when added to neurosphere preparations from sedentary mice. We also investigated the role of CX(3)CL1, a chemokine that is known to provide a more neuroprotective microglial phenotype. Intraparenchymal infusion of a blocking antibody against the CX(3)CL1 receptor, CX(3)CR1, but not control IgG, dramatically reduced the neurosphere formation frequency in mice that had exercised. While an increase in soluble CX(3)CL1 was observed following running, reduced levels of this chemokine were found in the aged brain. Lower levels of CX(3)CL1 with advancing age correlated with the natural decline in neural precursor cell activity, a state that could be partially alleviated through removal of microglia. These findings provide the first direct evidence that endogenous microglia can exert a dual and opposing influence on neural precursor cell activity within the hippocampus, and that signaling through the CX(3)CL1-CX(3)CR1 axis critically contributes toward this process.

Topology and computational performance of attractor neural networks

International Nuclear Information System (INIS)

McGraw, Patrick N.; Menzinger, Michael

2003-01-01

To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies. The random configuration is the most efficient for storage and retrieval of patterns by the network as a whole. However, in the scale-free case retrieval errors are not distributed uniformly among the nodes. The portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. The implications of these findings for brain function and social dynamics are suggestive

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

Neural basis of disgust perception in racial prejudice.

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Liu, Yunzhe; Lin, Wanjun; Xu, Pengfei; Zhang, Dandan; Luo, Yuejia

2015-12-01

Worldwide racial prejudice is originated from in-group/out-group discrimination. This prejudice can bias face perception at the very beginning of social interaction. However, little is known about the neurocognitive mechanism underlying the influence of racial prejudice on facial emotion perception. Here, we examined the neural basis of disgust perception in racial prejudice using a passive viewing task and functional magnetic resonance imaging. We found that compared with the disgusted faces of in-groups, the disgusted faces of out-groups result in increased amygdala and insular engagement, positive coupling of the insula with amygdala-based emotional system, and negative coupling of the insula with anterior cingulate cortex (ACC)-based regulatory system. Furthermore, machine-learning algorithms revealed that the level of implicit racial prejudice could be predicted by functional couplings of the insula with both the amygdala and the ACC, which suggests that the insula is largely involved in racially biased disgust perception through two distinct neural circuits. In addition, individual difference in disgust sensitivity was found to be predictive of implicit racial prejudice. Taken together, our results suggest a crucial role of insula-centered circuits for disgust perception in racial prejudice. © 2015 Wiley Periodicals, Inc.

Computational modeling of neural plasticity for self-organization of neural networks.

Science.gov (United States)

Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Two Distinct Moral Mechanisms for Ascribing and Denying Intentionality.

Science.gov (United States)

Ngo, Lawrence; Kelly, Meagan; Coutlee, Christopher G; Carter, R McKell; Sinnott-Armstrong, Walter; Huettel, Scott A

2015-12-04

Philosophers and legal scholars have long theorized about how intentionality serves as a critical input for morality and culpability, but the emerging field of experimental philosophy has revealed a puzzling asymmetry. People judge actions leading to negative consequences as being more intentional than those leading to positive ones. The implications of this asymmetry remain unclear because there is no consensus regarding the underlying mechanism. Based on converging behavioral and neural evidence, we demonstrate that there is no single underlying mechanism. Instead, two distinct mechanisms together generate the asymmetry. Emotion drives ascriptions of intentionality for negative consequences, while the consideration of statistical norms leads to the denial of intentionality for positive consequences. We employ this novel two-mechanism model to illustrate that morality can paradoxically shape judgments of intentionality. This is consequential for mens rea in legal practice and arguments in moral philosophy pertaining to terror bombing, abortion, and euthanasia among others.

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information.

Science.gov (United States)

Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter

2016-12-07

Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

ATM deficiency results in accumulation of DNA-topoisomerase I covalent intermediates in neural cells.

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Meryem Alagoz

Full Text Available Accumulation of peptide-linked DNA breaks contributes to neurodegeration in humans. This is typified by defects in tyrosyl DNA phosphodiesterase 1 (TDP1 and human hereditary ataxia. TDP1 primarily operates at single-strand breaks (SSBs created by oxidative stress or by collision of transcription machinery with topoisomerase I intermediates (Top1-CCs. Cellular and cell-free studies have shown that Top1 at stalled Top1-CCs is first degraded to a small peptide resulting in Top1-SSBs, which are the primary substrates for TDP1. Here we established an assay to directly compare Top1-SSBs and Top1-CCs. We subsequently employed this assay to reveal an increased steady state level of Top1-CCs in neural cells lacking Atm; the protein mutated in ataxia telangiectasia. Our data suggest that the accumulation of endogenous Top1-CCs in Atm-/- neural cells is primarily due to elevated levels of reactive oxygen species. Biochemical purification of Top1-CCs from neural cell extract and the use of Top1 poisons further confirmed a role for Atm during the formation/resolution of Top1-CCs. Finally, we report that global transcription is reduced in Atm-/- neural cells and fails to recover to normal levels following Top1-mediated DNA damage. Together, these data identify a distinct role for ATM during the formation/resolution of neural Top1-CCs and suggest that their accumulation contributes to the neuropathology of ataxia telangiectasia.

Acute stress evokes sexually dimorphic, stressor-specific patterns of neural activation across multiple limbic brain regions in adult rats.

Science.gov (United States)

Sood, Ankit; Chaudhari, Karina; Vaidya, Vidita A

2018-03-01

Stress enhances the risk for psychiatric disorders such as anxiety and depression. Stress responses vary across sex and may underlie the heightened vulnerability to psychopathology in females. Here, we examined the influence of acute immobilization stress (AIS) and a two-day short-term forced swim stress (FS) on neural activation in multiple cortical and subcortical brain regions, implicated as targets of stress and in the regulation of neuroendocrine stress responses, in male and female rats using Fos as a neural activity marker. AIS evoked a sex-dependent pattern of neural activation within the cingulate and infralimbic subdivisions of the medial prefrontal cortex (mPFC), lateral septum (LS), habenula, and hippocampal subfields. The degree of neural activation in the mPFC, LS, and habenula was higher in males. Female rats exhibited reduced Fos positive cell numbers in the dentate gyrus hippocampal subfield, an effect not observed in males. We addressed whether the sexually dimorphic neural activation pattern noted following AIS was also observed with the short-term stress of FS. In the paraventricular nucleus of the hypothalamus and the amygdala, FS similar to AIS resulted in robust increases in neural activation in both sexes. The pattern of neural activation evoked by FS was distinct across sexes, with a heightened neural activation noted in the prelimbic mPFC subdivision and hippocampal subfields in females and differed from the pattern noted with AIS. This indicates that the sex differences in neural activation patterns observed within stress-responsive brain regions are dependent on the nature of stressor experience.

Recursive N-way partial least squares for brain-computer interface.

Directory of Open Access Journals (Sweden)

Andrey Eliseyev

Full Text Available In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken. The RNPLS algorithm demonstrates fast and stable convergence of regression coefficients. Applied to Brain Computer Interface system calibration, the algorithm provides an efficient adjustment of the decoding model. Combining the online adaptation with easy interpretation of results, the method can be effectively applied in a variety of multi-modal neural activity flow modeling tasks.

Criterion for testing multiparticle negative-partial-transpose entanglement

International Nuclear Information System (INIS)

Zeng, B.; Zhou, D.L.; Zhang, P.; Xu, Z.; You, L.

2003-01-01

We revisit the criterion of multiparticle entanglement based on the overlaps of a given quantum state ρ with maximally entangled states. For a system of m particles, each with N distinct states, we prove that ρ is m-particle negative partial transpose entangled, if there exists a maximally entangled state vertical bar MES>, such that >1/N. While this sufficiency condition is weaker than the Peres-Horodecki criterion in all cases, it applies to multi-particle systems, and becomes especially useful when the number of particles (m) is large. We also consider the converse of this criterion and illustrate its invalidity with counter examples

[MEG]PLS: A pipeline for MEG data analysis and partial least squares statistics.

Science.gov (United States)

Cheung, Michael J; Kovačević, Natasa; Fatima, Zainab; Mišić, Bratislav; McIntosh, Anthony R

2016-01-01

The emphasis of modern neurobiological theories has recently shifted from the independent function of brain areas to their interactions in the context of whole-brain networks. As a result, neuroimaging methods and analyses have also increasingly focused on network discovery. Magnetoencephalography (MEG) is a neuroimaging modality that captures neural activity with a high degree of temporal specificity, providing detailed, time varying maps of neural activity. Partial least squares (PLS) analysis is a multivariate framework that can be used to isolate distributed spatiotemporal patterns of neural activity that differentiate groups or cognitive tasks, to relate neural activity to behavior, and to capture large-scale network interactions. Here we introduce [MEG]PLS, a MATLAB-based platform that streamlines MEG data preprocessing, source reconstruction and PLS analysis in a single unified framework. [MEG]PLS facilitates MRI preprocessing, including segmentation and coregistration, MEG preprocessing, including filtering, epoching, and artifact correction, MEG sensor analysis, in both time and frequency domains, MEG source analysis, including multiple head models and beamforming algorithms, and combines these with a suite of PLS analyses. The pipeline is open-source and modular, utilizing functions from FieldTrip (Donders, NL), AFNI (NIMH, USA), SPM8 (UCL, UK) and PLScmd (Baycrest, CAN), which are extensively supported and continually developed by their respective communities. [MEG]PLS is flexible, providing both a graphical user interface and command-line options, depending on the needs of the user. A visualization suite allows multiple types of data and analyses to be displayed and includes 4-D montage functionality. [MEG]PLS is freely available under the GNU public license (http://meg-pls.weebly.com). Copyright © 2015 Elsevier Inc. All rights reserved.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Equivalent neural responses in children and adolescents with and without autism during judgments of affect

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Brent C. Vander Wyk

2014-04-01

Full Text Available Previous research has noted disrupted patterns of neural activation during emotion, processing in individuals with autism spectrum disorders (ASD. However, prior research relied on, designs that may place greater cognitive load on individuals with ASD. In order to address this issue, we adapted the fMRI task of Ochsner et al. (2004a for children by, presenting fewer stimuli, with fewer valence levels, and longer stimuli duration. A localizer sample of, typically developing children (n = 26 was used to construct regions of interest involved in emotional, processing. Activations in these regions during self- and other-referential emotion processing was, compared in age, IQ, gender matched groups (n = 17 ASD, n = 16 TD. Matched samples replicate, condition contrasts of the localizer, but no group differences were found in behavior measures or, neural activation. An exploratory functional connectivity analysis in a subset of the matched groups, also did not detect striking differences between the groups. These findings suggest that disruptions in activation in emotion processing neural networks in ASD is partially a function of task related cognitive load.

Pay What You Want! A Pilot Study on Neural Correlates of Voluntary Payments for Music

Science.gov (United States)

Waskow, Simon; Markett, Sebastian; Montag, Christian; Weber, Bernd; Trautner, Peter; Kramarz, Volkmar; Reuter, Martin

2016-01-01

Pay-what-you-want (PWYW) is an alternative pricing mechanism for consumer goods. It describes an exchange situation in which the price for a given good is not set by the seller but freely chosen by the buyer. In recent years, many enterprises have made use of PWYW auctions. The somewhat contra-intuitive success of PWYW has sparked a great deal of behavioral work on economical decision making in PWYW contexts in the past. Empirical studies on the neural basis of PWYW decisions, however, are scarce. In the present paper, we present an experimental protocol to study PWYW decision making while simultaneously acquiring functional magnetic resonance imaging data. Participants have the possibility to buy music either under a traditional “fixed-price” (FP) condition or in a condition that allows them to freely decide on the price. The behavioral data from our experiment replicate previous results on the general feasibility of the PWYW mechanism. On the neural level, we observe distinct differences between the two conditions: In the FP-condition, neural activity in frontal areas during decision-making correlates positively with the participants’ willingness to pay. No such relationship was observed under PWYW conditions in any neural structure. Directly comparing neural activity during PWYW and the FP-condition we observed stronger activity of the lingual gyrus during PWYW decisions. Results demonstrate the usability of our experimental paradigm for future investigations into PWYW decision-making and provides first insights into neural mechanisms during self-determined pricing decisions. PMID:27458416

Gamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure

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Dimitris Pinotsis

2014-05-01

Full Text Available This paper shows how gamma oscillations can be combined with neural population models and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical excitability and microstructure. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. Neural field models are used to evaluate model evidence and obtain parameter estimates using invasive and non-invasive gamma recordings. Our overview comprises two parts: in the first part, we use neural fields to simulate neural activity and distinguish the effects of post synaptic filtering on predicted responses in terms of synaptic rate constants that correspond to different timescales and distinct neurotransmitters. We focus on model predictions of conductance and convolution based field models and show that these can yield spectral responses that are sensitive to biophysical properties of local cortical circuits like synaptic kinetics and filtering; we also consider two different mechanisms for this filtering: a nonlinear mechanism involving specific conductances and a linear convolution of afferent firing rates producing post synaptic potentials. In the second part of this paper, we use neural fields quantitatively—to fit empirical data recorded during visual stimulation. We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception, while in the second study, we exploited high density electrocorticographic (ECoG data to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.

Neural chips, neural computers and application in high and superhigh energy physics experiments

International Nuclear Information System (INIS)

Nikityuk, N.M.; )

2001-01-01

Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

Differences in the neural correlates of frontal lobe tests.

