Automatic Speech Recognition from Neural Signals: A Focused Review.

PubMed

Herff, Christian; Schultz, Tanja

2016-01-01

Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.

An FGF3-BMP Signaling Axis Regulates Caudal Neural Tube Closure, Neural Crest Specification and Anterior-Posterior Axis Extension

PubMed Central

Anderson, Matthew J.; Schimmang, Thomas; Lewandoski, Mark

2016-01-01

During vertebrate axis extension, adjacent tissue layers undergo profound morphological changes: within the neuroepithelium, neural tube closure and neural crest formation are occurring, while within the paraxial mesoderm somites are segmenting from the presomitic mesoderm (PSM). Little is known about the signals between these tissues that regulate their coordinated morphogenesis. Here, we analyze the posterior axis truncation of mouse Fgf3 null homozygotes and demonstrate that the earliest role of PSM-derived FGF3 is to regulate BMP signals in the adjacent neuroepithelium. FGF3 loss causes elevated BMP signals leading to increased neuroepithelium proliferation, delay in neural tube closure and premature neural crest specification. We demonstrate that elevated BMP4 depletes PSM progenitors in vitro, phenocopying the Fgf3 mutant, suggesting that excessive BMP signals cause the Fgf3 axis defect. To test this in vivo we increased BMP signaling in Fgf3 mutants by removing one copy of Noggin, which encodes a BMP antagonist. In such mutants, all parameters of the Fgf3 phenotype were exacerbated: neural tube closure delay, premature neural crest specification, and premature axis termination. Conversely, genetically decreasing BMP signaling in Fgf3 mutants, via loss of BMP receptor activity, alleviates morphological defects. Aberrant apoptosis is observed in the Fgf3 mutant tailbud. However, we demonstrate that cell death does not cause the Fgf3 phenotype: blocking apoptosis via deletion of pro-apoptotic genes surprisingly increases all Fgf3 defects including causing spina bifida. We demonstrate that this counterintuitive consequence of blocking apoptosis is caused by the increased survival of BMP-producing cells in the neuroepithelium. Thus, we show that FGF3 in the caudal vertebrate embryo regulates BMP signaling in the neuroepithelium, which in turn regulates neural tube closure, neural crest specification and axis termination. Uncovering this FGF3-BMP signaling axis is

Ion fluxes and neurotransmitters signaling in neural development.

PubMed

Andäng, Michael; Lendahl, Urban

2008-06-01

The brain develops and functions in a complex ionic milieu, which is a prerequisite for neurotransmitter function and neuronal signaling. Neurotransmitters and ion fluxes are, however, important not only in neuronal signaling, but also in the control of neural differentiation, and in this review, we highlight the recent advances in our understanding of how the gamma-amino butyric acid (GABA) neurotransmitter and ion fluxes are relevant for cell cycle control and neural differentiation. Conversely, proteins previously associated with ion transport across membranes have been endowed with novel ion-independent functions, and we discuss this in the context of gap junctions in cell adhesion and of the neuron-specific K(+)-Cl(-) cotransporter KCC2 in dendritic spine development. Collectively, these findings provide a richer and more complex picture of when ion fluxes are needed in neural development and when they are not.

Towards a magnetoresistive platform for neural signal recording

NASA Astrophysics Data System (ADS)

Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.

2017-05-01

A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.

Inferring neural activity from BOLD signals through nonlinear optimization.

PubMed

Vakorin, Vasily A; Krakovska, Olga O; Borowsky, Ron; Sarty, Gordon E

2007-11-01

The blood oxygen level-dependent (BOLD) fMRI signal does not measure neuronal activity directly. This fact is a key concern for interpreting functional imaging data based on BOLD. Mathematical models describing the path from neural activity to the BOLD response allow us to numerically solve the inverse problem of estimating the timing and amplitude of the neuronal activity underlying the BOLD signal. In fact, these models can be viewed as an advanced substitute for the impulse response function. In this work, the issue of estimating the dynamics of neuronal activity from the observed BOLD signal is considered within the framework of optimization problems. The model is based on the extended "balloon" model and describes the conversion of neuronal signals into the BOLD response through the transitional dynamics of the blood flow-inducing signal, cerebral blood flow, cerebral blood volume and deoxyhemoglobin concentration. Global optimization techniques are applied to find a control input (the neuronal activity and/or the biophysical parameters in the model) that causes the system to follow an admissible solution to minimize discrepancy between model and experimental data. As an alternative to a local linearization (LL) filtering scheme, the optimization method escapes the linearization of the transition system and provides a possibility to search for the global optimum, avoiding spurious local minima. We have found that the dynamics of the neural signals and the physiological variables as well as the biophysical parameters can be robustly reconstructed from the BOLD responses. Furthermore, it is shown that spiking off/on dynamics of the neural activity is the natural mathematical solution of the model. Incorporating, in addition, the expansion of the neural input by smooth basis functions, representing a low-pass filtering, allows us to model local field potential (LFP) solutions instead of spiking solutions.

Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

PubMed

Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

2008-09-01

In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

PubMed Central

Holca-Lamarre, Raphaël; Lücke, Jörg; Obermayer, Klaus

2017-01-01

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates. PMID:28690509

Regulation of adult neural progenitor cell functions by purinergic signaling.

PubMed

Tang, Yong; Illes, Peter

2017-02-01

Extracellular purines are signaling molecules in the neurogenic niches of the brain and spinal cord, where they activate cell surface purinoceptors at embryonic neural stem cells (NSCs) and adult neural progenitor cells (NPCs). Although mRNA and protein are expressed at NSCs/NPCs for almost all subtypes of the nucleotide-sensitive P2X/P2Y, and the nucleoside-sensitive adenosine receptors, only a few of those have acquired functional significance. ATP is sequentially degraded by ecto-nucleotidases to ADP, AMP, and adenosine with agonistic properties for distinct receptor-classes. Nucleotides/nucleosides facilitate or inhibit NSC/NPC proliferation, migration and differentiation. The most ubiquitous effect of all agonists (especially of ATP and ADP) appears to be the facilitation of cell proliferation, usually through P2Y1Rs and sometimes through P2X7Rs. However, usually P2X7R activation causes necrosis/apoptosis of NPCs. Differentiation can be initiated by P2Y2R-activation or P2X7R-blockade. A key element in the transduction mechanism of either receptor is the increase of the intracellular free Ca 2+ concentration, which may arise due to its release from intracellular storage sites (G protein-coupling; P2Y) or due to its passage through the receptor-channel itself from the extracellular space (ATP-gated ion channel; P2X). Further research is needed to clarify how purinergic signaling controls NSC/NPC fate and how the balance between the quiescent and activated states is established with fine and dynamic regulation. GLIA 2017;65:213-230. © 2016 Wiley Periodicals, Inc.

Slit/Robo1 signaling regulates neural tube development by balancing neuroepithelial cell proliferation and differentiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Guang; Li, Yan; Wang, Xiao-yu

2013-05-01

Formation of the neural tube is the morphological hallmark for development of the embryonic central nervous system (CNS). Therefore, neural tube development is a crucial step in the neurulation process. Slit/Robo signaling was initially identified as a chemo-repellent that regulated axon growth cone elongation, but its role in controlling neural tube development is currently unknown. To address this issue, we investigated Slit/Robo1 signaling in the development of chick neCollege of Life Sciences Biocentre, University of Dundee, Dundee DD1 5EH, UKural tube and transgenic mice over-expressing Slit2. We disrupted Slit/Robo1 signaling by injecting R5 monoclonal antibodies into HH10 neural tubes tomore » block the Robo1 receptor. This inhibited the normal development of the ventral body curvature and caused the spinal cord to curl up into a S-shape. Next, Slit/Robo1 signaling on one half-side of the chick embryo neural tube was disturbed by electroporation in ovo. We found that the morphology of the neural tube was dramatically abnormal after we interfered with Slit/Robo1 signaling. Furthermore, we established that silencing Robo1 inhibited cell proliferation while over-expressing Robo1 enhanced cell proliferation. We also investigated the effects of altering Slit/Robo1 expression on Sonic Hedgehog (Shh) and Pax7 expression in the developing neural tube. We demonstrated that over-expressing Robo1 down-regulated Shh expression in the ventral neural tube and resulted in the production of fewer HNK-1{sup +} migrating neural crest cells (NCCs). In addition, Robo1 over-expression enhanced Pax7 expression in the dorsal neural tube and increased the number of Slug{sup +} pre-migratory NCCs. Conversely, silencing Robo1 expression resulted in an enhanced Shh expression and more HNK-1{sup +} migrating NCCs but reduced Pax7 expression and fewer Slug{sup +} pre-migratory NCCs were observed. In conclusion, we propose that Slit/Robo1 signaling is involved in regulating neural tube

Dysregulation of neural calcium signaling in Alzheimer disease, bipolar disorder and schizophrenia

PubMed Central

Berridge, Michael J.

2013-01-01

Neurons have highly developed Ca2+ signaling systems responsible for regulating a large number of neural functions such as the control of brain rhythms, information processing and the changes in synaptic plasticity that underpin learning and memory. The tonic excitatory drive, which is activated by the ascending arousal system, is particularly important for processes such as sensory perception, cognition and consciousness. The Ca2+ signaling pathway is a key component of this arousal system that regulates the neuronal excitability responsible for controlling the neural brain rhythms required for information processing and cognition. Dysregulation of the Ca2+ signaling pathway responsible for many of these neuronal processes has been implicated in the development of some of the major neural diseases in man such as Alzheimer disease, bipolar disorder and schizophrenia. Various treatments, which are known to act by reducing the activity of Ca2+ signaling, have proved successful in alleviating the symptoms of some of these neural diseases. PMID:22895098

Weak correlations between hemodynamic signals and ongoing neural activity during the resting state

PubMed Central

Winder, Aaron T.; Echagarruga, Christina; Zhang, Qingguang; Drew, Patrick J.

2017-01-01

Spontaneous fluctuations in hemodynamic signals in the absence of a task or overt stimulation are used to infer neural activity. We tested this coupling by simultaneously measuring neural activity and changes in cerebral blood volume (CBV) in the somatosensory cortex of awake, head-fixed mice during periods of true rest, and during whisker stimulation and volitional whisking. Here we show that neurovascular coupling was similar across states, and large spontaneous CBV changes in the absence of sensory input were driven by volitional whisker and body movements. Hemodynamic signals during periods of rest were weakly correlated with neural activity. Spontaneous fluctuations in CBV and vessel diameter persisted when local neural spiking and glutamatergic input was blocked, and during blockade of noradrenergic receptors, suggesting a non-neuronal origin for spontaneous CBV fluctuations. Spontaneous hemodynamic signals reflect a combination of behavior, local neural activity, and putatively non-neural processes. PMID:29184204

Weak correlations between hemodynamic signals and ongoing neural activity during the resting state.

PubMed

Winder, Aaron T; Echagarruga, Christina; Zhang, Qingguang; Drew, Patrick J

2017-12-01

Spontaneous fluctuations in hemodynamic signals in the absence of a task or overt stimulation are used to infer neural activity. We tested this coupling by simultaneously measuring neural activity and changes in cerebral blood volume (CBV) in the somatosensory cortex of awake, head-fixed mice during periods of true rest and during whisker stimulation and volitional whisking. We found that neurovascular coupling was similar across states and that large, spontaneous CBV changes in the absence of sensory input were driven by volitional whisker and body movements. Hemodynamic signals during periods of rest were weakly correlated with neural activity. Spontaneous fluctuations in CBV and vessel diameter persisted when local neural spiking and glutamatergic input were blocked, as well as during blockade of noradrenergic receptors, suggesting a non-neuronal origin for spontaneous CBV fluctuations. Spontaneous hemodynamic signals reflect a combination of behavior, local neural activity, and putatively non-neural processes.

Seismic signal auto-detecing from different features by using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Tissue Inhibitor of Metalloproteinase-2 (TIMP-2) expression is regulated by multiple neural differentiation signals

PubMed Central

Jaworski, Diane M.; Pérez-Martínez, Leonor

2010-01-01

Neuronal differentiation requires exquisitely timed cell cycle arrest for progenitors to acquire an appropriate neuronal cell fate and is achieved by communication between soluble signals, such as growth factors and extracellular matrix molecules. Here we report that the expression of TIMP-2, a matrix metalloproteinase inhibitor, is up-regulated by signals that control proliferation (bFGF and EGF) and differentiation (retinoic acid and NGF) in neural progenitor and neuroblastoma cell lines. TIMP-2 expression coincides with the appearance of neurofilament-positive neurons, indicating that TIMP-2 may play a role in neurogenesis. The up-regulation of TIMP-2 expression by proliferative signals suggests a role in the transition from proliferation to neuronal differentiation. Live labeling experiments demonstrate TIMP-2 expression only on α3 integrin-positive cells. Thus, TIMP-2 function may be mediated via interaction with integrin receptor(s). We propose that TIMP-2 represents a component of the neurogenic signaling cascade induced by mitogenic stimuli that may withdraw progenitor cells from the cell cycle permitting their terminal neuronal differentiation. PMID:16805810

Screen for Slit/Robo signaling in trunk neural cells reveals new players.

PubMed

Martinez, Darwin; Zuhdi, Nora; Reyes, Michelle; Ortega, Blanca; Giovannone, Dion; Lee, Vivian M; de Bellard, Maria Elena

2018-06-01

Slits ligands and their Robo receptors are involved in quite disparate cell signaling pathways that include axon guidance, cell proliferation, cell motility and angiogenesis. Neural crest cells emerge by delamination from neural cells in the dorsal neural tube, and give rise to various components of the peripheral nervous system in vertebrates. It is well established that these cells change from a non-migratory to a highly migratory state allowing them to reach distant regions before they differentiate. However, but the mechanism controlling this delamination and subsequent migration are still not fully understood. The repulsive Slit ligand family members, have been classified also as true tumor suppressor molecules. The present study explored in further detail what possible Slit/Robo signals are at play in the trunk neural cells and neural crest cells by carrying out a microarray after Slit2 gain of function in trunk neural tubes. We found that in addition to molecules known to be downstream of Slit/Robo signaling, there were a large set of molecules known to be important in maintaining cells in non-motile, epithelia phenotype. Furthermore, we found new molecules previously not associated with Slit/Robo signaling: cell proliferation markers, Ankyrins and RAB intracellular transporters. Our findings suggest that neural crest cells use and array of different Slit/Robo pathways during their transformation from non-motile to highly motile cells. Copyright © 2018. Published by Elsevier B.V.

Signal template generation from acquired mammographic images for the non-prewhitening model observer with eye-filter

NASA Astrophysics Data System (ADS)

Balta, Christiana; Bouwman, Ramona W.; Sechopoulos, Ioannis; Broeders, Mireille J. M.; Karssemeijer, Nico; van Engen, Ruben E.; Veldkamp, Wouter J. H.

2017-03-01

Model observers (MOs) are being investigated for image quality assessment in full-field digital mammography (FFDM). Signal templates for the non-prewhitening MO with eye filter (NPWE) were formed using acquired FFDM images. A signal template was generated from acquired images by averaging multiple exposures resulting in a low noise signal template. Noise elimination while preserving the signal was investigated and a methodology which results in a noise-free template is proposed. In order to deal with signal location uncertainty, template shifting was implemented. The procedure to generate the template was evaluated on images of an anthropomorphic breast phantom containing microcalcification-related signals. Optimal reduction of the background noise was achieved without changing the signal. Based on a validation study in simulated images, the difference (bias) in MO performance from the ground truth signal was calculated and found to be <1%. As template generation is a building stone of the entire image quality assessment framework, the proposed method to construct templates from acquired images facilitates the use of the NPWE MO in acquired images.

Losartan prevents acquired epilepsy via TGF-β signaling suppression.

PubMed

Bar-Klein, Guy; Cacheaux, Luisa P; Kamintsky, Lyn; Prager, Ofer; Weissberg, Itai; Schoknecht, Karl; Cheng, Paul; Kim, Soo Young; Wood, Lydia; Heinemann, Uwe; Kaufer, Daniela; Friedman, Alon

2014-06-01

Acquired epilepsy is frequently associated with structural lesions after trauma, stroke, and infections. Although seizures are often difficult to treat, there is no clinically applicable strategy to prevent the development of epilepsy in patients at risk. We have recently shown that vascular injury is associated with activation of albumin-mediated transforming growth factor β (TGF-β) signaling, and followed by local inflammatory response and epileptiform activity ex vivo. Here we investigated albumin-mediated TGF-β signaling and tested the efficacy of blocking the TGF-β pathway in preventing epilepsy. We addressed the role of TGF-β signaling in epileptogenesis in 2 different rat models of vascular injury, combining in vitro and in vivo biochemical assays, gene expression, and magnetic resonance and direct optical imaging for blood-brain barrier permeability and vascular reactivity. Long-term electrocorticographic recordings were acquired in freely behaving animals. We demonstrate that serum-derived albumin preferentially induces activation of the activin receptor-like kinase 5 pathway of TGF-β receptor I in astrocytes. We further show that the angiotensin II type 1 receptor antagonist, losartan, previously identified as a blocker of peripheral TGF-β signaling, effectively blocks albumin-induced TGF-β activation in the brain. Most importantly, losartan prevents the development of delayed recurrent spontaneous seizures, an effect that persists weeks after drug withdrawal. TGF-β signaling, activated in astrocytes by serum-derived albumin, is involved in epileptogenesis. We propose losartan, a drug approved by the US Food and Drug Administration, as an efficient antiepileptogenic therapy for epilepsy associated with vascular injury. © 2014 American Neurological Association.

Losartan prevents acquired epilepsy via TGF-β signaling suppression

PubMed Central

Bar-Klein, Guy; Cacheaux, Luisa P.; Kamintsky, Lyn; Prager, Ofer; Weissberg, Itai; Schoknecht, Karl; Cheng, Paul; Kim, Soo Young; Wood, Lydia; Heinemann, Uwe; Kaufer, Daniela; Friedman, Alon

2014-01-01

Objective Acquired epilepsy is frequently associated with structural lesions following trauma, stroke and infections. While seizures are often difficult to treat, there is no clinically applicable strategy to prevent the development of epilepsy in patients at risk. We have recently shown that vascular injury is associated with activation of albumin-mediated transforming growth factor β (TGF-β) signaling, and followed by local inflammatory response and epileptiform activity ex vivo. Here we investigated albumin-mediated TGF-β signaling and tested the efficacy of blocking the TGF-β pathway in preventing epilepsy. Methods We addressed the role of TGF-β signaling in epiletogenesis in two different rat models of vascular injury, combining in vitro and in vivo biochemical assays, gene expression, magnetic resonance and direct optical imaging for blood-brain barrier (BBB) permeability and vascular reactivity. Long-term electrocorticographic (ECoG) recordings were acquired in freely behaving animals. Results We demonstrate that serum-derived albumin preferentially induces activation of the activin receptor-like kinase 5 (ALK5) pathway of TGF-β receptor I in astrocytes. We further show that the angiotensin II type 1 receptor antagonist (AT1), losartan, previously identified as a blocker of peripheral TGF-β signaling, effectively blocks albumin-induced TGF-β activation in the brain. Most importantly, losartan prevents the development of delayed recurrent spontaneous seizures, an effect that persists weeks after drug withdrawal. Interpretation TGF-β signaling, activated in astrocytes by serum-derived albumin, is involved in epileptogenesis. We propose losartan, an FDA-approved drug, as an efficient anti-epileptogenic therapy for epilepsy associated with vascular injury. PMID:24659129

Biophotons as neural communication signals demonstrated by in situ biophoton autography.

PubMed

Sun, Yan; Wang, Chao; Dai, Jiapei

2010-03-01

Cell to cell communication by biophotons has been demonstrated in plants, bacteria, animal neutrophil granulocytes and kidney cells. Whether such signal communication exists in neural cells is unclear. By developing a new biophoton detection method, called in situ biophoton autography (IBA), we have investigated biophotonic activities in rat spinal nerve roots in vitro. We found that different spectral light stimulation (infrared, red, yellow, blue, green and white) at one end of the spinal sensory or motor nerve roots resulted in a significant increase in the biophotonic activity at the other end. Such effects could be significantly inhibited by procaine (a regional anaesthetic for neural conduction block) or classic metabolic inhibitors, suggesting that light stimulation can generate biophotons that conduct along the neural fibers, probably as neural communication signals. The mechanism of biophotonic conduction along neural fibers may be mediated by protein-protein biophotonic interactions. This study may provide a better understanding of the fundamental mechanisms of neural communication, the functions of the nervous system, such as vision, learning and memory, as well as the mechanisms of human neurological diseases.

Long-distance communication and signal amplification in systemic acquired resistance

PubMed Central

Shah, Jyoti; Zeier, Jürgen

2013-01-01

Systemic acquired resistance (SAR) is an inducible defense mechanism in plants that confers enhanced resistance against a variety of pathogens. SAR is activated in the uninfected systemic (distal) organs in response to a prior (primary) infection elsewhere in the plant. SAR is associated with the activation of salicylic acid (SA) signaling and the priming of defense responses for robust activation in response to subsequent infections. The activation of SAR requires communication by the primary infected tissues with the distal organs. The vasculature functions as a conduit for the translocation of factors that facilitate long-distance intra-plant communication. In recent years, several metabolites putatively involved in long-distance signaling have been identified. These include the methyl ester of SA (MeSA), the abietane diterpenoid dehydroabietinal (DA), the dicarboxylic acid azelaic acid (AzA), and a glycerol-3-phosphate (G3P)-dependent factor. Long-distance signaling by some of these metabolites also requires the lipid-transfer protein DIR1 (DEFECTIVE IN INDUCED RESISTANCE 1). The relative contribution of these factors in long-distance signaling is likely influenced by environmental conditions, for example light. In the systemic leaves, the AGD2-LIKE DEFENSE RESPONSE PROTEIN1 (ALD1)-dependent production of the lysine catabolite pipecolic acid (Pip), FLAVIN-DEPENDENT MONOOXYGENASE1 (FMO1) signaling, as well as SA synthesis and downstream signaling are required for the activation of SAR. This review summarizes the involvement and interaction between long-distance SAR signals and details the recently discovered role of Pip in defense amplification and priming that allows plants to acquire immunity at the systemic level. Recent advances in SA signaling and perception are also highlighted. PMID:23440336

An intermediate level of BMP signaling directly specifies cranial neural crest progenitor cells in zebrafish.

PubMed

Schumacher, Jennifer A; Hashiguchi, Megumi; Nguyen, Vu H; Mullins, Mary C

2011-01-01

The specification of the neural crest progenitor cell (NCPC) population in the early vertebrate embryo requires an elaborate network of signaling pathways, one of which is the Bone Morphogenetic Protein (BMP) pathway. Based on alterations in neural crest gene expression in zebrafish BMP pathway component mutants, we previously proposed a model in which the gastrula BMP morphogen gradient establishes an intermediate level of BMP activity establishing the future NCPC domain. Here, we tested this model and show that an intermediate level of BMP signaling acts directly to specify the NCPC. We quantified the effects of reducing BMP signaling on the number of neural crest cells and show that neural crest cells are significantly increased when BMP signaling is reduced and that this increase is not due to an increase in cell proliferation. In contrast, when BMP signaling is eliminated, NCPC fail to be specified. We modulated BMP signaling levels in BMP pathway mutants with expanded or no NCPCs to demonstrate that an intermediate level of BMP signaling specifies the NCPC. We further investigated the ability of Smad5 to act in a graded fashion by injecting smad5 antisense morpholinos and show that increasing doses first expand the NCPCs and then cause a loss of NCPCs, consistent with Smad5 acting directly in neural crest progenitor specification. Using Western blot analysis, we show that P-Smad5 levels are dose-dependently reduced in smad5 morphants, consistent with an intermediate level of BMP signaling acting through Smad5 to specify the neural crest progenitors. Finally, we performed chimeric analysis to demonstrate for the first time that BMP signal reception is required directly by NCPCs for their specification. Together these results add substantial evidence to a model in which graded BMP signaling acts as a morphogen to pattern the ectoderm, with an intermediate level acting in neural crest specification.

Hepatocyte growth factor/scatter factor-MET signaling in neural crest-derived melanocyte development.

PubMed

Kos, L; Aronzon, A; Takayama, H; Maina, F; Ponzetto, C; Merlino, G; Pavan, W

1999-02-01

The mechanisms governing development of neural crest-derived melanocytes, and how alterations in these pathways lead to hypopigmentation disorders, are not completely understood. Hepatocyte growth factor/scatter factor (HGF/SF) signaling through the tyrosine-kinase receptor, MET, is capable of promoting the proliferation, increasing the motility, and maintaining high tyrosinase activity and melanin synthesis of melanocytes in vitro. In addition, transgenic mice that ubiquitously overexpress HGF/SF demonstrate hyperpigmentation in the skin and leptomenigenes and develop melanomas. To investigate whether HGF/ SF-MET signaling is involved in the development of neural crest-derived melanocytes, transgenic embryos, ubiquitously overexpressing HGF/SF, were analyzed. In HGF/SF transgenic embryos, the distribution of melanoblasts along the characteristic migratory pathway was not affected. However, additional ectopically localized melanoblasts were also observed in the dorsal root ganglia and neural tube, as early as 11.5 days post coitus (p.c.). We utilized an in vitro neural crest culture assay to further explore the role of HGF/SF-MET signaling in neural crest development. HGF/SF added to neural crest cultures increased melanoblast number, permitted differentiation into pigmented melanocytes, promoted melanoblast survival, and could replace mast-cell growth factor/Steel factor (MGF) in explant cultures. To examine whether HGF/SF-MET signaling is required for the proper development of melanocytes, embryos with a targeted Met null mutation (Met-/-) were analysed. In Met-/- embryos, melanoblast number and location were not overtly affected up to 14 days p.c. These results demonstrate that HGF/SF-MET signaling influences, but is not required for, the initial development of neural crest-derived melanocytes in vivo and in vitro.

Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

NASA Astrophysics Data System (ADS)

Dasgupta, Hirak

2016-12-01

The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

PubMed

Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M

2011-11-01

Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

Gamma Oscillations of Spiking Neural Populations Enhance Signal Discrimination

PubMed Central

Masuda, Naoki; Doiron, Brent

2007-01-01

Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive. PMID:18052541

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

PubMed

Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

2016-09-01

The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

Detecting modulated signals in modulated noise: (II) neural thresholds in the songbird forebrain.

PubMed

Bee, Mark A; Buschermöhle, Michael; Klump, Georg M

2007-10-01

Sounds in the real world fluctuate in amplitude. The vertebrate auditory system exploits patterns of amplitude fluctuations to improve signal detection in noise. One experimental paradigm demonstrating these general effects has been used in psychophysical studies of 'comodulation detection difference' (CDD). The CDD effect refers to the fact that thresholds for detecting a modulated, narrowband noise signal are lower when the envelopes of flanking bands of modulated noise are comodulated with each other, but fluctuate independently of the signal compared with conditions in which the envelopes of the signal and flanking bands are all comodulated. Here, we report results from a study of the neural correlates of CDD in European starlings (Sturnus vulgaris). We manipulated: (i) the envelope correlations between a narrowband noise signal and a masker comprised of six flanking bands of noise; (ii) the signal onset delay relative to masker onset; (iii) signal duration; and (iv) masker spectrum level. Masked detection thresholds were determined from neural responses using signal detection theory. Across conditions, the magnitude of neural CDD ranged between 2 and 8 dB, which is similar to that reported in a companion psychophysical study of starlings [U. Langemann & G.M. Klump (2007) Eur. J. Neurosci., 26, 1969-1978]. We found little evidence to suggest that neural CDD resulted from the across-channel processing of auditory grouping cues related to common envelope fluctuations and synchronous onsets between the signal and flanking bands. We discuss a within-channel model of peripheral processing that explains many of our results.

Enhancement of electrical signaling in neural networks on graphene films.

PubMed

Tang, Mingliang; Song, Qin; Li, Ning; Jiang, Ziyun; Huang, Rong; Cheng, Guosheng

2013-09-01

One of the key challenges for neural tissue engineering is to exploit supporting materials with robust functionalities not only to govern cell-specific behaviors, but also to form functional neural network. The unique electrical and mechanical properties of graphene imply it as a promising candidate for neural interfaces, but little is known about the details of neural network formation on graphene as a scaffold material for tissue engineering. Therapeutic regenerative strategies aim to guide and enhance the intrinsic capacity of the neurons to reorganize by promoting plasticity mechanisms in a controllable manner. Here, we investigated the impact of graphene on the formation and performance in the assembly of neural networks in neural stem cell (NSC) culture. Using calcium imaging and electrophysiological recordings, we demonstrate the capabilities of graphene to support the growth of functional neural circuits, and improve neural performance and electrical signaling in the network. These results offer a better understanding of interactions between graphene and NSCs, also they clearly present the great potentials of graphene as neural interface in tissue engineering. Copyright © 2013 Elsevier Ltd. All rights reserved.

A digitally assisted, signal folding neural recording amplifier.

PubMed

Chen, Yi; Basu, Arindam; Liu, Lei; Zou, Xiaodan; Rajkumar, Ramamoorthy; Dawe, Gavin Stewart; Je, Minkyu

2014-08-01

A novel signal folding and reconstruction scheme for neural recording applications that exploits the 1/f(n) characteristics of neural signals is described in this paper. The amplified output is 'folded' into a predefined range of voltages by using comparison and reset circuits along with the core amplifier. After this output signal is digitized and transmitted, a reconstruction algorithm can be applied in the digital domain to recover the amplified signal from the folded waveform. This scheme enables the use of an analog-to-digital convertor with less number of bits for the same effective dynamic range. It also reduces the transmission data rate of the recording chip. Both of these features allow power and area savings at the system level. Other advantages of the proposed topology are increased reliability due to the removal of pseudo-resistors, lower harmonic distortion and low-voltage operation. An analysis of the reconstruction error introduced by this scheme is presented along with a behavioral model to provide a quick estimate of the post reconstruction dynamic range. Measurement results from two different core amplifier designs in 65 nm and 180 nm CMOS processes are presented to prove the generality of the proposed scheme in the neural recording applications. Operating from a 1 V power supply, the amplifier in 180 nm CMOS has a gain of 54.2 dB, bandwidth of 5.7 kHz, input referred noise of 3.8 μVrms and power dissipation of 2.52 μW leading to a NEF of 3.1 in spike band. It exhibits a dynamic range of 66 dB and maximum SNDR of 43 dB in LFP band. It also reduces system level power (by reducing the number of bits in the ADC by 2) as well as data rate to 80% of a conventional design. In vivo measurements validate the ability of this amplifier to simultaneously record spike and LFP signals.

Identification of cytokine-specific sensory neural signals by decoding murine vagus nerve activity.

PubMed

Zanos, Theodoros P; Silverman, Harold A; Levy, Todd; Tsaava, Tea; Battinelli, Emily; Lorraine, Peter W; Ashe, Jeffrey M; Chavan, Sangeeta S; Tracey, Kevin J; Bouton, Chad E

2018-05-22

The nervous system maintains physiological homeostasis through reflex pathways that modulate organ function. This process begins when changes in the internal milieu (e.g., blood pressure, temperature, or pH) activate visceral sensory neurons that transmit action potentials along the vagus nerve to the brainstem. IL-1β and TNF, inflammatory cytokines produced by immune cells during infection and injury, and other inflammatory mediators have been implicated in activating sensory action potentials in the vagus nerve. However, it remains unclear whether neural responses encode cytokine-specific information. Here we develop methods to isolate and decode specific neural signals to discriminate between two different cytokines. Nerve impulses recorded from the vagus nerve of mice exposed to IL-1β and TNF were sorted into groups based on their shape and amplitude, and their respective firing rates were computed. This revealed sensory neural groups responding specifically to TNF and IL-1β in a dose-dependent manner. These cytokine-mediated responses were subsequently decoded using a Naive Bayes algorithm that discriminated between no exposure and exposures to IL-1β and TNF (mean successful identification rate 82.9 ± 17.8%, chance level 33%). Recordings obtained in IL-1 receptor-KO mice were devoid of IL-1β-related signals but retained their responses to TNF. Genetic ablation of TRPV1 neurons attenuated the vagus neural signals mediated by IL-1β, and distal lidocaine nerve block attenuated all vagus neural signals recorded. The results obtained in this study using the methodological framework suggest that cytokine-specific information is present in sensory neural signals within the vagus nerve. Copyright © 2018 the Author(s). Published by PNAS.

TECHNICAL NOTE: The development of a PZT-based microdrive for neural signal recording

NASA Astrophysics Data System (ADS)

Park, Sangkyu; Yoon, Euisung; Lee, Sukchan; Shin, Hee-sup; Park, Hyunjun; Kim, Byungkyu; Kim, Daesoo; Park, Jongoh; Park, Sukho

2008-04-01

A hand-controlled microdrive has been used to obtain neural signals from rodents such as rats and mice. However, it places severe physical stress on the rodents during its manipulation, and this stress leads to alertness in the mice and low efficiency in obtaining neural signals from the mice. To overcome this issue, we developed a novel microdrive, which allows one to adjust the electrodes by a piezoelectric device (PZT) with high precision. Its mass is light enough to install on the mouse's head. The proposed microdrive has three H-type PZT actuators and their guiding structure. The operation principle of the microdrive is based on the well known inchworm mechanism. When the three PZT actuators are synchronized, linear motion of the electrode is produced along the guiding structure. The electrodes used for the recording of the neural signals from neuron cells were fixed at one of the PZT actuators. Our proposed microdrive has an accuracy of about 400 nm and a long stroke of about 5 mm. In response to formalin-induced pain, single unit activities are robustly measured at the thalamus with electrodes whose vertical depth is adjusted by the microdrive under urethane anesthesia. In addition, the microdrive was efficient in detecting neural signals from mice that were moving freely. Thus, the present study suggests that the PZT-based microdrive could be an alternative for the efficient detection of neural signals from mice during behavioral states without any stress to the mice.

[Neural mechanism underlying autistic savant and acquired savant syndrome].

PubMed

Takahata, Keisuke; Kato, Motoichiro

2008-07-01

It is well known that the cases with savant syndrome, demonstrate outstanding mental capability despite coexisting severe mental disabilities. In many cases, savant skills are characterized by its domain-specificity, enhanced memory capability, and excessive focus on low-level perceptual processing. In addition, impaired integrative cognitive processing such as social cognition or executive function, restricted interest, and compulsive repetition of the same act are observed in savant individuals. All these are significantly relevant to the behavioral characteristics observed in individuals with autistic spectrum disorders (ASD). A neurocognitive model of savant syndrome should explain these cognitive features and the juxtaposition of outstanding talents with cognitive disabilities. In recent neuropsychological studies, Miller (1998) reported clinical cases of "acquired savant," i.e., patients who improved or newly acquired an artistic savant-like skill in the early stage of frontotemporal dementia (FTD). Although the relationship between an autistic savant and acquired savant remains to be elucidated, the advent of neuroimaging study of ASD and the clarification of FTD patients with savant-like skills may clarify the shared neural mechanisms of both types of talent. In this review, we classified current cognitive models of savant syndrome into the following 3 categories. (1) A hypermnesic model that suggests that savant skills develop from existing or dormant cognitive functions such as memory. However, recent findings obtained through neuropsychological examinations imply that savant individuals solve problems using a strategy that is fairly different from a non-autistic one. (2) A paradoxical functional facilitation model (Kapur, 1996) that offers possible explanations about how pathological states in the brain lead to development of prodigious skills. This model emphasizes the role of reciprocal inhibitory interaction among adjacent or distant cortical regions

Model for neural signaling leap statistics

NASA Astrophysics Data System (ADS)

Chevrollier, Martine; Oriá, Marcos

2011-03-01

We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.

Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

PubMed Central

Pagan, Marino

2014-01-01

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance (d′). PMID:24920017

A method for compression of intra-cortically-recorded neural signals dedicated to implantable brain-machine interfaces.

PubMed

Shaeri, Mohammad Ali; Sodagar, Amir M

2015-05-01

This paper proposes an efficient data compression technique dedicated to implantable intra-cortical neural recording devices. The proposed technique benefits from processing neural signals in the Discrete Haar Wavelet Transform space, a new spike extraction approach, and a novel data framing scheme to telemeter the recorded neural information to the outside world. Based on the proposed technique, a 64-channel neural signal processor was designed and prototyped as a part of a wireless implantable extra-cellular neural recording microsystem. Designed in a 0.13- μ m standard CMOS process, the 64-channel neural signal processor reported in this paper occupies ∼ 0.206 mm(2) of silicon area, and consumes 94.18 μW when operating under a 1.2-V supply voltage at a master clock frequency of 1.28 MHz.