Science.gov (United States)

Matsuoka, Teruyuki; Kato, Yuka; Imai, Ayu; Fujimoto, Hiroshi; Shibata, Keisuke; Nakamura, Kaeko; Yamada, Kei; Narumoto, Jin

2018-01-01

The Executive Interview (EXIT25), the executive clock-drawing task (CLOX1), and the Frontal Assessment Battery (FAB) are used to assess executive function at the bedside. These tests assess distinct psychometric properties. The aim of this study was to examine differences in the neural correlates of the EXIT25, CLOX1, and FAB based on magnetic resonance imaging. Fifty-eight subjects (30 with Alzheimer's disease, 10 with mild cognitive impairment, and 18 healthy controls) participated in this study. Multiple regression analyses were performed to examine the brain regions correlated with the EXIT25, CLOX1, and FAB scores. Age, gender, and years of education were included as covariates. Statistical thresholds were set to uncorrected P-values of 0.001 at the voxel level and 0.05 at the cluster level. The EXIT25 score correlated inversely with the regional grey matter volume in the left lateral frontal lobe (Brodmann areas 6, 9, 44, and 45). The CLOX1 score correlated positively with the regional grey matter volume in the right orbitofrontal cortex (Brodmann area 11) and the left supramarginal gyrus (Brodmann area 40). The FAB score correlated positively with the regional grey matter volume in the right precentral gyrus (Brodmann area 6). The left lateral frontal lobe (Brodmann area 9) and the right lateral frontal lobe (Brodmann area 46) were identified as common brain regions that showed association with EXIT25, CLOX1, and FAB based only a voxel-level threshold. The results of this study suggest that the EXIT25, CLOX1, and FAB may be associated with the distinct neural correlates of the frontal cortex. © 2018 Japanese Psychogeriatric Society.

The effects of gratitude expression on neural activity.

Science.gov (United States)

Kini, Prathik; Wong, Joel; McInnis, Sydney; Gabana, Nicole; Brown, Joshua W

2016-03-01

Gratitude is a common aspect of social interaction, yet relatively little is known about the neural bases of gratitude expression, nor how gratitude expression may lead to longer-term effects on brain activity. To address these twin issues, we recruited subjects who coincidentally were entering psychotherapy for depression and/or anxiety. One group participated in a gratitude writing intervention, which required them to write letters expressing gratitude. The therapy-as-usual control group did not perform a writing intervention. After three months, subjects performed a "Pay It Forward" task in the fMRI scanner. In the task, subjects were repeatedly endowed with a monetary gift and then asked to pass it on to a charitable cause to the extent they felt grateful for the gift. Operationalizing gratitude as monetary gifts allowed us to engage the subjects and quantify the gratitude expression for subsequent analyses. We measured brain activity and found regions where activity correlated with self-reported gratitude experience during the task, even including related constructs such as guilt motivation and desire to help as statistical controls. These were mostly distinct from brain regions activated by empathy or theory of mind. Also, our between groups cross-sectional study found that a simple gratitude writing intervention was associated with significantly greater and lasting neural sensitivity to gratitude - subjects who participated in gratitude letter writing showed both behavioral increases in gratitude and significantly greater neural modulation by gratitude in the medial prefrontal cortex three months later. Copyright © 2015 Elsevier Inc. All rights reserved.

Distinct effects of ASD and ADHD symptoms on reward anticipation in participants with ADHD, their unaffected siblings and healthy controls: a cross-sectional study.

Science.gov (United States)

van Dongen, Eelco V; von Rhein, Daniel; O'Dwyer, Laurence; Franke, Barbara; Hartman, Catharina A; Heslenfeld, Dirk J; Hoekstra, Pieter J; Oosterlaan, Jaap; Rommelse, Nanda; Buitelaar, Jan

2015-01-01

Autism spectrum disorder (ASD) traits are continuously distributed throughout the population, and ASD symptoms are also frequently observed in patients with attention-deficit/hyperactivity disorder (ADHD). Both ASD and ADHD have been linked to alterations in reward-related neural processing. However, whether both symptom domains interact and/or have distinct effects on reward processing in healthy and ADHD populations is currently unknown. We examined how variance in ASD and ADHD symptoms in individuals with ADHD and healthy participants was related to the behavioural and neural response to reward during a monetary incentive delay (MID) task. Participants (mean age: 17.7 years, range: 10-28 years) from the NeuroIMAGE study with a confirmed diagnosis of ADHD (n = 136), their unaffected siblings (n = 83), as well as healthy controls (n = 105) performed an MID task in a magnetic resonance imaging (MRI) scanner. ASD and ADHD symptom scores were used as predictors of the neural response to reward anticipation and reward receipt. Behavioural responses were modeled using linear mixed models; neural responses were analysed using FMRIB's Software Library (FSL) proprietary mixed effects analysis (FLAMEO). ASD and ADHD symptoms were associated with alterations in BOLD activity during reward anticipation, but not reward receipt. Specifically, ASD scores were related to increased insular activity during reward anticipation across the sample. No interaction was found between this effect and the presence of ADHD, suggesting that ASD symptoms had no differential effect in ADHD and healthy populations. ADHD symptom scores were associated with reduced dorsolateral prefrontal activity during reward anticipation. No interactions were found between the effects of ASD and ADHD symptoms on reward processing. Variance in ASD and ADHD symptoms separately influence neural processing during reward anticipation in both individuals with (an increased risk of) ADHD and healthy

Neural tissue-spheres

DEFF Research Database (Denmark)

Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

2007-01-01

By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

The neural circuits that generate tics in Tourette's syndrome.

Science.gov (United States)

Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

2011-12-01

The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico

The feasibility of using neural networks for determination of control rod elevation in a PWR

International Nuclear Information System (INIS)

Garis, N.S.; Temesvari, E.; Pazsit, I.

1996-08-01

This paper presents the results of a preliminary study on using neural networks for determination of the axial position of control rods in PWRs. The method is based on the dependence of the axial flux profile on control rod elevation in a reactor. This flux profile can be measured by e.g. a moveable detector in an operating plant. However, in this preliminary study the flux profile is only calculated using an advanced core code for several axial positions of a partially inserted control rod. The calculated fluxes with corresponding positions of the control rod are used for training a neural network. Using the trained network it is then possible to determine the unknown axial position of a control rod elevation from the corresponding axial flux profile. 10 refs

Using Dual Process Models to Examine Impulsivity Throughout Neural Maturation.

Science.gov (United States)

Leshem, Rotem

2016-01-01

The multivariate construct of impulsivity is examined through neural systems and connections that comprise the executive functioning system. It is proposed that cognitive and behavioral components of impulsivity can be divided into two distinct groups, mediated by (1) the cognitive control system: deficits in top-down cognitive control processes referred to as action/cognitive impulsivity and (2) the socioemotional system: related to bottom-up affective/motivational processes referred to as affective impulsivity. Examination of impulsivity from a developmental viewpoint can guide future research, potentially enabling the selection of more effective interventions for impulsive individuals, based on the cognitive components requiring improvement.

Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

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Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

2018-05-10

The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

Autism spectrum disorders and drug addiction: Common pathways, common molecules, distinct disorders?

Directory of Open Access Journals (Sweden)

Patrick E. Rothwell

2016-02-01

Full Text Available Autism spectrum disorders (ASDs and drug addiction do not share substantial comorbidity or obvious similarities in etiology or symptomatology. It is thus surprising that a number of recent studies implicate overlapping neural circuits and molecular signaling pathways in both disorders. The purpose of this review is to highlight this emerging intersection and consider implications for understanding the pathophysiology of these seemingly distinct disorders. One area of overlap involves neural circuits and neuromodulatory systems in the striatum and basal ganglia, which play an established role in addiction and reward but are increasingly implicated in clinical and preclinical studies of ASDs. A second area of overlap relates to molecules like Fragile X mental retardation protein (FMRP and methyl CpG-binding protein-2 (MECP2, which are best known for their contribution to the pathogenesis of syndromic ASDs, but have recently been shown to regulate behavioral and neurobiological responses to addictive drug exposure. These shared pathways and molecules point to common dimensions of behavioral dysfunction, including the repetition of behavioral patterns and aberrant reward processing. The synthesis of knowledge gained through parallel investigations of ASDs and addiction may inspire the design of new therapeutic interventions to correct common elements of striatal dysfunction.

Autism Spectrum Disorders and Drug Addiction: Common Pathways, Common Molecules, Distinct Disorders?

Science.gov (United States)

Rothwell, Patrick E

2016-01-01

Autism spectrum disorders (ASDs) and drug addiction do not share substantial comorbidity or obvious similarities in etiology or symptomatology. It is thus surprising that a number of recent studies implicate overlapping neural circuits and molecular signaling pathways in both disorders. The purpose of this review is to highlight this emerging intersection and consider implications for understanding the pathophysiology of these seemingly distinct disorders. One area of overlap involves neural circuits and neuromodulatory systems in the striatum and basal ganglia, which play an established role in addiction and reward but are increasingly implicated in clinical and preclinical studies of ASDs. A second area of overlap relates to molecules like Fragile X mental retardation protein (FMRP) and methyl CpG-binding protein-2 (MECP2), which are best known for their contribution to the pathogenesis of syndromic ASDs, but have recently been shown to regulate behavioral and neurobiological responses to addictive drug exposure. These shared pathways and molecules point to common dimensions of behavioral dysfunction, including the repetition of behavioral patterns and aberrant reward processing. The synthesis of knowledge gained through parallel investigations of ASDs and addiction may inspire the design of new therapeutic interventions to correct common elements of striatal dysfunction.

The Neural Substrates of Recognition Memory for Verbal Information: Spanning the Divide between Short- and Long-Term Memory

Science.gov (United States)

Buchsbaum, Bradley R.; Padmanabhan, Aarthi; Berman, Karen Faith

2011-01-01

One of the classic categorical divisions in the history of memory research is that between short-term and long-term memory. Indeed, because memory for the immediate past (a few seconds) and memory for the relatively more remote past (several seconds and beyond) are assumed to rely on distinct neural systems, more often than not, memory research…

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

Science.gov (United States)

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in

Development and function of human cerebral cortex neural networks from pluripotent stem cells in vitro.

Science.gov (United States)

Kirwan, Peter; Turner-Bridger, Benita; Peter, Manuel; Momoh, Ayiba; Arambepola, Devika; Robinson, Hugh P C; Livesey, Frederick J

2015-09-15

A key aspect of nervous system development, including that of the cerebral cortex, is the formation of higher-order neural networks. Developing neural networks undergo several phases with distinct activity patterns in vivo, which are thought to prune and fine-tune network connectivity. We report here that human pluripotent stem cell (hPSC)-derived cerebral cortex neurons form large-scale networks that reflect those found in the developing cerebral cortex in vivo. Synchronised oscillatory networks develop in a highly stereotyped pattern over several weeks in culture. An initial phase of increasing frequency of oscillations is followed by a phase of decreasing frequency, before giving rise to non-synchronous, ordered activity patterns. hPSC-derived cortical neural networks are excitatory, driven by activation of AMPA- and NMDA-type glutamate receptors, and can undergo NMDA-receptor-mediated plasticity. Investigating single neuron connectivity within PSC-derived cultures, using rabies-based trans-synaptic tracing, we found two broad classes of neuronal connectivity: most neurons have small numbers (40). These data demonstrate that the formation of hPSC-derived cortical networks mimics in vivo cortical network development and function, demonstrating the utility of in vitro systems for mechanistic studies of human forebrain neural network biology. © 2015. Published by The Company of Biologists Ltd.

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

Science.gov (United States)

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Atrophy in distinct corticolimbic networks in frontotemporal dementia relates to social impairments measured using the Social Impairment Rating Scale

Science.gov (United States)

Bickart, Kevin C; Brickhouse, Michael; Negreira, Alyson; Sapolsky, Daisy

2015-01-01

Patients with frontotemporal dementia (FTD) often exhibit prominent, early and progressive impairments in social behaviour. We developed the Social Impairment Rating Scale (SIRS), rated by a clinician after a structured interview, which grades the types and severity of social behavioural symptoms in seven domains. In 20 FTD patients, we used the SIRS to study the anatomic basis of social impairments. In support of hypotheses generated from a prior study of healthy adults, we found that the relative magnitude of brain atrophy in three partially dissociable corticolimbic networks anchored in the amygdala predicted the severity of distinct social impairments measured using the SIRS. Patients with the greatest atrophy in a mesolimbic, reward-related (affiliation) network exhibited the most severe socioemotional detachment, whereas patients with the greatest atrophy in an interoceptive, pain-related (aversion) network exhibited the most severe lack of social apprehension. Patients with the greatest atrophy in a perceptual network exhibited the most severe lack of awareness or understanding of others’ social and emotional behaviour. Our findings underscore observations that FTD is associated with heterogeneous social symptoms that can be understood in a refined manner by measuring impairments in component processes subserved by dissociable neural networks. Furthermore, these findings support the validity of the SIRS as an instrument to measure the social symptoms of patients with FTD. Ultimately, we hope it will be useful as a longitudinal outcome measure in natural history studies and in clinical trials of putative interventions to improve social functioning. PMID:24133285

Neural dichotomy of word concreteness: a view from functional neuroimaging.

Science.gov (United States)

Kumar, Uttam

2016-02-01

Our perception about the representation and processing of concrete and abstract concepts is based on the fact that concrete words are highly imagined and remembered faster than abstract words. In order to explain the processing differences between abstract and concrete concepts, various theories have been proposed, yet there is no unanimous consensus about its neural implication. The present study investigated the processing of concrete and abstract words during an orthography judgment task (implicit semantic processing) using functional magnetic resonance imaging to validate the involvement of the neural regions. Relative to non-words, both abstract and concrete words show activation in the regions of bilateral hemisphere previously associated with semantic processing. The common areas (conjunction analyses) observed for abstract and concrete words are bilateral inferior frontal gyrus (BA 44/45), left superior parietal (BA 7), left fusiform gyrus and bilateral middle occipital. The additional areas for abstract words were noticed in bilateral superior temporal and bilateral middle temporal region, whereas no distinct region was noticed for concrete words. This suggests that words with abstract concepts recruit additional language regions in the brain.

Lattice Boltzmann model for high-order nonlinear partial differential equations.

Science.gov (United States)

Chai, Zhenhua; He, Nanzhong; Guo, Zhaoli; Shi, Baochang

2018-01-01

In this paper, a general lattice Boltzmann (LB) model is proposed for the high-order nonlinear partial differential equation with the form ∂_{t}ϕ+∑_{k=1}^{m}α_{k}∂_{x}^{k}Π_{k}(ϕ)=0 (1≤k≤m≤6), α_{k} are constant coefficients, Π_{k}(ϕ) are some known differential functions of ϕ. As some special cases of the high-order nonlinear partial differential equation, the classical (m)KdV equation, KdV-Burgers equation, K(n,n)-Burgers equation, Kuramoto-Sivashinsky equation, and Kawahara equation can be solved by the present LB model. Compared to the available LB models, the most distinct characteristic of the present model is to introduce some suitable auxiliary moments such that the correct moments of equilibrium distribution function can be achieved. In addition, we also conducted a detailed Chapman-Enskog analysis, and found that the high-order nonlinear partial differential equation can be correctly recovered from the proposed LB model. Finally, a large number of simulations are performed, and it is found that the numerical results agree with the analytical solutions, and usually the present model is also more accurate than the existing LB models [H. Lai and C. Ma, Sci. China Ser. G 52, 1053 (2009)1672-179910.1007/s11433-009-0149-3; H. Lai and C. Ma, Phys. A (Amsterdam) 388, 1405 (2009)PHYADX0378-437110.1016/j.physa.2009.01.005] for high-order nonlinear partial differential equations.

Lattice Boltzmann model for high-order nonlinear partial differential equations

Science.gov (United States)

Chai, Zhenhua; He, Nanzhong; Guo, Zhaoli; Shi, Baochang

2018-01-01

In this paper, a general lattice Boltzmann (LB) model is proposed for the high-order nonlinear partial differential equation with the form ∂tϕ +∑k=1mαk∂xkΠk(ϕ ) =0 (1 ≤k ≤m ≤6 ), αk are constant coefficients, Πk(ϕ ) are some known differential functions of ϕ . As some special cases of the high-order nonlinear partial differential equation, the classical (m)KdV equation, KdV-Burgers equation, K (n ,n ) -Burgers equation, Kuramoto-Sivashinsky equation, and Kawahara equation can be solved by the present LB model. Compared to the available LB models, the most distinct characteristic of the present model is to introduce some suitable auxiliary moments such that the correct moments of equilibrium distribution function can be achieved. In addition, we also conducted a detailed Chapman-Enskog analysis, and found that the high-order nonlinear partial differential equation can be correctly recovered from the proposed LB model. Finally, a large number of simulations are performed, and it is found that the numerical results agree with the analytical solutions, and usually the present model is also more accurate than the existing LB models [H. Lai and C. Ma, Sci. China Ser. G 52, 1053 (2009), 10.1007/s11433-009-0149-3; H. Lai and C. Ma, Phys. A (Amsterdam) 388, 1405 (2009), 10.1016/j.physa.2009.01.005] for high-order nonlinear partial differential equations.

ESC-Derived BDNF-Overexpressing Neural Progenitors Differentially Promote Recovery in Huntington's Disease Models by Enhanced Striatal Differentiation

Directory of Open Access Journals (Sweden)

Tina Zimmermann

2016-10-01

Full Text Available Huntington's disease (HD is characterized by fatal motoric failures induced by loss of striatal medium spiny neurons. Neuronal cell death has been linked to impaired expression and axonal transport of the neurotrophin BDNF (brain-derived neurotrophic factor. By transplanting embryonic stem cell-derived neural progenitors overexpressing BDNF, we combined cell replacement and BDNF supply as a potential HD therapy approach. Transplantation of purified neural progenitors was analyzed in a quinolinic acid (QA chemical and two genetic HD mouse models (R6/2 and N171-82Q on the basis of distinct behavioral parameters, including CatWalk gait analysis. Explicit rescue of motor function by BDNF neural progenitors was found in QA-lesioned mice, whereas genetic mouse models displayed only minor improvements. Tumor formation was absent, and regeneration was attributed to enhanced neuronal and striatal differentiation. In addition, adult neurogenesis was preserved in a BDNF-dependent manner. Our findings provide significant insight for establishing therapeutic strategies for HD to ameliorate neurodegenerative symptoms.

Plasmid-based generation of induced neural stem cells from adult human fibroblasts

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Philipp Capetian

2016-10-01

Full Text Available Direct reprogramming from somatic to neural cell types has become an alternative to induced pluripotent stem cells. Most protocols employ viral expression systems, posing the risk of random genomic integration. Recent developments led to plasmid-based protocols, lowering this risk. However, these protocols either relied on continuous presence of a variety of small molecules or were only able to reprogram murine cells. We therefore established a reprogramming protocol based on vectors containing the Epstein-Barr virus (EBV-derived oriP/EBNA1 as well as the defined expression factors Oct3/4, Sox2, Klf4, L-myc, Lin28, and a small hairpin directed against p53. We employed a defined neural medium in combination with the neurotrophins bFGF, EGF and FGF4 for cultivation without the addition of small molecules. After reprogramming, cells demonstrated a temporary increase in the expression of endogenous Oct3/4. We obtained induced neural stem cells (iNSC 30 days after transfection. In contrast to previous results, plasmid vectors as well as a residual expression of reprogramming factors remained detectable in all cell lines. Cells showed a robust differentiation into neuronal (72% and glial cells (9% astrocytes, 6% oligodendrocytes. Despite the temporary increase of pluripotency-associated Oct3/4 expression during reprogramming, we did not detect pluripotent stem cells or non-neural cells in culture (except occasional residual fibroblasts. Neurons showed electrical activity and functional glutamatergic synapses. Our results demonstrate that reprogramming adult human fibroblasts to iNSC by plasmid vectors and basic neural medium without small molecules is possible and feasible. However, a full set of pluripotency-associated transcription factors may indeed result in the acquisition of a transient (at least partial pluripotent intermediate during reprogramming. In contrast to previous reports, the EBV-based plasmid system remained present and active inside

Distinct neural substrates of affective and cognitive theory of mind impairment in semantic dementia.

Science.gov (United States)

Bejanin, Alexandre; Chételat, Gaël; Laisney, Mickael; Pélerin, Alice; Landeau, Brigitte; Merck, Catherine; Belliard, Serge; de La Sayette, Vincent; Eustache, Francis; Desgranges, Béatrice

2017-06-01

Using structural MRI, we investigated the brain substrates of both affective and cognitive theory of mind (ToM) in 19 patients with semantic dementia. We also ran intrinsic connectivity analyses to identify the networks to which the substrates belong and whether they are functionally disturbed in semantic dementia. In line with previous studies, we observed a ToM impairment in patients with semantic dementia even when semantic memory was regressed out. Our results also highlighted different neural bases according to the nature (affective or cognitive) of the representations being inferred. The affective ToM deficit was associated with atrophy in the amygdala, suggesting the involvement of emotion-processing deficits in this impairment. By contrast, cognitive ToM performances were correlated with the volume of medial prefrontal and parietal regions, as well as the right frontal operculum. Intrinsic connectivity analyses revealed decreased functional connectivity, mainly between midline cortical regions and temporal regions. They also showed that left medial temporal regions were functionally isolated, a further possible hindrance to normal social cognitive functioning in semantic dementia. Overall, this study addressed for the first time the neuroanatomical substrates of both cognitive and affective ToM disruption in semantic dementia, highlighting disturbed connectivity within the networks that sustain these abilities.

Two Distinct Moral Mechanisms for Ascribing and Denying Intentionality

Science.gov (United States)

Ngo, Lawrence; Kelly, Meagan; Coutlee, Christopher G.; Carter, R. McKell; Sinnott-Armstrong, Walter; Huettel, Scott A.

2015-01-01

Philosophers and legal scholars have long theorized about how intentionality serves as a critical input for morality and culpability, but the emerging field of experimental philosophy has revealed a puzzling asymmetry. People judge actions leading to negative consequences as being more intentional than those leading to positive ones. The implications of this asymmetry remain unclear because there is no consensus regarding the underlying mechanism. Based on converging behavioral and neural evidence, we demonstrate that there is no single underlying mechanism. Instead, two distinct mechanisms together generate the asymmetry. Emotion drives ascriptions of intentionality for negative consequences, while the consideration of statistical norms leads to the denial of intentionality for positive consequences. We employ this novel two-mechanism model to illustrate that morality can paradoxically shape judgments of intentionality. This is consequential for mens rea in legal practice and arguments in moral philosophy pertaining to terror bombing, abortion, and euthanasia among others. PMID:26634909

Fusion of neural computing and PLS techniques for load estimation

Energy Technology Data Exchange (ETDEWEB)

Lu, M.; Xue, H.; Cheng, X. [Northwestern Polytechnical Univ., Xi' an (China); Zhang, W. [Xi' an Inst. of Post and Telecommunication, Xi' an (China)

2007-07-01

A method to predict the electric load of a power system in real time was presented. The method is based on neurocomputing and partial least squares (PLS). Short-term load forecasts for power systems are generally determined by conventional statistical methods and Computational Intelligence (CI) techniques such as neural computing. However, statistical modeling methods often require the input of questionable distributional assumptions, and neural computing is weak, particularly in determining topology. In order to overcome the problems associated with conventional techniques, the authors developed a CI hybrid model based on neural computation and PLS techniques. The theoretical foundation for the designed CI hybrid model was presented along with its application in a power system. The hybrid model is suitable for nonlinear modeling and latent structure extracting. It can automatically determine the optimal topology to maximize the generalization. The CI hybrid model provides faster convergence and better prediction results compared to the abductive networks model because it incorporates a load conversion technique as well as new transfer functions. In order to demonstrate the effectiveness of the hybrid model, load forecasting was performed on a data set obtained from the Puget Sound Power and Light Company. Compared with the abductive networks model, the CI hybrid model reduced the forecast error by 32.37 per cent on workday, and by an average of 27.18 per cent on the weekend. It was concluded that the CI hybrid model has a more powerful predictive ability. 7 refs., 1 tab., 3 figs.

Sadness is unique: Neural processing of emotions in speech prosody in musicians and non-musicians

Directory of Open Access Journals (Sweden)

Mona ePark

2015-01-01

Full Text Available Musical training has been shown to have positive effects on several aspects of speech processing, however, the effects of musical training on the neural processing of speech prosody conveying distinct emotions are yet to be better understood. We used functional magnetic resonance imaging (fMRI to investigate whether the neural responses to speech prosody conveying happiness, sadness, and fear differ between musicians and non-musicians. Differences in processing of emotional speech prosody between the two groups were only observed when sadness was expressed. Musicians showed increased activation in the middle frontal gyrus, the anterior medial prefrontal cortex, the posterior cingulate cortex and the retrosplenial cortex. Our results suggest an increased sensitivity of emotional processing in musicians with respect to sadness expressed in speech, possibly reflecting empathic processes.

Chemical analysis of multicomponent aqueous solutions using a system of nonselective sensor and artificial neural networks

International Nuclear Information System (INIS)

Vlasov, Yu.G.; Legin, A.V.; Rudnitskaya, A.M.; Amiko, A.D.; Natale, K.D.

1997-01-01

With the aim of creating a multisensor system for determining heavy-metal cations (Cu 2+ , Pb 2+ , Cd 2+ , and Zn 2+ ) and inorganic anions (Cl - , F - , and SO 4 2- ), measurements in mixed solutions were carried out with the use of an array of sensors based on chalcogenide glass electrodes, and the possibility of using various methods of mathematical processing of the resulting intricate signals was studied. Three methods of data processing were used: multilinear regression, partial least squares, and artificial neural networks. It was found that the multisensor system proposed were suitable for determining all of the analytes with an accuracy of 1-10%. Because the responses of sensors in solutions of complex composition deviated from linearity, the lowest determination errors were obtained with the use of an artificial neural network. As to the method of data securing (nonselective response of a sensor array) and processing (artificial neural network), the multisensor system developed may be considered a prototype of a device of the electronic tongue type

Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults.

Science.gov (United States)

Sebastian, Catherine L; Fontaine, Nathalie M G; Bird, Geoffrey; Blakemore, Sarah-Jayne; Brito, Stephane A De; McCrory, Eamon J P; Viding, Essi

2012-01-01

Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11-16 years) and 15 adult (aged 24-40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing.

Neural networks for aircraft control

Science.gov (United States)

Linse, Dennis

1990-01-01

Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

Dynamic simulation of a steam generator by neural networks

International Nuclear Information System (INIS)

Masini, R.; Padovani, E.; Ricotti, M.E.; Zio, E.