Music Signal Processing Using Vector Product Neural Networks

NASA Astrophysics Data System (ADS)

Fan, Z. C.; Chan, T. S.; Yang, Y. H.; Jang, J. S. R.

2017-05-01

We propose a novel neural network model for music signal processing using vector product neurons and dimensionality transformations. Here, the inputs are first mapped from real values into three-dimensional vectors then fed into a three-dimensional vector product neural network where the inputs, outputs, and weights are all three-dimensional values. Next, the final outputs are mapped back to the reals. Two methods for dimensionality transformation are proposed, one via context windows and the other via spectral coloring. Experimental results on the iKala dataset for blind singing voice separation confirm the efficacy of our model.

SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

PubMed

Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

2013-04-30

The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

Notch signaling patterns neurogenic ectoderm and regulates the asymmetric division of neural progenitors in sea urchin embryos.

PubMed

Mellott, Dan O; Thisdelle, Jordan; Burke, Robert D

2017-10-01

We have examined regulation of neurogenesis by Delta/Notch signaling in sea urchin embryos. At gastrulation, neural progenitors enter S phase coincident with expression of Sp-SoxC. We used a BAC containing GFP knocked into the Sp-SoxC locus to label neural progenitors. Live imaging and immunolocalizations indicate that Sp-SoxC-expressing cells divide to produce pairs of adjacent cells expressing GFP. Over an interval of about 6 h, one cell fragments, undergoes apoptosis and expresses high levels of activated Caspase3. A Notch reporter indicates that Notch signaling is activated in cells adjacent to cells expressing Sp-SoxC. Inhibition of γ-secretase, injection of Sp-Delta morpholinos or CRISPR/Cas9-induced mutation of Sp-Delta results in supernumerary neural progenitors and neurons. Interfering with Notch signaling increases neural progenitor recruitment and pairs of neural progenitors. Thus, Notch signaling restricts the number of neural progenitors recruited and regulates the fate of progeny of the asymmetric division. We propose a model in which localized signaling converts ectodermal and ciliary band cells to neural progenitors that divide asymmetrically to produce a neural precursor and an apoptotic cell. © 2017. Published by The Company of Biologists Ltd.

The neural basis of functional neuroimaging signal with positron and single-photon emission tomography.

PubMed

Sestini, S

2007-07-01

Functional imaging techniques such as positron and single-photon emission tomography exploit the relationship between neural activity, energy demand and cerebral blood flow to functionally map the brain. Despite the fact that neurobiological processes are not completely understood, several results have revealed the signals that trigger the metabolic and vascular changes accompanying variations in neural activity. Advances in this field have demonstrated that release of the major excitatory neurotransmitter glutamate initiates diverse signaling processes between neurons, astrocytes and blood perfusion, and that this signaling is crucial for the occurrence of brain imaging signals. Better understanding of the neural sites of energy consumption and the temporal correlation between energy demand, energy consumption and associated cerebrovascular hemodynamics gives novel insight into the potential of these imaging tools in the study of metabolic neurodegenerative disorders.

[Neural Mechanisms That Facilitate Adaptive Behavior Based on Acquired Stimulus-Outcome Information].

PubMed

Ogawa, Masaaki

2017-11-01

In response to changing internal and external situations, we always need to adapt our behavior based on previous experiences, particularly, acquired stimulus-outcome information. The orbitofrontal cortex (OFC), a prefrontal cortical region, is critical for this type of decision-making. The current understanding of the fundamental functions of the OFC has been reviewed by introducing, as an example, how the OFC contributes to the processing of uncertain rewards. Furthermore, the importance of revealing context and temporally specific causal roles of neural circuits including the OFC in decision-making, as well as the techniques to achieve the goal, have been discussed.

Signal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe -decay searches

NASA Astrophysics Data System (ADS)

Caldwell, A.; Cossavella, F.; Majorovits, B.; Palioselitis, D.; Volynets, O.

2015-07-01

A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5 %. This uncertainty is due to differences between signal and calibration samples.

Bayesian model of signal rewiring reveals mechanisms of gene dysregulation in acquired drug resistance in breast cancer

PubMed Central

Azad, A. K. M.; Keith, Jonathan M.

2017-01-01

Small molecule inhibitors, such as lapatinib, are effective against breast cancer in clinical trials, but tumor cells ultimately acquire resistance to the drug. Maintaining sensitization to drug action is essential for durable growth inhibition. Recently, adaptive reprogramming of signaling circuitry has been identified as a major cause of acquired resistance. We developed a computational framework using a Bayesian statistical approach to model signal rewiring in acquired resistance. We used the p1-model to infer potential aberrant gene-pairs with differential posterior probabilities of appearing in resistant-vs-parental networks. Results were obtained using matched gene expression profiles under resistant and parental conditions. Using two lapatinib-treated ErbB2-positive breast cancer cell-lines: SKBR3 and BT474, our method identified similar dysregulated signaling pathways including EGFR-related pathways as well as other receptor-related pathways, many of which were reported previously as compensatory pathways of EGFR-inhibition via signaling cross-talk. A manual literature survey provided strong evidence that aberrant signaling activities in dysregulated pathways are closely related to acquired resistance in EGFR tyrosine kinase inhibitors. Our approach predicted literature-supported dysregulated pathways complementary to both node-centric (SPIA, DAVID, and GATHER) and edge-centric (ESEA and PAGI) methods. Moreover, by proposing a novel pattern of aberrant signaling called V-structures, we observed that genes were dysregulated in resistant-vs-sensitive conditions when they were involved in the switch of dependencies from targeted to bypass signaling events. A literature survey of some important V-structures suggested they play a role in breast cancer metastasis and/or acquired resistance to EGFR-TKIs, where the mRNA changes of TGFBR2, LEF1 and TP53 in resistant-vs-sensitive conditions were related to the dependency switch from targeted to bypass signaling links

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Oscillation-Induced Signal Transmission and Gating in Neural Circuits

PubMed Central

Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

2014-01-01

Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

Characterization of Radar Signals Using Neural Networks

DTIC Science & Technology

1990-12-01

e***e*e*eeeeeeeeeeeesseeeeeese*eee*e*e************s /* Function Name: load.input.ptterns Number: 4.1 /* Description: This function determines wether ...XSE.last.layer Number: 8.5 */ /* Description: The function determines wether to backpropate the *f /* parameter by the sigmoidal or linear update...Sigmoidal Function," Mathematics of Control, Signals and Systems, 2:303-314 (March 1989). 6. Dayhoff, Judith E. Neural Network Architectures. New York: Van

Neural Networks For Demodulation Of Phase-Modulated Signals

NASA Technical Reports Server (NTRS)

Altes, Richard A.

1995-01-01

Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

Analysing 21cm signal with artificial neural network

NASA Astrophysics Data System (ADS)

Shimabukuro, Hayato; a Semelin, Benoit

2018-05-01

The 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

In vivo optical modulation of neural signals using monolithically integrated two-dimensional neural probe arrays

PubMed Central

Son, Yoojin; Jenny Lee, Hyunjoo; Kim, Jeongyeon; Shin, Hyogeun; Choi, Nakwon; Justin Lee, C.; Yoon, Eui-Sung; Yoon, Euisik; Wise, Kensall D.; Geun Kim, Tae; Cho, Il-Joo

2015-01-01

Integration of stimulation modalities (e.g. electrical, optical, and chemical) on a large array of neural probes can enable an investigation of important underlying mechanisms of brain disorders that is not possible through neural recordings alone. Furthermore, it is important to achieve this integration of multiple functionalities in a compact structure to utilize a large number of the mouse models. Here we present a successful optical modulation of in vivo neural signals of a transgenic mouse through our compact 2D MEMS neural array (optrodes). Using a novel fabrication method that embeds a lower cladding layer in a silicon substrate, we achieved a thin silicon 2D optrode array that is capable of delivering light to multiple sites using SU-8 as a waveguide core. Without additional modification to the microelectrodes, the measured impedance of the multiple microelectrodes was below 1 MΩ at 1 kHz. In addition, with a low background noise level (±25 μV), neural spikes from different individual neurons were recorded on each microelectrode. Lastly, we successfully used our optrodes to modulate the neural activity of a transgenic mouse through optical stimulation. These results demonstrate the functionality of the 2D optrode array and its potential as a next-generation tool for optogenetic applications. PMID:26494437

Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Flekova, L.; Schott, M.

2017-10-01

Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.

Neural Crest-Derived Mesenchymal Cells Require Wnt Signaling for Their Development and Drive Invagination of the Telencephalic Midline

PubMed Central

Choe, Youngshik; Zarbalis, Konstantinos S.; Pleasure, Samuel J.

2014-01-01

Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs) leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis. PMID:24516524

Enhancement of digital radiography image quality using a convolutional neural network.

PubMed

Sun, Yuewen; Li, Litao; Cong, Peng; Wang, Zhentao; Guo, Xiaojing

2017-01-01

Digital radiography system is widely used for noninvasive security check and medical imaging examination. However, the system has a limitation of lower image quality in spatial resolution and signal to noise ratio. In this study, we explored whether the image quality acquired by the digital radiography system can be improved with a modified convolutional neural network to generate high-resolution images with reduced noise from the original low-quality images. The experiment evaluated on a test dataset, which contains 5 X-ray images, showed that the proposed method outperformed the traditional methods (i.e., bicubic interpolation and 3D block-matching approach) as measured by peak signal to noise ratio (PSNR) about 1.3 dB while kept highly efficient processing time within one second. Experimental results demonstrated that a residual to residual (RTR) convolutional neural network remarkably improved the image quality of object structural details by increasing the image resolution and reducing image noise. Thus, this study indicated that applying this RTR convolutional neural network system was useful to improve image quality acquired by the digital radiography system.

Design and experiment of a neural signal detection using a FES driving system.

PubMed

Zonghao, Huang; Zhigong, Wang; Xiaoying, Lu; Wenyuan, Li; Xiaoyan, Shen; Xintai, Zhao; Shushan, Xie; Haixian, Pan; Cunliang, Zhu

2010-01-01

The channel bridging, signal regenerating, and functional rebuilding of injured nerves is one of the most important issues in life science research. In recent years, some progresses in the research area have been made in repairing injured nerves with microelectronic neural bridge. Based on the previous work, this paper presents a neural signal detection and functional electrical stimulation (FES) driving system with using high performance operational amplifiers, which has been realized. The experimental results show that the designed system meets requirements. In animal experiments, sciatic nerve signal detection, regeneration and function rebuilding between two toads have been accomplished successfully by using the designed system.

Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

An Intelligent Pattern Recognition System Based on Neural Network and Wavelet Decomposition for Interpretation of Heart Sounds

DTIC Science & Technology

2001-10-25

wavelet decomposition of signals and classification using neural network. Inputs to the system are the heart sound signals acquired by a stethoscope in a...Proceedings. pp. 415–418, 1990. [3] G. Ergun, “An intelligent diagnostic system for interpretation of arterpartum fetal heart rate tracings based on ANNs and...AN INTELLIGENT PATTERN RECOGNITION SYSTEM BASED ON NEURAL NETWORK AND WAVELET DECOMPOSITION FOR INTERPRETATION OF HEART SOUNDS I. TURKOGLU1, A

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Brain substrates of implicit and explicit memory: the importance of concurrently acquired neural signals of both memory types.

PubMed

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.

A preferential design approach for energy-efficient and robust implantable neural signal processing hardware.

PubMed

Narasimhan, Seetharam; Chiel, Hillel J; Bhunia, Swarup

2009-01-01

For implantable neural interface applications, it is important to compress data and analyze spike patterns across multiple channels in real time. Such a computational task for online neural data processing requires an innovative circuit-architecture level design approach for low-power, robust and area-efficient hardware implementation. Conventional microprocessor or Digital Signal Processing (DSP) chips would dissipate too much power and are too large in size for an implantable system. In this paper, we propose a novel hardware design approach, referred to as "Preferential Design" that exploits the nature of the neural signal processing algorithm to achieve a low-voltage, robust and area-efficient implementation using nanoscale process technology. The basic idea is to isolate the critical components with respect to system performance and design them more conservatively compared to the noncritical ones. This allows aggressive voltage scaling for low power operation while ensuring robustness and area efficiency. We have applied the proposed approach to a neural signal processing algorithm using the Discrete Wavelet Transform (DWT) and observed significant improvement in power and robustness over conventional design.

Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

PubMed

Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

2015-08-01

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed

Transcriptional response of Hoxb genes to retinoid signalling is regionally restricted along the neural tube rostrocaudal axis.

PubMed

Carucci, Nicoletta; Cacci, Emanuele; Nisi, Paola S; Licursi, Valerio; Paul, Yu-Lee; Biagioni, Stefano; Negri, Rodolfo; Rugg-Gunn, Peter J; Lupo, Giuseppe

2017-04-01

During vertebrate neural development, positional information is largely specified by extracellular morphogens. Their distribution, however, is very dynamic due to the multiple roles played by the same signals in the developing and adult neural tissue. This suggests that neural progenitors are able to modify their competence to respond to morphogen signalling and autonomously maintain positional identities after their initial specification. In this work, we take advantage of in vitro culture systems of mouse neural stem/progenitor cells (NSPCs) to show that NSPCs isolated from rostral or caudal regions of the mouse neural tube are differentially responsive to retinoic acid (RA), a pivotal morphogen for the specification of posterior neural fates. Hoxb genes are among the best known RA direct targets in the neural tissue, yet we found that RA could promote their transcription only in caudal but not in rostral NSPCs. Correlating with these effects, key RA-responsive regulatory regions in the Hoxb cluster displayed opposite enrichment of activating or repressing histone marks in rostral and caudal NSPCs. Finally, RA was able to strengthen Hoxb chromatin activation in caudal NSPCs, but was ineffective on the repressed Hoxb chromatin of rostral NSPCs. These results suggest that the response of NSPCs to morphogen signalling across the rostrocaudal axis of the neural tube may be gated by the epigenetic configuration of target patterning genes, allowing long-term maintenance of intrinsic positional values in spite of continuously changing extrinsic signals.

Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles

PubMed Central

Smith, Alec; Lohrenz, Terry; King, Justin; Montague, P. Read; Camerer, Colin F.

2014-01-01

Groups of humans routinely misassign value to complex future events, especially in settings involving the exchange of resources. If properly structured, experimental markets can act as excellent probes of human group-level valuation mechanisms during pathological overvaluations—price bubbles. The connection between the behavioral and neural underpinnings of such phenomena has been absent, in part due to a lack of enabling technology. We used a multisubject functional MRI paradigm to measure neural activity in human subjects participating in experimental asset markets in which endogenous price bubbles formed and crashed. Although many ideas exist about how and why such bubbles may form and how to identify them, our experiment provided a window on the connection between neural responses and behavioral acts (buying and selling) that created the bubbles. We show that aggregate neural activity in the nucleus accumbens (NAcc) tracks the price bubble and that NAcc activity aggregated within a market predicts future price changes and crashes. Furthermore, the lowest-earning subjects express a stronger tendency to buy as a function of measured NAcc activity. Conversely, we report a signal in the anterior insular cortex in the highest earners that precedes the impending price peak, is associated with a higher propensity to sell in high earners, and that may represent a neural early warning signal in these subjects. Such markets could be a model system to understand neural and behavior mechanisms in other settings where emergent group-level activity exhibits mistaken belief or valuation. PMID:25002476

Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles.

PubMed

Smith, Alec; Lohrenz, Terry; King, Justin; Montague, P Read; Camerer, Colin F

2014-07-22

Groups of humans routinely misassign value to complex future events, especially in settings involving the exchange of resources. If properly structured, experimental markets can act as excellent probes of human group-level valuation mechanisms during pathological overvaluations--price bubbles. The connection between the behavioral and neural underpinnings of such phenomena has been absent, in part due to a lack of enabling technology. We used a multisubject functional MRI paradigm to measure neural activity in human subjects participating in experimental asset markets in which endogenous price bubbles formed and crashed. Although many ideas exist about how and why such bubbles may form and how to identify them, our experiment provided a window on the connection between neural responses and behavioral acts (buying and selling) that created the bubbles. We show that aggregate neural activity in the nucleus accumbens (NAcc) tracks the price bubble and that NAcc activity aggregated within a market predicts future price changes and crashes. Furthermore, the lowest-earning subjects express a stronger tendency to buy as a function of measured NAcc activity. Conversely, we report a signal in the anterior insular cortex in the highest earners that precedes the impending price peak, is associated with a higher propensity to sell in high earners, and that may represent a neural early warning signal in these subjects. Such markets could be a model system to understand neural and behavior mechanisms in other settings where emergent group-level activity exhibits mistaken belief or valuation.

A study on a robot arm driven by three-dimensional trajectories predicted from non-invasive neural signals.

PubMed

Kim, Yoon Jae; Park, Sung Woo; Yeom, Hong Gi; Bang, Moon Suk; Kim, June Sic; Chung, Chun Kee; Kim, Sungwan

2015-08-20

A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although invasive neural signals have a high spatial resolution, non-invasive neural signals are valuable because they provide an interface without surgery. Thus, various researchers have developed robot arms driven by non-invasive neural signals. However, robot arm control based on the imagined trajectory of a human hand can be more intuitive for patients. In this study, therefore, an integrated robot arm-gripper system (IRAGS) that is driven by three-dimensional (3D) hand trajectories predicted from non-invasive neural signals was developed and verified. The IRAGS was developed by integrating a six-degree of freedom robot arm and adaptive robot gripper. The system was used to perform reaching and grasping motions for verification. The non-invasive neural signals, magnetoencephalography (MEG) and electroencephalography (EEG), were obtained to control the system. The 3D trajectories were predicted by multiple linear regressions. A target sphere was placed at the terminal point of the real trajectories, and the system was commanded to grasp the target at the terminal point of the predicted trajectories. The average correlation coefficient between the predicted and real trajectories in the MEG case was [Formula: see text] ([Formula: see text]). In the EEG case, it was [Formula: see text] ([Formula: see text]). The success rates in grasping the target plastic sphere were 18.75 and 7.50 % with MEG and EEG, respectively. The success rates of touching the target were 52.50 and 58.75 % respectively. A robot arm driven by 3D trajectories predicted from non-invasive neural signals was implemented, and reaching and grasping motions were performed. In most cases, the robot closely approached the target, but the success rate was not very high because

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system

PubMed Central

Perge, János A.; Homer, Mark L.; Malik, Wasim Q.; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P.; Hochberg, Leigh R.

2013-01-01

Objective Motor Neural Interface Systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. Approach To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). Main results Eighty-four percent of the recorded units showed a statistically significant change in apparent firing rate (3.8±8.71Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and seventy-four percent of the units showed a significant change in spike amplitude (3.7±6.5μV or 5.5% of mean spike amplitude). Forty percent of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional “bias” in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in fifty-six percent of all performance assessments in participant cursor control (n=2 participants, 108 and 20 assessments over two years), resulting in

Intra-day signal instabilities affect decoding performance in an intracortical neural interface system.

PubMed

Perge, János A; Homer, Mark L; Malik, Wasim Q; Cash, Sydney; Eskandar, Emad; Friehs, Gerhard; Donoghue, John P; Hochberg, Leigh R

2013-06-01

Motor neural interface systems (NIS) aim to convert neural signals into motor prosthetic or assistive device control, allowing people with paralysis to regain movement or control over their immediate environment. Effector or prosthetic control can degrade if the relationship between recorded neural signals and intended motor behavior changes. Therefore, characterizing both biological and technological sources of signal variability is important for a reliable NIS. To address the frequency and causes of neural signal variability in a spike-based NIS, we analyzed within-day fluctuations in spiking activity and action potential amplitude recorded with silicon microelectrode arrays implanted in the motor cortex of three people with tetraplegia (BrainGate pilot clinical trial, IDE). 84% of the recorded units showed a statistically significant change in apparent firing rate (3.8 ± 8.71 Hz or 49% of the mean rate) across several-minute epochs of tasks performed on a single session, and 74% of the units showed a significant change in spike amplitude (3.7 ± 6.5 µV or 5.5% of mean spike amplitude). 40% of the recording sessions showed a significant correlation in the occurrence of amplitude changes across electrodes, suggesting array micro-movement. Despite the relatively frequent amplitude changes, only 15% of the observed within-day rate changes originated from recording artifacts such as spike amplitude change or electrical noise, while 85% of the rate changes most likely emerged from physiological mechanisms. Computer simulations confirmed that systematic rate changes of individual neurons could produce a directional 'bias' in the decoded neural cursor movements. Instability in apparent neuronal spike rates indeed yielded a directional bias in 56% of all performance assessments in participant cursor control (n = 2 participants, 108 and 20 assessments over two years), resulting in suboptimal performance in these sessions. We anticipate that signal acquisition and

Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking.

PubMed

Zacksenhouse, Miriam; Lebedev, Mikhail A; Nicolelis, Miguel A L

2014-01-01

What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA) is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i) reaching experiments with Brain-Machine Interface (BMI), and (ii) locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.

Hippo signaling is required for Notch-dependent smooth muscle differentiation of neural crest.

PubMed

Manderfield, Lauren J; Aghajanian, Haig; Engleka, Kurt A; Lim, Lillian Y; Liu, Feiyan; Jain, Rajan; Li, Li; Olson, Eric N; Epstein, Jonathan A

2015-09-01

Notch signaling has well-defined roles in the assembly of arterial walls and in the development of the endothelium and smooth muscle of the vasculature. Hippo signaling regulates cellular growth in many tissues, and contributes to regulation of organ size, in addition to other functions. Here, we show that the Notch and Hippo pathways converge to regulate smooth muscle differentiation of the neural crest, which is crucial for normal development of the aortic arch arteries and cranial vasculature during embryonic development. Neural crest-specific deletion of the Hippo effectors Yap and Taz produces neural crest precursors that migrate normally, but fail to produce vascular smooth muscle, and Notch target genes such as Jagged1 fail to activate normally. We show that Yap is normally recruited to a tissue-specific Jagged1 enhancer by directly interacting with the Notch intracellular domain (NICD). The Yap-NICD complex is recruited to chromatin by the DNA-binding protein Rbp-J in a Tead-independent fashion. Thus, Hippo signaling can modulate Notch signaling outputs, and components of the Hippo and Notch pathways physically interact. Convergence of Hippo and Notch pathways by the mechanisms described here might be relevant for the function of these signaling cascades in many tissues and in diseases such as cancer. © 2015. Published by The Company of Biologists Ltd.

Dual Roles for Spike Signaling in Cortical Neural Populations

PubMed Central

Ballard, Dana H.; Jehee, Janneke F. M.

2011-01-01

A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding. PMID:21687798

Feedback modulation of neural network synchrony and seizure susceptibility by Mdm2-p53-Nedd4-2 signaling.

PubMed

Jewett, Kathryn A; Christian, Catherine A; Bacos, Jonathan T; Lee, Kwan Young; Zhu, Jiuhe; Tsai, Nien-Pei

2016-03-22

Neural network synchrony is a critical factor in regulating information transmission through the nervous system. Improperly regulated neural network synchrony is implicated in pathophysiological conditions such as epilepsy. Despite the awareness of its importance, the molecular signaling underlying the regulation of neural network synchrony, especially after stimulation, remains largely unknown. In this study, we show that elevation of neuronal activity by the GABA(A) receptor antagonist, Picrotoxin, increases neural network synchrony in primary mouse cortical neuron cultures. The elevation of neuronal activity triggers Mdm2-dependent degradation of the tumor suppressor p53. We show here that blocking the degradation of p53 further enhances Picrotoxin-induced neural network synchrony, while promoting the inhibition of p53 with a p53 inhibitor reduces Picrotoxin-induced neural network synchrony. These data suggest that Mdm2-p53 signaling mediates a feedback mechanism to fine-tune neural network synchrony after activity stimulation. Furthermore, genetically reducing the expression of a direct target gene of p53, Nedd4-2, elevates neural network synchrony basally and occludes the effect of Picrotoxin. Finally, using a kainic acid-induced seizure model in mice, we show that alterations of Mdm2-p53-Nedd4-2 signaling affect seizure susceptibility. Together, our findings elucidate a critical role of Mdm2-p53-Nedd4-2 signaling underlying the regulation of neural network synchrony and seizure susceptibility and reveal potential therapeutic targets for hyperexcitability-associated neurological disorders.

Semantic Congruence Accelerates the Onset of the Neural Signals of Successful Memory Encoding.

PubMed

Packard, Pau A; Rodríguez-Fornells, Antoni; Bunzeck, Nico; Nicolás, Berta; de Diego-Balaguer, Ruth; Fuentemilla, Lluís

2017-01-11

As the stream of experience unfolds, our memory system rapidly transforms current inputs into long-lasting meaningful memories. A putative neural mechanism that strongly influences how input elements are transformed into meaningful memory codes relies on the ability to integrate them with existing structures of knowledge or schemas. However, it is not yet clear whether schema-related integration neural mechanisms occur during online encoding. In the current investigation, we examined the encoding-dependent nature of this phenomenon in humans. We showed that actively integrating words with congruent semantic information provided by a category cue enhances memory for words and increases false recall. The memory effect of such active integration with congruent information was robust, even with an interference task occurring right after each encoding word list. In addition, via electroencephalography, we show in 2 separate studies that the onset of the neural signals of successful encoding appeared early (∼400 ms) during the encoding of congruent words. That the neural signals of successful encoding of congruent and incongruent information followed similarly ∼200 ms later suggests that this earlier neural response contributed to memory formation. We propose that the encoding of events that are congruent with readily available contextual semantics can trigger an accelerated onset of the neural mechanisms, supporting the integration of semantic information with the event input. This faster onset would result in a long-lasting and meaningful memory trace for the event but, at the same time, make it difficult to distinguish it from plausible but never encoded events (i.e., related false memories). Conceptual or schema congruence has a strong influence on long-term memory. However, the question of whether schema-related integration neural mechanisms occur during online encoding has yet to be clarified. We investigated the neural mechanisms reflecting how the active

Combinatorial Fgf and Bmp signalling patterns the gastrula ectoderm into prospective neural and epidermal domains

PubMed Central

Kudoh, Tetsuhiro; Concha, Miguel L.; Houart, Corinne; Dawid, Igor B.; Wilson, Stephen W.

2009-01-01

Summary Studies in fish and amphibia have shown that graded Bmp signalling activity regulates dorsal-to-ventral (DV) patterning of the gastrula embryo. In the ectoderm, it is thought that high levels of Bmp activity promote epidermal development ventrally, whereas secreted Bmp antagonists emanating from the organiser induce neural tissue dorsally. However, in zebrafish embryos, the domain of cells destined to contribute to the spinal cord extends all the way to the ventral side of the gastrula, a long way from the organiser. We show that in vegetal (trunk and tail) regions of the zebrafish gastrula, neural specification is initiated at all DV positions of the ectoderm in a manner that is unaffected by levels of Bmp activity and independent of organiser-derived signals. Instead, we find that Fgf activity is required to induce vegetal prospective neural markers and can do so without suppressing Bmp activity. We further show that Bmp signalling does occur within the vegetal prospective neural domain and that Bmp activity promotes the adoption of caudal fate by this tissue. PMID:15262889

Calcium signaling mediates five types of cell morphological changes to form neural rosettes.

PubMed

Hříbková, Hana; Grabiec, Marta; Klemová, Dobromila; Slaninová, Iva; Sun, Yuh-Man

2018-02-12

Neural rosette formation is a critical morphogenetic process during neural development, whereby neural stem cells are enclosed in rosette niches to equipoise proliferation and differentiation. How neural rosettes form and provide a regulatory micro-environment remains to be elucidated. We employed the human embryonic stem cell-based neural rosette system to investigate the structural development and function of neural rosettes. Our study shows that neural rosette formation consists of five types of morphological change: intercalation, constriction, polarization, elongation and lumen formation. Ca 2+ signaling plays a pivotal role in the five steps by regulating the actions of the cytoskeletal complexes, actin, myosin II and tubulin during intercalation, constriction and elongation. These, in turn, control the polarizing elements, ZO-1, PARD3 and β-catenin during polarization and lumen production for neural rosette formation. We further demonstrate that the dismantlement of neural rosettes, mediated by the destruction of cytoskeletal elements, promotes neurogenesis and astrogenesis prematurely, indicating that an intact rosette structure is essential for orderly neural development. © 2018. Published by The Company of Biologists Ltd.

Functional neural changes associated with acquired amusia across different stages of recovery after stroke.

PubMed

Sihvonen, Aleksi J; Särkämö, Teppo; Ripollés, Pablo; Leo, Vera; Saunavaara, Jani; Parkkola, Riitta; Rodríguez-Fornells, Antoni; Soinila, Seppo

2017-09-12

Brain damage causing acquired amusia disrupts the functional music processing system, creating a unique opportunity to investigate the critical neural architectures of musical processing in the brain. In this longitudinal fMRI study of stroke patients (N = 41) with a 6-month follow-up, we used natural vocal music (sung with lyrics) and instrumental music stimuli to uncover brain activation and functional network connectivity changes associated with acquired amusia and its recovery. In the acute stage, amusic patients exhibited decreased activation in right superior temporal areas compared to non-amusic patients during instrumental music listening. During the follow-up, the activation deficits expanded to comprise a wide-spread bilateral frontal, temporal, and parietal network. The amusics showed less activation deficits to vocal music, suggesting preserved processing of singing in the amusic brain. Compared to non-recovered amusics, recovered amusics showed increased activation to instrumental music in bilateral frontoparietal areas at 3 months and in right middle and inferior frontal areas at 6 months. Amusia recovery was also associated with increased functional connectivity in right and left frontoparietal attention networks to instrumental music. Overall, our findings reveal the dynamic nature of deficient activation and connectivity patterns in acquired amusia and highlight the role of dorsal networks in amusia recovery.

Performance sustaining intracortical neural prostheses

NASA Astrophysics Data System (ADS)

Nuyujukian, Paul; Kao, Jonathan C.; Fan, Joline M.; Stavisky, Sergey D.; Ryu, Stephen I.; Shenoy, Krishna V.

2014-12-01

Objective. Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr-1. In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days’ experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder

Genetic deletion of Rnd3 in neural stem cells promotes proliferation via upregulation of Notch signaling.

PubMed

Dong, Huimin; Lin, Xi; Li, Yuntao; Hu, Ronghua; Xu, Yang; Guo, Xiaojie; La, Qiong; Wang, Shun; Fang, Congcong; Guo, Junli; Li, Qi; Mao, Shanping; Liu, Baohui

2017-10-31

Rnd3, a Rho GTPase, is involved in the inhibition of actin cytoskeleton dynamics through the Rho kinase-dependent signaling pathway. We previously demonstrated that mice with genetic deletion of Rnd3 developed a markedly larger brain compared with wild-type mice. Here, we demonstrate that Rnd3 knockout mice developed an enlarged subventricular zone, and we identify a novel role for Rnd3 as an inhibitor of Notch signaling in neural stem cells. Rnd3 deficiency, both in vivo and in vitro , resulted in increased levels of Notch intracellular domain protein. This led to enhanced Notch signaling and promotion of aberrant neural stem cell growth, thereby resulting in a larger subventricular zone and a markedly larger brain. Inhibition of Notch activity abrogated this aberrant neural stem cell growth.

Protein S Negatively Regulates Neural Stem Cell Self-Renewal through Bmi-1 Signaling

PubMed Central

Zelentsova-Levytskyi, Katya; Talmi, Ziv; Abboud-Jarrous, Ghada; Capucha, Tal; Sapir, Tamar; Burstyn-Cohen, Tal

2017-01-01

Revealing the molecular mechanisms underlying neural stem cell self-renewal is a major goal toward understanding adult brain homeostasis. The self-renewing potential of neural stem and progenitor cells (NSPCs) must be tightly regulated to maintain brain homeostasis. We recently reported the expression of Protein S (PROS1) in adult hippocampal NSPCs, and revealed its role in regulation of NSPC quiescence and neuronal differentiation. Here, we investigate the effect of PROS1 on NSPC self-renewal and show that genetic ablation of Pros1 in neural progenitors increased NSPC self-renewal by 50%. Mechanistically, we identified the upregulation of the polycomb complex protein Bmi-1 and repression of its downstream effectors p16Ink4a and p19Arf to promote NSPC self-renewal in Pros1-ablated cells. Rescuing Pros1 expression restores normal levels of Bmi-1 signaling, and reverts the proliferation and enhanced self-renewal phenotypes observed in Pros1-deleted cells. Our study identifies PROS1 as a novel negative regulator of NSPC self-renewal. We conclude PROS1 is instructive for NSPC differentiation by negatively regulating Bmi-1 signaling in adult and embryonic neural stem cells. PMID:28512399

Spatiotemporal characteristics and vascular sources of neural-specific and -nonspecific fMRI signals at submillimeter columnar resolution

PubMed Central

Moon, Chan Hong; Fukuda, Mitsuhiro; Kim, Seong-Gi

2012-01-01

The neural specificity of hemodynamic-based functional magnetic resonance imaging (fMRI) signals are dependent on both the vascular regulation and the sensitivity of the applied fMRI technique to different types and sizes of blood vessels. In order to examine the specificity of MRI-detectable hemodynamic responses, submillimeter blood oxygenation-level dependent (BOLD) and cerebral blood volume (CBV) fMRI studies were performed in a well-established cat orientation column model at 9.4 Tesla. Neural-nonspecific and -specific signals were separated by comparing the fMRI responses of orthogonal orientation stimuli. The BOLD response was dominantly neural-nonspecific, mostly originating from pial and intracortical emerging veins, and thus was highly correlated with baseline blood volume. Uneven baseline CBV may displace or distort small functional domains in high-resolution BOLD maps. The CBV response in the parenchyma exhibited dual spatiotemporal characteristics, a fast and early neural-nonspecific response (with 4.3-s time constant) and a slightly slower and delayed neural-specific response (with 9.4-s time constant). The nonspecific CBV signal originates from early-responding arteries and arterioles, while the specific CBV response, which is not correlated with baseline blood volume, arises from late-responding microvessels including small pre-capillary arterioles and capillaries. Our data indicate that although the neural specificity of CBV fMRI signals is dependent on stimulation duration, high-resolution functional maps can be obtained from steady-state CBV studies. PMID:22960251

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

PubMed

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation

PubMed Central

Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits. PMID:24672422

A mixed-signal implementation of a polychronous spiking neural network with delay adaptation.

PubMed

Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André

2014-01-01

We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.

Ultra-low-power and robust digital-signal-processing hardware for implantable neural interface microsystems.

PubMed

Narasimhan, S; Chiel, H J; Bhunia, S

2011-04-01

Implantable microsystems for monitoring or manipulating brain activity typically require on-chip real-time processing of multichannel neural data using ultra low-power, miniaturized electronics. In this paper, we propose an integrated-circuit/architecture-level hardware design framework for neural signal processing that exploits the nature of the signal-processing algorithm. First, we consider different power reduction techniques and compare the energy efficiency between the ultra-low frequency subthreshold and conventional superthreshold design. We show that the superthreshold design operating at a much higher frequency can achieve comparable energy dissipation by taking advantage of extensive power gating. It also provides significantly higher robustness of operation and yield under large process variations. Next, we propose an architecture level preferential design approach for further energy reduction by isolating the critical computation blocks (with respect to the quality of the output signal) and assigning them higher delay margins compared to the noncritical ones. Possible delay failures under parameter variations are confined to the noncritical components, allowing graceful degradation in quality under voltage scaling. Simulation results using prerecorded neural data from the sea-slug (Aplysia californica) show that the application of the proposed design approach can lead to significant improvement in total energy, without compromising the output signal quality under process variations, compared to conventional design approaches.

Role of TGF-β signaling in inherited and acquired myopathies

PubMed Central

2011-01-01

The transforming growth factor-beta (TGF-β) superfamily consists of a variety of cytokines expressed in many different cell types including skeletal muscle. Members of this superfamily that are of particular importance in skeletal muscle are TGF-β1, mitogen-activated protein kinases (MAPKs), and myostatin. These signaling molecules play important roles in skeletal muscle homeostasis and in a variety of inherited and acquired neuromuscular disorders. Expression of these molecules is linked to normal processes in skeletal muscle such as growth, differentiation, regeneration, and stress response. However, chronic elevation of TGF-β1, MAPKs, and myostatin is linked to various features of muscle pathology, including impaired regeneration and atrophy. In this review, we focus on the aberrant signaling of TGF-β in various disorders such as Marfan syndrome, muscular dystrophies, sarcopenia, and critical illness myopathy. We also discuss how the inhibition of several members of the TGF-β signaling pathway has been implicated in ameliorating disease phenotypes, opening up novel therapeutic avenues for a large group of neuromuscular disorders. PMID:21798096

Vector neural network signal integration for radar application

NASA Astrophysics Data System (ADS)

Bierman, Gregory S.