1999-01-01

Numerical simulation by computers of the dynamic evolution of complex systems and components is a fundamental phase of any modern engineering design activity. This is of particular importance for risk-based design projects which require that the system behavior be analyzed under several and often extreme conditions. The traditional methods of simulation typically entail long, iterative, processes which lead to large simulation times, often exceeding the transients real time. Artificial neural networks (ANNs) may be exploited in this context, their advantages residing mainly in the speed of computation, in the capability of generalizing from few examples, in the robustness to noisy and partially incomplete data and in the capability of performing empirical input-output mapping without complete knowledge of the underlying physics. In this paper we present a novel approach to dynamic simulation by ANNs based on a superposition scheme in which a set of networks are individually trained, each one to respond to a different input forcing function. The dynamic simulation of a steam generator is considered as an example to show the potentialities of this tool and to point out the difficulties and crucial issues which typically arise when attempting to establish an efficient neural network simulator. The structure of the networks system is such to feedback, at each time step, a portion of the past evolution of the transient and this allows a good reproduction of also non-linear dynamic behaviors. A nice characteristic of the approach is that the modularization of the training reduces substantially its burden and gives this neural simulation tool a nice feature of transportability. (orig.)

Hysteretic recurrent neural networks: a tool for modeling hysteretic materials and systems

International Nuclear Information System (INIS)

Veeramani, Arun S; Crews, John H; Buckner, Gregory D

2009-01-01

This paper introduces a novel recurrent neural network, the hysteretic recurrent neural network (HRNN), that is ideally suited to modeling hysteretic materials and systems. This network incorporates a hysteretic neuron consisting of conjoined sigmoid activation functions. Although similar hysteretic neurons have been explored previously, the HRNN is unique in its utilization of simple recurrence to 'self-select' relevant activation functions. Furthermore, training is facilitated by placing the network weights on the output side, allowing standard backpropagation of error training algorithms to be used. We present two- and three-phase versions of the HRNN for modeling hysteretic materials with distinct phases. These models are experimentally validated using data collected from shape memory alloys and ferromagnetic materials. The results demonstrate the HRNN's ability to accurately generalize hysteretic behavior with a relatively small number of neurons. Additional benefits lie in the network's ability to identify statistical information concerning the macroscopic material by analyzing the weights of the individual neurons

Robust-BD Estimation and Inference for General Partially Linear Models

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Chunming Zhang

2017-11-01

Full Text Available The classical quadratic loss for the partially linear model (PLM and the likelihood function for the generalized PLM are not resistant to outliers. This inspires us to propose a class of “robust-Bregman divergence (BD” estimators of both the parametric and nonparametric components in the general partially linear model (GPLM, which allows the distribution of the response variable to be partially specified, without being fully known. Using the local-polynomial function estimation method, we propose a computationally-efficient procedure for obtaining “robust-BD” estimators and establish the consistency and asymptotic normality of the “robust-BD” estimator of the parametric component β o . For inference procedures of β o in the GPLM, we show that the Wald-type test statistic W n constructed from the “robust-BD” estimators is asymptotically distribution free under the null, whereas the likelihood ratio-type test statistic Λ n is not. This provides an insight into the distinction from the asymptotic equivalence (Fan and Huang 2005 between W n and Λ n in the PLM constructed from profile least-squares estimators using the non-robust quadratic loss. Numerical examples illustrate the computational effectiveness of the proposed “robust-BD” estimators and robust Wald-type test in the appearance of outlying observations.

Integration of donor mesenchymal stem cell-derived neuron-like cells into host neural network after rat spinal cord transection.

Science.gov (United States)

Zeng, Xiang; Qiu, Xue-Cheng; Ma, Yuan-Huan; Duan, Jing-Jing; Chen, Yuan-Feng; Gu, Huai-Yu; Wang, Jun-Mei; Ling, Eng-Ang; Wu, Jin-Lang; Wu, Wutian; Zeng, Yuan-Shan

2015-06-01

Functional deficits following spinal cord injury (SCI) primarily attribute to loss of neural connectivity. We therefore tested if novel tissue engineering approaches could enable neural network repair that facilitates functional recovery after spinal cord transection (SCT). Rat bone marrow-derived mesenchymal stem cells (MSCs), genetically engineered to overexpress TrkC, receptor of neurotrophin-3 (NT-3), were pre-differentiated into cells carrying neuronal features via co-culture with NT-3 overproducing Schwann cells in 3-dimensional gelatin sponge (GS) scaffold for 14 days in vitro. Intra-GS formation of MSC assemblies emulating neural network (MSC-GS) were verified morphologically via electron microscopy (EM) and functionally by whole-cell patch clamp recording of spontaneous post-synaptic currents. The differentiated MSCs still partially maintained prototypic property with the expression of some mesodermal cytokines. MSC-GS or GS was then grafted acutely into a 2 mm-wide transection gap in the T9-T10 spinal cord segments of adult rats. Eight weeks later, hindlimb function of the MSC-GS-treated SCT rats was significantly improved relative to controls receiving the GS or lesion only as indicated by BBB score. The MSC-GS transplantation also significantly recovered cortical motor evoked potential (CMEP). Histologically, MSC-derived neuron-like cells maintained their synapse-like structures in vivo; they additionally formed similar connections with host neurites (i.e., mostly serotonergic fibers plus a few corticospinal axons; validated by double-labeled immuno-EM). Moreover, motor cortex electrical stimulation triggered c-fos expression in the grafted and lumbar spinal cord cells of the treated rats only. Our data suggest that MSC-derived neuron-like cells resulting from NT-3-TrkC-induced differentiation can partially integrate into transected spinal cord and this strategy should be further investigated for reconstructing disrupted neural circuits. Copyright �

First report of partial albinism in genus Thrichomys (Rodentia: Echimyidae

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Antonio Carlos da S.A. Neves

2014-01-01

Full Text Available Reports about albinism in rodents are common. In the family Echimyidae, however, albinism is very rare. This is the second case of coat color variation reported within Echimyidae and the first for the genus Thrichomys. The pelages of Thrichomys pachyurus individuals with normal and variant coat color were observed under a fluorescent artificial light and were examined with a stereoscopic microscope. The descriptions of pelage color were based on the book "Color Standards and Color Nomenclature". The predominantly white pattern of coat color in individuals of T. pachyurus suggests a partial albinism caused by delay in migration time of melanoblasts from neural crest to epidermis. The habitat of T. pachyurus has a heavy vegetative cover, which offers natural protection against predators and high-quality nutrition.

The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

Science.gov (United States)

Pla, Patrick; Monsoro-Burq, Anne H

2018-05-28

The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

Review of the Neural Oscillations Underlying Meditation

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Darrin J. Lee

2018-03-01

Full Text Available Objective: Meditation is one type of mental training that has been shown to produce many cognitive benefits. Meditation practice is associated with improvement in concentration and reduction of stress, depression, and anxiety symptoms. Furthermore, different forms of meditation training are now being used as interventions for a variety of psychological and somatic illnesses. These benefits are thought to occur as a result of neurophysiologic changes. The most commonly studied specific meditation practices are focused attention (FA, open-monitoring (OM, as well as transcendental meditation (TM, and loving-kindness (LK meditation. In this review, we compare the neural oscillatory patterns during these forms of meditation.Method: We performed a systematic review of neural oscillations during FA, OM, TM, and LK meditation practices, comparing meditators to meditation-naïve adults.Results: FA, OM, TM, and LK meditation are associated with global increases in oscillatory activity in meditators compared to meditation-naïve adults, with larger changes occurring as the length of meditation training increases. While FA and OM are related to increases in anterior theta activity, only FA is associated with changes in posterior theta oscillations. Alpha activity increases in posterior brain regions during both FA and OM. In anterior regions, FA shows a bilateral increase in alpha power, while OM shows a decrease only in left-sided power. Gamma activity in these meditation practices is similar in frontal regions, but increases are variable in parietal and occipital regions.Conclusions: The current literature suggests distinct differences in neural oscillatory activity among FA, OM, TM, and LK meditation practices. Further characterizing these oscillatory changes may better elucidate the cognitive and therapeutic effects of specific meditation practices, and potentially lead to the development of novel neuromodulation targets to take advantage of their

Inductive differentiation of two neural lineages reconstituted in a microculture system from Xenopus early gastrula cells.

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Mitani, S; Okamoto, H

1991-05-01

Neural induction of ectoderm cells has been reconstituted and examined in a microculture system derived from dissociated early gastrula cells of Xenopus laevis. We have used monoclonal antibodies as specific markers to monitor cellular differentiation from three distinct ectoderm lineages in culture (N1 for CNS neurons from neural tube, Me1 for melanophores from neural crest and E3 for skin epidermal cells from epidermal lineages). CNS neurons and melanophores differentiate when deep layer cells of the ventral ectoderm (VE, prospective epidermis region; 150 cells/culture) and an appropriate region of the marginal zone (MZ, prospective mesoderm region; 5-150 cells/culture) are co-cultured, but not in cultures of either cell type on their own; VE cells cultured alone yield epidermal cells as we have previously reported. The extent of inductive neural differentiation in the co-culture system strongly depends on the origin and number of MZ cells initially added to culture wells. The potency to induce CNS neurons is highest for dorsal MZ cells and sharply decreases as more ventrally located cells are used. The same dorsoventral distribution of potency is seen in the ability of MZ cells to inhibit epidermal differentiation. In contrast, the ability of MZ cells to induce melanophores shows the reverse polarity, ventral to dorsal. These data indicate that separate developmental mechanisms are used for the induction of neural tube and neural crest lineages. Co-differentiation of CNS neurons or melanophores with epidermal cells can be obtained in a single well of co-cultures of VE cells (150) and a wide range of numbers of MZ cells (5 to 100). Further, reproducible differentiation of both neural lineages requires intimate association between cells from the two gastrula regions; virtually no differentiation is obtained when cells from the VE and MZ are separated in a culture well. These results indicate that the inducing signals from MZ cells for both neural tube and neural

Differences in Neural Activation as a Function of Risk-taking Task Parameters

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Eliza eCongdon

2013-09-01

Full Text Available Despite evidence supporting a relationship between impulsivity and naturalistic risk-taking, the relationship of impulsivity with laboratory-based measures of risky decision-making remains unclear. One factor contributing to this gap in our understanding is the degree to which different risky decision-making tasks vary in their details. We conducted an fMRI investigation of the Angling Risk Task (ART, which is an improved behavioral measure of risky decision-making. In order to examine whether the observed pattern of neural activation was specific to the ART or generalizable, we also examined correlates of the Balloon Analogue Risk Taking (BART task in the same sample of 23 healthy adults. Exploratory analyses were conducted to examine the relationship between neural activation, performance, impulsivity and self-reported risk-taking. While activation in a valuation network was associated with reward tracking during the ART but not the BART, increased fronto-cingulate activation was seen during risky choice trials in the BART as compared to the ART. Thus, neural activation during risky decision-making trials differed between the two tasks, and this observation was likely driven by differences in task parameters, namely the absence vs. presence of ambiguity and/or stationary vs. increasing probability of loss on the ART and BART, respectively. Exploratory association analyses suggest that sensitivity of neural response to the magnitude of potential reward during the ART was associated with a suboptimal performance strategy, higher scores on a scale of dysfunctional impulsivity and a greater likelihood of engaging in risky behaviors, while this pattern was not seen for the BART. Our results suggest that the ART is decomposable and associated with distinct patterns of neural activation; this represents a preliminary step towards characterizing a behavioral measure of risky decision-making that may support a better understanding of naturalistic risk-taking.

Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program

Science.gov (United States)

Benaouda, D.; Wadge, G.; Whitmarsh, R. B.; Rothwell, R. G.; MacLeod, C.

1999-02-01

In boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2-3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively.

Neuroplasticity beyond Sounds: Neural Adaptations Following Long-Term Musical Aesthetic Experiences

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Mark Reybrouck

2015-03-01

Full Text Available Capitalizing from neuroscience knowledge on how individuals are affected by the sound environment, we propose to adopt a cybernetic and ecological point of view on the musical aesthetic experience, which includes subprocesses, such as feature extraction and integration, early affective reactions and motor actions, style mastering and conceptualization, emotion and proprioception, evaluation and preference. In this perspective, the role of the listener/composer/performer is seen as that of an active “agent” coping in highly individual ways with the sounds. The findings concerning the neural adaptations in musicians, following long-term exposure to music, are then reviewed by keeping in mind the distinct subprocesses of a musical aesthetic experience. We conclude that these neural adaptations can be conceived of as the immediate and lifelong interactions with multisensorial stimuli (having a predominant auditory component, which result in lasting changes of the internal state of the “agent”. In a continuous loop, these changes affect, in turn, the subprocesses involved in a musical aesthetic experience, towards the final goal of achieving better perceptual, motor and proprioceptive responses to the immediate demands of the sounding environment. The resulting neural adaptations in musicians closely depend on the duration of the interactions, the starting age, the involvement of attention, the amount of motor practice and the musical genre played.

Anger under control: neural correlates of frustration as a function of trait aggression.

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Christina M Pawliczek

Full Text Available Antisocial behavior and aggression are prominent symptoms in several psychiatric disorders including antisocial personality disorder. An established precursor to aggression is a frustrating event, which can elicit anger or exasperation, thereby prompting aggressive responses. While some studies have investigated the neural correlates of frustration and aggression, examination of their relation to trait aggression in healthy populations are rare. Based on a screening of 550 males, we formed two extreme groups, one including individuals reporting high (n=21 and one reporting low (n=18 trait aggression. Using functional magnetic resonance imaging (fMRI at 3T, all participants were put through a frustration task comprising unsolvable anagrams of German nouns. Despite similar behavioral performance, males with high trait aggression reported higher ratings of negative affect and anger after the frustration task. Moreover, they showed relatively decreased activation in the frontal brain regions and the dorsal anterior cingulate cortex (dACC as well as relatively less amygdala activation in response to frustration. Our findings indicate distinct frontal and limbic processing mechanisms following frustration modulated by trait aggression. In response to a frustrating event, HA individuals show some of the personality characteristics and neural processing patterns observed in abnormally aggressive populations. Highlighting the impact of aggressive traits on the behavioral and neural responses to frustration in non-psychiatric extreme groups can facilitate further characterization of neural dysfunctions underlying psychiatric disorders that involve abnormal frustration processing and aggression.

Neural network regulation driven by autonomous neural firings

Science.gov (United States)

Cho, Myoung Won

2016-07-01

Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

Global and local missions of cAMP signaling in neural plasticity, learning and memory

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Daewoo eLee

2015-08-01

Full Text Available The fruit fly Drosophila melanogaster has been a popular model to study cAMP signaling and resultant behaviors due to its powerful genetic approaches. All molecular components (AC, PDE, PKA, CREB, etc essential for cAMP signaling have been identified in the fly. Among them, adenylyl cyclase (AC gene rutabaga and phosphodiesterase (PDE gene dunce have been intensively studied to understand the role of cAMP signaling. Interestingly, these two mutant genes were originally identified on the basis of associative learning deficits. This commentary summarizes findings on the role of cAMP in Drosophila neuronal excitability, synaptic plasticity and memory. It mainly focuses on two distinct mechanisms (global versus local regulating excitatory and inhibitory synaptic plasticity related to cAMP homeostasis. This dual regulatory role of cAMP is to increase the strength of excitatory neural circuits on one hand, but to act locally on postsynaptic GABA receptors to decrease inhibitory synaptic plasticity on the other. Thus the action of cAMP could result in a global increase in the neural circuit excitability and memory. Implications of this cAMP signaling related to drug discovery for neural diseases are also described.

Dynamics of neural cryptography.

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Dynamics of neural cryptography

International Nuclear Information System (INIS)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-01-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

Dynamics of neural cryptography

Science.gov (United States)

Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

2007-05-01

Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

Intrinsic gain modulation and adaptive neural coding.

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Sungho Hong

2008-07-01

Full Text Available In many cases, the computation of a neural system can be reduced to a receptive field, or a set of linear filters, and a thresholding function, or gain curve, which determines the firing probability; this is known as a linear/nonlinear model. In some forms of sensory adaptation, these linear filters and gain curve adjust very rapidly to changes in the variance of a randomly varying driving input. An apparently similar but previously unrelated issue is the observation of gain control by background noise in cortical neurons: the slope of the firing rate versus current (f-I curve changes with the variance of background random input. Here, we show a direct correspondence between these two observations by relating variance-dependent changes in the gain of f-I curves to characteristics of the changing empirical linear/nonlinear model obtained by sampling. In the case that the underlying system is fixed, we derive relationships relating the change of the gain with respect to both mean and variance with the receptive fields derived from reverse correlation on a white noise stimulus. Using two conductance-based model neurons that display distinct gain modulation properties through a simple change in parameters, we show that coding properties of both these models quantitatively satisfy the predicted relationships. Our results describe how both variance-dependent gain modulation and adaptive neural computation result from intrinsic nonlinearity.

Bioprinting for Neural Tissue Engineering.

Science.gov (United States)

Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

2018-01-01

Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

Input Selection for Return Temperature Estimation in Mixing Loops using Partial Mutual Information with Flow Variable Delay

DEFF Research Database (Denmark)

Overgaard, Anders; Kallesøe, Carsten Skovmose; Bendtsen, Jan Dimon

2017-01-01

adgang til data, er ønsker at skabe en datadreven model til kontrol. Grundet den store mængde tilgængelig data anvendes der en metode til valg af inputs kaldet "Partial Mutual Information" (PMI). Denne artikel introducerer en metode til at inkluderer flow variable forsinkelser i PMI. Data fra en...... kontorbygning i Bjerringbro anvendes til analyse. Det vises at "Mutual Information" og et "Generalized Regression Neural Network" begge forbedres ved at anvende flow variabelt forsinkelse i forhold til at anvende konstante delay....

Neural Response to Biological Motion in Healthy Adults Varies as a Function of Autistic-Like Traits

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Meghan H. Puglia

2017-07-01

Full Text Available Perception of biological motion is an important social cognitive ability that has been mapped to specialized brain regions. Perceptual deficits and neural differences during biological motion perception have previously been associated with autism, a disorder classified by social and communication difficulties and repetitive and restricted interests and behaviors. However, the traits associated with autism are not limited to diagnostic categories, but are normally distributed within the general population and show the same patterns of heritability across the continuum. In the current study, we investigate whether self-reported autistic-like traits in healthy adults are associated with variable neural response during passive viewing of biological motion displays. Results show that more autistic-like traits, particularly those associated with the communication domain, are associated with increased neural response in key regions involved in social cognitive processes, including prefrontal and left temporal cortices. This distinct pattern of activation might reflect differential neurodevelopmental processes for individuals with varying autistic-like traits, and highlights the importance of considering the full trait continuum in future work.

Basic perceptual changes that alter meaning and neural correlates of recognition memory.

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Gao, Chuanji; Hermiller, Molly S; Voss, Joel L; Guo, Chunyan

2015-01-01

It is difficult to pinpoint the border between perceptual and conceptual processing, despite their treatment as distinct entities in many studies of recognition memory. For instance, alteration of simple perceptual characteristics of a stimulus can radically change meaning, such as the color of bread changing from white to green. We sought to better understand the role of perceptual and conceptual processing in memory by identifying the effects of changing a basic perceptual feature (color) on behavioral and neural correlates of memory in circumstances when this change would be expected to either change the meaning of a stimulus or to have no effect on meaning (i.e., to influence conceptual processing or not). Abstract visual shapes ("squiggles") were colorized during study and presented during test in either the same color or a different color. Those squiggles that subjects found to resemble meaningful objects supported behavioral measures of conceptual priming, whereas meaningless squiggles did not. Further, changing color from study to test had a selective effect on behavioral correlates of priming for meaningful squiggles, indicating that color change altered conceptual processing. During a recognition memory test, color change altered event-related brain potential (ERP) correlates of memory for meaningful squiggles but not for meaningless squiggles. Specifically, color change reduced the amplitude of frontally distributed N400 potentials (FN400), implying that these potentials indicated conceptual processing during recognition memory that was sensitive to color change. In contrast, color change had no effect on FN400 correlates of recognition for meaningless squiggles, which were overall smaller in amplitude than for meaningful squiggles (further indicating that these potentials signal conceptual processing during recognition). Thus, merely changing the color of abstract visual shapes can alter their meaning, changing behavioral and neural correlates of memory

Impaired neurogenesis, learning and memory and low seizure threshold associated with loss of neural precursor cell survivin

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Eisch Amelia

2010-01-01

Full Text Available Abstract Background Survivin is a unique member of the inhibitor of apoptosis protein (IAP family in that it exhibits antiapoptotic properties and also promotes the cell cycle and mediates mitosis as a chromosome passenger protein. Survivin is highly expressed in neural precursor cells in the brain, yet its function there has not been elucidated. Results To examine the role of neural precursor cell survivin, we first showed that survivin is normally expressed in periventricular neurogenic regions in the embryo, becoming restricted postnatally to proliferating and migrating NPCs in the key neurogenic sites, the subventricular zone (SVZ and the subgranular zone (SGZ. We then used a conditional gene inactivation strategy to delete the survivin gene prenatally in those neurogenic regions. Lack of embryonic NPC survivin results in viable, fertile mice (SurvivinCamcre with reduced numbers of SVZ NPCs, absent rostral migratory stream, and olfactory bulb hypoplasia. The phenotype can be partially rescued, as intracerebroventricular gene delivery of survivin during embryonic development increases olfactory bulb neurogenesis, detected postnatally. SurvivinCamcre brains have fewer cortical inhibitory interneurons, contributing to enhanced sensitivity to seizures, and profound deficits in memory and learning. Conclusions The findings highlight the critical role that survivin plays during neural development, deficiencies of which dramatically impact on postnatal neural function.

Quantum Distinction: Quantum Distinctiones!

OpenAIRE

Zeps, Dainis

2009-01-01

10 pages; How many distinctions, in Latin, quantum distinctiones. We suggest approach of anthropic principle based on anthropic reference system which should be applied equally both in theoretical physics and in mathematics. We come to principle that within reference system of life subject of mathematics (that of thinking) should be equated with subject of physics (that of nature). For this reason we enter notions of series of distinctions, quantum distinction, and argue that quantum distinct...

Neural induction in Xenopus: requirement for ectodermal and endomesodermal signals via Chordin, Noggin, beta-Catenin, and Cerberus.

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Hiroki Kuroda

2004-05-01

Full Text Available The origin of the signals that induce the differentiation of the central nervous system (CNS is a long-standing question in vertebrate embryology. Here we show that Xenopus neural induction starts earlier than previously thought, at the blastula stage, and requires the combined activity of two distinct signaling centers. One is the well-known Nieuwkoop center, located in dorsal-vegetal cells, which expresses Nodal-related endomesodermal inducers. The other is a blastula Chordin- and Noggin-expressing (BCNE center located in dorsal animal cells that contains both prospective neuroectoderm and Spemann organizer precursor cells. Both centers are downstream of the early beta-Catenin signal. Molecular analyses demonstrated that the BCNE center was distinct from the Nieuwkoop center, and that the Nieuwkoop center expressed the secreted protein Cerberus (Cer. We found that explanted blastula dorsal animal cap cells that have not yet contacted a mesodermal substratum can, when cultured in saline solution, express definitive neural markers and differentiate histologically into CNS tissue. Transplantation experiments showed that the BCNE region was required for brain formation, even though it lacked CNS-inducing activity when transplanted ventrally. Cell-lineage studies demonstrated that BCNE cells give rise to a large part of the brain and retina and, in more posterior regions of the embryo, to floor plate and notochord. Loss-of-function experiments with antisense morpholino oligos (MO showed that the CNS that forms in mesoderm-less Xenopus embryos (generated by injection with Cerberus-Short [CerS] mRNA required Chordin (Chd, Noggin (Nog, and their upstream regulator beta-Catenin. When mesoderm involution was prevented in dorsal marginal-zone explants, the anterior neural tissue formed in ectoderm was derived from BCNE cells and had a complete requirement for Chd. By injecting Chd morpholino oligos (Chd-MO into prospective neuroectoderm and Cerberus

Behavioral and neural reactions to emotions of others in the distribution of resources.

Science.gov (United States)

Lelieveld, Gert-Jan; Van Dijk, Eric; Güroğlu, Berna; Van Beest, Ilja; Van Kleef, Gerben A; Rombouts, Serge A R B; Crone, Eveline A

2013-01-01

This study investigated the neural mechanisms involved in the interpersonal effects of emotions--i.e., how people are influenced by other people's emotions. Participants were allocators in a version of the dictator game and made a choice between two offers after receiving written emotional expressions of the recipients. The results showed that participants more often made a self-serving offer when dealing with an angry recipient than when dealing with a happy or disappointed recipient. Compared to disappointment, expressions of anger increased activation in regions associated with self-referential thinking (anterior medial prefrontal cortex, aMPFC) and (emotional) conflict (anterior cingulate cortex). We found increased activation in temporoparietal junction for receiving happy reactions in comparison with receiving angry or disappointed reactions. This study thus emphasizes that distinct emotions have distinct effects on people in terms of behavior and underlying neurological mechanisms.

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

Directory of Open Access Journals (Sweden)

Alexander eHuk

2012-10-01

Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

Directory of Open Access Journals (Sweden)

Chernoded Andrey

2017-01-01

Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

Neural Tube Defects

Science.gov (United States)

Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

Decision-making conflict and the neural efficiency hypothesis of intelligence: a functional near-infrared spectroscopy investigation.

Science.gov (United States)

Di Domenico, Stefano I; Rodrigo, Achala H; Ayaz, Hasan; Fournier, Marc A; Ruocco, Anthony C

2015-04-01

Research on the neural efficiency hypothesis of intelligence (NEH) has revealed that the brains of more intelligent individuals consume less energy when performing easy cognitive tasks but more energy when engaged in difficult mental operations. However, previous studies testing the NEH have relied on cognitive tasks that closely resemble psychometric tests of intelligence, potentially confounding efficiency during intelligence-test performance with neural efficiency per se. The present study sought to provide a novel test of the NEH by examining patterns of prefrontal activity while participants completed an experimental paradigm that is qualitatively distinct from the contents of psychometric tests of intelligence. Specifically, participants completed a personal decision-making task (e.g., which occupation would you prefer, dancer or chemist?) in which they made a series of forced choices according to their subjective preferences. The degree of decisional conflict (i.e., choice difficulty) between the available response options was manipulated on the basis of participants' unique preference ratings for the target stimuli, which were obtained prior to scanning. Evoked oxygenation of the prefrontal cortex was measured using 16-channel continuous-wave functional near-infrared spectroscopy. Consistent with the NEH, intelligence predicted decreased activation of the right inferior frontal gyrus (IFG) during low-conflict situations and increased activation of the right-IFG during high-conflict situations. This pattern of right-IFG activity among more intelligent individuals was complemented by faster reaction times in high-conflict situations. These results provide new support for the NEH and suggest that the neural efficiency of more intelligent individuals generalizes to the performance of cognitive tasks that are distinct from intelligence tests. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural correlates of improved executive function following erythropoietin treatment in mood disorders

DEFF Research Database (Denmark)

Miskowiak, K. W.; Vinberg, M.; Glerup, L.