1994-07-01

The Litton Data Systems Vector Neural Network (VNN) is a unique multi-scan integration algorithm currently in development. The target of interest is a low-flying cruise missile. Current tactical radar cannot detect and track the missile in ground clutter at tactically useful ranges. The VNN solves this problem by integrating the energy from multiple frames to effectively increase the target's signal-to-noise ratio. The implementation plan is addressing the APG-63 radar. Real-time results will be available by March 1994.

Top-Down Inhibition of BMP Signaling Enables Robust Induction of hPSCs Into Neural Crest in Fully Defined, Xeno-free Conditions.

PubMed

Hackland, James O S; Frith, Tom J R; Thompson, Oliver; Marin Navarro, Ana; Garcia-Castro, Martin I; Unger, Christian; Andrews, Peter W

2017-10-10

Defects in neural crest development have been implicated in many human disorders, but information about human neural crest formation mostly depends on extrapolation from model organisms. Human pluripotent stem cells (hPSCs) can be differentiated into in vitro counterparts of the neural crest, and some of the signals known to induce neural crest formation in vivo are required during this process. However, the protocols in current use tend to produce variable results, and there is no consensus as to the precise signals required for optimal neural crest differentiation. Using a fully defined culture system, we have now found that the efficient differentiation of hPSCs to neural crest depends on precise levels of BMP signaling, which are vulnerable to fluctuations in endogenous BMP production. We present a method that controls for this phenomenon and could be applied to other systems where endogenous signaling can also affect the outcome of differentiation protocols. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

NASA Astrophysics Data System (ADS)

Kim, Kyungmin; Harry, Ian W.; Hodge, Kari A.; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.

2015-12-01

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%-14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs.

Dynamic neural activity during stress signals resilient coping

PubMed Central

Sinha, Rajita; Lacadie, Cheryl M.; Constable, R. Todd; Seo, Dongju

2016-01-01

Active coping underlies a healthy stress response, but neural processes supporting such resilient coping are not well-known. Using a brief, sustained exposure paradigm contrasting highly stressful, threatening, and violent stimuli versus nonaversive neutral visual stimuli in a functional magnetic resonance imaging (fMRI) study, we show significant subjective, physiologic, and endocrine increases and temporally related dynamically distinct patterns of neural activation in brain circuits underlying the stress response. First, stress-specific sustained increases in the amygdala, striatum, hypothalamus, midbrain, right insula, and right dorsolateral prefrontal cortex (DLPFC) regions supported the stress processing and reactivity circuit. Second, dynamic neural activation during stress versus neutral runs, showing early increases followed by later reduced activation in the ventrolateral prefrontal cortex (VLPFC), dorsal anterior cingulate cortex (dACC), left DLPFC, hippocampus, and left insula, suggested a stress adaptation response network. Finally, dynamic stress-specific mobilization of the ventromedial prefrontal cortex (VmPFC), marked by initial hypoactivity followed by increased VmPFC activation, pointed to the VmPFC as a key locus of the emotional and behavioral control network. Consistent with this finding, greater neural flexibility signals in the VmPFC during stress correlated with active coping ratings whereas lower dynamic activity in the VmPFC also predicted a higher level of maladaptive coping behaviors in real life, including binge alcohol intake, emotional eating, and frequency of arguments and fights. These findings demonstrate acute functional neuroplasticity during stress, with distinct and separable brain networks that underlie critical components of the stress response, and a specific role for VmPFC neuroflexibility in stress-resilient coping. PMID:27432990

Harmonic stochastic resonance-enhanced signal detecting in NW small-world neural network

NASA Astrophysics Data System (ADS)

Wang, Dao-Guang; Liang, Xiao-Ming; Wang, Jing; Yang, Cheng-Fang; Liu, Kai; Lü, Hua-Ping

2010-11-01

The harmonic stochastic resonance-enhanced signal detecting in Newman-Watts small-world neural network is studied using the Hodgkin-Huxley dynamical equation with noise. If the connection probability p, coupling strength gsyn and noise intensity D matches well, higher order resonance will be found and an optimal signal-to-noise ratio will be obtained. Then, the reasons are given to explain the mechanism of this appearance.

A fully integrated mixed-signal neural processor for implantable multichannel cortical recording.

PubMed

Sodagar, Amir M; Wise, Kensall D; Najafi, Khalil

2007-06-01

A 64-channel neural processor has been developed for use in an implantable neural recording microsystem. In the Scan Mode, the processor is capable of detecting neural spikes by programmable positive, negative, or window thresholding. Spikes are tagged with their associated channel addresses and formed into 18-bit data words that are sent serially to the external host. In the Monitor Mode, two channels can be selected and viewed at high resolution for studies where the entire signal is of interest. The processor runs from a 3-V supply and a 2-MHz clock, with a channel scan rate of 64 kS/s and an output bit rate of 2 Mbps.

Dopamine controls the neural dynamics of memory signals and retrieval accuracy.

PubMed

Apitz, Thore; Bunzeck, Nico

2013-11-01

The human brain is capable of differentiating between new and already stored information rapidly to allow optimal behavior and decision-making. Although the neural mechanisms of novelty discrimination were often described as temporally constant (ie, with specific latencies), recent electrophysiological studies have demonstrated that the onset of neural novelty signals (ie, differences in event-related responses to new and old items) can be accelerated by reward motivation. While the precise physiological mechanisms underlying this acceleration remain unclear, the involvement of the neurotransmitter dopamine in both novelty and reward processing suggests that enhanced dopamine levels in the context of reward prospect may have a role. To investigate this hypothesis, we used magnetoencephalography (MEG) in combination with an old/new recognition memory task in which correct discrimination between old and new items was rewarded. Importantly, before the task, human subjects received either 150 mg of the dopamine precursor levodopa or placebo. For the placebo group, old/new signals peaked at ∼100 ms after stimulus onset over left temporal/occipital sensors. In contrast, after levodopa administration earliest old/new effects only emerged after ∼400 ms and retrieval accuracy was reduced as expressed in lower d' values. As such, our results point towards a previously unreported role of dopamine in controlling the chronometry of neural processes underlying the distinction between old and new information. They also suggest that this relationship follows a nonlinear function whereby slightly enhanced dopamine levels accelerate neural/cognitive processes and excessive dopamine levels impair them.

Tonic signaling from O2 sensors sets neural circuit activity and behavioral state

PubMed Central

Busch, Karl Emanuel; Laurent, Patrick; Soltesz, Zoltan; Murphy, Robin Joseph; Faivre, Olivier; Hedwig, Berthold; Thomas, Martin; Smith, Heather L.; de Bono, Mario

2012-01-01

Tonic receptors convey stimulus duration and intensity and are implicated in homeostatic control. However, how tonic homeostatic signals are generated, and how they reconfigure neural circuits and modify animal behavior is poorly understood. Here we show that C. elegans O2-sensing neurons are tonic receptors that continuously signal ambient [O2] to set the animal’s behavioral state. Sustained signalling relies on a Ca2+ relay involving L-type voltage-gated Ca2+ channels, the ryanodine and the IP3 receptors. Tonic activity evokes continuous neuropeptide release, which helps elicit the enduring behavioral state associated with high [O2]. Sustained O2 receptor signalling is propagated to downstream neural circuits, including the hub interneuron RMG. O2 receptors evoke similar locomotory states at particular [O2], regardless of previous d[O2]/dt. However, a phasic component of the URX receptors’ response to high d[O2]/dt, as well as tonic-to-phasic transformations in downstream interneurons, enable transient reorientation movements shaped by d[O2]/dt. Our results highlight how tonic homeostatic signals can generate both transient and enduring behavioral change. PMID:22388961

Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

2018-05-01

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

Plasmodesmata localizing proteins regulate transport and signaling during systemic acquired immunity in plants

USDA-ARS?s Scientific Manuscript database

Systemic acquired resistance (SAR) in plants is mediated by the signaling molecules azelaic acid (AzA),glycerol-3-phosphate (G3P), and salicylic acid (SA).Here, we show that AzA and G3P transport occurs via the symplastic route, which is regulated by channels known as plasmodesmata (PD). In contrast...

Radio frequency interference mitigation using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

2017-01-01

We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

The COP9 Signalosome Converts Temporal Hormone Signaling to Spatial Restriction on Neural Competence

PubMed Central

Huang, Yi-Chun; Lu, Yu-Nung; Wu, June-Tai; Chien, Cheng-Ting; Pi, Haiwei

2014-01-01

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. PMID:25393278

Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images.

PubMed

Johnson, J L

1994-09-10

The linking-field neural network model of Eckhorn et al. [Neural Comput. 2, 293-307 (1990)] was introduced to explain the experimentally observed synchronous activity among neural assemblies in the cat cortex induced by feature-dependent visual activity. The model produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency. It gives a basic new function: grouping by similarity. The synchronous bursts are obtained in the limit of strong linking strengths. The linking-field model in the limit of moderate-to-weak linking characterized by few if any multiple bursts is investigated. In this limit dynamic, locally periodic traveling waves exist whose time signal encodes the geometrical structure of a two-dimensional input image. The signal can be made insensitive to translation, scale, rotation, distortion, and intensity. The waves transmit information beyond the physical interconnect distance. The model is implemented in an optical hybrid demonstration system. Results of the simulations and the optical system are presented.

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

PubMed

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-06-07

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals

PubMed Central

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-01-01

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process. PMID:28590456

Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise

NASA Astrophysics Data System (ADS)

Keeler, James D.; Pichler, Elgar E.; Ross, John

1989-03-01

We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may ``quench'' the neural patterns of activity to enhance the ability to learn details.

Automated embolic signal detection using Deep Convolutional Neural Network.

PubMed

Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

2017-07-01

This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

ADAM13 Induces Cranial Neural Crest by Cleaving Class B Ephrins and Regulating Wnt Signaling

PubMed Central

Wei, Shuo; Xu, Guofeng; Bridges, Lance C.; Williams, Phoebe; White, Judith M.; DeSimone, Douglas W.

2010-01-01

SUMMARY The cranial neural crest (CNC) are multipotent embryonic cells that contribute to craniofacial structures and other cells and tissues of the vertebrate head. During embryogenesis, CNC is induced at the neural plate boundary through the interplay of several major signaling pathways. Here we report that the metalloproteinase activity of ADAM13 is required for early induction of CNC in Xenopus. In both cultured cells and X. tropicalis embryos, membrane-bound Ephrins (Efns) B1 and B2 were identified as substrates for ADAM13. ADAM13 upregulates canonical Wnt signaling and early expression of the transcription factor snail2, whereas EfnB1 inhibits the canonical Wnt pathway and snail2 expression. We propose that by cleaving class B Efns, ADAM13 promotes canonical Wnt signaling and early CNC induction. PMID:20708595

Predictable information in neural signals during resting state is reduced in autism spectrum disorder.

PubMed

Brodski-Guerniero, Alla; Naumer, Marcus J; Moliadze, Vera; Chan, Jason; Althen, Heike; Ferreira-Santos, Fernando; Lizier, Joseph T; Schlitt, Sabine; Kitzerow, Janina; Schütz, Magdalena; Langer, Anne; Kaiser, Jochen; Freitag, Christine M; Wibral, Michael

2018-04-04

The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients. © 2018 Wiley Periodicals, Inc.

Social discounting involves modulation of neural value signals by temporoparietal junction

PubMed Central

Strombach, Tina; Weber, Bernd; Hangebrauk, Zsofia; Kenning, Peter; Karipidis, Iliana I.; Tobler, Philippe N.; Kalenscher, Tobias

2015-01-01

Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation. PMID:25605887

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

Inhibition of BMP signaling overcomes acquired resistance to cetuximab in oral squamous cell carcinomas.

PubMed

Yin, Jinlong; Jung, Ji-Eun; Choi, Sun Il; Kim, Sung Soo; Oh, Young Taek; Kim, Tae-Hoon; Choi, Eunji; Lee, Sun Joo; Kim, Hana; Kim, Eun Ok; Lee, Yu Sun; Chang, Hee Jin; Park, Joo Yong; Kim, Yeejeong; Yun, Tak; Heo, Kyun; Kim, Youn-Jae; Kim, Hyunggee; Kim, Yun-Hee; Park, Jong Bae; Choi, Sung Weon

2018-02-01

Despite expressing high levels of the epidermal growth factor receptor (EGFR), a majority of oral squamous cell carcinoma (OSCC) patients show limited response to cetuximab and ultimately develop drug resistance. However, mechanism underlying cetuximab resistance in OSCC is not clearly understood. Here, using a mouse orthotopic xenograft model of OSCC, we show that bone morphogenic protein-7-phosphorylated Smad-1, -5, -8 (BMP7-p-Smad1/5/8) signaling contributes to cetuximab resistance. Tumor cells isolated from the recurrent cetuximab-resistant xenograft models exhibited low EGFR expression but extremely high levels of p-Smad1/5/8. Treatment with the bone morphogenic protein receptor type 1 (BMPRI) inhibitor, DMH1 significantly reduced cetuximab-resistant OSCC tumor growth, and combined treatment of DMH1 and cetuximab remarkably reduced relapsed tumor growth in vivo. Importantly, p-Smad1/5/8 level was elevated in cetuximab-resistant patients and this correlated with poor prognosis. Collectively, our results indicate that the BMP7-p-Smad1/5/8 signaling is a key pathway to acquired cetuximab resistance, and demonstrate that combination therapy of cetuximab and a BMP signaling inhibitor as potentially a new therapeutic strategy for overcoming acquired resistance to cetuximab in OSCC. Copyright © 2017 Elsevier B.V. All rights reserved.

SGK3 sustains ERα signaling and drives acquired aromatase inhibitor resistance through maintaining endoplasmic reticulum homeostasis.

PubMed

Wang, Yuanzhong; Zhou, Dujin; Phung, Sheryl; Warden, Charles; Rashid, Rumana; Chan, Nymph; Chen, Shiuan

2017-02-21

Many estrogen receptor alpha (ERα)-positive breast cancers initially respond to aromatase inhibitors (AIs), but eventually acquire resistance. Here, we report that serum- and glucocorticoid-inducible kinase 3 (SGK3), a kinase transcriptionally regulated by ERα in breast cancer, sustains ERα signaling and drives acquired AI resistance. SGK3 is up-regulated and essential for endoplasmic reticulum (EnR) homeostasis through preserving sarcoplasmic/EnR calcium ATPase 2b (SERCA2b) function in AI-resistant cells. We have further found that EnR stress response down-regulates ERα expression through the protein kinase RNA-like EnR kinase (PERK) arm, and SGK3 retains ERα expression and signaling by preventing excessive EnR stress. Our study reveals regulation of ERα expression mediated by the EnR stress response and the feed-forward regulation between SGK3 and ERα in breast cancer. Given SGK3 inhibition reduces AI-resistant cell survival by eliciting excessive EnR stress and also depletes ERα expression/function, we propose SGK3 inhibition as a potential effective treatment of acquired AI-resistant breast cancer.

Angiogenesis within the developing mouse neural tube is dependent on sonic hedgehog signaling: possible roles of motor neurons.

PubMed

Nagase, Takashi; Nagase, Miki; Yoshimura, Kotaro; Fujita, Toshiro; Koshima, Isao

2005-06-01

Embryonic morphogenesis of vascular and nervous systems is tightly coordinated, and recent studies revealed that some neurogenetic factors such as Sonic hedgehog (Shh) also exhibit angiogenetic potential. Vascularization within the developing mouse neural tube depends on vessel sprouting from the surrounding vascular plexus. Previous studies implicated possible roles of VEGF/Flk-1 and Angiopoietin-1(Ang-1)/Tie-2 signaling as candidate molecules functioning in this process. Examining gene expressions of these factors at embryonic day (E) 9.5 and 10.5, we unexpectedly found that both VEGF and Ang-1 were expressed in the motor neurons in the ventral neural tube. The motor neurons were indeed located in the close vicinity of the infiltrating vessels, suggesting involvement of motor neurons in the sprouting. To substantiate this possibility, we inhibited induction of the motor neurons in the cultured mouse embryos by cyclopamine, a Shh signaling blocker. The vessel sprouting was dramatically impaired by inhibition of Shh signaling, together with nearly complete loss of the motor neurons. Expression of Ang-1, but not VEGF, within the neural tube was remarkably reduced in the cyclopamine treated embryos. These results suggest that the neural tube angiogenesis is dependent on Shh signaling, and mediated, at least in part, by the Ang-1 positive motor neurons.

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance

PubMed Central

Buran, Bradley N.; Sen, Kamal; Semple, Malcolm N.; Sanes, Dan H.

2016-01-01

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. SIGNIFICANCE STATEMENT The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we

tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

NASA Astrophysics Data System (ADS)

Akeret, Joel; Chang, Chihway; Lucchi, Aurelien; Refregier, Alexandre

2016-11-01

tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

The neural bases of acquisitiveness: decisions to acquire and discard everyday goods differ across frames, items, and individuals.

PubMed

Wang, John M; Seidler, Rachael D; Hall, Julie L; Preston, Stephanie D

2012-04-01

The human tendency to acquire and keep large quantities of goods has become a serious concern, but has yet to be examined from a neuroscientific perspective. The mesolimbocortical system, particularly the orbitofrontal cortex (OFC) and nucleus accumbens (NAcc), is implicated when humans and animals acquire rewards. However, this may not extend to acquisitiveness per se, which involves fairly mundane items and is interconnected with a failure to discard. Moreover, the NAcc has not been implicated in neuroimaging studies of the extreme acquisitiveness of compulsive hoarders. In a study of the neural bases of normal acquisitiveness, subjects made decisions during functional neuroimaging to acquire or remove everyday items from a hypothetical collection, while maximizing personal preference or monetary profit. All decisions engaged the OFC, but the OFC and all regions of interest shifted in their relative involvement across the four decision contexts. The NAcc was only engaged during personal acquisition to the extent of problematic hoarding, suggesting that even common items can acquire an incentive salience that makes them hard to resist for acquisitive individuals. The types of items preferred also shifted with condition, with subjects only being biased toward expensive items when instructed to maximize profit. Item preferences even differed depending on whether participants were acquiring versus removing items, even though the task only differed superficially in the two conditions. Acquisitiveness reflects a complex mix of affective, cognitive, and personality factors that extend well beyond the drive to acquire valuable resources, with important implications for basic decision science, sustainability, and pathologies associated with compulsive acquisition. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dangerous mating systems: signal complexity, signal content and neural capacity in spiders.

PubMed

Herberstein, M E; Wignall, A E; Hebets, E A; Schneider, J M

2014-10-01

Spiders are highly efficient predators in possession of exquisite sensory capacities for ambushing prey, combined with machinery for launching rapid and determined attacks. As a consequence, any sexually motivated approach carries a risk of ending up as prey rather than as a mate. Sexual selection has shaped courtship to effectively communicate the presence, identity, motivation and/or quality of potential mates, which help ameliorate these risks. Spiders communicate this information via several sensory channels, including mechanical (e.g. vibrational), visual and/or chemical, with examples of multimodal signalling beginning to emerge in the literature. The diverse environments that spiders inhabit have further shaped courtship content and form. While our understanding of spider neurobiology remains in its infancy, recent studies are highlighting the unique and considerable capacities of spiders to process and respond to complex sexual signals. As a result, the dangerous mating systems of spiders are providing important insights into how ecology shapes the evolution of communication systems, with future work offering the potential to link this complex communication with its neural processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Disentangling neural representations of value and salience in the human brain

PubMed Central

Kahnt, Thorsten; Park, Soyoung Q; Haynes, John-Dylan; Tobler, Philippe N.

2014-01-01

A large body of evidence has implicated the posterior parietal and orbitofrontal cortex in the processing of value. However, value correlates perfectly with salience when appetitive stimuli are investigated in isolation. Accordingly, considerable uncertainty has remained about the precise nature of the previously identified signals. In particular, recent evidence suggests that neurons in the primate parietal cortex signal salience instead of value. To investigate neural signatures of value and salience, here we apply multivariate (pattern-based) analyses to human functional MRI data acquired during a noninstrumental outcome-prediction task involving appetitive and aversive outcomes. Reaction time data indicated additive and independent effects of value and salience. Critically, we show that multivoxel ensemble activity in the posterior parietal cortex encodes predicted value and salience in superior and inferior compartments, respectively. These findings reinforce the earlier reports of parietal value signals and reconcile them with the recent salience report. Moreover, we find that multivoxel patterns in the orbitofrontal cortex correlate with value. Importantly, the patterns coding for the predicted value of appetitive and aversive outcomes are similar, indicating a common neural scale for appetite and aversive values in the orbitofrontal cortex. Thus orbitofrontal activity patterns satisfy a basic requirement for a neural value signal. PMID:24639493

Influence of the measuring condition on vibrocardiographic signals acquired on the thorax with a laser Doppler vibrometer

NASA Astrophysics Data System (ADS)

Mignanelli, L.; Bauer, G.; Klarmann, M.; Wang, H.; Rembe, C.

2017-07-01

Velocity signals acquired with a Laser Doppler Vibrometer on the thorax (Optical Vibrocardiography) contain important information, which have a relation to cardiovascular parameters and cardiovascular diseases. The acquired signal results in a superimposition of vibrations originated from different sources of the human body. Since we study the vibration generated by the heart to reliably detect a characteristic time interval corresponding to the PR interval in the ECG, these disturbance have to be removed by filtering. Moreover, the Laser Doppler Vibrometer measures only in the direction of the laser beam and, thus, the velocity signal is only a projection of the tridimensional movement of the thorax. This work presents an analysis of the influences of the filters and of the measurement direction on the characteristic time interval in Vibrocardiographic signals. Our analysis results in recommended settings for filters and we demonstrate that reliable detection of vibrocardiographic parameters is possible within an angle deviation of 30° in respect to the perpendicular irradiation on the front side of the subject.

Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

NASA Astrophysics Data System (ADS)

Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

2001-07-01

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

NGF reprograms metastatic melanoma to a bipotent glial-melanocyte neural crest-like precursor

PubMed Central

Kasemeier-Kulesa, Jennifer C.; Romine, Morgan H.; Morrison, Jason A.; Bailey, Caleb M.; Welch, Danny R.

2018-01-01

ABSTRACT Melanoma pathogenesis from normal neural crest-derived melanocytes is often fatal due to aggressive cell invasion throughout the body. The identification of signals that reprogram de-differentiated, metastatic melanoma cells to a less aggressive and stable phenotype would provide a novel strategy to limit disease progression. In this study, we identify and test the function of developmental signals within the chick embryonic neural crest microenvironment to reprogram and sustain the transition of human metastatic melanoma to a neural crest cell-like phenotype. Results reveal that co-culture of the highly aggressive and metastatic human melanoma cell line C8161 upregulate a marker of melanosome formation (Mart-1) in the presence of embryonic day 3.5 chick trunk dorsal root ganglia. We identify nerve growth factor (NGF) as the signal within this tissue driving Mart-1 re-expression and show that NGF receptors trkA and p75 cooperate to induce Mart-1 re-expression. Furthermore, Mart-1 expressing C8161 cells acquire a gene signature of poorly aggressive C81-61 cells. These data suggest that targeting NGF signaling may yield a novel strategy to reprogram metastatic melanoma toward a benign cell type. PMID:29175861

Analysis of Acoustic Depth Sounder Signals with Artificial Neural Networks

DTIC Science & Technology

1991-04-01

battery pack, processor, and mode switches and (2) a stainless steel shaft 1 meter long and 27 millimeters in diameter, containing 8 milliCurie of...returned signal which is not used in conventional depth sounders due to lack of real-time tools for interpreting the 36 information. The shape and...develop some software tools for conducting the research. Commercial programs for neural network implementation were available, but were "black box" in

Altered neural reward and loss processing and prediction error signalling in depression

PubMed Central

Ubl, Bettina; Kuehner, Christine; Kirsch, Peter; Ruttorf, Michaela

2015-01-01

Dysfunctional processing of reward and punishment may play an important role in depression. However, functional magnetic resonance imaging (fMRI) studies have shown heterogeneous results for reward processing in fronto-striatal regions. We examined neural responsivity associated with the processing of reward and loss during anticipation and receipt of incentives and related prediction error (PE) signalling in depressed individuals. Thirty medication-free depressed persons and 28 healthy controls performed an fMRI reward paradigm. Regions of interest analyses focused on neural responses during anticipation and receipt of gains and losses and related PE-signals. Additionally, we assessed the relationship between neural responsivity during gain/loss processing and hedonic capacity. When compared with healthy controls, depressed individuals showed reduced fronto-striatal activity during anticipation of gains and losses. The groups did not significantly differ in response to reward and loss outcomes. In depressed individuals, activity increases in the orbitofrontal cortex and nucleus accumbens during reward anticipation were associated with hedonic capacity. Depressed individuals showed an absence of reward-related PEs but encoded loss-related PEs in the ventral striatum. Depression seems to be linked to blunted responsivity in fronto-striatal regions associated with limited motivational responses for rewards and losses. Alterations in PE encoding might mirror blunted reward- and enhanced loss-related associative learning in depression. PMID:25567763

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

PubMed

von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

2016-10-26

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance.

PubMed

Dellett, Margaret; Hu, Wanzhou; Papadaki, Vasiliki; Ohnuma, Shin-ichi

2012-04-01

The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

Concurrent OCT imaging of stimulus evoked retinal neural activation and hemodynamic responses

NASA Astrophysics Data System (ADS)

Son, Taeyoon; Wang, Benquan; Lu, Yiming; Chen, Yanjun; Cao, Dingcai; Yao, Xincheng

2017-02-01

It is well established that major retinal diseases involve distortions of the retinal neural physiology and blood vascular structures. However, the details of distortions in retinal neurovascular coupling associated with major eye diseases are not well understood. In this study, a multi-modal optical coherence tomography (OCT) imaging system was developed to enable concurrent imaging of retinal neural activity and vascular hemodynamics. Flicker light stimulation was applied to mouse retinas to evoke retinal neural responses and hemodynamic changes. The OCT images were acquired continuously during the pre-stimulation, light-stimulation, and post-stimulation phases. Stimulus-evoked intrinsic optical signals (IOSs) and hemodynamic changes were observed over time in blood-free and blood regions, respectively. Rapid IOSs change occurred almost immediately after stimulation. Both positive and negative signals were observed in adjacent retinal areas. The hemodynamic changes showed time delays after stimulation. The signal magnitudes induced by light stimulation were observed in blood regions and did not show significant changes in blood-free regions. These differences may arise from different mechanisms in blood vessels and neural tissues in response to light stimulation. These characteristics agreed well with our previous observations in mouse retinas. Further development of the multimodal OCT may provide a new imaging method for studying how retinal structures and metabolic and neural functions are affected by age-related macular degeneration (AMD), glaucoma, diabetic retinopathy (DR), and other diseases, which promises novel noninvasive biomarkers for early disease detection and reliable treatment evaluations of eye diseases.

Orphan nuclear receptor TLX activates Wnt/β-catenin signalling to stimulate neural stem cell proliferation and self-renewal

PubMed Central

Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T.; Gage, Fred H.; Evans, Ronald M.; Shi, Yanhong

2010-01-01

The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/β-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/β-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active β-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a β-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active β-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active β-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active β-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/β-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode. PMID:20010817

Orphan nuclear receptor TLX activates Wnt/beta-catenin signalling to stimulate neural stem cell proliferation and self-renewal.

PubMed

Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T; Gage, Fred H; Evans, Ronald M; Shi, Yanhong

2010-01-01

The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/beta-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/beta-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active beta-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a beta-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active beta-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active beta-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active beta-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/beta-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode.

Control of excessive neural circuit excitability and prevention of epileptic seizures by endocannabinoid signaling.

PubMed

Sugaya, Yuki; Kano, Masanobu

2018-05-08

Progress in research on endocannabinoid signaling has greatly advanced our understanding of how it controls neural circuit excitability in health and disease. In general, endocannabinoid signaling at excitatory synapses suppresses seizures by inhibiting glutamate release. In contrast, endocannabinoid signaling promotes seizures by inhibiting GABA release at inhibitory synapses. The physiological distribution of endocannabinoid signaling molecules becomes disrupted with the development of epileptic focus in patients with mesial temporal lobe epilepsy and in animal models of experimentally induced epilepsy. Augmentation of endocannabinoid signaling can promote the development of epileptic focus at initial stages. However, at later stages, increased endocannabinoid signaling delays it and suppresses spontaneous seizures. Thus, the regulation of endocannabinoid signaling at specific synapses that cause hyperexcitability during particular stages of disease development may be effective for treating epilepsy and epileptogenesis.

System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

PubMed

Delgado-Restituto, Manuel; Rodriguez-Perez, Alberto; Darie, Angela; Soto-Sanchez, Cristina; Fernandez-Jover, Eduardo; Rodriguez-Vazquez, Angel

2017-04-01

This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330 μW.

The application of neural networks to myoelectric signal analysis: a preliminary study.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1990-03-01

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect

PubMed Central

Römer, Heiner

2015-01-01

species produces an extremely noisy sound, yet the second species still detects its own song. Using intracellular recording techniques we identified two neural mechanisms underlying the surprising behavioral signal detection at the level of single identified interneurons. These neural mechanisms for signal detection are likely to be important for other sensory modalities as well, where noise in the communication channel creates similar problems. Also, they may be used for the development of algorithms for the filtering of specific signals in technical microphones or hearing aids. PMID:26203150

Neuregulin 1 Type II-ErbB Signaling Promotes Cell Divisions Generating Neurons from Neural Progenitor Cells in the Developing Zebrafish Brain.

PubMed

Sato, Tomomi; Sato, Fuminori; Kamezaki, Aosa; Sakaguchi, Kazuya; Tanigome, Ryoma; Kawakami, Koichi; Sehara-Fujisawa, Atsuko

2015-01-01

Post-mitotic neurons are generated from neural progenitor cells (NPCs) at the expense of their proliferation. Molecular and cellular mechanisms that regulate neuron production temporally and spatially should impact on the size and shape of the brain. While transcription factors such as neurogenin1 (neurog1) and neurod govern progression of neurogenesis as cell-intrinsic mechanisms, recent studies show regulatory roles of several cell-extrinsic or intercellular signaling molecules including Notch, FGF and Wnt in production of neurons/neural progenitor cells from neural stem cells/radial glial cells (NSCs/RGCs) in the ventricular zone (VZ). However, it remains elusive how production of post-mitotic neurons from neural progenitor cells is regulated in the sub-ventricular zone (SVZ). Here we show that newborn neurons accumulate in the basal-to-apical direction in the optic tectum (OT) of zebrafish embryos. While neural progenitor cells are amplified by mitoses in the apical ventricular zone, neurons are exclusively produced through mitoses of neural progenitor cells in the sub-basal zone, later in the sub-ventricular zone, and accumulate apically onto older neurons. This neurogenesis depends on Neuregulin 1 type II (NRG1-II)-ErbB signaling. Treatment with an ErbB inhibitor, AG1478 impairs mitoses in the sub-ventricular zone of the optic tectum. Removal of AG1478 resumes sub-ventricular mitoses without precedent mitoses in the apical ventricular zone prior to basal-to-apical accumulation of neurons, suggesting critical roles of ErbB signaling in mitoses for post-mitotic neuron production. Knockdown of NRG1-II impairs both mitoses in the sub-basal/sub-ventricular zone and the ventricular zone. Injection of soluble human NRG1 into the developing brain ameliorates neurogenesis of NRG1-II-knockdown embryos, suggesting a conserved role of NRG1 as a cell-extrinsic signal. From these results, we propose that NRG1-ErbB signaling stimulates cell divisions generating neurons from

Classification of Acousto-Optic Correlation Signatures of Spread Spectrum Signals Using Artificial Neural Networks

DTIC Science & Technology

1989-12-01

Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION

An Implantable Neural Sensing Microsystem with Fiber-Optic Data Transmission and Power Delivery

PubMed Central

Park, Sunmee; Borton, David A.; Kang, Mingyu; Nurmikko, Arto V.; Song, Yoon-Kyu

2013-01-01

We have developed a prototype cortical neural sensing microsystem for brain implantable neuroengineering applications. Its key feature is that both the transmission of broadband, multichannel neural data and power required for the embedded microelectronics are provided by optical fiber access. The fiber-optic system is aimed at enabling neural recording from rodents and primates by converting cortical signals to a digital stream of infrared light pulses. In the full microsystem whose performance is summarized in this paper, an analog-to-digital converter and a low power digital controller IC have been integrated with a low threshold, semiconductor laser to extract the digitized neural signals optically from the implantable unit. The microsystem also acquires electrical power and synchronization clocks via optical fibers from an external laser by using a highly efficient photovoltaic cell on board. The implantable unit employs a flexible polymer substrate to integrate analog and digital microelectronics and on-chip optoelectronic components, while adapting to the anatomical and physiological constraints of the environment. A low power analog CMOS chip, which includes preamplifier and multiplexing circuitry, is directly flip-chip bonded to the microelectrode array to form the cortical neurosensor device. PMID:23666130

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2008-01-01

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

Novel association of VACTERL, neural tube defect and crossed renal ectopia: sonic hedgehog signaling: a point of coherence?

PubMed

Vaze, Dhananjay; Mahalik, Santosh; Rao, Katragadda L N

2012-12-01

The present case report describes two patients with a novel combination of VACTERL (vertebral, anorectal, cardiac, tracheoesophageal, renal, limb), neural tube defect and crossed renal ectopia. Though cases of VACTERL associated with crossed renal ectopia have been described, the present case report is the first to describe its combination with neural tube defect. The cases reported here are significant because central nervous system manifestations are scarce in VACTERL syndrome. The role of sonic hedgehog pathway has been proposed in VACTERL association and neural tube defects. Axial Sonic hedgehog signaling has also been implicated in the mediolateral positioning of the renal parenchyma. With this knowledge, the etiopathogenesis of this novel combination is discussed to highlight the role of sonic hedgehog signaling as a point of coherence. © 2011 The Authors. Congenital Anomalies © 2011 Japanese Teratology Society.

Fatty acid–induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans

PubMed Central

Van Oudenhove, Lukas; McKie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

2011-01-01

Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an intragastric infusion of fatty acid solution or saline during neutral or sad emotion induction and rated sensations of hunger, fullness, and mood. We found an interaction between fatty acid infusion and emotion induction both in the behavioral readouts (hunger, mood) and at the level of neural activity in multiple pre-hypothesized regions of interest. Specifically, the behavioral and neural responses to sad emotion induction were attenuated by fatty acid infusion. These findings increase our understanding of the interplay among emotions, hunger, food intake, and meal-induced sensations in health, which may have important implications for a wide range of disorders, including obesity, eating disorders, and depression. PMID:21785220

Fatty acid-induced gut-brain signaling attenuates neural and behavioral effects of sad emotion in humans.

PubMed

Van Oudenhove, Lukas; McKie, Shane; Lassman, Daniel; Uddin, Bilal; Paine, Peter; Coen, Steven; Gregory, Lloyd; Tack, Jan; Aziz, Qasim

2011-08-01

Although a relationship between emotional state and feeding behavior is known to exist, the interactions between signaling initiated by stimuli in the gut and exteroceptively generated emotions remain incompletely understood. Here, we investigated the interaction between nutrient-induced gut-brain signaling and sad emotion induced by musical and visual cues at the behavioral and neural level in healthy nonobese subjects undergoing functional magnetic resonance imaging. Subjects received an intragastric infusion of fatty acid solution or saline during neutral or sad emotion induction and rated sensations of hunger, fullness, and mood. We found an interaction between fatty acid infusion and emotion induction both in the behavioral readouts (hunger, mood) and at the level of neural activity in multiple pre-hypothesized regions of interest. Specifically, the behavioral and neural responses to sad emotion induction were attenuated by fatty acid infusion. These findings increase our understanding of the interplay among emotions, hunger, food intake, and meal-induced sensations in health, which may have important implications for a wide range of disorders, including obesity, eating disorders, and depression.

Theta signal as the neural signature of social exclusion.

PubMed

Cristofori, Irene; Moretti, Laura; Harquel, Sylvain; Posada, Andres; Deiana, Gianluca; Isnard, Jean; Mauguière, François; Sirigu, Angela

2013-10-01

The feeling of being excluded from a social interaction triggers social pain, a sensation as intense as actual physical pain. Little is known about the neurophysiological underpinnings of social pain. We addressed this issue using intracranial electroencephalography in 15 patients performing a ball game where inclusion and exclusion blocks were alternated. Time-frequency analyses showed an increase in power of theta-band oscillations during exclusion in the anterior insula (AI) and posterior insula, the subgenual anterior cingulate cortex (sACC), and the fusiform "face area" (FFA). Interestingly, the AI showed an initial fast response to exclusion but the signal rapidly faded out. Activity in the sACC gradually increased and remained significant thereafter. This suggests that the AI may signal social pain by detecting emotional distress caused by the exclusion, whereas the sACC may be linked to the learning aspects of social pain. Theta activity in the FFA was time-locked to the observation of a player poised to exclude the participant, suggesting that the FFA encodes the social value of faces. Taken together, our findings suggest that theta activity represents the neural signature of social pain. The time course of this signal varies across regions important for processing emotional features linked to social information.

A low-power current-reuse dual-band analog front-end for multi-channel neural signal recording.

PubMed

Sepehrian, H; Gosselin, B

2014-01-01

Thoroughly studying the brain activity of freely moving subjects requires miniature data acquisition systems to measure and wirelessly transmit neural signals in real time. In this application, it is mandatory to simultaneously record the bioelectrical activity of a large number of neurons to gain a better knowledge of brain functions. However, due to limitations in transferring the entire raw data to a remote base station, employing dedicated data reduction techniques to extract the relevant part of neural signals is critical to decrease the amount of data to transfer. In this work, we present a new dual-band neural amplifier to separate the neuronal spike signals (SPK) and the local field potential (LFP) simultaneously in the analog domain, immediately after the pre-amplification stage. By separating these two bands right after the pre-amplification stage, it is possible to process LFP and SPK separately. As a result, the required dynamic range of the entire channel, which is determined by the signal-to-noise ratio of the SPK signal of larger bandwidth, can be relaxed. In this design, a new current-reuse low-power low-noise amplifier and a new dual-band filter that separates SPK and LFP while saving capacitors and pseudo resistors. A four-channel dual-band (SPK, LFP) analog front-end capable of simultaneously separating SPK and LFP is implemented in a TSMC 0.18 μm technology. Simulation results present a total power consumption per channel of 3.1 μw for an input referred noise of 3.28 μV and a NEF for 2.07. The cutoff frequency of the LFP band is fc=280 Hz, and fL=725 Hz and fL=11.2 KHz for SPK, with 36 dB gain for LFP band 46 dB gain for SPK band.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

PubMed

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Serotonin 2A Receptor Signaling Underlies LSD-induced Alteration of the Neural Response to Dynamic Changes in Music.

PubMed

Barrett, Frederick S; Preller, Katrin H; Herdener, Marcus; Janata, Petr; Vollenweider, Franz X

2017-09-28

Classic psychedelic drugs (serotonin 2A, or 5HT2A, receptor agonists) have notable effects on music listening. In the current report, blood oxygen level-dependent (BOLD) signal was collected during music listening in 25 healthy adults after administration of placebo, lysergic acid diethylamide (LSD), and LSD pretreated with the 5HT2A antagonist ketanserin, to investigate the role of 5HT2A receptor signaling in the neural response to the time-varying tonal structure of music. Tonality-tracking analysis of BOLD data revealed that 5HT2A receptor signaling alters the neural response to music in brain regions supporting basic and higher-level musical and auditory processing, and areas involved in memory, emotion, and self-referential processing. This suggests a critical role of 5HT2A receptor signaling in supporting the neural tracking of dynamic tonal structure in music, as well as in supporting the associated increases in emotionality, connectedness, and meaningfulness in response to music that are commonly observed after the administration of LSD and other psychedelics. Together, these findings inform the neuropsychopharmacology of music perception and cognition, meaningful music listening experiences, and altered perception of music during psychedelic experiences. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Neural network classification of myoelectric signal for prosthesis control.

PubMed

Kelly, M F; Parker, P A; Scott, R N

1991-12-01

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions. Copyright © 1991. Published by Elsevier Ltd.

The tangled web of non-canonical Wnt signalling in neural migration.

PubMed

Clark, Charlotte E J; Nourse, C Cathrin; Cooper, Helen M

2012-01-01

In all multicellular animals, successful embryogenesis is dependent on the ability of cells to detect the status of the local environment and respond appropriately. The nature of the extracellular environment is communicated to the intracellular compartment by ligand/receptor interactions at the cell surface. The Wnt canonical and non-canonical signalling pathways are found in the most primitive metazoans, and they play an essential role in the most fundamental developmental processes in all multicellular organisms. Vertebrates have expanded the number of Wnts and Frizzled receptors and have additionally evolved novel Wnt receptor families (Ryk, Ror). The multiplicity of potential interactions between Wnts, their receptors and downstream effectors has exponentially increased the complexity of the signal transduction network. Signalling through each of the Wnt pathways, as well as crosstalk between them, plays a critical role in the establishment of the complex architecture of the vertebrate central nervous system. In this review, we explore the signalling networks triggered by non-canonical Wnt/receptor interactions, focussing on the emerging roles of the non-conventional Wnt receptors Ryk and Ror. We describe the role of these pathways in neural tube formation and axon guidance where Wnt signalling controls tissue polarity, coordinated cell migration and axon guidance via remodelling of the cytoskeleton. Copyright © 2012 S. Karger AG, Basel.

CNV amplitude as a neural correlate for stuttering frequency: A case report of acquired stuttering.

PubMed

Vanhoutte, Sarah; Van Borsel, John; Cosyns, Marjan; Batens, Katja; van Mierlo, Pieter; Hemelsoet, Dimitri; Van Roost, Dirk; Corthals, Paul; De Letter, Miet; Santens, Patrick

2014-11-01

A neural hallmark of developmental stuttering is abnormal articulatory programming. One of the neurophysiological substrates of articulatory preparation is the contingent negative variation (CNV). Unfortunately, CNV tasks are rarely performed in persons who stutter and mainly focus on the effect of task variation rather than on interindividual variation in stutter related variables. However, variations in motor programming seem to be related to variation in stuttering frequency. The current study presents a case report of acquired stuttering following stroke and stroke related surgery in the left superior temporal gyrus. A speech related CNV task was administered at four points in time with differences in stuttering severity and frequency. Unexpectedly, CNV amplitudes at electrode sites approximating bilateral motor and left inferior frontal gyrus appeared to be inversely proportional to stuttering frequency. The higher the stuttering frequency, the lower the activity for articulatory preparation. Thus, the amount of disturbance in motor programming seems to determine stuttering frequency. At right frontal electrodes, a relative increase in CNV amplitude was seen at the test session with most severe stuttering. Right frontal overactivation is cautiously suggested to be a compensation strategy. In conclusion, late CNV amplitude elicited by a relatively simple speech task seems to be able to provide an objective, neural correlate of stuttering frequency. The present case report supports the hypothesis that motor preparation has an important role in stuttering. Copyright © 2014 Elsevier Ltd. All rights reserved.

Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

NASA Astrophysics Data System (ADS)

Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

2017-10-01

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.

RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

NASA Astrophysics Data System (ADS)

Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

2008-10-01

A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and <1.5Hz at frequency in Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

Real-time classification of signals from three-component seismic sensors using neural nets

NASA Astrophysics Data System (ADS)

Bowman, B. C.; Dowla, F.

1992-05-01

Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

LKB1 signaling in cephalic neural crest cells is essential for vertebrate head development.

PubMed

Creuzet, Sophie E; Viallet, Jean P; Ghawitian, Maya; Torch, Sakina; Thélu, Jacques; Alrajeh, Moussab; Radu, Anca G; Bouvard, Daniel; Costagliola, Floriane; Borgne, Maïlys Le; Buchet-Poyau, Karine; Aznar, Nicolas; Buschlen, Sylvie; Hosoya, Hiroshi; Thibert, Chantal; Billaud, Marc

2016-10-15

Head development in vertebrates proceeds through a series of elaborate patterning mechanisms and cell-cell interactions involving cephalic neural crest cells (CNCC). These cells undergo extensive migration along stereotypical paths after their separation from the dorsal margins of the neural tube and they give rise to most of the craniofacial skeleton. Here, we report that the silencing of the LKB1 tumor suppressor affects the delamination of pre-migratory CNCC from the neural primordium as well as their polarization and survival, thus resulting in severe facial and brain defects. We further show that LKB1-mediated effects on the development of CNCC involve the sequential activation of the AMP-activated protein kinase (AMPK), the Rho-dependent kinase (ROCK) and the actin-based motor protein myosin II. Collectively, these results establish that the complex morphogenetic processes governing head formation critically depends on the activation of the LKB1 signaling network in CNCC. Copyright © 2016 Elsevier Inc. All rights reserved.

Modulation of hippocampal neural plasticity by glucose-related signaling.

PubMed

Mainardi, Marco; Fusco, Salvatore; Grassi, Claudio

2015-01-01

Hormones and peptides involved in glucose homeostasis are emerging as important modulators of neural plasticity. In this regard, increasing evidence shows that molecules such as insulin, insulin-like growth factor-I, glucagon-like peptide-1, and ghrelin impact on the function of the hippocampus, which is a key area for learning and memory. Indeed, all these factors affect fundamental hippocampal properties including synaptic plasticity (i.e., synapse potentiation and depression), structural plasticity (i.e., dynamics of dendritic spines), and adult neurogenesis, thus leading to modifications in cognitive performance. Here, we review the main mechanisms underlying the effects of glucose metabolism on hippocampal physiology. In particular, we discuss the role of these signals in the modulation of cognitive functions and their potential implications in dysmetabolism-related cognitive decline.

Sub-meninges implantation reduces immune response to neural implants.

PubMed

Markwardt, Neil T; Stokol, Jodi; Rennaker, Robert L

2013-04-15

Glial scar formation around neural interfaces inhibits their ability to acquire usable signals from the surrounding neurons. To improve neural recording performance, the inflammatory response and glial scarring must be minimized. Previous work has indicated that meningeally derived cells participate in the immune response, and it is possible that the meninges may grow down around the shank of a neural implant, contributing to the formation of the glial scar. This study examines whether the glial scar can be reduced by placing a neural probe completely below the meninges. Rats were implanted with sets of loose microwire implants placed either completely below the meninges or implanted conventionally with the upper end penetrating the meninges, but not attached to the skull. Histological analysis was performed 4 weeks following surgical implantation to evaluate the glial scar. Our results found that sub-meninges implants showed an average reduction in reactive astrocyte activity of 63% compared to trans-meninges implants. Microglial activity was also reduced for sub-meninges implants. These results suggest that techniques that isolate implants from the meninges offer the potential to reduce the encapsulation response which should improve chronic recording quality and stability. Published by Elsevier B.V.

Sub-meninges Implantation Reduces Immune Response to Neural Implants

PubMed Central

Markwardt, Neil T.; Stokol, Jodi; Rennaker, Robert L.

2013-01-01

Glial scar formation around neural interfaces inhibits their ability to acquire usable signals from the surrounding neurons. To improve neural recording performance, the inflammatory response and glial scarring must be minimized. Previous work has indicated that meningeally derived cells participate in the immune response, and it is possible that the meninges may grow down around the shank of a neural implant, contributing to the formation of the glial scar. This study examines whether the glial scar can be reduced by placing a neural probe completely below the meninges. Rats were implanted with sets of loose microwire implants placed either completely below the meninges or implanted conventionally with the upper end penetrating the meninges, but not attached to the skull. Histological analysis was performed 4 weeks following surgical implantation to evaluate the glial scar. Our results found that sub-meninges implants showed an average reduction in reactive astrocyte activity of 63% compared to trans-meninges implants. Microglial activity was also reduced for sub-meninges implants. These results suggest that techniques that isolate implants from the meninges offer the potential to reduce the encapsulation response which should improve chronic recording quality and stability. PMID:23370311

Socially acquired predator avoidance: is it just classical conditioning?

PubMed

Griffin, Andrea S

2008-06-15

Associative learning theories presume the existence of a general purpose learning process, the structure of which does not mirror the demands of any particular learning problem. In contrast, learning scientists working within an Evolutionary Biology tradition believe that learning processes have been shaped by ecological demands. One potential means of exploring how ecology may have modified properties of acquisition is to use associative learning theory as a framework within which to analyse a particular learning phenomenon. Recent work has used this approach to examine whether socially transmitted predator avoidance can be conceptualised as a classical conditioning process in which a novel predator stimulus acts as a conditioned stimulus (CS) and acquires control over an avoidance response after it has become associated with alarm signals of social companions, the unconditioned stimulus (US). I review here a series of studies examining the effect of CS/US presentation timing on the likelihood of acquisition. Results suggest that socially acquired predator avoidance may be less sensitive to forward relationships than traditional classical conditioning paradigms. I make the case that socially acquired predator avoidance is an exciting novel one-trial learning paradigm that could be studied along side fear conditioning. Comparisons between social and non-social learning of danger at both the behavioural and neural level may yield a better understanding of how ecology might shape properties and mechanisms of learning.

SOX1 links the function of neural patterning and Notch signalling in the ventral spinal cord during the neuron-glial fate switch

DOE Office of Scientific and Technical Information (OSTI.GOV)

Genethliou, Nicholas; Panayiotou, Elena; Department of Biological Sciences, University of Cyprus, P.O. Box 20537, 1678 Nicosia

2009-12-25

During neural development the transition from neurogenesis to gliogenesis, known as the neuron-glial ({Nu}/G) fate switch, requires the coordinated function of patterning factors, pro-glial factors and Notch signalling. How this process is coordinated in the embryonic spinal cord is poorly understood. Here, we demonstrate that during the N/G fate switch in the ventral spinal cord (vSC) SOX1 links the function of neural patterning and Notch signalling. We show that, SOX1 expression in the vSC is regulated by PAX6, NKX2.2 and Notch signalling in a domain-specific manner. We further show that SOX1 regulates the expression of Hes1 and that loss ofmore » Sox1 leads to enhanced production of oligodendrocyte precursors from the pMN. Finally, we show that Notch signalling functions upstream of SOX1 during this fate switch and is independently required for the acquisition of the glial fate perse by regulating Nuclear Factor I A expression in a PAX6/SOX1/HES1/HES5-independent manner. These data integrate functional roles of neural patterning factors, Notch signalling and SOX1 during gliogenesis.« less

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Heparan sulfate deficiency leads to Peters anomaly in mice by disturbing neural crest TGF-β2 signaling

PubMed Central

Iwao, Keiichiro; Inatani, Masaru; Matsumoto, Yoshihiro; Ogata-Iwao, Minako; Takihara, Yuji; Irie, Fumitoshi; Yamaguchi, Yu; Okinami, Satoshi; Tanihara, Hidenobu

2009-01-01

During human embryogenesis, neural crest cells migrate to the anterior chamber of the eye and then differentiate into the inner layers of the cornea, the iridocorneal angle, and the anterior portion of the iris. When proper development does not occur, this causes iridocorneal angle dysgenesis and intraocular pressure (IOP) elevation, which ultimately results in developmental glaucoma. Here, we show that heparan sulfate (HS) deficiency in mouse neural crest cells causes anterior chamber dysgenesis, including corneal endothelium defects, corneal stroma hypoplasia, and iridocorneal angle dysgenesis. These dysfunctions are phenotypes of the human developmental glaucoma, Peters anomaly. In the neural crest cells of mice embryos, disruption of the gene encoding exostosin 1 (Ext1), which is an indispensable enzyme for HS synthesis, resulted in disturbed TGF-β2 signaling. This led to reduced phosphorylation of Smad2 and downregulated expression of forkhead box C1 (Foxc1) and paired-like homeodomain transcription factor 2 (Pitx2), transcription factors that have been identified as the causative genes for developmental glaucoma. Furthermore, impaired interactions between HS and TGF-β2 induced developmental glaucoma, which was manifested as an IOP elevation caused by iridocorneal angle dysgenesis. These findings suggest that HS is necessary for neural crest cells to form the anterior chamber via TGF-β2 signaling. Disturbances of HS synthesis might therefore contribute to the pathology of developmental glaucoma. PMID:19509472

Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks.

PubMed

Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh

2017-05-01

In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music. Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively. The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

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

PubMed

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.

Complex relationship between BOLD-fMRI and electrophysiological signals in different olfactory bulb layers.

PubMed

Li, Bo; Gong, Ling; Wu, Ruiqi; Li, Anan; Xu, Fuqiang

2014-07-15

Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI), one of the most powerful technologies in neuroscience, measures neural activity indirectly. Therefore, systematic correlation of BOLD signals with other neural activity measurements is critical to understanding and then using the technology. Numerous studies have revealed that the BOLD signal is determined by many factors and is better correlated with local field potentials (LFP) than single/multiple unit firing. The relationship between BOLD and LFP signals under higher spatial resolution is complex and remains unclear. Here, changes of BOLD and LFP signals in the glomerular (GL), mitral cell (MCL), and granular cell layers (GCL) of the olfactory bulb were evoked by odor stimulation and sequentially acquired using high-resolution fMRI and electrode array. The experimental results revealed a rather complex relationship between BOLD and LFP signals. Both signal modalities were increased layer-dependently by odor stimulation, but the orders of signal intensity were significantly different: GL>MCL>GCL and GCL>GL>MCL for BOLD and LFP, respectively. During odor stimulation, the temporal features of LFPs were similar for a given band in different layers, but different for different frequency bands in a given layer. The BOLD and LFP signals in the low gamma frequency band correlated the best. This study provides new evidence for the consistency between structure and function in understanding the neurophysiological basis of BOLD signals, but also reminds that caution must be taken in interpreting of BOLD signals in regard to neural activity. Copyright © 2014 Elsevier Inc. All rights reserved.

Estimation of Locomotion States of a Rat by Neural Signals from the Motor Cortices Based on a Linear Correlation Model

NASA Astrophysics Data System (ADS)

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

We are developing a brain-machine interface (BMI) called “RatCar," a small vehicle controlled by the neural signals of a rat's brain. An unconfined adult rat with a set of bundled neural electrodes in the brain rides on the vehicle. Each bundle consists of four tungsten wires isolated with parylene polymer. These bundles were implanted in the primary motor and premotor cortices in both hemispheres of the brain. In this paper, methods and results for estimating locomotion speed and directional changes are described. Neural signals were recorded as the rat moved in a straight line and as it changed direction in a curve. Spike-like waveforms were then detected and classified into several clusters to calculate a firing rate for each neuron. The actual locomotion velocity and directional changes of the rat were recorded concurrently. Finally, the locomotion states were correlated with the neural firing rates using a simple linear model. As a result, the abstract estimation of the locomotion velocity and directional changes were achieved.

Neural timing signal for precise tactile timing judgments

PubMed Central

Watanabe, Junji; Nishida, Shin'ya

2016-01-01

The brain can precisely encode the temporal relationship between tactile inputs. While behavioural studies have demonstrated precise interfinger temporal judgments, the underlying neural mechanism remains unknown. Computationally, two kinds of neural responses can act as the information source. One is the phase-locked response to the phase of relatively slow inputs, and the other is the response to the amplitude change of relatively fast inputs. To isolate the contributions of these components, we measured performance of a synchrony judgment task for sine wave and amplitude-modulation (AM) wave stimuli. The sine wave stimulus was a low-frequency sinusoid, with the phase shifted in the asynchronous stimulus. The AM wave stimulus was a low-frequency sinusoidal AM of a 250-Hz carrier, with only the envelope shifted in the asynchronous stimulus. In the experiment, three stimulus pairs, two synchronous ones and one asynchronous one, were sequentially presented to neighboring fingers, and participants were asked to report which one was the asynchronous pair. We found that the asynchrony of AM waves could be detected as precisely as single impulse pair, with the threshold asynchrony being ∼20 ms. On the other hand, the asynchrony of sine waves could not be detected at all in the range from 5 to 30 Hz. Our results suggest that the timing signal for tactile judgments is provided not by the stimulus phase information but by the envelope of the response of the high-frequency-sensitive Pacini channel (PC), although they do not exclude a possible contribution of the envelope of non-PCs. PMID:26843600

A CMOS power-efficient low-noise current-mode front-end amplifier for neural signal recording.

PubMed

Wu, Chung-Yu; Chen, Wei-Ming; Kuo, Liang-Ting

2013-04-01

In this paper, a new current-mode front-end amplifier (CMFEA) for neural signal recording systems is proposed. In the proposed CMFEA, a current-mode preamplifier with an active feedback loop operated at very low frequency is designed as the first gain stage to bypass any dc offset current generated by the electrode-tissue interface and to achieve a low high-pass cutoff frequency below 0.5 Hz. No reset signal or ultra-large pseudo resistor is required. The current-mode preamplifier has low dc operation current to enhance low-noise performance and decrease power consumption. A programmable current gain stage is adopted to provide adjustable gain for adaptive signal scaling. A following current-mode filter is designed to adjust the low-pass cutoff frequency for different neural signals. The proposed CMFEA is designed and fabricated in 0.18-μm CMOS technology and the area of the core circuit is 0.076 mm(2). The measured high-pass cutoff frequency is as low as 0.3 Hz and the low-pass cutoff frequency is adjustable from 1 kHz to 10 kHz. The measured maximum current gain is 55.9 dB. The measured input-referred current noise density is 153 fA /√Hz , and the power consumption is 13 μW at 1-V power supply. The fabricated CMFEA has been successfully applied to the animal test for recording the seizure ECoG of Long-Evan rats.

Uncoupling High Light Responses from Singlet Oxygen Retrograde Signaling and Spatial-Temporal Systemic Acquired Acclimation.

PubMed

Carmody, Melanie; Crisp, Peter A; d'Alessandro, Stefano; Ganguly, Diep; Gordon, Matthew; Havaux, Michel; Albrecht-Borth, Verónica; Pogson, Barry J

2016-07-01

Distinct ROS signaling pathways initiated by singlet oxygen ((1)O2) or superoxide and hydrogen peroxide have been attributed to either cell death or acclimation, respectively. Recent studies have revealed that more complex antagonistic and synergistic relationships exist within and between these pathways. As specific chloroplastic ROS signals are difficult to study, rapid systemic signaling experiments using localized high light (HL) stress or ROS treatments were used in this study to uncouple signals required for direct HL and ROS perception and distal systemic acquired acclimation (SAA). A qPCR approach was chosen to determine local perception and distal signal reception. Analysis of a thylakoidal ascorbate peroxidase mutant (tapx), the (1)O2-retrograde signaling double mutant (ex1/ex2), and an apoplastic signaling double mutant (rbohD/F) revealed that tAPX and EXECUTER 1 are required for both HL and systemic acclimation stress perception. Apoplastic membrane-localized RBOHs were required for systemic spread of the signal but not for local signal induction in directly stressed tissues. Endogenous ROS treatments revealed a very strong systemic response induced by a localized 1 h induction of (1)O2 using the conditional flu mutant. A qPCR time course of (1)O2 induced systemic marker genes in directly and indirectly connected leaves revealed a direct vascular connection component of both immediate and longer term SAA signaling responses. These results reveal the importance of an EXECUTER-dependent (1)O2 retrograde signal for both local and long distance RBOH-dependent acclimation signaling that is distinct from other HL signaling pathways, and that direct vascular connections have a role in spatial-temporal SAA induction. © 2016 American Society of Plant Biologists. All Rights Reserved.

Low-dose x-ray tomography through a deep convolutional neural network

DOE PAGES

Yang, Xiaogang; De Andrade, Vincent; Scullin, William; ...

2018-02-07

Synchrotron-based X-ray tomography offers the potential of rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short exposure time projections enhanced with CNN show similar signal to noise ratios as compared with long exposure time projections and muchmore » lower noise and more structural information than low-dose fats acquisition without CNN. We optimized this approach using simulated samples and further validated on experimental nano-computed tomography data of radiation sensitive mouse brains acquired with a transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in datasets collected with low dose-CNN. As a result, this method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.« less

Low-dose x-ray tomography through a deep convolutional neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiaogang; De Andrade, Vincent; Scullin, William

Synchrotron-based X-ray tomography offers the potential of rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short exposure time projections enhanced with CNN show similar signal to noise ratios as compared with long exposure time projections and muchmore » lower noise and more structural information than low-dose fats acquisition without CNN. We optimized this approach using simulated samples and further validated on experimental nano-computed tomography data of radiation sensitive mouse brains acquired with a transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in datasets collected with low dose-CNN. As a result, this method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.« less

Transmission of wireless neural signals through a 0.18 µm CMOS low-power amplifier.

PubMed

Gazziro, M; Braga, C F R; Moreira, D A; Carvalho, A C P L F; Rodrigues, J F; Navarro, J S; Ardila, J C M; Mioni, D P; Pessatti, M; Fabbro, P; Freewin, C; Saddow, S E

2015-01-01

In the field of Brain Machine Interfaces (BMI) researchers still are not able to produce clinically viable solutions that meet the requirements of long-term operation without the use of wires or batteries. Another problem is neural compatibility with the electrode probes. One of the possible ways of approaching these problems is the use of semiconductor biocompatible materials (silicon carbide) combined with an integrated circuit designed to operate with low power consumption. This paper describes a low-power neural signal amplifier chip, named Cortex, fabricated using 0.18 μm CMOS process technology with all electronics integrated in an area of 0.40 mm(2). The chip has 4 channels, total power consumption of only 144 μW, and is impedance matched to silicon carbide biocompatible electrodes.

Signal processing in local neuronal circuits based on activity-dependent noise and competition

NASA Astrophysics Data System (ADS)

Volman, Vladislav; Levine, Herbert

2009-09-01

We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For nonperiodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity) and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease in input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit.

A Fully Integrated Wireless Compressed Sensing Neural Signal Acquisition System for Chronic Recording and Brain Machine Interface.

PubMed

Liu, Xilin; Zhang, Milin; Xiong, Tao; Richardson, Andrew G; Lucas, Timothy H; Chin, Peter S; Etienne-Cummings, Ralph; Tran, Trac D; Van der Spiegel, Jan

2016-07-18

Reliable, multi-channel neural recording is critical to the neuroscience research and clinical treatment. However, most hardware development of fully integrated, multi-channel wireless neural recorders to-date, is still in the proof-of-concept stage. To be ready for practical use, the trade-offs between performance, power consumption, device size, robustness, and compatibility need to be carefully taken into account. This paper presents an optimized wireless compressed sensing neural signal recording system. The system takes advantages of both custom integrated circuits and universal compatible wireless solutions. The proposed system includes an implantable wireless system-on-chip (SoC) and an external wireless relay. The SoC integrates 16-channel low-noise neural amplifiers, programmable filters and gain stages, a SAR ADC, a real-time compressed sensing module, and a near field wireless power and data transmission link. The external relay integrates a 32 bit low-power microcontroller with Bluetooth 4.0 wireless module, a programming interface, and an inductive charging unit. The SoC achieves high signal recording quality with minimized power consumption, while reducing the risk of infection from through-skin connectors. The external relay maximizes the compatibility and programmability. The proposed compressed sensing module is highly configurable, featuring a SNDR of 9.78 dB with a compression ratio of 8×. The SoC has been fabricated in a 180 nm standard CMOS technology, occupying 2.1 mm × 0.6 mm silicon area. A pre-implantable system has been assembled to demonstrate the proposed paradigm. The developed system has been successfully used for long-term wireless neural recording in freely behaving rhesus monkey.

Controlled levels of canonical Wnt signaling are required for neural crest migration.

PubMed

Maj, Ewa; Künneke, Lutz; Loresch, Elisabeth; Grund, Anita; Melchert, Juliane; Pieler, Tomas; Aspelmeier, Timo; Borchers, Annette

2016-09-01

Canonical Wnt signaling plays a dominant role in the development of the neural crest (NC), a highly migratory cell population that generates a vast array of cell types. Canonical Wnt signaling is required for NC induction as well as differentiation, however its role in NC migration remains largely unknown. Analyzing nuclear localization of β-catenin as readout for canonical Wnt activity, we detect nuclear β-catenin in premigratory but not migratory Xenopus NC cells suggesting that canonical Wnt activity has to decrease to basal levels to enable NC migration. To define a possible function of canonical Wnt signaling in Xenopus NC migration, canonical Wnt signaling was modulated at different time points after NC induction. This was accomplished using either chemical modulators affecting β-catenin stability or inducible glucocorticoid fusion constructs of Lef/Tcf transcription factors. In vivo analysis of NC migration by whole mount in situ hybridization demonstrates that ectopic activation of canonical Wnt signaling inhibits cranial NC migration. Further, NC transplantation experiments confirm that this effect is tissue-autonomous. In addition, live-cell imaging in combination with biophysical data analysis of explanted NC cells confirms the in vivo findings and demonstrates that modulation of canonical Wnt signaling affects the ability of NC cells to perform single cell migration. Thus, our data support the hypothesis that canonical Wnt signaling needs to be tightly controlled to enable migration of NC cells. Copyright © 2016 Elsevier Inc. All rights reserved.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes

PubMed Central

Plis, Sergey M.; Calhoun, Vince D.; Weisend, Michael P.; Eichele, Tom; Lane, Terran

2010-01-01

The combined analysis of magnetoencephalography (MEG)/electroencephalography and functional magnetic resonance imaging (fMRI) measurements can lead to improvement in the description of the dynamical and spatial properties of brain activity. In this paper we empirically demonstrate this improvement using simulated and recorded task related MEG and fMRI activity. Neural activity estimates were derived using a dynamic Bayesian network with continuous real valued parameters by means of a sequential Monte Carlo technique. In synthetic data, we show that MEG and fMRI fusion improves estimation of the indirectly observed neural activity and smooths tracking of the blood oxygenation level dependent (BOLD) response. In recordings of task related neural activity the combination of MEG and fMRI produces a result with greater signal-to-noise ratio, that confirms the expectation arising from the nature of the experiment. The highly non-linear model of the BOLD response poses a difficult inference problem for neural activity estimation; computational requirements are also high due to the time and space complexity. We show that joint analysis of the data improves the system's behavior by stabilizing the differential equations system and by requiring fewer computational resources. PMID:21120141

Cognitive Neural Prosthetics

PubMed Central

Andersen, Richard A.; Hwang, Eun Jung; Mulliken, Grant H.

2010-01-01

The cognitive neural prosthetic (CNP) is a very versatile method for assisting paralyzed patients and patients with amputations. The CNP records the cognitive state of the subject, rather than signals strictly related to motor execution or sensation. We review a number of high-level cortical signals and their application for CNPs, including intention, motor imagery, decision making, forward estimation, executive function, attention, learning, and multi-effector movement planning. CNPs are defined by the cognitive function they extract, not the cortical region from which the signals are recorded. However, some cortical areas may be better than others for particular applications. Signals can also be extracted in parallel from multiple cortical areas using multiple implants, which in many circumstances can increase the range of applications of CNPs. The CNP approach relies on scientific understanding of the neural processes involved in cognition, and many of the decoding algorithms it uses also have parallels to underlying neural circuit functions. PMID:19575625

The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

PubMed

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.

Radar signal categorization using a neural network

NASA Technical Reports Server (NTRS)

Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

1991-01-01

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Neural crest contribution to the cardiovascular system.

PubMed

Brown, Christopher B; Baldwin, H Scott

2006-01-01

Normal cardiovascular development requires complex remodeling of the outflow tract and pharyngeal arch arteries to create the separate pulmonic and systemic circulations. During remodeling, the outflow tract is septated to form the ascending aorta and the pulmonary trunk. The initially symmetrical pharyngeal arch arteries are remodeled to form the aortic arch, subclavian and carotid arteries. Remodeling is mediated by a population of neural crest cells arising between the mid-otic placode and somite four called the cardiac neural crest. Cardiac neural crest cells form smooth muscle and pericytes in the great arteries, and the neurons of cardiac innervation. In addition to the physical contribution of smooth muscle to the cardiovascular system, cardiac neural crest cells also provide signals required for the maintenance and differentiation of the other cell layers in the pharyngeal apparatus. Reciprocal signaling between the cardiac neural crest cells and cardiogenic mesoderm of the secondary heart field is required for elaboration of the conotruncus and disruption in this signaling results in primary myocardial dysfunction. Cardiovascular defects attributed to the cardiac neural crest cells may reflect either cell autonomous defects in the neural crest or defects in signaling between the neural crest and adjacent cell layers.

Biologically-based signal processing system applied to noise removal for signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren I.

2004-07-13

The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex.

PubMed

Vanni, Simo; Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

2015-08-01

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. Copyright © 2015 the American Physiological Society.

Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

PubMed Central

Sharifian, Fariba; Heikkinen, Hanna; Vigário, Ricardo

2015-01-01

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals. PMID:25972586

Lineage-specific effects of Notch/Numb signaling in post-embryonic development of the Drosophila brain.

PubMed

Lin, Suewei; Lai, Sen-Lin; Yu, Huang-Hsiang; Chihara, Takahiro; Luo, Liqun; Lee, Tzumin

2010-01-01

Numb can antagonize Notch signaling to diversify the fates of sister cells. We report here that paired sister cells acquire different fates in all three Drosophila neuronal lineages that make diverse types of antennal lobe projection neurons (PNs). Only one in each pair of postmitotic neurons survives into the adult stage in both anterodorsal (ad) and ventral (v) PN lineages. Notably, Notch signaling specifies the PN fate in the vPN lineage but promotes programmed cell death in the missing siblings in the adPN lineage. In addition, Notch/Numb-mediated binary sibling fates underlie the production of PNs and local interneurons from common precursors in the lAL lineage. Furthermore, Numb is needed in the lateral but not adPN or vPN lineages to prevent the appearance of ectopic neuroblasts and to ensure proper self-renewal of neural progenitors. These lineage-specific outputs of Notch/Numb signaling show that a universal mechanism of binary fate decision can be utilized to govern diverse neural sibling differentiations.

A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

PubMed Central

Sriraam, N.

2012-01-01

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications. PMID:22489238

A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors.

PubMed

Sriraam, N

2012-01-01

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

ITO/gold nanoparticle/RGD peptide composites to enhance electrochemical signals and proliferation of human neural stem cells.

PubMed

Kim, Tae-Hyung; El-Said, Waleed Ahmed; An, Jeung Hee; Choi, Jeong-Woo

2013-04-01

A cell chip composed of ITO, gold nanoparticles (GNP) and RGD-MAP-C peptide composites was fabricated to enhance the electrochemical signals and proliferation of undifferentiated human neural stem cells (HB1.F3). The structural characteristics of the fabricated surfaces were confirmed by both scanning electron microscopy and surface-enhanced Raman spectroscopy. HB1.F3 cells were allowed to attach to various composites electrodes in the cell chip and the material-dependent effects on electrochemical signals and cell proliferation were analyzed. The ITO/60 nm GNP/RGD-MAP-C composite electrode was found to be the best material in regards to enhancing the voltammetric signals of HB1.F3 cells when exposed to cyclic voltammetry, as well as for increasing cell proliferation. Differential pulse voltammetry was performed to evaluate the adverse effects of doxorubicin on HB1.F3 cells. In these experiments, negative correlations between cell viability and chemical concentrations were obseved, which were more sensitive than MTT viability assay especially at low concentrations (<0.1 μg/mL). In this basic science study, a cell chip composed of ITO, gold nanoparticles and RGD-MAP-C peptide composites was fabricated to enhance electrochemical signals and proliferation of undifferentiated human neural stem cells (HB1.F3). The ITO/60 nm GNP/RGD-MAP-C composite electrode was found to best enhance the voltammetric signals of the studied cells. Copyright © 2013 Elsevier Inc. All rights reserved.

Effects of Nerve Injury and Segmental Regeneration on the Cellular Correlates of Neural Morphallaxis

PubMed Central

Martinez, Veronica G.; Manson, Josiah M.B.; Zoran, Mark J.