2016-01-01

magnetic resonance imaging (fMRI) study investigated the effects of EPO on neural circuitry activity during working memory (WM) performance. METHOD: Patients with treatment-resistant major depression, who were moderately depressed, or with BD in partial remission, were randomized to eight weekly infusions...... of EPO (40 000 IU) (N = 30) or saline (N = 26) in a double-blind, parallel-group design. Patients underwent fMRI, mood ratings and blood tests at baseline and week 14. During fMRI patients performed an n-back WM task. RESULTS: EPO improved WM accuracy compared with saline (p = 0.045). Whole...

Neural Basis of Anhedonia and Amotivation in Patients with Schizophrenia: The Role of Reward System.

Science.gov (United States)

Lee, Jung Suk; Jung, Suwon; Park, Il Ho; Kim, Jae-Jin

2015-01-01

Anhedonia, the inability to feel pleasure, and amotivation, the lack of motivation, are two prominent negative symptoms of schizophrenia, which contribute to the poor social and occupational behaviors in the patients. Recently growing evidence shows that anhedonia and amotivation are tied together, but have distinct neural correlates. It is important to note that both of these symptoms may derive from deficient functioning of the reward network. A further analysis into the neuroimaging findings of schizophrenia shows that the neural correlates overlap in the reward network including the ventral striatum, anterior cingulate cortex and orbitofrontal cortex. Other neuroimaging studies have demonstrated the involvement of the default mode network in anhedonia. The identification of aspecific deficit in hedonic and motivational capacity may help to elucidate the mechanisms behind social functioning deficits in schizophrenia, and may also lead to more targeted treatment of negative symptoms.

On the improvement of neural cryptography using erroneous transmitted information with error prediction.

Science.gov (United States)

Allam, Ahmed M; Abbas, Hazem M

2010-12-01

Neural cryptography deals with the problem of "key exchange" between two neural networks using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between the two communicating parties is eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process. Therefore, diminishing the probability of such a threat improves the reliability of exchanging the output bits through a public channel. The synchronization with feedback algorithm is one of the existing algorithms that enhances the security of neural cryptography. This paper proposes three new algorithms to enhance the mutual learning process. They mainly depend on disrupting the attacker confidence in the exchanged outputs and input patterns during training. The first algorithm is called "Do not Trust My Partner" (DTMP), which relies on one party sending erroneous output bits, with the other party being capable of predicting and correcting this error. The second algorithm is called "Synchronization with Common Secret Feedback" (SCSFB), where inputs are kept partially secret and the attacker has to train its network on input patterns that are different from the training sets used by the communicating parties. The third algorithm is a hybrid technique combining the features of the DTMP and SCSFB. The proposed approaches are shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

Science.gov (United States)

Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Brain substrates of implicit and explicit memory: the importance of concurrently acquired neural signals of both memory types.

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Voss, Joel L; Paller, Ken A

2008-11-01

A comprehensive understanding of human memory requires cognitive and neural descriptions of memory processes along with a conception of how memory processing drives behavioral responses and subjective experiences. One serious challenge to this endeavor is that an individual memory process is typically operative within a mix of other contemporaneous memory processes. This challenge is particularly disquieting in the context of implicit memory, which, unlike explicit memory, transpires without the subject necessarily being aware of memory retrieval. Neural correlates of implicit memory and neural correlates of explicit memory are often investigated in different experiments using very different memory tests and procedures. This strategy poses difficulties for elucidating the interactions between the two types of memory process that may result in explicit remembering, and for determining the extent to which certain neural processing events uniquely contribute to only one type of memory. We review recent studies that have succeeded in separately assessing neural correlates of both implicit memory and explicit memory within the same paradigm using event-related brain potentials (ERPs) and functional magnetic resonance imaging (fMRI), with an emphasis on studies from our laboratory. The strategies we describe provide a methodological framework for achieving valid assessments of memory processing, and the findings support an emerging conceptualization of the distinct neurocognitive events responsible for implicit and explicit memory.

Classes of n-copy undistillable quantum states with negative partial transposition

International Nuclear Information System (INIS)

Bandyopadhyay, Somshubhro; Roychowdhury, Vwani

2003-01-01

The discovery of entangled quantum states from which one cannot distill pure entanglement constitutes a fundamental recent advance in the field of quantum information. Such bipartite bound-entangled (BE) quantum states could fall into two distinct categories: (1) Inseparable states with positive partial transposition (PPT), and (2) states with negative partial transposition (NPT). While the existence of PPT BE states has been confirmed, only one class of conjectured NPT BE states has been discovered so far. We provide explicit constructions of a variety of multicopy undistillable NPT states, and conjecture that they constitute families of NPT BE states. For example, we show that for every pure state of Schmidt rank greater than or equal to 3, one can construct n-copy undistillable NPT states, for any n≥1. The abundance of such conjectured NPT BE states, we believe, considerably strengthens the notion that being NPT is only a necessary condition for a state to be distillable

Predicting CYP2C19 Catalytic Parameters for Enantioselective Oxidations Using Artificial Neural Networks and a Chirality Code

Science.gov (United States)

Hartman, Jessica H.; Cothren, Steven D.; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A.; Miller, Grover P.

2013-01-01

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds. PMID:23673224

Application of neural network and pattern recognition software to the automated analysis of continuous nuclear monitoring of on-load reactors

Energy Technology Data Exchange (ETDEWEB)

Howell, J.A.; Eccleston, G.W.; Halbig, J.K.; Klosterbuer, S.F. [Los Alamos National Lab., NM (United States); Larson, T.W. [California Polytechnic State Univ., San Luis Obispo, CA (US)

1993-08-01

Automated analysis using pattern recognition and neural network software can help interpret data, call attention to potential anomalies, and improve safeguards effectiveness. Automated software analysis, based on pattern recognition and neural networks, was applied to data collected from a radiation core discharge monitor system located adjacent to an on-load reactor core. Unattended radiation sensors continuously collect data to monitor on-line refueling operations in the reactor. The huge volume of data collected from a number of radiation channels makes it difficult for a safeguards inspector to review it all, check for consistency among the measurement channels, and find anomalies. Pattern recognition and neural network software can analyze large volumes of data from continuous, unattended measurements, thereby improving and automating the detection of anomalies. The authors developed a prototype pattern recognition program that determines the reactor power level and identifies the times when fuel bundles are pushed through the core during on-line refueling. Neural network models were also developed to predict fuel bundle burnup to calculate the region on the on-load reactor face from which fuel bundles were discharged based on the radiation signals. In the preliminary data set, which was limited and consisted of four distinct burnup regions, the neural network model correctly predicted the burnup region with an accuracy of 92%.

Overlap in the functional neural systems involved in semantic and episodic memory retrieval.

Science.gov (United States)

Rajah, M N; McIntosh, A R

2005-03-01

Neuroimaging and neuropsychological data suggest that episodic and semantic memory may be mediated by distinct neural systems. However, an alternative perspective is that episodic and semantic memory represent different modes of processing within a single declarative memory system. To examine whether the multiple or the unitary system view better represents the data we conducted a network analysis using multivariate partial least squares (PLS ) activation analysis followed by covariance structural equation modeling (SEM) of positron emission tomography data obtained while healthy adults performed episodic and semantic verbal retrieval tasks. It is argued that if performance of episodic and semantic retrieval tasks are mediated by different memory systems, then there should differences in both regional activations and interregional correlations related to each type of retrieval task, respectively. The PLS results identified brain regions that were differentially active during episodic retrieval versus semantic retrieval. Regions that showed maximal differences in regional activity between episodic retrieval tasks were used to construct separate functional models for episodic and semantic retrieval. Omnibus tests of these functional models failed to find a significant difference across tasks for both functional models. The pattern of path coefficients for the episodic retrieval model were not different across tasks, nor were the path coefficients for the semantic retrieval model. The SEM results suggest that the same memory network/system was engaged across tasks, given the similarities in path coefficients. Therefore, activation differences between episodic and semantic retrieval may ref lect variation along a continuum of processing during task performance within the context of a single memory system.

A review on mechanical considerations for chronically-implanted neural probes

Science.gov (United States)

Lecomte, Aziliz; Descamps, Emeline; Bergaud, Christian

2018-06-01

This review intends to present a comprehensive analysis of the mechanical considerations for chronically-implanted neural probes. Failure of neural electrical recordings or stimulation over time has shown to arise from foreign body reaction and device material stability. It seems that devices that match most closely with the mechanical properties of the brain would be more likely to reduce the mechanical stress at the probe/tissue interface, thus improving body acceptance. The use of low Young’s modulus polymers instead of hard substrates is one way to enhance this mechanical mimetism, though compliance can be achieved through a variety of means. The reduction of probe width and thickness in comparison to a designated length, the use of soft hydrogel coatings and the release in device tethering to the skull, can also improve device compliance. Paradoxically, the more compliant the device, the more likely it will fail during the insertion process in the brain. Strategies have multiplied this past decade to offer partial or temporary stiffness to the device to overcome this buckling effect. A detailed description of the probe insertion mechanisms is provided to analyze potential sources of implantation failure and the need for a mechanically-enhancing structure. This leads us to present an overview of the strategies that have been put in place over the last ten years to overcome buckling issues. Particularly, great emphasis is put on bioresorbable polymers and their assessment for neural applications. Finally, a discussion is provided on some of the key features for the design of mechanically-reliable, polymer-based next generation of chronic neuroprosthetic devices.

Three tesla magnetic resonance imaging of the anterior cruciate ligament of the knee: can we differentiate complete from partial tears?

Energy Technology Data Exchange (ETDEWEB)

Dyck, Pieter van; Gielen, Jan L.; Parizel, Paul M. [University Hospital Antwerp and University of Antwerp, Department of Radiology, Antwerp (Edegem) (Belgium); Vanhoenacker, Filip M. [University Hospital Antwerp and University of Antwerp, Department of Radiology, Antwerp (Edegem) (Belgium); AZ St-Maarten Duffel/Mechelen, Department of Radiology, Duffel (Belgium); Dossche, Lieven; Gestel, Jozef van [University Hospital Antwerp and University of Antwerp, Department of Orthopedics, Antwerp (Edegem) (Belgium); Wouters, Kristien [University Hospital Antwerp and University of Antwerp, Department of Scientific Coordination and Biostatistics, Antwerp (Edegem) (Belgium)

2011-06-15

To determine the ability of 3.0T magnetic resonance (MR) imaging to identify partial tears of the anterior cruciate ligament (ACL) and to allow distinction of complete from partial ACL tears. One hundred seventy-two patients were prospectively studied by 3.0T MR imaging and arthroscopy in our institution. MR images were interpreted in consensus by two experienced reviewers, and the ACL was diagnosed as being normal, partially torn, or completely torn. Diagnostic accuracy of 3.0T MR for the detection of both complete and partial tears of the ACL was calculated using arthroscopy as the standard of reference. There were 132 patients with an intact ACL, 17 had a partial, and 23 had a complete tear of the ACL seen at arthroscopy. Sensitivity, specificity, and accuracy of 3.0T MR for complete ACL tears were 83, 99, and 97%, respectively, and, for partial ACL tears, 77, 97, and 95%, respectively. Five of 40 ACL lesions (13%) could not correctly be identified as complete or partial ACL tears. MR imaging at 3.0T represents a highly accurate method for identifying tears of the ACL. However, differentiation between complete and partial ACL tears and identification of partial tears of this ligament remains difficult, even at 3.0T. (orig.)

Three tesla magnetic resonance imaging of the anterior cruciate ligament of the knee: can we differentiate complete from partial tears?

International Nuclear Information System (INIS)

Dyck, Pieter van; Gielen, Jan L.; Parizel, Paul M.; Vanhoenacker, Filip M.; Dossche, Lieven; Gestel, Jozef van; Wouters, Kristien

2011-01-01

To determine the ability of 3.0T magnetic resonance (MR) imaging to identify partial tears of the anterior cruciate ligament (ACL) and to allow distinction of complete from partial ACL tears. One hundred seventy-two patients were prospectively studied by 3.0T MR imaging and arthroscopy in our institution. MR images were interpreted in consensus by two experienced reviewers, and the ACL was diagnosed as being normal, partially torn, or completely torn. Diagnostic accuracy of 3.0T MR for the detection of both complete and partial tears of the ACL was calculated using arthroscopy as the standard of reference. There were 132 patients with an intact ACL, 17 had a partial, and 23 had a complete tear of the ACL seen at arthroscopy. Sensitivity, specificity, and accuracy of 3.0T MR for complete ACL tears were 83, 99, and 97%, respectively, and, for partial ACL tears, 77, 97, and 95%, respectively. Five of 40 ACL lesions (13%) could not correctly be identified as complete or partial ACL tears. MR imaging at 3.0T represents a highly accurate method for identifying tears of the ACL. However, differentiation between complete and partial ACL tears and identification of partial tears of this ligament remains difficult, even at 3.0T. (orig.)

Classification of PD sources in HV cables using neural networks and the LN-FDTD method

Energy Technology Data Exchange (ETDEWEB)

Barros, W.H.; Oliveira, R.M.; Sobrinho, C.L.; Leite, R.C. [Univ. Federal do Para, Para (Brazil). Dept. of Computational and Electrical Engineering

2008-07-01

Partial discharges can be generated by the presence of several defects, and are often the cause of failures in electrical equipment insulators. In this study, a local non-orthogonal finite difference time-domain method (LN-FDTD) was used to simulate the sources of partial discharges in a high voltage coaxial cable model. The artificial neural network (ANN) technique used a Marquandt-Levenberg training algorithm to detect and classify cable PD sources. A set of harmonics obtained from the difference between the registered signals in the time domain was used as part of the training procedure. A failure was inserted on an electrical cable in each simulation in order to obtain the correlated data. Input signals were injected using a voltage pulse represented by a Gaussian function. A total of 90 simulations were conducted to generate 360 data groups in order to consider 6 different types of failures. Results of the study showed that the method can be used to detect and classify partial discharges in cables. 12 refs., 21 figs.