2009-01-01

Functional recovery of neural networks after injury requires a series of signaling events similar to the embryonic processes that governed initial network construction. Neural morphallaxis, a form of nervous system regeneration, involves reorganization of adult neural connectivity patterns. Neural morphallaxis in the worm, Lumbriculus variegatus, occurs during asexual reproduction and segmental regeneration, as body fragments acquire new positional identities along the anterior–posterior axis. Ectopic head (EH) formation, induced by ventral nerve cord lesion, generated morphallactic plasticity including the reorganization of interneuronal sensory fields and the induction of a molecular marker of neural morphallaxis. Morphallactic changes occurred only in segments posterior to an EH. Neither EH formation, nor neural morphallaxis was observed after dorsal body lesions, indicating a role for nerve cord injury in morphallaxis induction. Furthermore, a hierarchical system of neurobehavioral control was observed, where anterior heads were dominant and an EH controlled body movements only in the absence of the anterior head. Both suppression of segmental regeneration and blockade of asexual fission, after treatment with boric acid, disrupted the maintenance of neural morphallaxis, but did not block its induction. Therefore, segmental regeneration (i.e., epimorphosis) may not be required for the induction of morphallactic remodeling of neural networks. However, on-going epimorphosis appears necessary for the long-term consolidation of cellular and molecular mechanisms underlying the morphallaxis of neural circuitry. PMID:18561185

Spatio-temporal Model of Endogenous ROS and Raft-Dependent WNT/Beta-Catenin Signaling Driving Cell Fate Commitment in Human Neural Progenitor Cells

PubMed Central

Haack, Fiete; Lemcke, Heiko; Ewald, Roland; Rharass, Tareck; Uhrmacher, Adelinde M.

2015-01-01

Canonical WNT/β-catenin signaling is a central pathway in embryonic development, but it is also connected to a number of cancers and developmental disorders. Here we apply a combined in-vitro and in-silico approach to investigate the spatio-temporal regulation of WNT/β-catenin signaling during the early neural differentiation process of human neural progenitors cells (hNPCs), which form a new prospect for replacement therapies in the context of neurodegenerative diseases. Experimental measurements indicate a second signal mechanism, in addition to canonical WNT signaling, being involved in the regulation of nuclear β-catenin levels during the cell fate commitment phase of neural differentiation. We find that the biphasic activation of β-catenin signaling observed experimentally can only be explained through a model that combines Reactive Oxygen Species (ROS) and raft dependent WNT/β-catenin signaling. Accordingly after initiation of differentiation endogenous ROS activates DVL in a redox-dependent manner leading to a transient activation of down-stream β-catenin signaling, followed by continuous auto/paracrine WNT signaling, which crucially depends on lipid rafts. Our simulation studies further illustrate the elaborate spatio-temporal regulation of DVL, which, depending on its concentration and localization, may either act as direct inducer of the transient ROS/β-catenin signal or as amplifier during continuous auto-/parcrine WNT/β-catenin signaling. In addition we provide the first stochastic computational model of WNT/β-catenin signaling that combines membrane-related and intracellular processes, including lipid rafts/receptor dynamics as well as WNT- and ROS-dependent β-catenin activation. The model’s predictive ability is demonstrated under a wide range of varying conditions for in-vitro and in-silico reference data sets. Our in-silico approach is realized in a multi-level rule-based language, that facilitates the extension and modification of the

Neural network based system for equipment surveillance

DOEpatents

Vilim, Richard B.; Gross, Kenneth C.; Wegerich, Stephan W.

1998-01-01

A method and system for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process.

Real-time GMAW quality classification using an artificial neural network with airborne acoustic signals as inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matteson, A.; Morris, R.; Tate, R.

1993-12-31

The acoustic signal produced by the gas metal arc welding (GMAW) arc contains information about the behavior of the arc column, the molten pool and droplet transfer. It is possible to detect some defect producing conditions from the acoustic signal from the GMAW arc. An intelligent sensor, called the Weld Acoustic Monitor (WAM) has been developed to take advantage of this acoustic information in order to provide real-time quality assessment information for process control. The WAM makes use of an Artificial Neural Network (ANN) to classify the characteristic arc acoustic signals of acceptable and unacceptable welds. The ANN used inmore » the Weld Acoustic Monitor developed its own set of rules for this classification problem by learning a data base of known GMAW acoustic signals.« less

Signals that regulate the oncogenic fate of neural stem cells and progenitors

PubMed Central

Swartling, Fredrik J.; Bolin, Sara; Phillips, Joanna J.; Persson, Anders I.

2013-01-01

Brain tumors have frequently been associated with a neural stem cell (NSC) origin and contain stem-like tumor cells, so-called brain tumor stem cells (BTSCs) that share many features with normal NSCs. A stem cell state of BTSCs confers resistance to radiotherapy and treatment with alkylating agents. It is also a hallmark of aggressive brain tumors and is maintained by transcriptional networks that are also active in embryonic stem cells. Advances in reprogramming of somatic cells into induced pluripotent stem (iPS) cells have further identified genes that drive stemness. In this review, we will highlight the possible drivers of stemness in medulloblastoma and glioma, the most frequent types of primary malignant brain cancer in children and adults, respectively. Signals that drive expansion of developmentally defined neural precursor cells are also active in corresponding brain tumors. Transcriptomal subgroups of human medulloblastoma and glioma match features of NSCs but also more restricted progenitors. Lessons from genetically-engineered mouse (GEM) models show that temporally and regionally defined NSCs can give rise to distinct subgroups of medulloblastoma and glioma. We will further discuss how acquisition of stem cell features may drive brain tumorigenesis from a non-NSC origin. Genetic alterations, signaling pathways, and therapy-induced changes in the tumor microenvironment can drive reprogramming networks and induce stemness in brain tumors. Finally, we propose a model where dysregulation of microRNAs (miRNAs) that normally provide barriers against reprogramming plays an integral role in promoting stemness in brain tumors. PMID:23376224

Lymphotropic Virions Affect Chemokine Receptor-Mediated Neural Signaling and Apoptosis: Implications for Human Immunodeficiency Virus Type 1-Associated Dementia

PubMed Central

Zheng, Jialin; Ghorpade, Anuja; Niemann, Douglas; Cotter, Robin L.; Thylin, Michael R.; Epstein, Leon; Swartz, Jennifer M.; Shepard, Robin B.; Liu, Xiaojuan; Nukuna, Adeline; Gendelman, Howard E.

1999-01-01

Chemokine receptors pivotal for human immunodeficiency virus type 1 (HIV-1) infection in lymphocytes and macrophages (CCR3, CCR5, and CXCR4) are expressed on neural cells (microglia, astrocytes, and/or neurons). It is these cells which are damaged during progressive HIV-1 infection of the central nervous system. We theorize that viral coreceptors could effect neural cell damage during HIV-1-associated dementia (HAD) without simultaneously affecting viral replication. To these ends, we studied the ability of diverse viral strains to affect intracellular signaling and apoptosis of neurons, astrocytes, and monocyte-derived macrophages. Inhibition of cyclic AMP, activation of inositol 1,4,5-trisphosphate, and apoptosis were induced by diverse HIV-1 strains, principally in neurons. Virions from T-cell-tropic (T-tropic) strains (MN, IIIB, and Lai) produced the most significant alterations in signaling of neurons and astrocytes. The HIV-1 envelope glycoprotein, gp120, induced markedly less neural damage than purified virions. Macrophage-tropic (M-tropic) strains (ADA, JR-FL, Bal, MS-CSF, and DJV) produced the least neural damage, while 89.6, a dual-tropic HIV-1 strain, elicited intermediate neural cell damage. All T-tropic strain-mediated neuronal impairments were blocked by the CXCR4 antibody, 12G5. In contrast, the M-tropic strains were only partially blocked by 12G5. CXCR4-mediated neuronal apoptosis was confirmed in pure populations of rat cerebellar granule neurons and was blocked by HA1004, an inhibitor of calcium/calmodulin-dependent protein kinase II, protein kinase A, and protein kinase C. Taken together, these results suggest that progeny HIV-1 virions can influence neuronal signal transduction and apoptosis. This process occurs, in part, through CXCR4 and is independent of CD4 binding. T-tropic viruses that traffic in and out of the brain during progressive HIV-1 disease may play an important role in HAD neuropathogenesis. PMID:10482576

Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

2015-04-01

The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

PubMed

Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

2017-07-12

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

Neural Signaling of Food Healthiness Associated with Emotion Processing.

PubMed

Herwig, Uwe; Dhum, Matthias; Hittmeyer, Anna; Opialla, Sarah; Scherpiet, Sigrid; Keller, Carmen; Brühl, Annette B; Siegrist, Michael

2016-01-01

The ability to differentiate healthy from unhealthy foods is important in order to promote good health. Food, however, may have an emotional connotation, which could be inversely related to healthiness. The neurobiological background of differentiating healthy and unhealthy food and its relations to emotion processing are not yet well understood. We addressed the neural activations, particularly considering the single subject level, when one evaluates a food item to be of a higher, compared to a lower grade of healthiness with a particular view on emotion processing brain regions. Thirty-seven healthy subjects underwent functional magnetic resonance imaging while evaluating the healthiness of food presented as photographs with a subsequent rating on a visual analog scale. We compared individual evaluations of high and low healthiness of food items and also considered gender differences. We found increased activation when food was evaluated to be healthy in the left dorsolateral prefrontal cortex and precuneus in whole brain analyses. In ROI analyses, perceived and rated higher healthiness was associated with lower amygdala activity and higher ventral striatal and orbitofrontal cortex activity. Females exerted a higher activation in midbrain areas when rating food items as being healthy. Our results underline the close relationship between food and emotion processing, which makes sense considering evolutionary aspects. Actively evaluating and deciding whether food is healthy is accompanied by neural signaling associated with reward and self-relevance, which could promote salutary nutrition behavior. The involved brain regions may be amenable to mechanisms of emotion regulation in the context of psychotherapeutic regulation of food intake.

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

PubMed Central

Presacco, Alessandro; Goodman, Ronald; Forrester, Larry

2011-01-01

Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function. PMID:21768121

EDITORIAL: Focus on the neural interface Focus on the neural interface

NASA Astrophysics Data System (ADS)

Durand, Dominique M.

2009-10-01

The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

Neural responses in songbird forebrain reflect learning rates, acquired salience, and stimulus novelty after auditory discrimination training.

PubMed

Bell, Brittany A; Phan, Mimi L; Vicario, David S

2015-03-01

How do social interactions form and modulate the neural representations of specific complex signals? This question can be addressed in the songbird auditory system. Like humans, songbirds learn to vocalize by imitating tutors heard during development. These learned vocalizations are important in reproductive and social interactions and in individual recognition. As a model for the social reinforcement of particular songs, male zebra finches were trained to peck for a food reward in response to one song stimulus (GO) and to withhold responding for another (NoGO). After performance reached criterion, single and multiunit neural responses to both trained and novel stimuli were obtained from multiple electrodes inserted bilaterally into two songbird auditory processing areas [caudomedial mesopallium (CMM) and caudomedial nidopallium (NCM)] of awake, restrained birds. Neurons in these areas undergo stimulus-specific adaptation to repeated song stimuli, and responses to familiar stimuli adapt more slowly than to novel stimuli. The results show that auditory responses differed in NCM and CMM for trained (GO and NoGO) stimuli vs. novel song stimuli. When subjects were grouped by the number of training days required to reach criterion, fast learners showed larger neural responses and faster stimulus-specific adaptation to all stimuli than slow learners in both areas. Furthermore, responses in NCM of fast learners were more strongly left-lateralized than in slow learners. Thus auditory responses in these sensory areas not only encode stimulus familiarity, but also reflect behavioral reinforcement in our paradigm, and can potentially be modulated by social interactions. Copyright © 2015 the American Physiological Society.

Endocrine Pancreas Development and Regeneration: Noncanonical Ideas From Neural Stem Cell Biology.

PubMed

Masjkur, Jimmy; Poser, Steven W; Nikolakopoulou, Polyxeni; Chrousos, George; McKay, Ronald D; Bornstein, Stefan R; Jones, Peter M; Androutsellis-Theotokis, Andreas

2016-02-01

Loss of insulin-producing pancreatic islet β-cells is a hallmark of type 1 diabetes. Several experimental paradigms demonstrate that these cells can, in principle, be regenerated from multiple endogenous sources using signaling pathways that are also used during pancreas development. A thorough understanding of these pathways will provide improved opportunities for therapeutic intervention. It is now appreciated that signaling pathways should not be seen as "on" or "off" but that the degree of activity may result in wildly different cellular outcomes. In addition to the degree of operation of a signaling pathway, noncanonical branches also play important roles. Thus, a pathway, once considered as "off" or "low" may actually be highly operational but may be using noncanonical branches. Such branches are only now revealing themselves as new tools to assay them are being generated. A formidable source of noncanonical signal transduction concepts is neural stem cells because these cells appear to have acquired unusual signaling interpretations to allow them to maintain their unique dual properties (self-renewal and multipotency). We discuss how such findings from the neural field can provide a blueprint for the identification of new molecular mechanisms regulating pancreatic biology, with a focus on Notch, Hes/Hey, and hedgehog pathways. © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

Dual role of Brg chromatin remodeling factor in Sonic hedgehog signaling during neural development.

PubMed

Zhan, Xiaoming; Shi, Xuanming; Zhang, Zilai; Chen, Yu; Wu, Jiang I

2011-08-02

Sonic hedgehog (Shh) signaling plays diverse roles during animal development and adult tissue homeostasis through differential regulation of Gli family transcription factors. Dysregulated Shh signaling activities have been linked to birth defects and tumorigenesis. Here we report that Brg, an ATP-dependent chromatin remodeling factor, has dual functions in regulating Shh target gene expression. Using a Brg conditional deletion in Shh-responding neural progenitors and fibroblasts, we demonstrate that Brg is required both for repression of the basal expression and for the activation of signal-induced transcription of Shh target genes. In developing telencephalons deficient for Brg, Shh target genes were derepressed, whereas Brg-deleted cerebellar granule neuron precursors failed to respond to Shh to increase their proliferation. The repressor function of Brg was mediated through Gli3 and both the repressor and activator functions of Brg appeared to be independent of its ATPase activity. Furthermore, Brg facilitates Gli coactivator histone deacetylase (HDAC) binding to the regulatory regions of Shh target genes, providing a possible mechanism for its positive role in Shh signaling. Our results thus reveal that a complex chromatin regulation mechanism underlies the precise transcription outcomes of Shh signaling and its diverse roles during development.

Neurons in the barrel cortex turn into processing whisker and odor signals: a cellular mechanism for the storage and retrieval of associative signals

PubMed Central

Wang, Dangui; Zhao, Jun; Gao, Zilong; Chen, Na; Wen, Bo; Lu, Wei; Lei, Zhuofan; Chen, Changfeng; Liu, Yahui; Feng, Jing; Wang, Jin-Hui

2015-01-01

Associative learning and memory are essential to logical thinking and cognition. How the neurons are recruited as associative memory cells to encode multiple input signals for their associated storage and distinguishable retrieval remains unclear. We studied this issue in the barrel cortex by in vivo two-photon calcium imaging, electrophysiology, and neural tracing in our mouse model that the simultaneous whisker and olfaction stimulations led to odorant-induced whisker motion. After this cross-modal reflex arose, the barrel and piriform cortices connected. More than 40% of barrel cortical neurons became to encode odor signal alongside whisker signal. Some of these neurons expressed distinct activity patterns in response to acquired odor signal and innate whisker signal, and others encoded similar pattern in response to these signals. In the meantime, certain barrel cortical astrocytes encoded odorant and whisker signals. After associative learning, the neurons and astrocytes in the sensory cortices are able to store the newly learnt signal (cross-modal memory) besides the innate signal (native-modal memory). Such associative memory cells distinguish the differences of these signals by programming different codes and signify the historical associations of these signals by similar codes in information retrievals. PMID:26347609

Neural network based system for equipment surveillance

DOEpatents

Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

1998-04-28

A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

Neural Network Prediction of Failure of Damaged Composite Pressure Vessels from Strain Field Data Acquired by a Computer Vision Method

NASA Technical Reports Server (NTRS)

Russell, Samuel S.; Lansing, Matthew D.

1997-01-01

This effort used a new and novel method of acquiring strains called Sub-pixel Digital Video Image Correlation (SDVIC) on impact damaged Kevlar/epoxy filament wound pressure vessels during a proof test. To predict the burst pressure, the hoop strain field distribution around the impact location from three vessels was used to train a neural network. The network was then tested on additional pressure vessels. Several variations on the network were tried. The best results were obtained using a single hidden layer. SDVIC is a fill-field non-contact computer vision technique which provides in-plane deformation and strain data over a load differential. This method was used to determine hoop and axial displacements, hoop and axial linear strains, the in-plane shear strains and rotations in the regions surrounding impact sites in filament wound pressure vessels (FWPV) during proof loading by internal pressurization. The relationship between these deformation measurement values and the remaining life of the pressure vessels, however, requires a complex theoretical model or numerical simulation. Both of these techniques are time consuming and complicated. Previous results using neural network methods had been successful in predicting the burst pressure for graphite/epoxy pressure vessels based upon acoustic emission (AE) measurements in similar tests. The neural network associates the character of the AE amplitude distribution, which depends upon the extent of impact damage, with the burst pressure. Similarly, higher amounts of impact damage are theorized to cause a higher amount of strain concentration in the damage effected zone at a given pressure and result in lower burst pressures. This relationship suggests that a neural network might be able to find an empirical relationship between the SDVIC strain field data and the burst pressure, analogous to the AE method, with greater speed and simplicity than theoretical or finite element modeling. The process of testing SDVIC

Uncoupling High Light Responses from Singlet Oxygen Retrograde Signaling and Spatial-Temporal Systemic Acquired Acclimation1[OPEN

PubMed Central

Gordon, Matthew; Havaux, Michel; Albrecht-Borth, Verónica

2016-01-01

Distinct ROS signaling pathways initiated by singlet oxygen (1O2) or superoxide and hydrogen peroxide have been attributed to either cell death or acclimation, respectively. Recent studies have revealed that more complex antagonistic and synergistic relationships exist within and between these pathways. As specific chloroplastic ROS signals are difficult to study, rapid systemic signaling experiments using localized high light (HL) stress or ROS treatments were used in this study to uncouple signals required for direct HL and ROS perception and distal systemic acquired acclimation (SAA). A qPCR approach was chosen to determine local perception and distal signal reception. Analysis of a thylakoidal ascorbate peroxidase mutant (tapx), the 1O2-retrograde signaling double mutant (ex1/ex2), and an apoplastic signaling double mutant (rbohD/F) revealed that tAPX and EXECUTER 1 are required for both HL and systemic acclimation stress perception. Apoplastic membrane-localized RBOHs were required for systemic spread of the signal but not for local signal induction in directly stressed tissues. Endogenous ROS treatments revealed a very strong systemic response induced by a localized 1 h induction of 1O2 using the conditional flu mutant. A qPCR time course of 1O2 induced systemic marker genes in directly and indirectly connected leaves revealed a direct vascular connection component of both immediate and longer term SAA signaling responses. These results reveal the importance of an EXECUTER-dependent 1O2 retrograde signal for both local and long distance RBOH-dependent acclimation signaling that is distinct from other HL signaling pathways, and that direct vascular connections have a role in spatial-temporal SAA induction. PMID:27288360

Cognitive Deficits Associated with Acquired Amusia after Stroke: A Neuropsychological Follow-Up Study

ERIC Educational Resources Information Center

Sarkamo, Teppo; Tervaniemi, Mari; Soinila, Seppo; Autti, Taina; Silvennoinen, Heli M.; Laine, Matti; Hietanen, Marja

2009-01-01

Recent evidence on amusia suggests that our ability to perceive music might be based on the same neural resources that underlie other higher cognitive functions, such as speech perception and spatial processing. We studied the neural correlates of acquired amusia by performing extensive neuropsychological assessments on 53 stroke patients with a…

Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

PubMed Central

Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

2009-01-01

This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms. PMID:22408487

Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.

PubMed

Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan

2009-01-01

This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.

Feature reconstruction of LFP signals based on PLSR in the neural information decoding study.

PubMed

Yonghui Dong; Zhigang Shang; Mengmeng Li; Xinyu Liu; Hong Wan

2017-07-01

To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

The neural subjective frame: from bodily signals to perceptual consciousness

PubMed Central

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-01-01

The report ‘I saw the stimulus’ operationally defines visual consciousness, but where does the ‘I’ come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness. PMID:24639580

The neural subjective frame: from bodily signals to perceptual consciousness.

PubMed

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

On the estimation of brain signal entropy from sparse neuroimaging data

PubMed Central

Grandy, Thomas H.; Garrett, Douglas D.; Schmiedek, Florian; Werkle-Bergner, Markus

2016-01-01

Multi-scale entropy (MSE) has been recently established as a promising tool for the analysis of the moment-to-moment variability of neural signals. Appealingly, MSE provides a measure of the predictability of neural operations across the multiple time scales on which the brain operates. An important limitation in the application of the MSE to some classes of neural signals is MSE’s apparent reliance on long time series. However, this sparse-data limitation in MSE computation could potentially be overcome via MSE estimation across shorter time series that are not necessarily acquired continuously (e.g., in fMRI block-designs). In the present study, using simulated, EEG, and fMRI data, we examined the dependence of the accuracy and precision of MSE estimates on the number of data points per segment and the total number of data segments. As hypothesized, MSE estimation across discontinuous segments was comparably accurate and precise, despite segment length. A key advance of our approach is that it allows the calculation of MSE scales not previously accessible from the native segment lengths. Consequently, our results may permit a far broader range of applications of MSE when gauging moment-to-moment dynamics in sparse and/or discontinuous neurophysiological data typical of many modern cognitive neuroscience study designs. PMID:27020961

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

NASA Astrophysics Data System (ADS)

Simeral, J. D.; Kim, S.-P.; Black, M. J.; Donoghue, J. P.; Hochberg, L. R.

2011-04-01

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point

Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array

PubMed Central

Simeral, J D; Kim, S-P; Black, M J; Donoghue, J P; Hochberg, L R

2013-01-01

The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking activity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were successfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical microelectrode array can provide repeatable, accurate point

Auto-programmable impulse neural circuits

NASA Technical Reports Server (NTRS)

Watula, D.; Meador, J.

1990-01-01

Impulse neural networks use pulse trains to communicate neuron activation levels. Impulse neural circuits emulate natural neurons at a more detailed level than that typically employed by contemporary neural network implementation methods. An impulse neural circuit which realizes short term memory dynamics is presented. The operation of that circuit is then characterized in terms of pulse frequency modulated signals. Both fixed and programmable synapse circuits for realizing long term memory are also described. The implementation of a simple and useful unsupervised learning law is then presented. The implementation of a differential Hebbian learning rule for a specific mean-frequency signal interpretation is shown to have a straightforward implementation using digital combinational logic with a variation of a previously developed programmable synapse circuit. This circuit is expected to be exploited for simple and straightforward implementation of future auto-adaptive neural circuits.

Lineage-specific effects of Notch/Numb signaling in post-embryonic development of the Drosophila brain

PubMed Central

Lin, Suewei; Lai, Sen-Lin; Yu, Huang-Hsiang; Chihara, Takahiro; Luo, Liqun; Lee, Tzumin

2010-01-01

Numb can antagonize Notch signaling to diversify the fates of sister cells. We report here that paired sister cells acquire different fates in all three Drosophila neuronal lineages that make diverse types of antennal lobe projection neurons (PNs). Only one in each pair of postmitotic neurons survives into the adult stage in both anterodorsal (ad) and ventral (v) PN lineages. Notably, Notch signaling specifies the PN fate in the vPN lineage but promotes programmed cell death in the missing siblings in the adPN lineage. In addition, Notch/Numb-mediated binary sibling fates underlie the production of PNs and local interneurons from common precursors in the lAL lineage. Furthermore, Numb is needed in the lateral but not adPN or vPN lineages to prevent the appearance of ectopic neuroblasts and to ensure proper self-renewal of neural progenitors. These lineage-specific outputs of Notch/Numb signaling show that a universal mechanism of binary fate decision can be utilized to govern diverse neural sibling differentiations. PMID:20023159

Processing Electromyographic Signals to Recognize Words

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Lee, D. D.

2009-01-01

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Multiple disturbances classifier for electric signals using adaptive structuring neural networks

NASA Astrophysics Data System (ADS)

Lu, Yen-Ling; Chuang, Cheng-Long; Fahn, Chin-Shyurng; Jiang, Joe-Air

2008-07-01

This work proposes a novel classifier to recognize multiple disturbances for electric signals of power systems. The proposed classifier consists of a series of pipeline-based processing components, including amplitude estimator, transient disturbance detector, transient impulsive detector, wavelet transform and a brand-new neural network for recognizing multiple disturbances in a power quality (PQ) event. Most of the previously proposed methods usually treated a PQ event as a single disturbance at a time. In practice, however, a PQ event often consists of various types of disturbances at the same time. Therefore, the performances of those methods might be limited in real power systems. This work considers the PQ event as a combination of several disturbances, including steady-state and transient disturbances, which is more analogous to the real status of a power system. Six types of commonly encountered power quality disturbances are considered for training and testing the proposed classifier. The proposed classifier has been tested on electric signals that contain single disturbance or several disturbances at a time. Experimental results indicate that the proposed PQ disturbance classification algorithm can achieve a high accuracy of more than 97% in various complex testing cases.

Swallow segmentation with artificial neural networks and multi-sensor fusion.

PubMed

Lee, Joon; Steele, Catriona M; Chau, Tom

2009-11-01

Swallow segmentation is a critical precursory step to the analysis of swallowing signal characteristics. In an effort to automatically segment swallows, we investigated artificial neural networks (ANN) with information from cervical dual-axis accelerometry, submental MMG, and nasal airflow. Our objectives were (1) to investigate the relationship between segmentation performance and the number of signal sources and (2) to identify the signals or signal combinations most useful for swallow segmentation. Signals were acquired from 17 healthy adults in both discrete and continuous swallowing tasks using five stimuli. Training and test feature vectors were constructed with variances from single or multiple signals, estimated within 200 ms moving windows with 50% overlap. Corresponding binary target labels (swallow or non-swallow) were derived by manual segmentation. A separate 3-layer ANN was trained for each participant-signal combination, and all possible signal combinations were investigated. As more signal sources were included, segmentation performance improved in terms of sensitivity, specificity, accuracy, and adjusted accuracy. The combination of all four signal sources achieved the highest mean accuracy and adjusted accuracy of 88.5% and 89.6%, respectively. A-P accelerometry proved to be the most discriminatory source, while the inclusion of MMG or nasal airflow resulted in the least performance improvement. These findings suggest that an ANN, multi-sensor fusion approach to segmentation is worthy of further investigation in swallowing studies.

Molecular stages of rapid and uniform neuralization of human embryonic stem cells.

PubMed

Bajpai, R; Coppola, G; Kaul, M; Talantova, M; Cimadamore, F; Nilbratt, M; Geschwind, D H; Lipton, S A; Terskikh, A V

2009-06-01

Insights into early human development are fundamental for our understanding of human biology. Efficient differentiation of human embryonic stem cells (hESCs) into neural precursor cells is critical for future cell-based therapies. Here, using defined conditions, we characterized a new method for rapid and uniform differentiation of hESCs into committed neural precursor cells (designated C-NPCs). Dynamic gene expression analysis identified several distinct stages of ESC neuralization and revealed functional modules of coregulated genes and pathways. The first wave of gene expression changes, likely corresponding to the transition through primitive ectoderm, started at day 3, preceding the formation of columnar neuroepithelial rosettes. The second wave started at day 5, coinciding with the formation of rosettes. The majority of C-NPCs were positive for both anterior and posterior markers of developing neuroepithelium. In culture, C-NPCs became electrophysiologically functional neurons; on transplantation into neonatal mouse brains, C-NPCs integrated into the cortex and olfactory bulb, acquiring appropriate neuronal morphologies and markers. Compared to rosette-NPCs,(1) C-NPCs exhibited limited in vitro expansion capacity and did not express potent oncogenes such as PLAG1 or RSPO3. Concordantly, we never detected tumors or excessive neural proliferation after transplantation of C-NPCs into mouse brains. In conclusion, our study provides a framework for future analysis of molecular signaling during ESC neuralization.

Neural transcription factors bias cleavage stage blastomeres to give rise to neural ectoderm

PubMed Central

Gaur, Shailly; Mandelbaum, Max; Herold, Mona; Majumdar, Himani Datta; Neilson, Karen M.; Maynard, Thomas M.; Mood, Kathy; Daar, Ira O.; Moody, Sally A.

2016-01-01

The decision by embryonic ectoderm to give rise to epidermal versus neural derivatives is the result of signaling events during blastula and gastrula stages. However, there also is evidence in Xenopus that cleavage stage blastomeres contain maternally derived molecules that bias them toward a neural fate. We used a blastomere explant culture assay to test whether maternally deposited transcription factors bias 16-cell blastomere precursors of epidermal or neural ectoderm to express early zygotic neural genes in the absence of gastrulation interactions or exogenously supplied signaling factors. We found that Foxd4l1, Zic2, Gmnn and Sox11 each induced explants made from ventral, epidermis-producing blastomeres to express early neural genes, and that at least some of the Foxd4l1 and Zic2 activity is required at cleavage stages. Similarly, providing extra Foxd4l1 or Zic2 to explants made from dorsal, neural plate-producing blastomeres significantly increased expression of early neural genes, whereas knocking down either significantly reduced them. These results show that maternally delivered transcription factors bias cleavage stage blastomeres to a neural fate. We demonstrate that mouse and human homologues of Foxd4l1 have similar functional domains compared to the frog protein, as well as conserved transcriptional activities when expressed in Xenopus embryos and blastomere explants. PMID:27092474

Coenzyme Q10 protects neural stem cells against hypoxia by enhancing survival signals.

PubMed

Park, Jinse; Park, Hyun-Hee; Choi, Hojin; Kim, Young Seo; Yu, Hyun-Jeung; Lee, Kyu-Yong; Lee, Young Joo; Kim, Seung Hyun; Koh, Seong-Ho

2012-10-10

Recanalization and secondary prevention are the main therapeutic strategies for acute ischemic stroke. Neuroprotective therapies have also been investigated despite unsuccessful clinical results. Coenzyme Q10 (CoQ10), which is an essential cofactor for electron transport in mitochondria, is known to have an antioxidant effect. We investigated the protective effects of CoQ10 against hypoxia in neural stem cells (NSCs). We measured cell viability and levels of intracellular signaling proteins after treatment with several concentrations of CoQ10 under hypoxia-reperfusion. CoQ10 protected NSCs against hypoxia-reperfusion in a concentration-dependent manner by reducing growth inhibition and inhibiting free radical formation. It increased the expression of a number of survival-related proteins such as phosphorylated Akt (pAkt), phosphorylated glycogen synthase kinase 3-β (pGSK3-β), and B-cell lymphoma 2 (Bcl-2) in NSCs injured by hypoxia-reperfusion and reduced the expression of death-related proteins such as cleaved caspase-3. We conclude that CoQ10 has effects against hypoxia-reperfusion induced damage to NSCs by enhancing survival signals and decreasing death signals. Copyright © 2012 Elsevier B.V. All rights reserved.

Two multichannel integrated circuits for neural recording and signal processing.

PubMed

Obeid, Iyad; Morizio, James C; Moxon, Karen A; Nicolelis, Miguel A L; Wolf, Patrick D

2003-02-01

We have developed, manufactured, and tested two analog CMOS integrated circuit "neurochips" for recording from arrays of densely packed neural electrodes. Device A is a 16-channel buffer consisting of parallel noninverting amplifiers with a gain of 2 V/V. Device B is a 16-channel two-stage analog signal processor with differential amplification and high-pass filtering. It features selectable gains of 250 and 500 V/V as well as reference channel selection. The resulting amplifiers on Device A had a mean gain of 1.99 V/V with an equivalent input noise of 10 microV(rms). Those on Device B had mean gains of 53.4 and 47.4 dB with a high-pass filter pole at 211 Hz and an equivalent input noise of 4.4 microV(rms). Both devices were tested in vivo with electrode arrays implanted in the somatosensory cortex.

Classification of images acquired with colposcopy using artificial neural networks.

PubMed

Simões, Priscyla W; Izumi, Narjara B; Casagrande, Ramon S; Venson, Ramon; Veronezi, Carlos D; Moretti, Gustavo P; da Rocha, Edroaldo L; Cechinel, Cristian; Ceretta, Luciane B; Comunello, Eros; Martins, Paulo J; Casagrande, Rogério A; Snoeyer, Maria L; Manenti, Sandra A

2014-01-01

To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

The Neural Representations Underlying Human Episodic Memory.

PubMed

Xue, Gui

2018-06-01

A fundamental question of human episodic memory concerns the cognitive and neural representations and processes that give rise to the neural signals of memory. By integrating behavioral tests, formal computational models, and neural measures of brain activity patterns, recent studies suggest that memory signals not only depend on the neural processes and representations during encoding and retrieval, but also on the interaction between encoding and retrieval (e.g., transfer-appropriate processing), as well as on the interaction between the tested events and all other events in the episodic memory space (e.g., global matching). In addition, memory signals are also influenced by the compatibility of the event with the existing long-term knowledge (e.g., schema matching). These studies highlight the interactive nature of human episodic memory. Copyright © 2018 Elsevier Ltd. All rights reserved.

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

PubMed Central

Sun, Weifang; Yao, Bin; Zeng, Nianyin; He, Yuchao; Cao, Xincheng; He, Wangpeng

2017-01-01

As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features. PMID:28773148

Connectionist neuropsychology: uncovering ultimate causes of acquired dyslexia.

PubMed

Woollams, Anna M

2014-01-01

Acquired dyslexia offers a unique window on to the nature of the cognitive and neural architecture supporting skilled reading. This paper provides an integrative overview of recent empirical and computational work on acquired dyslexia within the context of the primary systems framework as implemented in connectionist neuropsychological models. This view proposes that damage to general visual, phonological or semantic processing abilities are the root causes of different forms of acquired dyslexia. Recent case-series behavioural evidence concerning pure alexia, phonological dyslexia and surface dyslexia that supports this perspective is presented. Lesion simulations of these findings within connectionist models of reading demonstrate the viability of this approach. The commitment of such models to learnt representations allows them to capture key aspects of performance in each type of acquired dyslexia, particularly the associated non-reading deficits, the role of relearning and the influence of individual differences in the premorbid state of the reading system. Identification of these factors not only advances our understanding of acquired dyslexia and the mechanisms of normal reading but they are also relevant to the complex interactions underpinning developmental reading disorders.

Neural dynamics based on the recognition of neural fingerprints

PubMed Central

Carrillo-Medina, José Luis; Latorre, Roberto

2015-01-01

Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g., individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e., specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible, and powerful strategy. PMID:25852531

FGF2 and insulin signaling converge to regulate cyclin D expression in multipotent neural stem cells.

PubMed

Adepoju, Adedamola; Micali, Nicola; Ogawa, Kazuya; Hoeppner, Daniel J; McKay, Ronald D G

2014-03-01

The ex vivo expansion of stem cells is making major contribution to biomedical research. The multipotent nature of neural precursors acutely isolated from the developing central nervous system has been established in a series of studies. Understanding the mechanisms regulating cell expansion in tissue culture would support their expanded use either in cell therapies or to define disease mechanisms. Basic fibroblast growth factor (FGF2) and insulin, ligands for tyrosine kinase receptors, are sufficient to sustain neural stem cells (NSCs) in culture. Interestingly, real-time imaging shows that these cells become multipotent every time they are passaged. Here, we analyze the role of FGF2 and insulin in the brief period when multipotent cells are present. FGF2 signaling results in the phosphorylation of Erk1/2, and activation of c-Fos and c-Jun that lead to elevated cyclin D mRNA levels. Insulin signals through the PI3k/Akt pathway to regulate cyclins at the post-transcriptional level. This precise Boolean regulation extends our understanding of the proliferation of multipotent NSCs and provides a basis for further analysis of proliferation control in the cell states defined by real-time mapping of the cell lineages that form the central nervous system. © 2013 AlphaMed Press.