A neural flow estimator

DEFF Research Database (Denmark)

Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

1995-01-01

This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

Hidden neural networks

DEFF Research Database (Denmark)

Krogh, Anders Stærmose; Riis, Søren Kamaric

1999-01-01

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

Partial tooth gear bearings

Science.gov (United States)

Vranish, John M. (Inventor)

2010-01-01

A partial gear bearing including an upper half, comprising peak partial teeth, and a lower, or bottom, half, comprising valley partial teeth. The upper half also has an integrated roller section between each of the peak partial teeth with a radius equal to the gear pitch radius of the radially outwardly extending peak partial teeth. Conversely, the lower half has an integrated roller section between each of the valley half teeth with a radius also equal to the gear pitch radius of the peak partial teeth. The valley partial teeth extend radially inwardly from its roller section. The peak and valley partial teeth are exactly out of phase with each other, as are the roller sections of the upper and lower halves. Essentially, the end roller bearing of the typical gear bearing has been integrated into the normal gear tooth pattern.

NeuroMEMS: Neural Probe Microtechnologies

Directory of Open Access Journals (Sweden)

Sam Musallam

2008-10-01

Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

Neural congruence between intertemporal and interpersonal self-control: Evidence from delay and social discounting.

Science.gov (United States)

Hill, Paul F; Yi, Richard; Spreng, R Nathan; Diana, Rachel A

2017-11-15

Behavioral studies using delay and social discounting as indices of self-control and altruism, respectively, have revealed functional similarities between farsighted and social decisions. However, neural evidence for this functional link is lacking. Twenty-five young adults completed a delay and social discounting task during fMRI scanning. A spatiotemporal partial least squares analysis revealed that both forms of discounting were well characterized by a pattern of brain activity in areas comprising frontoparietal control, default, and mesolimbic reward networks. Both forms of discounting appear to draw on common neurocognitive mechanisms, regardless of whether choices involve intertemporal or interpersonal outcomes. We also observed neural profiles differentiating between high and low discounters. High discounters were well characterized by increased medial temporal lobe and limbic activity. In contrast, low discount rates were associated with activity in the medial prefrontal cortex and right temporoparietal junction. This pattern may reflect biological mechanisms underlying behavioral heterogeneity in discount rates. Copyright © 2017 Elsevier Inc. All rights reserved.

Music enrichment programs improve the neural encoding of speech in at-risk children.

Science.gov (United States)

Kraus, Nina; Slater, Jessica; Thompson, Elaine C; Hornickel, Jane; Strait, Dana L; Nicol, Trent; White-Schwoch, Travis

2014-09-03

Musicians are often reported to have enhanced neurophysiological functions, especially in the auditory system. Musical training is thought to improve nervous system function by focusing attention on meaningful acoustic cues, and these improvements in auditory processing cascade to language and cognitive skills. Correlational studies have reported musician enhancements in a variety of populations across the life span. In light of these reports, educators are considering the potential for co-curricular music programs to provide auditory-cognitive enrichment to children during critical developmental years. To date, however, no studies have evaluated biological changes following participation in existing, successful music education programs. We used a randomized control design to investigate whether community music participation induces a tangible change in auditory processing. The community music training was a longstanding and successful program that provides free music instruction to children from underserved backgrounds who stand at high risk for learning and social problems. Children who completed 2 years of music training had a stronger neurophysiological distinction of stop consonants, a neural mechanism linked to reading and language skills. One year of training was insufficient to elicit changes in nervous system function; beyond 1 year, however, greater amounts of instrumental music training were associated with larger gains in neural processing. We therefore provide the first direct evidence that community music programs enhance the neural processing of speech in at-risk children, suggesting that active and repeated engagement with sound changes neural function. Copyright © 2014 the authors 0270-6474/14/3411913-06$15.00/0.

Diprosopia revisited in light of the recognized role of neural crest cells in facial development.

Science.gov (United States)

Carles, D; Weichhold, W; Alberti, E M; Léger, F; Pigeau, F; Horovitz, J

1995-01-01

The aim of this study is to compare the theory of embryogenesis of the face with human diprosopia. This peculiar form of conjoined twinning is of great interest because 1) only the facial structures are duplicated and 2) almost all cases have a rather monomorphic pattern. The hypothesis is that an initial duplication of the notochord leads to two neural plates and subsequently duplicated neural crests. In those conditions, derivatives of the neural crests will be partially or totally duplicated; therefore, in diprosopia, the duplicated facial structures would be considered to be neural crest derivatives. If these structures are identical to those that are experimentally demonstrated to be neural crest derivatives in animals, these findings are an argument to apply this theory of facial embryogenesis in man. Serial horizontal sections of the face of two diprosopic fetuses (11 and 21 weeks gestation) were studied macro- and microscopically to determine the external and internal structures that are duplicated. Complete postmortem examination was performed in search for additional malformations. The face of both fetuses showed a very similar morphologic pattern with duplication of ocular, nasal, and buccal structures. The nasal fossae and the anterior part of the tongue were also duplicated, albeit the posterior part and the pharyngolaryngeal structures were unique. Additional facial clefts were present in both fetuses. Extrafacial anomalies were represented by a craniorachischisis, two fused vertebral columns and, in the older fetus, by a complex cardiac malformation morphologically identical to malformations induced by removal or grafting of additional cardiac neural crest cells in animals. These pathological findings could identify the facial structures that are neural crest derivatives in man. They are similar to those experimentally demonstrated to be neural crest derivatives in animals. In this respect, diprosopia could be considered as the end of a spectrum

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

Science.gov (United States)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

IGF-II Promotes Stemness of Neural Restricted Precursors

Science.gov (United States)

Ziegler, Amber N.; Schneider, Joel S.; Qin, Mei; Tyler, William A.; Pintar, John E.; Fraidenraich, Diego; Wood, Teresa L.; Levison, Steven W.

2016-01-01

Insulin-like growth factor (IGF)-I and IGF-II regulate brain development and growth through the IGF type 1 receptor (IGF-1R). Less appreciated is that IGF-II, but not IGF-I, activates a splice variant of the insulin receptor (IR) known as IR-A. We hypothesized that IGF-II exerts distinct effects from IGF-I on neural stem/progenitor cells (NSPs) via its interaction with IR-A. Immunofluorescence revealed high IGF-II in the medial region of the subventricular zone (SVZ) comprising the neural stem cell niche, with IGF-II mRNA predominant in the adjacent choroid plexus. The IGF-1R and the IR isoforms were differentially expressed with IR-A predominant in the medial SVZ, whereas the IGF-1R was more abundant laterally. Similarly, IR-A was more highly expressed by NSPs, whereas the IGF-1R was more highly expressed by lineage restricted cells. In vitro, IGF-II was more potent in promoting NSP expansion than either IGF-I or standard growth medium. Limiting dilution and differentiation assays revealed that IGF-II was superior to IGF-I in promoting stemness. In vivo, NSPs propagated in IGF-II migrated to and took up residence in periventricular niches while IGF-I-treated NSPs predominantly colonized white matter. Knockdown of IR or IGF-1R using shRNAs supported the conclusion that the IGF-1R promotes progenitor proliferation, whereas the IR is important for self-renewal. Q-PCR revealed that IGF-II increased Oct4, Sox1, and FABP7 mRNA levels in NSPs. Our data support the conclusion that IGF-II promotes the self-renewal of neural stem/progenitors via the IR. By contrast, IGF-1R functions as a mitogenic receptor to increase precursor abundance. PMID:22593020

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

Science.gov (United States)

Hardy, N F; Buonomano, Dean V

2018-02-01

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

A patch-based convolutional neural network for remote sensing image classification.

Science.gov (United States)

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

Science.gov (United States)

Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

2014-01-01

Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

Science.gov (United States)

Lowet, Eric; Roberts, Mark J.; Bonizzi, Pietro; Karel, Joël; De Weerd, Peter

2016-01-01

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches.

Directory of Open Access Journals (Sweden)

Eric Lowet

Full Text Available Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT preceded by Singular Spectrum Decomposition (SSD of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization

Decoding the neural signatures of emotions expressed through sound.

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Sachs, Matthew E; Habibi, Assal; Damasio, Antonio; Kaplan, Jonas T

2018-03-01

Effective social functioning relies in part on the ability to identify emotions from auditory stimuli and respond appropriately. Previous studies have uncovered brain regions engaged by the affective information conveyed by sound. But some of the acoustical properties of sounds that express certain emotions vary remarkably with the instrument used to produce them, for example the human voice or a violin. Do these brain regions respond in the same way to different emotions regardless of the sound source? To address this question, we had participants (N = 38, 20 females) listen to brief audio excerpts produced by the violin, clarinet, and human voice, each conveying one of three target emotions-happiness, sadness, and fear-while brain activity was measured with fMRI. We used multivoxel pattern analysis to test whether emotion-specific neural responses to the voice could predict emotion-specific neural responses to musical instruments and vice-versa. A whole-brain searchlight analysis revealed that patterns of activity within the primary and secondary auditory cortex, posterior insula, and parietal operculum were predictive of the affective content of sound both within and across instruments. Furthermore, classification accuracy within the anterior insula was correlated with behavioral measures of empathy. The findings suggest that these brain regions carry emotion-specific patterns that generalize across sounds with different acoustical properties. Also, individuals with greater empathic ability have more distinct neural patterns related to perceiving emotions. These results extend previous knowledge regarding how the human brain extracts emotional meaning from auditory stimuli and enables us to understand and connect with others effectively. Copyright © 2018 Elsevier Inc. All rights reserved.

The neural basis of task switching changes with skill acquisition

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Koji eJimura

2014-05-01

Full Text Available Learning novel skills involves reorganization and optimization of cognitive processing involving a broad network of brain regions. Previous work has shown asymmetric costs of switching to a well-trained task versus a poorly-trained task, but the neural basis of these differential switch costs is unclear. The current study examined the neural signature of task switching in the context of acquisition of new skill. Human participants alternated randomly between a novel visual task (mirror-reversed word reading and a highly practiced one (plain word reading, allowing the isolation of task switching and skill set maintenance. Two scan sessions were separated by two weeks, with behavioral training on the mirror reading task in between the two sessions. Broad cortical regions, including bilateral prefrontal, parietal, and extrastriate cortices, showed decreased activity associated with learning of the mirror reading skill. In contrast, learning to switch to the novel skill was associated with decreased activity in a focal subcortical region in the dorsal striatum. Switching to the highly practiced task was associated with a non-overlapping set of regions, suggesting substantial differences in the neural substrates of switching as a function of task skill. Searchlight multivariate pattern analysis also revealed that learning was associated with decreased pattern information for mirror versus plain reading tasks in fronto-parietal regions. Inferior frontal junction and posterior parietal cortex showed a joint effect of univariate activation and pattern information. These results suggest distinct learning mechanisms task performance and executive control as a function of learning.

Frequency decoding of periodically timed action potentials through distinct activity patterns in a random neural network

International Nuclear Information System (INIS)

Reichenbach, Tobias; Hudspeth, A J

2012-01-01

Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus—they exhibit phase locking—and thus provide temporal information about the tone's frequency. Humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we employ statistical and numerical methods to demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans. (paper)

Neural Networks: Implementations and Applications

OpenAIRE

Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

1996-01-01

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control

Directory of Open Access Journals (Sweden)

Adenike A. Adewuyi

2016-10-01

Full Text Available Pattern recognition-based myoelectric control of upper limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1 the performance of non-linear and linear pattern recognition algorithms and (2 the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p<0.001 but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p=0.07, intrinsic (p=0.06, or combined extrinsic and intrinsic muscle EMG (p=0.08, respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p<0.001 and time domain/autoregressive feature sets (p<0.01. This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.

Transcranial magnetic stimulation reveals two functionally distinct stages of motor cortex involvement during perception of emotional body language.

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Borgomaneri, Sara; Gazzola, Valeria; Avenanti, Alessio

2015-09-01

Studies indicate that perceiving emotional body language recruits fronto-parietal regions involved in action execution. However, the nature of such motor activation is unclear. Using transcranial magnetic stimulation (TMS) we provide correlational and causative evidence of two distinct stages of motor cortex engagement during emotion perception. Participants observed pictures of body expressions and categorized them as happy, fearful or neutral while receiving TMS over the left or right motor cortex at 150 and 300 ms after picture onset. In the early phase (150 ms), we observed a reduction of excitability for happy and fearful emotional bodies that was specific to the right hemisphere and correlated with participants' disposition to feel personal distress. This 'orienting' inhibitory response to emotional bodies was also paralleled by a general drop in categorization accuracy when stimulating the right but not the left motor cortex. Conversely, at 300 ms, greater excitability for negative, positive and neutral movements was found in both hemispheres. This later motor facilitation marginally correlated with participants' tendency to assume the psychological perspectives of others and reflected simulation of the movement implied in the neutral and emotional body expressions. These findings highlight the motor system's involvement during perception of emotional bodies. They suggest that fast orienting reactions to emotional cues--reflecting neural processing necessary for visual perception--occur before motor features of the observed emotional expression are simulated in the motor system and that distinct empathic dispositions influence these two neural motor phenomena. Implications for theories of embodied simulation are discussed.

The flexible brain. On mind and brain, neural darwinism and psychiatry.