Neural Networks for Signal Processing and Control

NASA Astrophysics Data System (ADS)

Hesselroth, Ted Daniel

Neural networks are developed for controlling a robot-arm and camera system and for processing images. The networks are based upon computational schemes that may be found in the brain. In the first network, a neural map algorithm is employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm employed shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a set of pressures corresponding to the end effector positions, as well as a set of Jacobian matrices for interpolating between these positions. Because of the properties of the rubber-tube actuators of the arm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after two hundred learning steps. Applications of repeated corrections in each step via the Jacobian matrices leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that they yield a reduction of the distance between gripper and target. The second network is proposed as a model for the mammalian vision system in which backward connections from the primary visual cortex (V1) to the lateral geniculate nucleus play a key role. The application of hebbian learning to the forward and backward connections causes the formation of receptive fields which are sensitive to edges, bars, and spatial frequencies of preferred orientations. The receptive fields are learned in such a way as to maximize the rate of transfer of information from the LGN to V1. Orientational preferences are organized into a feature map in the primary visual

Brain Tissue Responses to Neural Implants Impact Signal Sensitivity and Intervention Strategies

PubMed Central

2015-01-01

Implantable biosensors are valuable scientific tools for basic neuroscience research and clinical applications. Neurotechnologies provide direct readouts of neurological signal and neurochemical processes. These tools are generally most valuable when performance capacities extend over months and years to facilitate the study of memory, plasticity, and behavior or to monitor patients’ conditions. These needs have generated a variety of device designs from microelectrodes for fast scan cyclic voltammetry (FSCV) and electrophysiology to microdialysis probes for sampling and detecting various neurochemicals. Regardless of the technology used, the breaching of the blood–brain barrier (BBB) to insert devices triggers a cascade of biochemical pathways resulting in complex molecular and cellular responses to implanted devices. Molecular and cellular changes in the microenvironment surrounding an implant include the introduction of mechanical strain, activation of glial cells, loss of perfusion, secondary metabolic injury, and neuronal degeneration. Changes to the tissue microenvironment surrounding the device can dramatically impact electrochemical and electrophysiological signal sensitivity and stability over time. This review summarizes the magnitude, variability, and time course of the dynamic molecular and cellular level neural tissue responses induced by state-of-the-art implantable devices. Studies show that insertion injuries and foreign body response can impact signal quality across all implanted central nervous system (CNS) sensors to varying degrees over both acute (seconds to minutes) and chronic periods (weeks to months). Understanding the underlying biological processes behind the brain tissue response to the devices at the cellular and molecular level leads to a variety of intervention strategies for improving signal sensitivity and longevity. PMID:25546652

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Immersive audiomotor game play enhances neural and perceptual salience of weak signals in noise

PubMed Central

Whitton, Jonathon P.; Hancock, Kenneth E.; Polley, Daniel B.

2014-01-01

All sensory systems face the fundamental challenge of encoding weak signals in noisy backgrounds. Although discrimination abilities can improve with practice, these benefits rarely generalize to untrained stimulus dimensions. Inspired by recent findings that action video game training can impart a broader spectrum of benefits than traditional perceptual learning paradigms, we trained adult humans and mice in an immersive audio game that challenged them to forage for hidden auditory targets in a 2D soundscape. Both species learned to modulate their angular search vectors and target approach velocities based on real-time changes in the level of a weak tone embedded in broadband noise. In humans, mastery of this tone in noise task generalized to an improved ability to comprehend spoken sentences in speech babble noise. Neural plasticity in the auditory cortex of trained mice supported improved decoding of low-intensity sounds at the training frequency and an enhanced resistance to interference from background masking noise. These findings highlight the potential to improve the neural and perceptual salience of degraded sensory stimuli through immersive computerized games. PMID:24927596

Linear-phase delay filters for ultra-low-power signal processing in neural recording implants.

PubMed

Gosselin, Benoit; Sawan, Mohamad; Kerherve, Eric

2010-06-01

We present the design and implementation of linear-phase delay filters for ultra-low-power signal processing in neural recording implants. We use these filters as low-distortion delay elements along with an automatic biopotential detector to perform integral waveform extraction and efficient power management. The presented delay elements are realized employing continuous-time OTA-C filters featuring 9th-order equiripple transfer functions with constant group delay. Such analog delay enables processing neural waveforms with reduced overhead compared to a digital delay since it does not requires sampling and digitization. It uses an allpass transfer function for achieving wider constant-delay bandwidth than all-pole does. Two filters realizations are compared for implementing the delay element: the Cascaded structure and the Inverse follow-the-leader feedback filter. Their respective strengths and drawbacks are assessed by modeling parasitics and non-idealities of OTAs, and by transistor-level simulations. A budget of 200 nA is used in both filters. Experimental measurements with the chosen filter topology are presented and discussed.

Immersive audiomotor game play enhances neural and perceptual salience of weak signals in noise.

PubMed

Whitton, Jonathon P; Hancock, Kenneth E; Polley, Daniel B

2014-06-24

All sensory systems face the fundamental challenge of encoding weak signals in noisy backgrounds. Although discrimination abilities can improve with practice, these benefits rarely generalize to untrained stimulus dimensions. Inspired by recent findings that action video game training can impart a broader spectrum of benefits than traditional perceptual learning paradigms, we trained adult humans and mice in an immersive audio game that challenged them to forage for hidden auditory targets in a 2D soundscape. Both species learned to modulate their angular search vectors and target approach velocities based on real-time changes in the level of a weak tone embedded in broadband noise. In humans, mastery of this tone in noise task generalized to an improved ability to comprehend spoken sentences in speech babble noise. Neural plasticity in the auditory cortex of trained mice supported improved decoding of low-intensity sounds at the training frequency and an enhanced resistance to interference from background masking noise. These findings highlight the potential to improve the neural and perceptual salience of degraded sensory stimuli through immersive computerized games.

Changes in expression and secretion patterns of fibroblast growth factor 8 and Sonic Hedgehog signaling pathway molecules during murine neural stem/progenitor cell differentiation in vitro☆

PubMed Central

Lu, Jiang; Lu, Kehuan; Li, Dongsheng

2012-01-01

In the present study, we investigated the dynamic expression of fibroblast growth factor 8 and Sonic Hedgehog signaling pathway related factors in the process of in vitro hippocampal neural stem/progenitor cell differentiation from embryonic Sprague-Dawley rats or embryonic Kunming species mice, using fluorescent quantitative reverse transcription-PCR and western blot analyses. Results demonstrated that the dynamic expression of fibroblast growth factor 8 was similar to fibroblast growth factor receptor 1 expression but not to other fibroblast growth factor receptors. Enzyme-linked immunosorbent assay demonstrated that fibroblast growth factor 8 and Sonic Hedgehog signaling pathway protein factors were secreted by neural cells into the intercellular niche. Our experimental findings indicate that fibroblast growth factor 8 and Sonic Hedgehog expression may be related to the differentiation of neural stem/progenitor cells. PMID:25624789

Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System Following Neural Injury (Addendum)

DTIC Science & Technology

2016-03-01

AD_________________ Award Number: W81XWH-12-1-0051 TITLE: Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the...Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System Following Neural Injury 5a. CONTRACT NUMBER...incapable of axon regeneration . There are currently two principal concepts that form the basis of our understanding of the inability of the mature

Signal, noise, and variation in neural and sensory-motor latency

PubMed Central

Lee, Joonyeol; Joshua, Mati; Medina, Javier F.; Lisberger, Stephen G.

2016-01-01

Analysis of the neural code for sensory-motor latency in smooth pursuit eye movements reveals general principles of neural variation and the specific origin of motor latency. The trial-by-trial variation in neural latency in MT comprises: a shared component expressed as neuron-neuron latency correlations; and an independent component that is local to each neuron. The independent component arises heavily from fluctuations in the underlying probability of spiking with an unexpectedly small contribution from the stochastic nature of spiking itself. The shared component causes the latency of single neuron responses in MT to be weakly predictive of the behavioral latency of pursuit. Neural latency deeper in the motor system is more strongly predictive of behavioral latency. A model reproduces both the variance of behavioral latency and the neuron-behavior latency correlations in MT if it includes realistic neural latency variation, neuron-neuron latency correlations in MT, and noisy gain control downstream from MT. PMID:26971946

Neural markers of loss aversion in resting-state brain activity.

PubMed

Canessa, Nicola; Crespi, Chiara; Baud-Bovy, Gabriel; Dodich, Alessandra; Falini, Andrea; Antonellis, Giulia; Cappa, Stefano F

2017-02-01

Neural responses in striatal, limbic and somatosensory brain regions track individual differences in loss aversion, i.e. the higher sensitivity to potential losses compared with equivalent gains in decision-making under risk. The engagement of structures involved in the processing of aversive stimuli and experiences raises a further question, i.e. whether the tendency to avoid losses rather than acquire gains represents a transient fearful overreaction elicited by choice-related information, or rather a stable component of one's own preference function, reflecting a specific pattern of neural activity. We tested the latter hypothesis by assessing in 57 healthy human subjects whether the relationship between behavioral and neural loss aversion holds at rest, i.e. when the BOLD signal is collected during 5minutes of cross-fixation in the absence of an explicit task. Within the resting-state networks highlighted by a spatial group Independent Component Analysis (gICA), we found a significant correlation between strength of activity and behavioral loss aversion in the left ventral striatum and right posterior insula/supramarginal gyrus, i.e. the very same regions displaying a pattern of neural loss aversion during explicit choices. Cross-study analyses confirmed that this correlation holds when voxels identified by gICA are used as regions of interest in task-related activity and vice versa. These results suggest that the individual degree of (neural) loss aversion represents a stable dimension of decision-making, which reflects in specific metrics of intrinsic brain activity at rest possibly modulating cortical excitability at choice. Copyright © 2016 Elsevier Inc. All rights reserved.

Cyclin-dependent kinase 4 signaling acts as a molecular switch between syngenic differentiation and neural transdifferentiation in human mesenchymal stem cells

PubMed Central

Lee, Janet; Baek, Jeong-Hwa; Choi, Kyu-Sil; Kim, Hyun-Soo; Park, Hye-Young; Ha, Geun-Hyoung; Park, Ho; Lee, Kyo-Won; Lee, Chang Geun; Yang, Dong-Yun; Moon, Hyo Eun; Paek, Sun Ha; Lee, Chang-Woo

2013-01-01

Multipotent mesenchymal stem/stromal cells (MSCs) are capable of differentiating into a variety of cell types from different germ layers. However, the molecular and biochemical mechanisms underlying the transdifferentiation of MSCs into specific cell types still need to be elucidated. In this study, we unexpectedly found that treatment of human adipose- and bone marrow-derived MSCs with cyclin-dependent kinase (CDK) inhibitor, in particular CDK4 inhibitor, selectively led to transdifferentiation into neural cells with a high frequency. Specifically, targeted inhibition of CDK4 expression using recombinant adenovial shRNA induced the neural transdifferentiation of human MSCs. However, the inhibition of CDK4 activity attenuated the syngenic differentiation of human adipose-derived MSCs. Importantly, the forced regulation of CDK4 activity showed reciprocal reversibility between neural differentiation and dedifferentiation of human MSCs. Together, these results provide novel molecular evidence underlying the neural transdifferentiation of human MSCs; in addition, CDK4 signaling appears to act as a molecular switch from syngenic differentiation to neural transdifferentiation of human MSCs. PMID:23324348

Shared developmental and evolutionary origins for neural basis of vocal–acoustic and pectoral–gestural signaling

PubMed Central

Bass, Andrew H.; Chagnaud, Boris P.

2012-01-01

Acoustic signaling behaviors are widespread among bony vertebrates, which include the majority of living fishes and tetrapods. Developmental studies in sound-producing fishes and tetrapods indicate that central pattern generating networks dedicated to vocalization originate from the same caudal hindbrain rhombomere (rh) 8-spinal compartment. Together, the evidence suggests that vocalization and its morphophysiological basis, including mechanisms of vocal–respiratory coupling that are widespread among tetrapods, are ancestral characters for bony vertebrates. Premotor-motor circuitry for pectoral appendages that function in locomotion and acoustic signaling develops in the same rh8-spinal compartment. Hence, vocal and pectoral phenotypes in fishes share both developmental origins and roles in acoustic communication. These findings lead to the proposal that the coupling of more highly derived vocal and pectoral mechanisms among tetrapods, including those adapted for nonvocal acoustic and gestural signaling, originated in fishes. Comparative studies further show that rh8 premotor populations have distinct neurophysiological properties coding for equally distinct behavioral attributes such as call duration. We conclude that neural network innovations in the spatiotemporal patterning of vocal and pectoral mechanisms of social communication, including forelimb gestural signaling, have their evolutionary origins in the caudal hindbrain of fishes. PMID:22723366

Effects of social sustainability signaling on neural valuation signals and taste-experience of food products

PubMed Central

Enax, Laura; Krapp, Vanessa; Piehl, Alexandra; Weber, Bernd

2015-01-01

Value-based decision making occurs when individuals choose between different alternatives and place a value on each alternative and its attributes. Marketing actions frequently manipulate product attributes, by adding, e.g., health claims on the packaging. A previous imaging study found that an emblem for organic products increased willingness to pay (WTP) and activity in the ventral striatum (VS). The current study investigated neural and behavioral processes underlying the influence of Fair Trade (FT) labeling on food valuation and choice. Sustainability is an important product attribute for many consumers, with FT signals being one way to highlight ethically sustainable production. Forty participants valuated products in combination with an FT emblem or no emblem and stated their WTP in a bidding task while in an MRI scanner. After that, participants tasted—objectively identical—chocolates, presented either as “FT” or as “conventionally produced”. In the fMRI task, WTP was significantly higher for FT products. FT labeling increased activity in regions important for reward-processing and salience, that is, in the VS, anterior and posterior cingulate, as well as superior frontal gyrus. Subjective value, that is, WTP was correlated with activity in the ventromedial prefrontal cortex (vmPFC). We find that the anterior cingulate, VS and superior frontal gyrus exhibit task-related increases in functional connectivity to the vmPFC when an FT product was evaluated. Effective connectivity analyses revealed a highly probable directed modulation of the vmPFC by those three regions, suggesting a network which alters valuation processes. We also found a significant taste-placebo effect, with higher experienced taste pleasantness and intensity for FT labeled chocolates. Our results reveal a possible neural mechanism underlying valuation processes of certified food products. The results are important in light of understanding current marketing trends as well as

Real-time neural network earthquake profile predictor

DOEpatents

Leach, R.R.; Dowla, F.U.

1996-02-06

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion. 17 figs.

Real-time neural network earthquake profile predictor

DOEpatents

Leach, Richard R.; Dowla, Farid U.

1996-01-01

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion.

Intelligent processing of acoustic emission signals

NASA Astrophysics Data System (ADS)

Sachse, Wolfgang; Grabec, Igor

1992-07-01

Recent developments in applying neural-like signal-processing procedures for analyzing acoustic emission signals are summarized. These procedures employ a set of learning signals to develop a memory that can subsequently be utilized to process other signals to recover information about an unknown source. A majority of the current applications to process ultrasonic waveforms are based on multilayered, feed-forward neural networks, trained with some type of back-error propagation rule.

Serotonin, neural markers, and memory

PubMed Central

Meneses, Alfredo

2015-01-01

Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals' species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6, and 5-HT7 receptors as well as SERT (serotonin transporter) seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence. PMID:26257650

Activation of EGFR Bypass Signaling by TGFα Overexpression Induces Acquired Resistance to Alectinib in ALK-Translocated Lung Cancer Cells.

PubMed

Tani, Tetsuo; Yasuda, Hiroyuki; Hamamoto, Junko; Kuroda, Aoi; Arai, Daisuke; Ishioka, Kota; Ohgino, Keiko; Miyawaki, Masayoshi; Kawada, Ichiro; Naoki, Katsuhiko; Hayashi, Yuichiro; Betsuyaku, Tomoko; Soejima, Kenzo

2016-01-01

Alectinib is a highly selective ALK inhibitor and shows promising efficacy in non-small cell lung cancers (NSCLC) harboring the EML4-ALK gene rearrangement. The precise mechanism of acquired resistance to alectinib is not well defined. The purpose of this study was to clarify the mechanism of acquired resistance to alectinib in ALK-translocated lung cancer cells. We established alectinib-resistant cells (H3122-AR) from the H3122 NSCLC cell line, harboring the EML4-ALK gene rearrangement, by long-term exposure to alectinib. The mechanism of acquired resistance to alectinib in H3122-AR cells was evaluated by phospho-receptor tyrosine kinase (phospho-RTK) array screening and Western blotting. No mutation of the ALK-TK domain was found. Phospho-RTK array analysis revealed that the phosphorylation level of EGFR was increased in H3122-AR cells compared with H3122. Expression of TGFα, one of the EGFR ligands, was significantly increased and knockdown of TGFα restored the sensitivity to alectinib in H3122-AR cells. We found combination therapy targeting ALK and EGFR with alectinib and afatinib showed efficacy both in vitro and in a mouse xenograft model. We propose a preclinical rationale to use the combination therapy with alectinib and afatinib in NSCLC that acquired resistance to alectinib by the activation of EGFR bypass signaling. ©2015 American Association for Cancer Research.

Separate Perceptual and Neural Processing of Velocity- and Disparity-Based 3D Motion Signals

PubMed Central

Czuba, Thaddeus B.; Cormack, Lawrence K.; Huk, Alexander C.

2016-01-01

Although the visual system uses both velocity- and disparity-based binocular information for computing 3D motion, it is unknown whether (and how) these two signals interact. We found that these two binocular signals are processed distinctly at the levels of both cortical activity in human MT and perception. In human MT, adaptation to both velocity-based and disparity-based 3D motions demonstrated direction-selective neuroimaging responses. However, when adaptation to one cue was probed using the other cue, there was no evidence of interaction between them (i.e., there was no “cross-cue” adaptation). Analogous psychophysical measurements yielded correspondingly weak cross-cue motion aftereffects (MAEs) in the face of very strong within-cue adaptation. In a direct test of perceptual independence, adapting to opposite 3D directions generated by different binocular cues resulted in simultaneous, superimposed, opposite-direction MAEs. These findings suggest that velocity- and disparity-based 3D motion signals may both flow through area MT but constitute distinct signals and pathways. SIGNIFICANCE STATEMENT Recent human neuroimaging and monkey electrophysiology have revealed 3D motion selectivity in area MT, which is driven by both velocity-based and disparity-based 3D motion signals. However, to elucidate the neural mechanisms by which the brain extracts 3D motion given these binocular signals, it is essential to understand how—or indeed if—these two binocular cues interact. We show that velocity-based and disparity-based signals are mostly separate at the levels of both fMRI responses in area MT and perception. Our findings suggest that the two binocular cues for 3D motion might be processed by separate specialized mechanisms. PMID:27798134

NFκB signaling regulates embryonic and adult neurogenesis

PubMed Central

ZHANG, Yonggang; HU, Wenhui

2013-01-01

Both embryonic and adult neurogenesis involves the self-renewal/proliferation, survival, migration and lineage differentiation of neural stem/progenitor cells. Such dynamic process is tightly regulated by intrinsic and extrinsic factors and complex signaling pathways. Misregulated neurogenesis contributes much to a large range of neurodevelopmental defects and neurodegenerative diseases. The signaling of NFκB regulates many genes important in inflammation, immunity, cell survival and neural plasticity. During neurogenesis, NFκB signaling mediates the effect of numerous niche factors such as cytokines, chemokines, growth factors, extracellular matrix molecules, but also crosstalks with other signaling pathways such as Notch, Shh, Wnt/β-catenin. This review summarizes current progress on the NFκB signaling in all aspects of neurogenesis, focusing on the novel role of NFκB signaling in initiating early neural differentiation of neural stem cells and embryonic stem cells. PMID:24324484

Persistent neural activity in head direction cells

NASA Technical Reports Server (NTRS)

Taube, Jeffrey S.; Bassett, Joshua P.; Oman, C. M. (Principal Investigator)

2003-01-01

Many neurons throughout the rat limbic system discharge in relation to the animal's directional heading with respect to its environment. These so-called head direction (HD) cells exhibit characteristics of persistent neural activity. This article summarizes where HD cells are found, their major properties, and some of the important experiments that have been conducted to elucidate how this signal is generated. The number of HD and angular head velocity cells was estimated for several brain areas involved in the generation of the HD signal, including the postsubiculum, anterior dorsal thalamus, lateral mammillary nuclei and dorsal tegmental nucleus. The HD cell signal has many features in common with what is known about how neural integration is accomplished in the oculomotor system. The nature of the HD cell signal makes it an attractive candidate for using neural network models to elucidate the signal's underlying mechanisms. The conditions that any network model must satisfy in order to accurately represent how the nervous system generates this signal are highlighted and areas where key information is missing are discussed.

Classification of subsurface objects using singular values derived from signal frames

DOEpatents

Chambers, David H; Paglieroni, David W

2014-05-06

The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

Suppression of IGF-I signals in neural stem cells enhances neurogenesis and olfactory function during aging.

PubMed

Chaker, Zayna; Aïd, Saba; Berry, Hugues; Holzenberger, Martin

2015-10-01

Downregulation of insulin-like growth factor (IGF) pathways prolongs lifespan in various species, including mammals. Still, the cellular mechanisms by which IGF signaling controls the aging trajectory of individual organs are largely unknown. Here, we asked whether suppression of IGF-I receptor (IGF-1R) in adult stem cells preserves long-term cell replacement, and whether this may prevent age-related functional decline in a regenerating tissue. Using neurogenesis as a paradigm, we showed that conditional knockout of IGF-1R specifically in adult neural stem cells (NSC) maintained youthful characteristics of olfactory bulb neurogenesis within an aging brain. We found that blocking IGF-I signaling in neural precursors increased cumulative neuroblast production and enhanced neuronal integration into the olfactory bulb. This in turn resulted in neuro-anatomical changes that improved olfactory function. Interestingly, mutants also displayed long-term alterations in energy metabolism, possibly related to IGF-1R deletion in NSCs throughout lifespan. We explored Akt and ERK signaling cascades and revealed differential regulation downstream of IGF-1R, with Akt phosphorylation preferentially decreased in IGF-1R(-/-) NSCs within the niche, and ERK pathway downregulated in differentiated neurons of the OB. These challenging experimental results were sustained by data from mathematical modeling, predicting that diminished stimulation of growth is indeed optimal for tissue aging. Thus, inhibiting growth and longevity gene IGF-1R in adult NSCs induced a gain-of-function phenotype during aging, marked by optimized management of cell renewal, and enhanced olfactory sensory function. © 2015 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

PACAP signaling to DREAM: a cAMP-dependent pathway that regulates cortical astrogliogenesis.

PubMed

Vallejo, Mario

2009-04-01

Astrocytes constitute a very abundant cell type in the mammalian central nervous system and play critical roles in brain function. During development, astrocytes are generated from neural progenitor cells only after these cells have generated neurons. This so called gliogenic switch is tightly regulated by intrinsic factors that inhibit the generation of astrocytes during the neurogenic period. Once neural progenitors acquire gliogenic competence, they differentiate into astrocytes in response to specific extracellular signals. Some of these signals are delivered by neurotrophic cytokines via activation of the gp130-JAK-signal transducer and activator of transcription system, whereas others depend on the activity of pituitary adenylate cyclase-activating polypeptide (PACAP) on specific PAC1 receptors that stimulate the production of cAMP. This results in the activation of the small GTPases Rap1 and Ras, and in the cAMP-dependent entry of extracellular calcium into the cell. Calcium, in turn, stimulates the transcription factor downstream regulatory element antagonist modulator (DREAM), which is bound to specific sites of the promoter of the glial fibrillary acidic protein gene, stimulating its expression during astrocyte differentiation. Lack of DREAM in vivo results in alterations in the number of neurons and astrocytes generated during development. Thus, the PACAP-cAMP-Ca(2+)-DREAM signaling cascade constitutes an important pathway to activate glial-specific gene expression during astrocyte differentiation.

Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner.

PubMed

Gopakumar, Gopalakrishna Pillai; Swetha, Murali; Sai Siva, Gorthi; Sai Subrahmanyam, Gorthi R K

2018-03-01

The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2-level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for point-of-care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Estimation of locomotion speed and directions changes to control a vehicle using neural signals from the motor cortex of rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2006-01-01

We have developed a brain-machine interface (BMI) in the form of a small vehicle, which we call the RatCar. In this system, we implanted wire electrodes in the motor cortices of rat's brain to continuously record neural signals. We applied a linear model to estimate the locomotion state (e.g., speed and directions) of a rat using a weighted summation model for the neural firing rates. With this information, we then determined the approximate movement of a rat. Although the estimation is still imprecise, results suggest that our model is able to control the system to some degree. In this paper, we give an overview of our system and describe the methods used, which include continuous neural recording, spike detection and a discrimination algorithm, and a locomotion estimation model minimizes the square error of the locomotion speed and changes in direction.

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, W; Jiang, M; Yin, F

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, amore » Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results

Connectionist neuropsychology: uncovering ultimate causes of acquired dyslexia

PubMed Central

Woollams, Anna M.

2014-01-01

Acquired dyslexia offers a unique window on to the nature of the cognitive and neural architecture supporting skilled reading. This paper provides an integrative overview of recent empirical and computational work on acquired dyslexia within the context of the primary systems framework as implemented in connectionist neuropsychological models. This view proposes that damage to general visual, phonological or semantic processing abilities are the root causes of different forms of acquired dyslexia. Recent case-series behavioural evidence concerning pure alexia, phonological dyslexia and surface dyslexia that supports this perspective is presented. Lesion simulations of these findings within connectionist models of reading demonstrate the viability of this approach. The commitment of such models to learnt representations allows them to capture key aspects of performance in each type of acquired dyslexia, particularly the associated non-reading deficits, the role of relearning and the influence of individual differences in the premorbid state of the reading system. Identification of these factors not only advances our understanding of acquired dyslexia and the mechanisms of normal reading but they are also relevant to the complex interactions underpinning developmental reading disorders. PMID:24324241

Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal.

PubMed

Wen, Haiguang; Liu, Zhongming

2016-06-01

Spontaneous activity observed with resting-state fMRI is used widely to uncover the brain's intrinsic functional networks in health and disease. Although many networks appear modular and specific, global and nonspecific fMRI fluctuations also exist and both pose a challenge and present an opportunity for characterizing and understanding brain networks. Here, we used a multimodal approach to investigate the neural correlates to the global fMRI signal in the resting state. Like fMRI, resting-state power fluctuations of broadband and arrhythmic, or scale-free, macaque electrocorticography and human magnetoencephalography activity were correlated globally. The power fluctuations of scale-free human electroencephalography (EEG) were coupled with the global component of simultaneously acquired resting-state fMRI, with the global hemodynamic change lagging the broadband spectral change of EEG by ∼5 s. The levels of global and nonspecific fluctuation and synchronization in scale-free population activity also varied across and depended on arousal states. Together, these results suggest that the neural origin of global resting-state fMRI activity is the broadband power fluctuation in scale-free population activity observable with macroscopic electrical or magnetic recordings. Moreover, the global fluctuation in neurophysiological and hemodynamic activity is likely modulated through diffuse neuromodulation pathways that govern arousal states and vigilance levels. This study provides new insights into the neural origin of resting-state fMRI. Results demonstrate that the broadband power fluctuation of scale-free electrophysiology is globally synchronized and directly coupled with the global component of spontaneous fMRI signals, in contrast to modularly synchronized fluctuations in oscillatory neural activity. These findings lead to a new hypothesis that scale-free and oscillatory neural processes account for global and modular patterns of functional connectivity observed

Activity-dependent signaling mechanisms regulating adult hippocampal neural stem cells and their progeny.

PubMed

Crowther, Andrew J; Song, Juan

2014-08-01

Adult neural stem cells (NSCs) reside in a restricted microenvironment, where their development is controlled by subtle and presently underexplored cues. This raises a significant question: what instructions must be provided by this supporting niche to regulate NSC development and functions? Signaling from the niche is proposed to control many aspects of NSC behavior, including balancing the quiescence and proliferation of NSCs, determining the cell division mode (symmetric versus asymmetric), and preventing premature depletion of stem cells to maintain neurogenesis throughout life. Interactions between neurogenic niches and NSCs also govern the homeostatic regulation of adult neurogenesis under diverse physiological, environmental, and pathological conditions. An important implication from revisiting many previously-identifi ed regulatory factors is that most of them (e.g., the antidepressant fluoxetine and exercise) affect gross neurogenesis by acting downstream of NSCs at the level of intermediate progenitors and neuroblasts, while leaving the NSC pool unaffected. Therefore, it is critically important to address how various niche components, signaling pathways, and environmental stimuli differentially regulate distinct stages of adult neurogenesis.

Depression and treatment response: dynamic interplay of signaling pathways and altered neural processes

PubMed Central

Duric, Vanja

2014-01-01

Since the 1960s, when the first tricyclic and monoamine oxidase inhibitor antidepressant drugs were introduced, most of the ensuing agents were designed to target similar brain pathways that elevate serotonin and/or norepinephrine signaling. Fifty years later, the main goal of the current depression research is to develop faster-acting, more effective therapeutic agents with fewer side effects, as currently available antidepressants are plagued by delayed therapeutic onset and low response rates. Clinical and basic science research studies have made significant progress towards deciphering the pathophysiological events within the brain involved in development, maintenance, and treatment of major depressive disorder. Imaging and postmortem brain studies in depressed human subjects, in combination with animal behavioral models of depression, have identified a number of different cellular events, intracellular signaling pathways, proteins, and target genes that are modulated by stress and are potentially vital mediators of antidepressant action. In this review, we focus on several neural mechanisms, primarily within the hippocampus and prefrontal cortex, which have recently been implicated in depression and treatment response. PMID:22585060

Investigations of Escherichia coli promoter sequences with artificial neural networks: New signals discovered upstream of the transcriptional startpoint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pedersen, A.G.; Engelbrecht, J.

1995-12-31

In this paper we present a novel method for using the learning ability of a neural network as a measure of information in local regions of input data. Using the method to analyze Escherichia coli promoters, we discover all previously described signals, and furthermore find new signals that are regularly spaced along the promoter region. The spacing of all signals correspond to the helical periodicity of DNA, meaning that the signals are all present on the same face of the DNA helix in the promoter region. This is consistent with a model where the RNA polymerase contacts the promoter onmore » one side of the DNA, and suggests that the regions important for promoter recognition may include more positions on the DNA than usually assumed. We furthermore analyze the E.coli promoters by calculating the Kullback Leibler distance, and by constructing sequence logos.« less

A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal.

PubMed

Bonfanti, A; Ceravolo, M; Zambra, G; Gusmeroli, R; Spinelli, A S; Lacaita, A L; Angotzi, G N; Baranauskas, G; Fadiga, L

2010-01-01

This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- εm CMOS process with a power consumption of 105 εW per channel (269 εW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm(2). The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments.

A role for chemokine signaling in neural crest cell migration and craniofacial development

PubMed Central

Killian, Eugenia C. Olesnicky; Birkholz, Denise A.; Artinger, Kristin Bruk

2009-01-01

Neural crest cells (NCCs) are a unique population of multipotent cells that migrate along defined pathways throughout the embryo and give rise to many diverse cell types including pigment cells, craniofacial cartilage and the peripheral nervous system (PNS). Aberrant migration of NCCs results in a wide variety of congenital birth defects including craniofacial abnormalities. The chemokine Sdf1 and its receptors, Cxcr4 and Cxcr7, have been identified as key components in the regulation of cell migration in a variety of tissues. Here we describe a novel role for the zebrafish chemokine receptor Cxcr4a in the development and migration of cranial NCCs (CNCCs). We find that loss of Cxcr4a, but not Cxcr7b results in aberrant CNCC migration, defects in the neurocranium, as well as cranial ganglia dismorphogenesis. Moreover, overexpression of either Sdf1b or Cxcr4a causes aberrant CNCC migration and results in ectopic craniofacial cartilages. We propose a model in which Sdf1b signaling from the pharyngeal arch endoderm and optic stalk to Cxcr4a expressing CNCCs is important for both the proper condensation of the CNCCs into pharyngeal arches and the subsequent patterning and morphogenesis of the neural crest derived tissues. PMID:19576198

Inferring Functional Neural Connectivity with Phase Synchronization Analysis: A Review of Methodology

PubMed Central

Sun, Junfeng; Li, Zhijun; Tong, Shanbao

2012-01-01

Functional neural connectivity is drawing increasing attention in neuroscience research. To infer functional connectivity from observed neural signals, various methods have been proposed. Among them, phase synchronization analysis is an important and effective one which examines the relationship of instantaneous phase between neural signals but neglecting the influence of their amplitudes. In this paper, we review the advances in methodologies of phase synchronization analysis. In particular, we discuss the definitions of instantaneous phase, the indexes of phase synchronization and their significance test, the issues that may affect the detection of phase synchronization and the extensions of phase synchronization analysis. In practice, phase synchronization analysis may be affected by observational noise, insufficient samples of the signals, volume conduction, and reference in recording neural signals. We make comments and suggestions on these issues so as to better apply phase synchronization analysis to inferring functional connectivity from neural signals. PMID:22577470

Role of FGF and noggin in neural crest induction.

PubMed

Mayor, R; Guerrero, N; Martínez, C

1997-09-01

A study of the molecules noggin and fibroblast growth factor (FGF) and its receptor in the induction of the prospective neural crest in Xenopus laevis embryos has been carried out, using the expression of the gene Xslu as a marker for the neural crest. We show that when a truncated FGF receptor (XFD) was expressed ectopically in order to block FGF signaling Xslu expression was inhibited. The effect of XFD on Xslu was specific and could be reversed by the coinjection of the wild-type FGF receptor (FGFR). Inhibition of Xslu expression by XFD is not a consequence of neural plate inhibition, as was shown by analyzing Xsox-2 expression. When ectoderm expressing XFD was transplanted into the prospective neural fold region of embryos Xslu induction was inhibited. The neural crest can also be induced by an interaction between neural plate and epidermis. As this induction is suppressed by the presence of XFD in the neural plate and not in the epidermis, it suggests that the neural crest is induced by FGF from the epidermis. However, treatment of neural plate with FGF was not able to induce Xslug expression, showing that in addition to FGF other non-FGF factors are also required. Previously we have suggested that the ectopic ventral expression of Xslu produced by overexpression of noggin mRNA resulted from an interaction of noggin with a ventral signal. Overexpression of XFD inhibits this effect, suggesting that FGF could be one component involved in this ventral signaling. Overexpression of FGFR produced a remarkable increase in the expression of Xslu in the posterior neural folds and around the blastopore. Injections in different blastomeres of the embryo suggest that the target cells of this effect are the ventral cells. Finally, we proposed a model in which the induction of the neural crests at the border of the neural plate requires functional FGF signaling, which possibly interacts with a neural inducer such as noggin.

Cold atmospheric plasma (CAP), a novel physicochemical source, induces neural differentiation through cross-talk between the specific RONS cascade and Trk/Ras/ERK signaling pathway.

PubMed

Jang, Ja-Young; Hong, Young June; Lim, Junsup; Choi, Jin Sung; Choi, Eun Ha; Kang, Seongman; Rhim, Hyangshuk

2018-02-01

Plasma, formed by ionization of gas molecules or atoms, is the most abundant form of matter and consists of highly reactive physicochemical species. In the physics and chemistry fields, plasma has been extensively studied; however, the exact action mechanisms of plasma on biological systems, including cells and humans, are not well known. Recent evidence suggests that cold atmospheric plasma (CAP), which refers to plasma used in the biomedical field, may regulate diverse cellular processes, including neural differentiation. However, the mechanism by which these physicochemical signals, elicited by reactive oxygen and nitrogen species (RONS), are transmitted to biological system remains elusive. In this study, we elucidated the physicochemical and biological (PCB) connection between the CAP cascade and Trk/Ras/ERK signaling pathway, which resulted in neural differentiation. Excited atomic oxygen in the plasma phase led to the formation of RONS in the PCB network, which then interacted with reactive atoms in the extracellular liquid phase to form nitric oxide (NO). Production of large amounts of superoxide radical (O 2 - ) in the mitochondria of cells exposed to CAP demonstrated that extracellular NO induced the reversible inhibition of mitochondrial complex IV. We also demonstrated that cytosolic hydrogen peroxide, formed by O 2 - dismutation, act as an intracellular messenger to specifically activate the Trk/Ras/ERK signaling pathway. This study is the first to elucidate the mechanism linking physicochemical signals from the CAP cascade to the intracellular neural differentiation signaling pathway, providing physical, chemical and biological insights into the development of therapeutic techniques to treat neurological diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

PubMed

Scheler, Gabriele

2013-01-01

We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species) with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of individual transfer

Separate Perceptual and Neural Processing of Velocity- and Disparity-Based 3D Motion Signals.