Science.gov (United States)

den Boer, J A

1997-09-01

A theoretical introduction is given in which several theoretical viewpoints concerning the mind-brain problem are discussed. During the last decade philosophers like Searle, Dennett and the Churchlands have taken a more or less pure materialistic position in explaining mental phenomena. Investigators in biological psychiatry have hardly ever taken a clear position in this discussion, whereas we believe it is important that the conclusions drawn from biological research are embedded in a theoretical framework related to the mind-brain problem. In this article the thesis is defended that the theory of neural darwinism represents a major step forward and may bridge previous distinctions between biological, clinical and social psychiatry.

The porous boundaries between explicit and implicit memory: behavioral and neural evidence.

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Dew, Ilana T Z; Cabeza, Roberto

2011-04-01

Explicit memory refers to the conscious retrieval of past information or experiences, whereas implicit memory refers to an unintentional or nonconscious form of retrieval. Much of the literature in cognitive psychology and cognitive neuroscience has focused on differences between explicit and implicit memory, and the traditional view is that they rely on distinct brain systems. However, the potential interplay between implicit and explicit memory is not always clear. This review draws from behavioral and functional neuroimaging evidence to evaluate three areas in which implicit and explicit memory may be interrelated. First, we discuss views of familiarity-based recognition in terms of its relationship with implicit memory. Second, we review the challenges of distinguishing between implicit memory and involuntary aware memory, at both behavioral and neural levels. Finally, we examine evidence indicating that implicit and explicit retrieval of relational information may rely on a common neural mechanism. Taken together, these areas indicate that, under certain circumstances, there may be an important and influential relationship between conscious and nonconscious expressions of memory. © 2011 New York Academy of Sciences.

Two social brains: neural mechanisms of intersubjectivity.

Science.gov (United States)

Vogeley, Kai

2017-08-19

It is the aim of this article to present an empirically justified hypothesis about the functional roles of the two social neural systems, namely the so-called 'mirror neuron system' (MNS) and the 'mentalizing system' (MENT, also 'theory of mind network' or 'social neural network'). Both systems are recruited during cognitive processes that are either related to interaction or communication with other conspecifics, thereby constituting intersubjectivity. The hypothesis is developed in the following steps: first, the fundamental distinction that we make between persons and things is introduced; second, communication is presented as the key process that allows us to interact with others; third, the capacity to 'mentalize' or to understand the inner experience of others is emphasized as the fundamental cognitive capacity required to establish successful communication. On this background, it is proposed that MNS serves comparably early stages of social information processing related to the 'detection' of spatial or bodily signals, whereas MENT is recruited during comparably late stages of social information processing related to the 'evaluation' of emotional and psychological states of others. This hypothesis of MNS as a social detection system and MENT as a social evaluation system is illustrated by findings in the field of psychopathology. Finally, new research questions that can be derived from this hypothesis are discussed.This article is part of the themed issue 'Physiological determinants of social behaviour in animals'. © 2017 The Author(s).

Distinct timing mechanisms produce discrete and continuous movements.

Directory of Open Access Journals (Sweden)

Raoul Huys

2008-04-01

Full Text Available The differentiation of discrete and continuous movement is one of the pillars of motor behavior classification. Discrete movements have a definite beginning and end, whereas continuous movements do not have such discriminable end points. In the past decade there has been vigorous debate whether this classification implies different control processes. This debate up until the present has been empirically based. Here, we present an unambiguous non-empirical classification based on theorems in dynamical system theory that sets discrete and continuous movements apart. Through computational simulations of representative modes of each class and topological analysis of the flow in state space, we show that distinct control mechanisms underwrite discrete and fast rhythmic movements. In particular, we demonstrate that discrete movements require a time keeper while fast rhythmic movements do not. We validate our computational findings experimentally using a behavioral paradigm in which human participants performed finger flexion-extension movements at various movement paces and under different instructions. Our results demonstrate that the human motor system employs different timing control mechanisms (presumably via differential recruitment of neural subsystems to accomplish varying behavioral functions such as speed constraints.

Identification of neural transcription factors required for the differentiation of three neuronal subtypes in the sea urchin embryo.

Science.gov (United States)

Slota, Leslie A; McClay, David R

2018-03-15

Correct patterning of the nervous system is essential for an organism's survival and complex behavior. Embryologists have used the sea urchin as a model for decades, but our understanding of sea urchin nervous system patterning is incomplete. Previous histochemical studies identified multiple neurotransmitters in the pluteus larvae of several sea urchin species. However, little is known about how, where and when neural subtypes are differentially specified during development. Here, we examine the molecular mechanisms of neuronal subtype specification in 3 distinct neural subtypes in the Lytechinus variegatus larva. We show that these subtypes are specified through Delta/Notch signaling and identify a different transcription factor required for the development of each neural subtype. Our results show achaete-scute and neurogenin are proneural for the serotonergic neurons of the apical organ and cholinergic neurons of the ciliary band, respectively. We also show that orthopedia is not proneural but is necessary for the differentiation of the cholinergic/catecholaminergic postoral neurons. Interestingly, these transcription factors are used similarly during vertebrate neurogenesis. We believe this study is a starting point for building a neural gene regulatory network in the sea urchin and for finding conserved deuterostome neurogenic mechanisms. Copyright © 2018 Elsevier Inc. All rights reserved.

Musicians' Enhanced Neural Differentiation of Speech Sounds Arises Early in Life: Developmental Evidence from Ages 3 to 30

Science.gov (United States)

Strait, Dana L.; O'Connell, Samantha; Parbery-Clark, Alexandra; Kraus, Nina

2014-01-01

The perception and neural representation of acoustically similar speech sounds underlie language development. Music training hones the perception of minute acoustic differences that distinguish sounds; this training may generalize to speech processing given that adult musicians have enhanced neural differentiation of similar speech syllables compared with nonmusicians. Here, we asked whether this neural advantage in musicians is present early in life by assessing musically trained and untrained children as young as age 3. We assessed auditory brainstem responses to the speech syllables /ba/ and /ga/ as well as auditory and visual cognitive abilities in musicians and nonmusicians across 3 developmental time-points: preschoolers, school-aged children, and adults. Cross-phase analyses objectively measured the degree to which subcortical responses differed to these speech syllables in musicians and nonmusicians for each age group. Results reveal that musicians exhibit enhanced neural differentiation of stop consonants early in life and with as little as a few years of training. Furthermore, the extent of subcortical stop consonant distinction correlates with auditory-specific cognitive abilities (i.e., auditory working memory and attention). Results are interpreted according to a corticofugal framework for auditory learning in which subcortical processing enhancements are engendered by strengthened cognitive control over auditory function in musicians. PMID:23599166

Supramolecular Isomers of Metal-Organic Frameworks Derived from a Partially Flexible Ligand with Distinct Binding Motifs

KAUST Repository

Abdul Halim, Racha Ghassan

2016-01-04

Three novel metal-organic frameworks (MOFs) were isolated upon reacting a heterofunctional ligand 4 (pyrimidin-5 yl)benzoic acid (4,5-pmbc) with mixed valence Cu(I,II) under solvothermal conditions. X-ray crystal structural analysis reveals that the first compound is a layered structure composed of one type of inorganic building block, dinuclear paddlewheel [Cu2(O2C–)4], which are linked through 4,5-pmbc ligands. The two other supramolecular isomers are composed of the same Cu(II) dinuclear paddlewheel and a dinuclear Cu2I2 cluster, which are linked via the 4,5-pmbc linkers to yield two different 3-periodic frameworks with underlying topologies related to lvt and nbo. The observed structural diversity in these structures is due to the distinct coordination modes of the two coordinating moieties (the carboxylate group on the phenyl ring and the N-donor atoms from the pyrimidine moiety).

Supramolecular Isomers of Metal-Organic Frameworks Derived from a Partially Flexible Ligand with Distinct Binding Motifs

KAUST Repository

AbdulHalim, Rasha; Shkurenko, Aleksander; Al Kordi, Mohamed; Eddaoudi, Mohamed

2016-01-01

Three novel metal-organic frameworks (MOFs) were isolated upon reacting a heterofunctional ligand 4 (pyrimidin-5 yl)benzoic acid (4,5-pmbc) with mixed valence Cu(I,II) under solvothermal conditions. X-ray crystal structural analysis reveals that the first compound is a layered structure composed of one type of inorganic building block, dinuclear paddlewheel [Cu2(O2C–)4], which are linked through 4,5-pmbc ligands. The two other supramolecular isomers are composed of the same Cu(II) dinuclear paddlewheel and a dinuclear Cu2I2 cluster, which are linked via the 4,5-pmbc linkers to yield two different 3-periodic frameworks with underlying topologies related to lvt and nbo. The observed structural diversity in these structures is due to the distinct coordination modes of the two coordinating moieties (the carboxylate group on the phenyl ring and the N-donor atoms from the pyrimidine moiety).

Neurophysiology and neural engineering: a review.

Science.gov (United States)

Prochazka, Arthur

2017-08-01

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

NARCIS (Netherlands)

D. Zambrano (Davide); S.M. Bohte (Sander)

2016-01-01

textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

Acidogenesis driven by hydrogen partial pressure towards bioethanol production through fatty acids reduction

International Nuclear Information System (INIS)

Sarkar, Omprakash; Butti, Sai Kishore; Venkata Mohan, S.

2017-01-01

H 2 partial pressure drives the reduction of carboxylic acid (short chain fatty acids) formed as primary metabolites in acidogenic fermentation to form bioalcohols. Microbial catalysis under the influence of H 2 partial pressure was evaluated in comparison with a reactor operated at atmospheric pressure under identical conditions. Carboxylic acid reduction gets regulated selectively by the influence of elevated pressures and redox conditions, resulting in the formation of alcohols. The non-equilibrium of the intra and extracellular H 2 ions causes the anaerobic bacteria to alter their pathways as a function of interspecies H 2 transfer. Ethanol production was quantified, as acetic acid was the major carboxylic acid synthesised during acidogenesis. H 2 pressure influenced the electrochemical activity which was reflected in the distinct variation of the electron transfer rates and the catalytic activity of redox mediators (NAD + /NADH, flavoproteins and iron-sulphur clusters). The bioprocess depicted in this communication depicted a non-genetic regulation of product formation, understanding the acidogenic metabolism and alternate route for alcohol production. - Highlights: • H 2 partial pressure in HPR aided in the reduction of carboxylic acids to alcohols. • Production and consumption rate of VFAs were correlating with alcohol formation. • Metabolic shift was evident with bioelectrochical analysis. • NADH/NAD + ratio and H 2 partial pressure coupled in enhanced solventogenesis.

Artificial neural networks in NDT

International Nuclear Information System (INIS)

Abdul Aziz Mohamed

2001-01-01

Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

Distinct Neurodegenerative Changes in an Induced Pluripotent Stem Cell Model of Frontotemporal Dementia Linked to Mutant TAU Protein

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Marc Ehrlich

2015-07-01

Full Text Available Frontotemporal dementia (FTD is a frequent form of early-onset dementia and can be caused by mutations in MAPT encoding the microtubule-associated protein TAU. Because of limited availability of neural cells from patients’ brains, the underlying mechanisms of neurodegeneration in FTD are poorly understood. Here, we derived induced pluripotent stem cells (iPSCs from individuals with FTD-associated MAPT mutations and differentiated them into mature neurons. Patient iPSC-derived neurons demonstrated pronounced TAU pathology with increased fragmentation and phospho-TAU immunoreactivity, decreased neurite extension, and increased but reversible oxidative stress response to inhibition of mitochondrial respiration. Furthermore, FTD neurons showed an activation of the unfolded protein response, and a transcriptome analysis demonstrated distinct, disease-associated gene expression profiles. These findings indicate distinct neurodegenerative changes in FTD caused by mutant TAU and highlight the unique opportunity to use neurons differentiated from patient-specific iPSCs to identify potential targets for drug screening purposes and therapeutic intervention.

Neural correlates of hate.

Directory of Open Access Journals (Sweden)

Semir Zeki

Full Text Available In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus. The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the putamen and the insula.

The neural dynamics of reward value and risk coding in the human orbitofrontal cortex.

Science.gov (United States)

Li, Yansong; Vanni-Mercier, Giovanna; Isnard, Jean; Mauguière, François; Dreher, Jean-Claude

2016-04-01

The orbitofrontal cortex is known to carry information regarding expected reward, risk and experienced outcome. Yet, due to inherent limitations in lesion and neuroimaging methods, the neural dynamics of these computations has remained elusive in humans. Here, taking advantage of the high temporal definition of intracranial recordings, we characterize the neurophysiological signatures of the intact orbitofrontal cortex in processing information relevant for risky decisions. Local field potentials were recorded from the intact orbitofrontal cortex of patients suffering from drug-refractory partial epilepsy with implanted depth electrodes as they performed a probabilistic reward learning task that required them to associate visual cues with distinct reward probabilities. We observed three successive signals: (i) around 400 ms after cue presentation, the amplitudes of the local field potentials increased with reward probability; (ii) a risk signal emerged during the late phase of reward anticipation and during the outcome phase; and (iii) an experienced value signal appeared at the time of reward delivery. Both the medial and lateral orbitofrontal cortex encoded risk and reward probability while the lateral orbitofrontal cortex played a dominant role in coding experienced value. The present study provides the first evidence from intracranial recordings that the human orbitofrontal cortex codes reward risk both during late reward anticipation and during the outcome phase at a time scale of milliseconds. Our findings offer insights into the rapid mechanisms underlying the ability to learn structural relationships from the environment. © The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.