PubMed

Joo, Sung Jun; Czuba, Thaddeus B; Cormack, Lawrence K; Huk, Alexander C

2016-10-19

Although the visual system uses both velocity- and disparity-based binocular information for computing 3D motion, it is unknown whether (and how) these two signals interact. We found that these two binocular signals are processed distinctly at the levels of both cortical activity in human MT and perception. In human MT, adaptation to both velocity-based and disparity-based 3D motions demonstrated direction-selective neuroimaging responses. However, when adaptation to one cue was probed using the other cue, there was no evidence of interaction between them (i.e., there was no "cross-cue" adaptation). Analogous psychophysical measurements yielded correspondingly weak cross-cue motion aftereffects (MAEs) in the face of very strong within-cue adaptation. In a direct test of perceptual independence, adapting to opposite 3D directions generated by different binocular cues resulted in simultaneous, superimposed, opposite-direction MAEs. These findings suggest that velocity- and disparity-based 3D motion signals may both flow through area MT but constitute distinct signals and pathways. Recent human neuroimaging and monkey electrophysiology have revealed 3D motion selectivity in area MT, which is driven by both velocity-based and disparity-based 3D motion signals. However, to elucidate the neural mechanisms by which the brain extracts 3D motion given these binocular signals, it is essential to understand how-or indeed if-these two binocular cues interact. We show that velocity-based and disparity-based signals are mostly separate at the levels of both fMRI responses in area MT and perception. Our findings suggest that the two binocular cues for 3D motion might be processed by separate specialized mechanisms. Copyright © 2016 the authors 0270-6474/16/3610791-12$15.00/0.

Expression of p53/HGF/c-met/STAT3 signal in fetuses with neural tube defects.

PubMed

Trovato, Maria; D'Armiento, Maria; Lavra, Luca; Ulivieri, Alessandra; Dominici, Roberto; Vitarelli, Enrica; Grosso, Maddalena; Vecchione, Raffaella; Barresi, Gaetano; Sciacchitano, Salvatore

2007-02-01

Neural tube defects (NTD) are morphogenetic alterations due to a defective closure of neural tube. Hepatocyte growth factor (HGF)/c-met system plays a role in morphogenesis of nervous system, lung, and kidney. HGF/c-met morphogenetic effects are mediated by signal transducers and activators of transcription (STAT)3 and both HGF and c-met genes are regulated from p53. The aim of our study was to analyze mRNA and protein expressions of p53, HGF, c-met, and STAT3 in fetuses with NTD. By reverse transcriptase-polymerase chain reaction and immunohistochemistry, we analyzed neural tissues from four NTD fetuses and the corresponding non-malformed lungs, kidneys and placentas. We found a reduced mRNA expression of HGF/c-met/STAT3 pathway, in the malformed nervous systems and placentas. The reduced expression of this pathway correlated with the absence of p53 in all these samples. On the contrary, detectable expression levels of p53, HGF, c-met, and STAT3 were observed in non-malformed lungs and kidneys obtained from the same fetuses. Comparable results were obtained by immunohistochemistry, with the exception of p53, which was undetected in all fetal tissues. In conclusion, in NTD fetuses, both the defective neural tube tissue and the placenta have a reduction in all components of the p53/HGF/c-met/STAT3 cascade. This raises the possibility of using the suppression of these genes for early diagnosis of NTD especially on chorionic villus sampling.

Ectopic cross-talk between thyroid and retinoic acid signaling: A possible etiology for spinal neural tube defects.

PubMed

Li, Huili; Bai, Baoling; Zhang, Qin; Bao, Yihua; Guo, Jin; Chen, Shuyuan; Miao, Chunyue; Liu, Xiaozhen; Zhang, Ting

2015-12-01

Previous studies have highlighted the connections between neural tube defects (NTDs) and both thyroid hormones (TH) and vitamin A. However, whether the two hormonal signaling pathways interact in NTDs has remained unclear. We measured the expression levels of TH signaling genes in human fetuses with spinal NTDs associated with maternal hyperthyroidism as well as levels of retinoic acid (RA) signaling genes in mouse fetuses exposed to an overdose of RA using NanoString or real-time PCR on spinal cord tissues. Interactions between the two signaling pathways were detected by ChIP assays. The data revealed attenuated DIO2/DIO3 switching in fetuses with NTDs born to hyperthyroid mothers. The promoters of the RA signaling genes CRABP1 and RARB were ectopically occupied by increased RXRG and RXRB but displayed decreased levels of inhibitory histone modifications, suggesting that elevated TH signaling abnormally stimulates RA signaling genes. Conversely, in the mouse model, the observed decrease in Dio3 expression could be explained by increased levels of inhibitory histone modifications in the Dio3 promoter region, suggesting that overactive RA signaling may ectopically derepress TH signaling. This study thus raises in vivo a possible abnormal cross-promotion between two different hormonal signals through their common RXRs and the subsequent recruitment of histone modifications, prompting further investigation into their involvement in the etiology of spinal NTDs. Copyright © 2015 Elsevier B.V. All rights reserved.

Targeted Knockdown of Bone Morphogenetic Protein Signaling within Neural Progenitors Protects the Brain and Improves Motor Function following Postnatal Hypoxia-Ischemia

PubMed Central

Dettman, Robert W.; Birch, Derin; Fernando, Augusta; Kessler, John A.; Dizon, Maria L.V.

2018-01-01

Hypoxic-ischemic injury (HI) to the neonatal human brain results in myelin loss that, in some children, can manifest as cerebral palsy. Previously, we had found that neuronal overexpression of the bone morphogenic protein (BMP) inhibitor noggin during development increased oligodendroglia and improved motor function in an experimental model of HI utilizing unilateral common carotid artery ligation followed by hypoxia. As BMPs are known to negatively regulate oligodendroglial fate specification of neural stem cells and alter differentiation of committed oligodendroglia, BMP signaling is likely an important mechanism leading to myelin loss. Here, we showed that BMP signaling is upregulated within oligodendroglia of the neonatal brain. We tested the hypothesis that inhibition of BMP signaling specifically within neural progenitor cells (NPCs) is sufficient to protect oligodendroglia. We conditionally deleted the BMP receptor 2 subtype (BMPR2) in NG2-expressing cells after HI. We found that BMPR2 deletion globally protects the brain as assessed by MRI and protects motor function as assessed by digital gait analysis, and that conditional deletion of BMPR2 maintains oligodendrocyte marker expression by immunofluorescence and Western blot and prevents loss of oligodendroglia. Finally, BMPR2 deletion after HI results in an increase in noncompacted myelin. Thus, our data indicate that inhibition of BMP signaling specifically in NPCs may be a tractable strategy to protect the newborn brain from HI. PMID:29324456

Training-free compressed sensing for wireless neural recording using analysis model and group weighted {{\\ell}_{1}} -minimization

NASA Astrophysics Data System (ADS)

Sun, Biao; Zhao, Wenfeng; Zhu, Xinshan

2017-06-01

Objective. Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed group weighted analysis {{\\ell}1} -minimization (GWALM), is proposed for wireless neural recording. Approach. The GWALM method consists of three parts: (1) the analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance. (2) A multi-fractional-order difference matrix is constructed as the analysis operator, thus avoiding the dictionary learning procedure and reducing the need for previously acquired data and computational complexities. (3) By exploiting the statistical properties of the analysis coefficients, a group weighting approach is developed to enhance the performance of analysis {{\\ell}1} -minimization. Main results. Experimental results on synthetic and real datasets reveal that the proposed approach outperforms state-of-the-art CS-based methods in terms of both spike recovery quality and classification accuracy. Significance. Energy and area efficiency of the GWALM make it an ideal candidate for resource-constrained, large scale wireless neural recording applications. The training-free feature of the GWALM further improves its robustness to spike shape variation, thus making it more practical for long term wireless neural recording.

Motion Control of Drives for Prosthetic Hand Using Continuous Myoelectric Signals

NASA Astrophysics Data System (ADS)

Purushothaman, Geethanjali; Ray, Kalyan Kumar

2016-03-01

In this paper the authors present motion control of a prosthetic hand, through continuous myoelectric signal acquisition, classification and actuation of the prosthetic drive. A four channel continuous electromyogram (EMG) signal also known as myoelectric signals (MES) are acquired from the abled-body to classify the six unique movements of hand and wrist, viz, hand open (HO), hand close (HC), wrist flexion (WF), wrist extension (WE), ulnar deviation (UD) and radial deviation (RD). The classification technique involves in extracting the features/pattern through statistical time domain (TD) parameter/autoregressive coefficients (AR), which are reduced using principal component analysis (PCA). The reduced statistical TD features and or AR coefficients are used to classify the signal patterns through k nearest neighbour (kNN) as well as neural network (NN) classifier and the performance of the classifiers are compared. Performance comparison of the above two classifiers clearly shows that kNN classifier in identifying the hidden intended motion in the myoelectric signals is better than that of NN classifier. Once the classifier identifies the intended motion, the signal is amplified to actuate the three low power DC motor to perform the above mentioned movements.

Feedback to distal dendrites links fMRI signals to neural receptive fields in a spiking network model of the visual cortex.

PubMed

Heikkinen, Hanna; Sharifian, Fariba; Vigario, Ricardo; Vanni, Simo

2015-07-01

The blood oxygenation level-dependent (BOLD) response has been strongly associated with neuronal activity in the brain. However, some neuronal tuning properties are consistently different from the BOLD response. We studied the spatial extent of neural and hemodynamic responses in the primary visual cortex, where the BOLD responses spread and interact over much longer distances than the small receptive fields of individual neurons would predict. Our model shows that a feedforward-feedback loop between V1 and a higher visual area can account for the observed spread of the BOLD response. In particular, anisotropic landing of inputs to compartmental neurons were necessary to account for the BOLD signal spread, while retaining realistic spiking responses. Our work shows that simple dendrites can separate tuning at the synapses and at the action potential output, thus bridging the BOLD signal to the neural receptive fields with high fidelity. Copyright © 2015 the American Physiological Society.

Classification of Respiratory Sounds by Using An Artificial Neural Network

DTIC Science & Technology

2001-10-28

CLASSIFICATION OF RESPIRATORY SOUNDS BY USING AN ARTIFICIAL NEURAL NETWORK M.C. Sezgin, Z. Dokur, T. Ölmez, M. Korürek Department of Electronics and...successfully classified by the GAL network. Keywords-Respiratory Sounds, Classification of Biomedical Signals, Artificial Neural Network . I. INTRODUCTION...process, feature extraction, and classification by the artificial neural network . At first, the RS signal obtained from a real-time measurement equipment is

Neural coding of syntactic structure in learned vocalizations in the songbird.

PubMed

Fujimoto, Hisataka; Hasegawa, Taku; Watanabe, Dai

2011-07-06

Although vocal signals including human languages are composed of a finite number of acoustic elements, complex and diverse vocal patterns can be created from combinations of these elements, linked together by syntactic rules. To enable such syntactic vocal behaviors, neural systems must extract the sequence patterns from auditory information and establish syntactic rules to generate motor commands for vocal organs. However, the neural basis of syntactic processing of learned vocal signals remains largely unknown. Here we report that the basal ganglia projecting premotor neurons (HVC(X) neurons) in Bengalese finches represent syntactic rules that generate variable song sequences. When vocalizing an alternative transition segment between song elements called syllables, sparse burst spikes of HVC(X) neurons code the identity of a specific syllable type or a specific transition direction among the alternative trajectories. When vocalizing a variable repetition sequence of the same syllable, HVC(X) neurons not only signal the initiation and termination of the repetition sequence but also indicate the progress and state-of-completeness of the repetition. These different types of syntactic information are frequently integrated within the activity of single HVC(X) neurons, suggesting that syntactic attributes of the individual neurons are not programmed as a basic cellular subtype in advance but acquired in the course of vocal learning and maturation. Furthermore, some auditory-vocal mirroring type HVC(X) neurons display transition selectivity in the auditory phase, much as they do in the vocal phase, suggesting that these songbirds may extract syntactic rules from auditory experience and apply them to form their own vocal behaviors.

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

PubMed Central

Ruiz-Gonzalez, Ruben; Gomez-Gil, Jaime; Gomez-Gil, Francisco Javier; Martínez-Martínez, Víctor

2014-01-01

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels. PMID:25372618

An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis.

PubMed

Ruiz-Gonzalez, Ruben; Gomez-Gil, Jaime; Gomez-Gil, Francisco Javier; Martínez-Martínez, Víctor

2014-11-03

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

BMP, Wnt and FGF signals are integrated through evolutionarily conserved enhancers to achieve robust expression of Pax3 and Zic genes at the zebrafish neural plate border

PubMed Central

Garnett, Aaron T.; Square, Tyler A.; Medeiros, Daniel M.

2012-01-01

Neural crest cells generate a range of cells and tissues in the vertebrate head and trunk, including peripheral neurons, pigment cells, and cartilage. Neural crest cells arise from the edges of the nascent central nervous system, a domain called the neural plate border (NPB). NPB induction is known to involve the BMP, Wnt and FGF signaling pathways. However, little is known about how these signals are integrated to achieve temporally and spatially specific expression of genes in NPB cells. Furthermore, the timing and relative importance of these signals in NPB formation appears to differ between vertebrate species. Here, we use heat-shock overexpression and chemical inhibitors to determine whether, and when, BMP, Wnt and FGF signaling are needed for expression of the NPB specifiers pax3a and zic3 in zebrafish. We then identify four evolutionarily conserved enhancers from the pax3a and zic3 loci and test their response to BMP, Wnt and FGF perturbations. We find that all three signaling pathways are required during gastrulation for the proper expression of pax3a and zic3 in the zebrafish NPB. We also find that, although the expression patterns driven by the pax3a and zic3 enhancers largely overlap, they respond to different combinations of BMP, Wnt and FGF signals. Finally, we show that the combination of the two pax3a enhancers is less susceptible to signaling perturbations than either enhancer alone. Taken together, our results reveal how BMPs, FGFs and Wnts act cooperatively and redundantly through partially redundant enhancers to achieve robust, specific gene expression in the zebrafish NPB. PMID:23034628

Dlx proteins position the neural plate border and determine adjacent cell fates.

PubMed

Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

2003-01-01

The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

Dlx proteins position the neural plate border and determine adjacent cell fates

PubMed Central

Woda, Juliana M.; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

2014-01-01

Summary The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates. PMID:12466200

Microbe-Induced Inflammatory Signals Triggering Acquired Bone Marrow Failure Syndromes.

PubMed

Espinoza, J Luis; Kotecha, Ritesh; Nakao, Shinji

2017-01-01

Acquired bone marrow failure syndromes encompass a unique set of disorders characterized by a reduction in the effective production of mature cells by the bone marrow (BM). In the majority of cases, these syndromes are the result of the immune-mediated destruction of hematopoietic stem cells or their progenitors at various stages of differentiation. Microbial infection has also been associated with hematopoietic stem cell injury and may lead to associated transient or persistent BM failure, and recent evidence has highlighted the potential impact of commensal microbes and their metabolites on hematopoiesis. We summarize the interactions between microorganisms and the host immune system and emphasize how they may impact the development of acquired BM failure.

Microbe-Induced Inflammatory Signals Triggering Acquired Bone Marrow Failure Syndromes

PubMed Central

Espinoza, J. Luis; Kotecha, Ritesh; Nakao, Shinji

2017-01-01

Acquired bone marrow failure syndromes encompass a unique set of disorders characterized by a reduction in the effective production of mature cells by the bone marrow (BM). In the majority of cases, these syndromes are the result of the immune-mediated destruction of hematopoietic stem cells or their progenitors at various stages of differentiation. Microbial infection has also been associated with hematopoietic stem cell injury and may lead to associated transient or persistent BM failure, and recent evidence has highlighted the potential impact of commensal microbes and their metabolites on hematopoiesis. We summarize the interactions between microorganisms and the host immune system and emphasize how they may impact the development of acquired BM failure. PMID:28286502

Analysis of acoustic emission signals at austempering of steels using neural networks

NASA Astrophysics Data System (ADS)

Łazarska, Malgorzata; Wozniak, Tadeusz Z.; Ranachowski, Zbigniew; Trafarski, Andrzej; Domek, Grzegorz

2017-05-01

Bearing steel 100CrMnSi6-4 and tool steel C105U were used to carry out this research with the steels being austempered to obtain a martensitic-bainitic structure. During the process quite a large number of acoustic emissions (AE) were observed. These signals were then analysed using neural networks resulting in the identification of three groups of events of: high, medium and low energy and in addition their spectral characteristics were plotted. The results were presented in the form of diagrams of AE incidence as a function of time. It was demonstrated that complex transformations of austenite into martensite and bainite occurred when austempering bearing steel at 160 °C and tool steel at 130 °C respectively. The selected temperatures of isothermal quenching of the tested steels were within the area near to MS temperature, which affected the complex course of phase transition. The high activity of AE is a typical occurrence for martensitic transformation and this is the transformation mechanism that induces the generation of AE signals of higher energy in the first stage of transition. In the second stage of transformation, the initially nucleated martensite accelerates the occurrence of the next bainitic transformation.

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback

PubMed Central

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence

A theory of how active behavior stabilises neural activity: Neural gain modulation by closed-loop environmental feedback.

PubMed

Buckley, Christopher L; Toyoizumi, Taro

2018-01-01

During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence

Strategies for improving neural signal detection using a neural-electronic interface.

PubMed

Szlavik, Robert B

2003-03-01

There have been various theoretical and experimental studies presented in the literature that focus on interfacing neurons with discrete electronic devices, such as transistors. From both a theoretical and experimental perspective, these studies have emphasized the variability in the characteristics of the detected action potential from the nerve cell. The demonstrated lack of reproducible fidelity of the nerve cell action potential at the device junction would make it impractical to implement these devices in any neural prosthetic application where reliable detection of the action potential was a prerequisite. In this study, the effects of several different physical parameters on the fidelity of the detected action potential at the device junction are investigated and discussed. The impact of variations in the extracellular resistivity, which directly affects the junction seal resistance, is studied along with the impact of variable nerve cell membrane capacitance and variations in the injected charge. These parameters are discussed in the context of their suitability to design manipulation for the purpose of improving the fidelity of the detected neural action potential. In addition to investigating the effects of variations in these parameters, the applicability of the linear equivalent circuit approach to calculating the junction potential is investigated.

BLOS2 negatively regulates Notch signaling during neural and hematopoietic stem and progenitor cell development

PubMed Central

Zhou, Wenwen; He, Qiuping; Zhang, Chunxia; He, Xin; Cui, Zongbin; Liu, Feng; Li, Wei

2016-01-01

Notch signaling plays a crucial role in controling the proliferation and differentiation of stem and progenitor cells during embryogenesis or organogenesis, but its regulation is incompletely understood. BLOS2, encoded by the Bloc1s2 gene, is a shared subunit of two lysosomal trafficking complexes, biogenesis of lysosome-related organelles complex-1 (BLOC-1) and BLOC-1-related complex (BORC). Bloc1s2−/− mice were embryonic lethal and exhibited defects in cortical development and hematopoiesis. Loss of BLOS2 resulted in elevated Notch signaling, which consequently increased the proliferation of neural progenitor cells and inhibited neuronal differentiation in cortices. Likewise, ablation of bloc1s2 in zebrafish or mice led to increased hematopoietic stem and progenitor cell production in the aorta-gonad-mesonephros region. BLOS2 physically interacted with Notch1 in endo-lysosomal trafficking of Notch1. Our findings suggest that BLOS2 is a novel negative player in regulating Notch signaling through lysosomal trafficking to control multiple stem and progenitor cell homeostasis in vertebrates. DOI: http://dx.doi.org/10.7554/eLife.18108.001 PMID:27719760

Training-free compressed sensing for wireless neural recording using analysis model and group weighted [Formula: see text]-minimization.

PubMed

Sun, Biao; Zhao, Wenfeng; Zhu, Xinshan

2017-06-01

Data compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and compressed sensing (CS) theory has successfully demonstrated its potential in neural recording applications. In this paper, an analytical, training-free CS recovery method, termed group weighted analysis [Formula: see text]-minimization (GWALM), is proposed for wireless neural recording. The GWALM method consists of three parts: (1) the analysis model is adopted to enforce sparsity of the neural signals, therefore overcoming the drawbacks of conventional synthesis models and enhancing the recovery performance. (2) A multi-fractional-order difference matrix is constructed as the analysis operator, thus avoiding the dictionary learning procedure and reducing the need for previously acquired data and computational complexities. (3) By exploiting the statistical properties of the analysis coefficients, a group weighting approach is developed to enhance the performance of analysis [Formula: see text]-minimization. Experimental results on synthetic and real datasets reveal that the proposed approach outperforms state-of-the-art CS-based methods in terms of both spike recovery quality and classification accuracy. Energy and area efficiency of the GWALM make it an ideal candidate for resource-constrained, large scale wireless neural recording applications. The training-free feature of the GWALM further improves its robustness to spike shape variation, thus making it more practical for long term wireless neural recording.

Neural mechanisms underlying the effects of face-based affective signals on memory for faces: a tentative model

PubMed Central

Tsukiura, Takashi

2012-01-01

In our daily lives, we form some impressions of other people. Although those impressions are affected by many factors, face-based affective signals such as facial expression, facial attractiveness, or trustworthiness are important. Previous psychological studies have demonstrated the impact of facial impressions on remembering other people, but little is known about the neural mechanisms underlying this psychological process. The purpose of this article is to review recent functional MRI (fMRI) studies to investigate the effects of face-based affective signals including facial expression, facial attractiveness, and trustworthiness on memory for faces, and to propose a tentative concept for understanding this affective-cognitive interaction. On the basis of the aforementioned research, three brain regions are potentially involved in the processing of face-based affective signals. The first candidate is the amygdala, where activity is generally modulated by both affectively positive and negative signals from faces. Activity in the orbitofrontal cortex (OFC), as the second candidate, increases as a function of perceived positive signals from faces; whereas activity in the insular cortex, as the third candidate, reflects a function of face-based negative signals. In addition, neuroscientific studies have reported that the three regions are functionally connected to the memory-related hippocampal regions. These findings suggest that the effects of face-based affective signals on memory for faces could be modulated by interactions between the regions associated with the processing of face-based affective signals and the hippocampus as a memory-related region. PMID:22837740

Neural networks and applications tutorial

NASA Astrophysics Data System (ADS)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

High-Density Stretchable Electrode Grids for Chronic Neural Recording

PubMed Central

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F.; Buzsáki, György; Vörös, János

2018-01-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. PMID:29488263

Neural Global Pattern Similarity Underlies True and False Memories.

PubMed

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.

Method for neural network control of motion using real-time environmental feedback

NASA Technical Reports Server (NTRS)

Buckley, Theresa M. (Inventor)

1997-01-01

A method of motion control for robotics and other automatically controlled machinery using a neural network controller with real-time environmental feedback. The method is illustrated with a two-finger robotic hand having proximity sensors and force sensors that provide environmental feedback signals. The neural network controller is taught to control the robotic hand through training sets using back- propagation methods. The training sets are created by recording the control signals and the feedback signal as the robotic hand or a simulation of the robotic hand is moved through a representative grasping motion. The data recorded is divided into discrete increments of time and the feedback data is shifted out of phase with the control signal data so that the feedback signal data lag one time increment behind the control signal data. The modified data is presented to the neural network controller as a training set. The time lag introduced into the data allows the neural network controller to account for the temporal component of the robotic motion. Thus trained, the neural network controlled robotic hand is able to grasp a wide variety of different objects by generalizing from the training sets.

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

PubMed

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.

Polarity-specific high-level information propagation in neural networks

PubMed Central

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. PMID:24672472

Proposal of a model of mammalian neural induction

PubMed Central

Levine, Ariel J.; Brivanlou, Ali H.

2009-01-01

How does the vertebrate embryo make a nervous system? This complex question has been at the center of developmental biology for many years. The earliest step in this process – the induction of neural tissue – is intimately linked to patterning of the entire early embryo, and the molecular and embryological basis these processes are beginning to emerge. Here, we analyze classic and cutting-edge findings on neural induction in the mouse. We find that data from genetics, tissue explants, tissue grafting, and molecular marker expression support a coherent framework for mammalian neural induction. In this model, the gastrula organizer of the mouse embryo inhibits BMP signaling to allow neural tissue to form as a default fate – in the absence of instructive signals. The first neural tissue induced is anterior and subsequent neural tissue is posteriorized to form the midbrain, hindbrain, and spinal cord. The anterior visceral endoderm protects the pre-specified anterior neural fate from similar posteriorization, allowing formation of forebrain. This model is very similar to the default model of neural induction in the frog, thus bridging the evolutionary gap between amphibians and mammals. PMID:17585896

Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, Patrick I.

2003-09-23

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. Neuralmore » 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

A novel method to acquire 3D data from serial 2D images of a dental cast

NASA Astrophysics Data System (ADS)

Yi, Yaxing; Li, Zhongke; Chen, Qi; Shao, Jun; Li, Xinshe; Liu, Zhiqin

2007-05-01

This paper introduced a newly developed method to acquire three-dimensional data from serial two-dimensional images of a dental cast. The system consists of a computer and a set of data acquiring device. The data acquiring device is used to take serial pictures of the a dental cast; an artificial neural network works to translate two-dimensional pictures to three-dimensional data; then three-dimensional image can reconstruct by the computer. The three-dimensional data acquiring of dental casts is the foundation of computer-aided diagnosis and treatment planning in orthodontics.

Curcumin Stimulates Proliferation of Spinal Cord Neural Progenitor Cells via a Mitogen-Activated Protein Kinase Signaling Pathway

PubMed Central

Son, Sihoon; Cho, Dae-Chul; Kim, Hye-Jeong; Sung, Joo-Kyung; Bae, Jae-Sung

2014-01-01

Objective The aims of our study are to evaluate the effect of curcumin on spinal cord neural progenitor cell (SC-NPC) proliferation and to clarify the mechanisms of mitogen-activated protein (MAP) kinase signaling pathways in SC-NPCs. Methods We established cultures of SC-NPCs, extracted from the spinal cord of Sprague-Dawley rats weighing 250 g to 350 g. We measured proliferation rates of SC-NPCs after curcumin treatment at different dosage. The immuno-blotting method was used to evaluate the MAP kinase signaling protein that contains extracellular signal-regulated kinases (ERKs), p38, c-Jun NH2-terminal kinases (JNKs) and β-actin as the control group. Results Curcumin has a biphasic effect on SC-NPC proliferation. Lower dosage (0.1, 0.5, 1 µM) of curcumin increased SC-NPC proliferation. However, higher dosage decreased SC-NPC proliferation. Also, curcumin stimulates proliferation of SC-NPCs via the MAP kinase signaling pathway, especially involving the p-ERK and p-38 protein. The p-ERK protein and p38 protein levels varied depending on curcumin dosage (0.5 and 1 µM, p<0.05). Conclusion Curcumin can stimulate proliferation of SC-NPCs via ERKs and the p38 signaling pathway in low concentrations. PMID:25289117

Neural recording and modulation technologies

NASA Astrophysics Data System (ADS)

Chen, Ritchie; Canales, Andres; Anikeeva, Polina

2017-01-01

In the mammalian nervous system, billions of neurons connected by quadrillions of synapses exchange electrical, chemical and mechanical signals. Disruptions to this network manifest as neurological or psychiatric conditions. Despite decades of neuroscience research, our ability to treat or even to understand these conditions is limited by the capability of tools to probe the signalling complexity of the nervous system. Although orders of magnitude smaller and computationally faster than neurons, conventional substrate-bound electronics do not recapitulate the chemical and mechanical properties of neural tissue. This mismatch results in a foreign-body response and the encapsulation of devices by glial scars, suggesting that the design of an interface between the nervous system and a synthetic sensor requires additional materials innovation. Advances in genetic tools for manipulating neural activity have fuelled the demand for devices that are capable of simultaneously recording and controlling individual neurons at unprecedented scales. Recently, flexible organic electronics and bio- and nanomaterials have been developed for multifunctional and minimally invasive probes for long-term interaction with the nervous system. In this Review, we discuss the design lessons from the quarter-century-old field of neural engineering, highlight recent materials-driven progress in neural probes and look at emergent directions inspired by the principles of neural transduction.

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Folic Acid supplementation stimulates notch signaling and cell proliferation in embryonic neural stem cells.

PubMed

Liu, Huan; Huang, Guo-Wei; Zhang, Xu-Mei; Ren, Da-Lin; X Wilson, John

2010-09-01

The present study investigated the effect of folic acid supplementation on the Notch signaling pathway and cell proliferation in rat embryonic neural stem cells (NSCs). The NSCs were isolated from E14-16 rat brain and grown as neurospheres in serum-free suspension culture. Individual cultures were assigned to one of 3 treatment groups that differed according to the concentration of folic acid in the medium: Control (baseline folic acid concentration of 4 mg/l), low folic acid supplementation (4 mg/l above baseline, Folate-L) and high folic acid supplementation (40 mg/l above baseline, Folate-H). NSCs were identified by their expression of immunoreactive nestin and proliferating cells by incorporation of 5'bromo-2'deoxyuridine. Cell proliferation was also assessed by methyl thiazolyl tetrazolium assay. Notch signaling was analyzed by real-time PCR and western blot analyses of the expression of Notch1 and hairy and enhancer of split 5 (Hes5). Supplementation of NSCs with folic acid increased the mRNA and protein expression levels of Notch1 and Hes5. Folic acid supplementation also stimulated NSC proliferation dose-dependently. Embryonic NSCs respond to folic acid supplementation with increased Notch signaling and cell proliferation. This mechanism may mediate the effects of folic acid supplementation on neurogenesis in the embryonic nervous system.

Segregating neural and mechanosensory fates in the developing ear: patterning, signaling, and transcriptional control

PubMed Central

Raft, Steven; Groves, Andrew K.

2014-01-01

The vertebrate inner ear is composed of multiple sensory receptor epithelia, each of which is specialized for detection of sound, gravity or angular acceleration. Each receptor epithelium contains mechanosensitive hair cells, which are connected to the brainstem by bipolar sensory neurons. Hair cells and their associated neurons are derived from the embryonic rudiment of the inner ear epithelium, but the precise spatial and temporal patterns of their generation, as well as the signals that coordinate these events, have only recently begun to be understood. Gene expression, lineage tracing, and mutant analyses suggest that both neurons and hair cells are generated from a common domain of neural and sensory competence in the embryonic inner ear rudiment. Members of the Shh, Wnt and FGF families, together with retinoic acid signals, regulate transcription factor genes within the inner ear rudiment to establish the axial identity of the ear and regionalize neurogenic activity. Close-range signaling, such as that of the Notch pathway, specifies the fate of sensory regions and individual cell types. We also describe positive and negative interactions between basic helix-loop-helix and SoxB family transcription factors that specify either neuronal or sensory fates in a context-dependent manner. Finally, we review recent work on inner ear development in zebrafish, which demonstrates that the relative timing of neurogenesis and sensory epithelial formation is not phylogenetically constrained. PMID:24902666

Ex vivo determination of chewing patterns using FBG and artificial neural networks

NASA Astrophysics Data System (ADS)

Karam, L. Z.; Pegorini, V.; Pitta, C. S. R.; Assmann, T. S.; Cardoso, R.; Kalinowski, H. J.; Silva, J. C. C.

2014-05-01

This paper reports the experimental procedures performed in a bovine head for the determination of chewing patterns during the mastication process. Mandible movements during the chewing have been simulated either by using two plasticine materials with different textures or without material. Fibre Bragg grating sensors were fixed in the jaw to monitor the biomechanical forces involved in the chewing process. The acquired signals from the sensors fed the input of an artificial neural network aiming at the classification of the measured chewing patterns for each material used in the experiment. The results obtained from the simulation of the chewing process presented different patterns for the different textures of plasticine, resulting on the determination of three chewing patterns with a classification error of 5%.

Analysis of Magnetic Resonance Image Signal Fluctuations Acquired During MR-Guided Radiotherapy.

PubMed

Breto, Adrian L; Padgett, Kyle R; Ford, John C; Kwon, Deukwoo; Chang, Channing; Fuss, Martin; Stoyanova, Radka; Mellon, Eric A

2018-03-28

Magnetic resonance-guided radiotherapy (MRgRT) is a new and evolving treatment modality that allows unprecedented visualization of the tumor and surrounding anatomy. MRgRT includes daily 3D magnetic resonance imaging (MRI) for setup and rapidly repeated near real-time MRI scans during treatment for target tracking. One of the more exciting potential benefits of MRgRT is the ability to analyze serial MRIs to monitor treatment response or predict outcomes. A typical radiation treatment (RT) over the span of 10-15 minutes on the MRIdian system (ViewRay, Cleveland, OH) yields thousands of "cine" images, each acquired in 250 ms. This unique data allows for a glimpse in image intensity changes during RT delivery. In this report, we analyze cine images from a single fraction RT of a glioblastoma patient on the ViewRay platform in order to characterize the dynamic signal changes occurring during RT therapy. The individual frames in the cines were saved into DICOM format and read into an MIM image analysis platform (MIM Software, Cleveland, OH) as a time series. The three possible states of the three Cobalt-60 radiation sources-OFF, READY, and ON-were also recorded. An in-house Java plugin for MIM was created in order to perform principal component analysis (PCA) on each of the datasets. The analysis resulted in first PC, related to monotonous signal increase over the course of the treatment fraction. We found several distortion patterns in the data that we postulate result from the perturbation of the magnetic field due to the moving metal parts in the platform while treatment was being administered. The largest variations were detected when all Cobalt-60 sources were OFF. During this phase of the treatment, the gantry and multi-leaf collimators (MLCs) are moving. Conversely, when all Cobalt-60 sources were in the ON position, the image signal fluctuations were minimal, relating to very little mechanical motion. At this phase, the gantry, the MLCs, and sources are fixed

Analysis of Magnetic Resonance Image Signal Fluctuations Acquired During MR-Guided Radiotherapy

PubMed Central

Breto, Adrian L; Padgett, Kyle R; Ford, John C; Kwon, Deukwoo; Chang, Channing; Fuss, Martin; Mellon, Eric A

2018-01-01

Magnetic resonance-guided radiotherapy (MRgRT) is a new and evolving treatment modality that allows unprecedented visualization of the tumor and surrounding anatomy. MRgRT includes daily 3D magnetic resonance imaging (MRI) for setup and rapidly repeated near real-time MRI scans during treatment for target tracking. One of the more exciting potential benefits of MRgRT is the ability to analyze serial MRIs to monitor treatment response or predict outcomes. A typical radiation treatment (RT) over the span of 10-15 minutes on the MRIdian system (ViewRay, Cleveland, OH) yields thousands of “cine” images, each acquired in 250 ms. This unique data allows for a glimpse in image intensity changes during RT delivery. In this report, we analyze cine images from a single fraction RT of a glioblastoma patient on the ViewRay platform in order to characterize the dynamic signal changes occurring during RT therapy. The individual frames in the cines were saved into DICOM format and read into an MIM image analysis platform (MIM Software, Cleveland, OH) as a time series. The three possible states of the three Cobalt-60 radiation sources—OFF, READY, and ON—were also recorded. An in-house Java plugin for MIM was created in order to perform principal component analysis (PCA) on each of the datasets. The analysis resulted in first PC, related to monotonous signal increase over the course of the treatment fraction. We found several distortion patterns in the data that we postulate result from the perturbation of the magnetic field due to the moving metal parts in the platform while treatment was being administered. The largest variations were detected when all Cobalt-60 sources were OFF. During this phase of the treatment, the gantry and multi-leaf collimators (MLCs) are moving. Conversely, when all Cobalt-60 sources were in the ON position, the image signal fluctuations were minimal, relating to very little mechanical motion. At this phase, the gantry, the MLCs, and sources

Neural networks for Higgs physics

NASA Astrophysics Data System (ADS)

Tentindo-Repond, Silvia; Bhat, Pushpalatha C.; Prosper, Harrison B.

2001-08-01

The main application of neural networks (NN) in Higgs physics so far has been to optimize the signal over background ratio. The positive result obtained imply that the use of NN will lead to a big reduction in the integrated luminosity required for the discovery of the Higgs in RunII. Neural Networks have also been recently used in Higgs physics to set up tagging algorithms to identify the heavy flavor content of jets. Whereas in the previous studies the NN b-tagging methods used are channel-independent, a channel-dependent method has been used in the present work. The signal pp¯→WH→lνbb¯ has been studied against the dominant background pp¯→Wbb¯, in an attempt to improve the signal over background ratio by trying to push the invariant mass of the background events further away from the signal. This result would get the equivalent effect of an improved mass resolution.

Welding quality evaluation of resistance spot welding using the time-varying inductive reactance signal

NASA Astrophysics Data System (ADS)

Zhang, Hongjie; Hou, Yanyan; Yang, Tao; Zhang, Qian; Zhao, Jian

2018-05-01

In the spot welding process, a high alternating current is applied, resulting in a time-varying electromagnetic field surrounding the welder. When measuring the welding voltage signal, the impedance of the measuring circuit consists of two parts: dynamic resistance relating to weld nugget nucleation event and inductive reactance caused by mutual inductance. The aim of this study is to develop a method to acquire the dynamic reactance signal and to discuss the possibility of using this signal to evaluate the weld quality. For this purpose, a series of experiments were carried out. The reactance signals under different welding conditions were compared and the results showed that the morphological feature of the reactance signal was closely related to the welding current and it was also significantly influenced by some abnormal welding conditions. Some features were extracted from the reactance signal and combined to construct weld nugget strength and diameter prediction models based on the radial basis function (RBF) neural network. In addition, several features were also used to monitor the expulsion in the welding process by using Fisher linear discriminant analysis. The results indicated that using the dynamic reactance signal to evaluate weld quality is possible and feasible.

Modulation Depth Estimation and Variable Selection in State-Space Models for Neural Interfaces

PubMed Central

Hochberg, Leigh R.; Donoghue, John P.; Brown, Emery N.

2015-01-01

Rapid developments in neural interface technology are making it possible to record increasingly large signal sets of neural activity. Various factors such as asymmetrical information distribution and across-channel redundancy may, however, limit the benefit of high-dimensional signal sets, and the increased computational complexity may not yield corresponding improvement in system performance. High-dimensional system models may also lead to overfitting and lack of generalizability. To address these issues, we present a generalized modulation depth measure using the state-space framework that quantifies the tuning of a neural signal channel to relevant behavioral covariates. For a dynamical system, we develop computationally efficient procedures for estimating modulation depth from multivariate data. We show that this measure can be used to rank neural signals and select an optimal channel subset for inclusion in the neural decoding algorithm. We present a scheme for choosing the optimal subset based on model order selection criteria. We apply this method to neuronal ensemble spike-rate decoding in neural interfaces, using our framework to relate motor cortical activity with intended movement kinematics. With offline analysis of intracortical motor imagery data obtained from individuals with tetraplegia using the BrainGate neural interface, we demonstrate that our variable selection scheme is useful for identifying and ranking the most information-rich neural signals. We demonstrate that our approach offers several orders of magnitude lower complexity but virtually identical decoding performance compared to greedy search and other selection schemes. Our statistical analysis shows that the modulation depth of human motor cortical single-unit signals is well characterized by the generalized Pareto distribution. Our variable selection scheme has wide applicability in problems involving multisensor signal modeling and estimation in biomedical engineering systems. PMID

Self-organizing neural network models for visual pattern recognition.

PubMed

Fukushima, K

1987-01-01

Two neural network models for visual pattern recognition are discussed. The first model, called a "neocognitron", is a hierarchical multilayered network which has only afferent synaptic connections. It can acquire the ability to recognize patterns by "learning-without-a-teacher": the repeated presentation of a set of training patterns is sufficient, and no information about the categories of the patterns is necessary. The cells of the highest stage eventually become "gnostic cells", whose response shows the final result of the pattern-recognition of the network. Pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the stimulus pattern. The second model has not only afferent but also efferent synaptic connections, and is endowed with the function of selective attention. The afferent and the efferent signals interact with each other in the hierarchical network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent signals, and at the same time, the afferent signals gate efferent signal flow. When a complex figure, consisting of two patterns or more, is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the models is paying selective attention is affected by noise or defects, the model can "recall" the complete pattern from which the noise has been eliminated and the defects corrected.

High-Density Stretchable Electrode Grids for Chronic Neural Recording.

PubMed

Tybrandt, Klas; Khodagholy, Dion; Dielacher, Bernd; Stauffer, Flurin; Renz, Aline F; Buzsáki, György; Vörös, János

2018-04-01

Electrical interfacing with neural tissue is key to advancing diagnosis and therapies for neurological disorders, as well as providing detailed information about neural signals. A challenge for creating long-term stable interfaces between electronics and neural tissue is the huge mechanical mismatch between the systems. So far, materials and fabrication processes have restricted the development of soft electrode grids able to combine high performance, long-term stability, and high electrode density, aspects all essential for neural interfacing. Here, this challenge is addressed by developing a soft, high-density, stretchable electrode grid based on an inert, high-performance composite material comprising gold-coated titanium dioxide nanowires embedded in a silicone matrix. The developed grid can resolve high spatiotemporal neural signals from the surface of the cortex in freely moving rats with stable neural recording quality and preserved electrode signal coherence during 3 months of implantation. Due to its flexible and stretchable nature, it is possible to minimize the size of the craniotomy required for placement, further reducing the level of invasiveness. The material and device technology presented herein have potential for a wide range of emerging biomedical applications. © 2018 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nonlinear channel equalization for QAM signal constellation using artificial neural networks.

PubMed

Patra, J C; Pal, R N; Baliarsingh, R; Panda, G

1999-01-01

Application of artificial neural networks (ANN's) to adaptive channel equalization in a digital communication system with 4-QAM signal constellation is reported in this paper. A novel computationally efficient single layer functional link ANN (FLANN) is proposed for this purpose. This network has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomials. Because of input pattern enhancement, the FLANN is capable of forming arbitrarily nonlinear decision boundaries and can perform complex pattern classification tasks. Considering channel equalization as a nonlinear classification problem, the FLANN has been utilized for nonlinear channel equalization. The performance of the FLANN is compared with two other ANN structures [a multilayer perceptron (MLP) and a polynomial perceptron network (PPN)] along with a conventional linear LMS-based equalizer for different linear and nonlinear channel models. The effect of eigenvalue ratio (EVR) of input correlation matrix on the equalizer performance has been studied. The comparison of computational complexity involved for the three ANN structures is also provided.

Innate signals overcome acquired TCR signaling pathway regulation and govern the fate of human CD161(hi) CD8α⁺ semi-invariant T cells.

PubMed

Turtle, Cameron J; Delrow, Jeff; Joslyn, Rochelle C; Swanson, Hillary M; Basom, Ryan; Tabellini, Laura; Delaney, Colleen; Heimfeld, Shelly; Hansen, John A; Riddell, Stanley R

2011-09-08

Type 17 programmed CD161(hi)CD8α(+) T cells contribute to mucosal immunity to bacteria and yeast. In early life, microbial colonization induces proliferation of CD161(hi) cells that is dependent on their expression of a semi-invariant Vα7.2(+) TCR. Although prevalent in adults, CD161(hi)CD8α(+) cells exhibit weak proliferative and cytokine responses to TCR ligation. The mechanisms responsible for the dichotomous response of neonatal and adult CD161(hi) cells, and the signals that enable their effector function, have not been established. We describe acquired regulation of TCR signaling in adult memory CD161(hi)CD8α(+) T cells that is absent in cord CD161(hi) cells and adult CD161(lo) cells. Regulated TCR signaling in CD161(hi) cells was due to profound alterations in TCR signaling pathway gene expression and could be overcome by costimulation through CD28 or innate cytokine receptors, which dictated the fate of their progeny. Costimulation with IL-1β during TCR ligation markedly increased proinflammatory IL-17 production, while IL-12-induced Tc1-like function and restored the response to TCR ligation without costimulation. CD161(hi) cells from umbilical cord blood and granulocyte colony stimulating factor-mobilized leukaphereses differed in frequency and function, suggesting future evaluation of the contribution of CD161(hi) cells in hematopoietic stem cell grafts to transplant outcomes is warranted.

Neural control of finger movement via intracortical brain-machine interface

NASA Astrophysics Data System (ADS)

Irwin, Z. T.; Schroeder, K. E.; Vu, P. P.; Bullard, A. J.; Tat, D. M.; Nu, C. S.; Vaskov, A.; Nason, S. R.; Thompson, D. E.; Bentley, J. N.; Patil, P. G.; Chestek, C. A.

2017-12-01

Objective. Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. Approach. In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets. In the physical control condition, four rhesus macaques performed this task by moving all four fingers together in order to acquire a single target. This movement was equivalent to controlling the aperture of a power grasp. During this task performance, we recorded neural spikes from intracortical electrode arrays in primary motor cortex. Main results. Using a standard Kalman filter, we could reconstruct continuous finger movement offline with an average correlation of ρ  =  0.78 between actual and predicted position across four rhesus macaques. For two of the monkeys, this movement prediction was performed in real-time to enable direct brain control of the virtual hand. Compared to physical control, neural control performance was slightly degraded; however, the monkeys were still able to successfully perform the task with an average target acquisition rate of 83.1%. The monkeys’ ability to arbitrarily specify fingertip position was also quantified using an information throughput metric. During brain control task performance, the monkeys achieved an average 1.01 bits s-1 throughput, similar to that achieved in previous studies which decoded upper-arm movements to control computer cursors using a standard Kalman filter. Significance. This is, to our knowledge, the first demonstration of brain control of finger-level fine motor skills. We believe

Homeostatic control of neural activity: from phenomenology to molecular design.

PubMed

Davis, Graeme W

2006-01-01

Homeostasis is a specialized form of regulation that precisely maintains the function of a system at a set point level of activity. Recently, homeostatic signaling has been suggested to control neural activity through the modulation of synaptic efficacy and membrane excitability ( Davis & Goodman 1998a, Turrigiano & Nelson 2000, Marder & Prinz 2002, Perez-Otano & Ehlers 2005 ). In this way, homeostatic signaling is thought to constrain neural plasticity and contribute to the stability of neural function over time. Using a restrictive definition of homeostasis, this review first evaluates the phenomenological and molecular evidence for homeostatic signaling in the nervous system. Then, basic principles underlying the design and molecular implementation of homeostatic signaling are reviewed on the basis of work in other, simplified biological systems such as bacterial chemotaxis and the heat shock response. Data from these systems are then discussed in the context of homeostatic signaling in the nervous system.

Neuroendocrine signaling modulates specific neural networks relevant to migraine.

PubMed

Martins-Oliveira, Margarida; Akerman, Simon; Holland, Philip R; Hoffmann, Jan R; Tavares, Isaura; Goadsby, Peter J

2017-05-01

Migraine is a disabling brain disorder involving abnormal trigeminovascular activation and sensitization. Fasting or skipping meals is considered a migraine trigger and altered fasting glucose and insulin levels have been observed in migraineurs. Therefore peptides involved in appetite and glucose regulation including insulin, glucagon and leptin could potentially influence migraine neurobiology. We aimed to determine the effect of insulin (10U·kg -1 ), glucagon (100μg·200μl -1 ) and leptin (0.3, 1 and 3mg·kg -1 ) signaling on trigeminovascular nociceptive processing at the level of the trigeminocervical-complex and hypothalamus. Male rats were anesthetized and prepared for craniovascular stimulation. In vivo electrophysiology was used to determine changes in trigeminocervical neuronal responses to dural electrical stimulation, and phosphorylated extracellular signal-regulated kinases 1 and 2 (pERK1/2) immunohistochemistry to determine trigeminocervical and hypothalamic neural activity; both in response to intravenous administration of insulin, glucagon, leptin or vehicle control in combination with blood glucose analysis. Blood glucose levels were significantly decreased by insulin (p<0.001) and leptin (p<0.01) whereas glucagon had the opposite effect (p<0.001). Dural-evoked neuronal firing in the trigeminocervical-complex was significantly inhibited by insulin (p<0.001), glucagon (p<0.05) and leptin (p<0.01). Trigeminocervical-complex pERK1/2 cell expression was significantly decreased by insulin and leptin (both p<0.001), and increased by glucagon (p<0.001), when compared to vehicle control. However, only leptin affected pERK1/2 expression in the hypothalamus, significantly decreasing pERK1/2 immunoreactive cell expression in the arcuate nucleus (p<0.05). These findings demonstrate that insulin, glucagon and leptin can alter the transmission of trigeminal nociceptive inputs. A potential neurobiological link between migraine and impaired metabolic

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

PubMed

Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

2008-01-01

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

It’s a lipid’s world: Bioactive lipid metabolism and signaling in neural stem cell differentiation

PubMed Central

Bieberich, Erhard

2012-01-01

Lipids are often considered membrane components whose function is to embed proteins into cell membranes. In the last two decades, studies on brain lipids have unequivocally demonstrated that many lipids have critical cell signaling functions; they are called “bioactive lipids”. Pioneering work in Dr. Robert Ledeen’s laboratory has shown that two bioactive brain sphingolipids, sphingomyelin and the ganglioside GM1 are major signaling lipids in the nuclear envelope. In addition to derivatives of the sphingolipid ceramide, the bioactive lipids discussed here belong to the classes of terpenoids and steroids, eicosanoids, and lysophospholipids. These lipids act mainly through two mechanisms: 1) direct interaction between the bioactive lipid and a specific protein binding partner such as a lipid receptor, protein kinase or phosphatase, ion exchanger, or other cell signaling protein; and 2) formation of lipid microdomains or rafts that regulate the activity of a group of raft-associated cell signaling proteins. In recent years, a third mechanism has emerged, which invokes lipid second messengers as a regulator for the energy and redox balance of differentiating neural stem cells (NSCs). Interestingly, developmental niches such as the stem cell niche for adult NSC differentiation may also be metabolic compartments that respond to a distinct combination of bioactive lipids. The biological function of these lipids as regulators of NSC differentiation will be reviewed and their application in stem cell therapy discussed. PMID:22246226

Species, Sex and Individual Differences in the Vasotocin/Vasopressin System: Relationship to Neurochemical Signaling in the Social Behavior Neural Network

PubMed Central

Albers, H. Elliott

2014-01-01

Arginine-vasotocin(AVT)/arginine vasopressin (AVP) are members of the AVP/oxytocin (OT) superfamily of peptides that are involved in the regulation of social behavior, social cognition and emotion. Comparative studies have revealed that AVT/AVP and their receptors are found throughout the “Social Behavior Neural Network” and display the properties expected from a signaling system that controls social behavior (i.e., species, sex and individual differences and modulation by gonadal hormones and social factors). Neurochemical signaling within the SBNN likely involves a complex combination of synaptic mechanisms that co-release multiple chemical signals (e.g., classical neurotransmitters and AVT/AVP as well as other peptides) and non-synaptic mechanisms (i.e., volume transmission). Crosstalk between AVP/OT peptides and receptors within the SBNN is likely. A better understanding of the functional properties of neurochemical signaling in the SBNN will allow for a more refined examination of the relationships between this peptide system and species, sex and individual differences in sociality. PMID:25102443

Signal Feature Analysis Using Neural Networks & Psychoacoustics

DTIC Science & Technology

1993-05-01

large class file on the DAT recording . This processing produced signals which ranged in length from 13200 and 39650 points. The extractions produced ... recorded . This signal set, denoted as "Air" signals , lacked the parameter of angle but added the parameter of striker (metal, plastic, and wood...the subjects were recorded . These became r.4 w data for confusion matrices which described how often a subject confused the class of a signal

Norepinephrine and Dopamine as Learning Signals

PubMed Central

Harley, Carolyn W.

2004-01-01

The present review focuses on the hypothesis that norepinephrine (NE) and dopamine (DA) act as learning signals. Both NE and DA are broadly distributed in areas concerned with the representation of the world and with the conjunction of sensory inputs and motor outputs. Both are released at times of novelty and uncertainty, providing plausible signal events for updating representations and associations. These catecholamines activate intracellular machinery postulated to serve as a memory-formation cascade. Yet, despite the plausibility of an NE and DA role in vertebrate learning and memory, most evidence that they provide a learning signal is circumstantial. The major weakness of the data available is the lack of a specific description of how the neural circuit modulated by NE or DA participates in the learning being analyzed. Identifying a conditioned stimuli (CS) representation would facilitate the identification of a learning signal role for NE or DA. Describing how the CS representation comes to relate to learned behavior, either through sensory-sensory associations, in which the CS acquires the motivational significance of reward or punishment, thus driving appropriate behavior, or through direct sensory-motor associations is necessary to identify how NE and DA participate in memory creation. As described here, evidence consistent with a direct learning signal role for NE and DA is seen in the changing of sensory circuits in odor preference learning (NE), defensive conditioning (NE), and auditory cortex remodeling in adult rats (DA). Evidence that NE and DA contribute to normal learning through unspecified mechanisms is extensive, but the details of that support role are lacking. PMID:15656268

Application of Neural Networks to Wind tunnel Data Response Surface Methods

NASA Technical Reports Server (NTRS)

Lo, Ching F.; Zhao, J. L.; DeLoach, Richard

2000-01-01

The integration of nonlinear neural network methods with conventional linear regression techniques is demonstrated for representative wind tunnel force balance data modeling. This work was motivated by a desire to formulate precision intervals for response surfaces produced by neural networks. Applications are demonstrated for representative wind tunnel data acquired at NASA Langley Research Center and the Arnold Engineering Development Center in Tullahoma, TN.

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

NASA Astrophysics Data System (ADS)

Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

2017-12-01

We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

Augmented Indian hedgehog signaling in cranial neural crest cells leads to craniofacial abnormalities and dysplastic temporomandibular joint in mice

PubMed Central

Yang, Ling; Gu, Shuping; Ye, Wenduo; Song, Yingnan; Chen, YiPing

2016-01-01

Extensive studies have pinpointed the crucial role of Indian hedgehog (Ihh) signaling in the development of the appendicular skeleton and the essential function of Ihh in the formation of the temporomandibular joint (TMJ). In this study, we have investigated the effect of augmented Ihh signaling in TMJ development. We took a transgenic gain-of-function approach by overexpressing Ihh in the cranial neural crest (CNC) cells using a conditional Ihh transgenic allele and the Wnt1-Cre allele. We found that Wnt1-Cre-mediated tissue-specific overexpression of Ihh in the CNC lineage caused severe craniofacial abnormalities, including cleft lip/palate, encephalocele, anophthalmos, micrognathia, and defective TMJ development. In the mutant TMJ, the glenoid fossa was completely absent, whereas the condyle and the articular disc appeared relatively normal with slightly delayed chondrocyte differentiation. Our findings thus demonstrate that augmented Ihh signaling is detrimental to craniofacial development, and that finely tuned Ihh signaling is critical for TMJ formation. Our results also provide additional evidence that the development of the condyle and articular disc is independent of the glenoid fossa. PMID:26553654

Wnt/Yes-Associated Protein Interactions During Neural Tissue Patterning of Human Induced Pluripotent Stem Cells.

PubMed

Bejoy, Julie; Song, Liqing; Zhou, Yi; Li, Yan

2018-04-01

Human induced pluripotent stem cells (hiPSCs) have special ability to self-assemble into neural spheroids or mini-brain-like structures. During the self-assembly process, Wnt signaling plays an important role in regional patterning and establishing positional identity of hiPSC-derived neural progenitors. Recently, the role of Wnt signaling in regulating Yes-associated protein (YAP) expression (nuclear or cytoplasmic), the pivotal regulator during organ growth and tissue generation, has attracted increasing interests. However, the interactions between Wnt and YAP expression for neural lineage commitment of hiPSCs remain poorly explored. The objective of this study is to investigate the effects of Wnt signaling and YAP expression on the cellular population in three-dimensional (3D) neural spheroids derived from hiPSCs. In this study, Wnt signaling was activated using CHIR99021 for 3D neural spheroids derived from human iPSK3 cells through embryoid body formation. Our results indicate that Wnt activation induces nuclear localization of YAP and upregulates the expression of HOXB4, the marker for hindbrain/spinal cord. By contrast, the cells exhibit more rostral forebrain neural identity (expression of TBR1) without Wnt activation. Cytochalasin D was then used to induce cytoplasmic YAP and the results showed the decreased HOXB4 expression. In addition, the incorporation of microparticles in the neural spheroids was investigated for the perturbation of neural patterning. This study may indicate the bidirectional interactions of Wnt signaling and YAP expression during neural tissue patterning, which have the significance in neurological disease modeling, drug screening, and neural tissue regeneration.

A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network.

PubMed

Gharehbaghi, Arash; Linden, Maria

2017-10-12

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.

Feasibility Study of Extended-Gate-Type Silicon Nanowire Field-Effect Transistors for Neural Recording

PubMed Central

Kang, Hongki; Kim, Jee-Yeon; Choi, Yang-Kyu; Nam, Yoonkey

2017-01-01

In this research, a high performance silicon nanowire field-effect transistor (transconductance as high as 34 µS and sensitivity as 84 nS/mV) is extensively studied and directly compared with planar passive microelectrode arrays for neural recording application. Electrical and electrochemical characteristics are carefully characterized in a very well-controlled manner. We especially focused on the signal amplification capability and intrinsic noise of the transistors. A neural recording system using both silicon nanowire field-effect transistor-based active-type microelectrode array and platinum black microelectrode-based passive-type microelectrode array are implemented and compared. An artificial neural spike signal is supplied as input to both arrays through a buffer solution and recorded simultaneously. Recorded signal intensity by the silicon nanowire transistor was precisely determined by an electrical characteristic of the transistor, transconductance. Signal-to-noise ratio was found to be strongly dependent upon the intrinsic 1/f noise of the silicon nanowire transistor. We found how signal strength is determined and how intrinsic noise of the transistor determines signal-to-noise ratio of the recorded neural signals. This study provides in-depth understanding of the overall neural recording mechanism using silicon nanowire transistors and solid design guideline for further improvement and development. PMID:28350370

Feasibility Study of Extended-Gate-Type Silicon Nanowire Field-Effect Transistors for Neural Recording.

PubMed

Kang, Hongki; Kim, Jee-Yeon; Choi, Yang-Kyu; Nam, Yoonkey

2017-03-28

In this research, a high performance silicon nanowire field-effect transistor (transconductance as high as 34 µS and sensitivity as 84 nS/mV) is extensively studied and directly compared with planar passive microelectrode arrays for neural recording application. Electrical and electrochemical characteristics are carefully characterized in a very well-controlled manner. We especially focused on the signal amplification capability and intrinsic noise of the transistors. A neural recording system using both silicon nanowire field-effect transistor-based active-type microelectrode array and platinum black microelectrode-based passive-type microelectrode array are implemented and compared. An artificial neural spike signal is supplied as input to both arrays through a buffer solution and recorded simultaneously. Recorded signal intensity by the silicon nanowire transistor was precisely determined by an electrical characteristic of the transistor, transconductance. Signal-to-noise ratio was found to be strongly dependent upon the intrinsic 1/f noise of the silicon nanowire transistor. We found how signal strength is determined and how intrinsic noise of the transistor determines signal-to-noise ratio of the recorded neural signals. This study provides in-depth understanding of the overall neural recording mechanism using silicon nanowire transistors and solid design guideline for further improvement and development.

On Design and Implementation of Neural-Machine Interface for Artificial Legs

PubMed Central

Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

2011-01-01

The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

Curcumin increased the differentiation rate of neurons in neural stem cells via wnt signaling in vitro study.

PubMed

Chen, Fei; Wang, Haoxiang; Xiang, Xin; Yuan, Jichao; Chu, Weihua; Xue, Xingsen; Zhu, Haitao; Ge, Hongfei; Zou, Mingming; Feng, Hua; Lin, Jiangkai

2014-12-01

The objective of the present study was to clarify the relationship between the neuroprotective effects of curcumin and the classical wnt signaling pathway. Using Sprague-Dawley rats at a gestational age of 14.5 d, we isolated neural stem cells from the anterior two-thirds of the fetal rat brain. The neural stem cells were passaged three times using the half media replacement method and identified using cellular immunofluorescence. After passaging for three generations, we cultured cells in media without basic fibroblast growth factor and epidermal growth factor. Then we treated cells in five different ways, including a blank control group, a group treated with IWR1 (10 μmol/L), a group treated with curcumin (500 nmol/L), a group treated with IWR1 + curcumin, and a group treated with dimethyl sulfoxide (10 μmol/L). We then measured the protein and RNA expression levels for wnt3a and β-catenin using Western blotting and Reverse transcription-polymerase chain reaction (RT-PCR). Western-blotting: after the third generation of cells had been treated for 72 h, we observed that wnt3a and β-catenin expression was significantly increased in the group receiving 500 nmol/L curcumin but not in the other groups. Furthermore, cells in the IWR1-treated group showed decreased wnt3a and β-catenin expression, and wnt3a and β-catenin was also decreased in the IWR1 + 500 nmol/L curcumin group. No obvious change was observed in the dimethyl sulfoxide group. RT-PCR showed similar changes to those observed with the Western blotting experiments. Our study suggests that curcumin can activate the wnt signaling pathway, which provides evidence that curcumin exhibits a neuroprotective effect through the classical wnt signaling pathway. Copyright © 2014 Elsevier Inc. All rights reserved.

New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.

PubMed

Cao, Yuting; Wen, Shiping; Chen, Michael Z Q; Huang, Tingwen; Zeng, Zhigang

2016-09-01

This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper. Copyright © 2016 Elsevier Ltd. All rights reserved.

An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

NASA Astrophysics Data System (ADS)

Su, Zhongqing; Ye, Lin

2004-08-01

The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Notch signaling and ageing.

PubMed

Polychronidou, Eleftheria; Vlachakis, Dimitrios; Vlamos, Panayiotis; Baumann, Marc; Kossida, Sophia

2015-01-01

Notch signaling is a master controller of the neural stem cell and neural development maintaining a significant role in the normal brain function. Notch genes are involved in embryogenesis, nervous system, and cardiovascular and endocrine function. On the other side, there are studies representing the involvement of Notch mutations in sporadic Alzheimer disease, other neurodegenerative diseases such as Down syndrome, Pick's and Prion's disease, and CADASIL. This manuscript attempts to present a holistic view of the positive or negative contribution of Notch signaling in the adult brain, and at the same time to present and promote the promising research fields of study.

The science of neural interface systems.

PubMed

Hatsopoulos, Nicholas G; Donoghue, John P

2009-01-01

The ultimate goal of neural interface research is to create links between the nervous system and the outside world either by stimulating or by recording from neural tissue to treat or assist people with sensory, motor, or other disabilities of neural function. Although electrical stimulation systems have already reached widespread clinical application, neural interfaces that record neural signals to decipher movement intentions are only now beginning to develop into clinically viable systems to help paralyzed people. We begin by reviewing state-of-the-art research and early-stage clinical recording systems and focus on systems that record single-unit action potentials. We then address the potential for neural interface research to enhance basic scientific understanding of brain function by offering unique insights in neural coding and representation, plasticity, brain-behavior relations, and the neurobiology of disease. Finally, we discuss technical and scientific challenges faced by these systems before they are widely adopted by severely motor-disabled patients.

Early driver fatigue detection from electroencephalography signals using artificial neural networks.

PubMed

King, L M; Nguyen, H T; Lal, S K L

2006-01-01

This paper describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver fatigue. The neural network uses a training optimization technique called the magnified gradient function (MGF). This technique reduces the time required for training by modifying the standard back propagation (SBP) algorithm. The MGF is shown to classify professional driver fatigue with 81.49% accuracy (80.53% sensitivity, 82.44% specificity) and non-professional driver fatigue with 83.06% accuracy (84.04% sensitivity and 82.08% specificity).

Conducting polymer coated neural recording electrodes.

PubMed

Harris, Alexander R; Morgan, Simeon J; Chen, Jun; Kapsa, Robert M I; Wallace, Gordon G; Paolini, Antonio G

2013-02-01

Neural recording electrodes suffer from poor signal to noise ratio, charge density, biostability and biocompatibility. This paper investigates the ability of conducting polymer coated electrodes to record acute neural response in a systematic manner, allowing in depth comparison of electrochemical and electrophysiological response. Polypyrrole (Ppy) and poly-3,4-ethylenedioxythiophene (PEDOT) doped with sulphate (SO4) or para-toluene sulfonate (pTS) were used to coat iridium neural recording electrodes. Detailed electrochemical and electrophysiological investigations were undertaken to compare the effect of these materials on acute in vivo recording. A range of charge density and impedance responses were seen with each respectively doped conducting polymer. All coatings produced greater charge density than uncoated electrodes, while PEDOT-pTS, PEDOT-SO4 and Ppy-SO4 possessed lower impedance values at 1 kHz than uncoated electrodes. Charge density increased with PEDOT-pTS thickness and impedance at 1 kHz was reduced with deposition times up to 45 s. Stable electrochemical response after acute implantation inferred biostability of PEDOT-pTS coated electrodes while other electrode materials had variable impedance and/or charge density after implantation indicative of a protein fouling layer forming on the electrode surface. Recording of neural response to white noise bursts after implantation of conducting polymer-coated electrodes into a rat model inferior colliculus showed a general decrease in background noise and increase in signal to noise ratio and spike count with reduced impedance at 1 kHz, regardless of the specific electrode coating, compared to uncoated electrodes. A 45 s PEDOT-pTS deposition time yielded the highest signal to noise ratio and spike count. A method for comparing recording electrode materials has been demonstrated with doped conducting polymers. PEDOT-pTS showed remarkable low fouling during acute implantation, inferring good biostability

Hemodynamic signals in fNIRS.

PubMed

Hoshi, Y

Near-infrared spectroscopy (NIRS) was originally designed for clinical monitoring of tissue oxygenation, and it has also been developed into a useful tool in neuroimaging studies, with the so-called functional NIRS (fNIRS). With NIRS, cerebral activation is detected by measuring the cerebral hemoglobin (Hb), where however, the precise correlation between NIRS signal and neural activity remains to be fully understood. This can in part be attributed to the situation that NIRS signals are inherently subject to contamination by signals arising from extracerebral tissue. In recent years, several approaches have been investigated to distinguish between NIRS signals originating in cerebral tissue and signals originating in extracerebral tissue. Selective measurements of cerebral Hb will enable a further evolution of fNIRS. This chapter is divided into six sections: first a summary of the basic theory of NIRS, NIRS signals arising in the activated areas, correlations between NIRS signals and fMRI signals, correlations between NIRS signals and neural activities, and the influence of a variety of extracerebral tissue on NIRS signals and approaches to this issue are reviewed. Finally, future prospects of fNIRS are described. © 2016 Elsevier B.V. All rights reserved.

Trait and state anxiety is marked by increased working memory-related parietal BOLD signal.

PubMed

Ford, Talitha C; Simpson, Tamara; McPhee, Grace; Stough, Con; Downey, Luke A

2018-05-16

Anxiety is associated with compromised cognitive control functions, such as working memory. State and trait anxiety within the non-clinical population can be utilised to investigate potential neural markers for anxiety, which may help to elucidate potential prevention and intervention methods. Thirty-two healthy adults (20 female, 12 male), aged between 30 and 65 years, performed a 2-back task whilst fMRI BOLD signal was acquired using a 3T scanner. Mean BOLD signal was obtained in cognitive control network regions of interest of: left and right dorsolateral prefrontal cortex (DLPFC) and posterior parietal lobe (PPL), and medial prefrontal cortex (MPFC). State and trait anxiety levels were recorded. Higher overall anxiety was moderately associated with more left and right PPL BOLD signal; there was a weak relationship between anxiety and left DLPFC BOLD signal. MPFC BOLD signal and trait anxiety were moderately associated with overall 2-back task performance. These findings suggest that non-clinical anxiety affects the recruitment of cortical resources during working memory, but that anxiety does not impair performance during a 2-back task. Copyright © 2018 Elsevier B.V. All rights reserved.

Neural Differentiation of Embryonic Stem Cells In Vitro: A Road Map to Neurogenesis in the Embryo

PubMed Central

Abranches, Elsa; Silva, Margarida; Pradier, Laurent; Schulz, Herbert; Hummel, Oliver; Henrique, Domingos; Bekman, Evguenia

2009-01-01

Background The in vitro generation of neurons from embryonic stem (ES) cells is a promising approach to produce cells suitable for neural tissue repair and cell-based replacement therapies of the nervous system. Available methods to promote ES cell differentiation towards neural lineages attempt to replicate, in different ways, the multistep process of embryonic neural development. However, to achieve this aim in an efficient and reproducible way, a better knowledge of the cellular and molecular events that are involved in the process, from the initial specification of neuroepithelial progenitors to their terminal differentiation into neurons and glial cells, is required. Methodology/Principal Findings In this work, we characterize the main stages and transitions that occur when ES cells are driven into a neural fate, using an adherent monolayer culture system. We established improved conditions to routinely produce highly homogeneous cultures of neuroepithelial progenitors, which organize into neural tube-like rosettes when they acquire competence for neuronal production. Within rosettes, neuroepithelial progenitors display morphological and functional characteristics of their embryonic counterparts, namely, apico-basal polarity, active Notch signalling, and proper timing of production of neurons and glia. In order to characterize the global gene activity correlated with each particular stage of neural development, the full transcriptome of different cell populations that arise during the in vitro differentiation protocol was determined by microarray analysis. By using embryo-oriented criteria to cluster the differentially expressed genes, we define five gene expression signatures that correlate with successive stages in the path from ES cells to neurons. These include a gene signature for a primitive ectoderm-like stage that appears after ES cells enter differentiation, and three gene signatures for subsequent stages of neural progenitor development, from an

The extraction of neural strategies from the surface EMG: an update

PubMed Central

Merletti, Roberto; Enoka, Roger M.

2014-01-01

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486–1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue. PMID:25277737

Differential roles of AVP and VIP signaling in the postnatal changes of neural networks for coherent circadian rhythms in the SCN

PubMed Central

Ono, Daisuke; Honma, Sato; Honma, Ken-ichi

2016-01-01

The suprachiasmatic nucleus (SCN) is the site of the master circadian clock in mammals. The SCN neural network plays a critical role in expressing the tissue-level circadian rhythm. Previously, we demonstrated postnatal changes in the SCN network in mice, in which the clock gene products CRYPTOCHROMES (CRYs) are involved. Here, we show that vasoactive intestinal polypeptide (VIP) signaling is essential for the tissue-level circadian PER2::LUC rhythm in the neonatal SCN of CRY double-deficient mice (Cry1,2−/−). VIP and arginine vasopressin (AVP) signaling showed redundancy in expressing the tissue-level circadian rhythm in the SCN. AVP synthesis was significantly attenuated in the Cry1,2−/− SCN, which contributes to aperiodicity in the adult mice together with an attenuation of VIP signaling as a natural process of ontogeny. The SCN network consists of multiple clusters of cellular circadian rhythms that are differentially integrated by AVP and VIP signaling, depending on the postnatal period. PMID:27626074

Neural Circuitry of the Bilingual Mental Lexicon: Effect of Age of Second Language Acquisition

ERIC Educational Resources Information Center

Isel, Frederic; Baumgaertner, Annette; Thran, Johannes; Meisel, Jurgen M.; Buchel, Christian

2010-01-01

Numerous studies have proposed that changes of the human language faculty caused by neural maturation can explain the substantial differences in ultimate attainment of grammatical competences between first language (L1) acquirers and second language (L2) learners. However, little evidence on the effect of neural maturation on the attainment of…

Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.

PubMed

Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon

2017-03-01

An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

Ereira, Sam; Dolan, Raymond J; Kurth-Nelson, Zeb

2018-04-01

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.

Neural learning rules for the vestibulo-ocular reflex

NASA Technical Reports Server (NTRS)

Raymond, J. L.; Lisberger, S. G.

1998-01-01

Mechanisms for the induction of motor learning in the vestibulo-ocular reflex (VOR) were evaluated by recording the patterns of neural activity elicited in the cerebellum by a range of stimuli that induce learning. Patterns of climbing-fiber, vestibular, and Purkinje cell simple-spike signals were examined during sinusoidal head movement paired with visual image movement at stimulus frequencies from 0.5 to 10 Hz. A comparison of simple-spike and vestibular signals contained the information required to guide learning only at low stimulus frequencies, and a comparison of climbing-fiber and simple-spike signals contained the information required to guide learning only at high stimulus frequencies. Learning could be guided by comparison of climbing-fiber and vestibular signals at all stimulus frequencies tested, but only if climbing fiber responses were compared with the vestibular signals present 100 msec earlier. Computational analysis demonstrated that this conclusion is valid even if there is a broad range of vestibular signals at the site of plasticity. Simulations also indicated that the comparison of vestibular and climbing-fiber signals across the 100 msec delay must be implemented by a subcellular "eligibility" trace rather than by neural circuits that delay the vestibular inputs to the site of plasticity. The results suggest two alternative accounts of learning in the VOR. Either there are multiple mechanisms of learning that use different combinations of neural signals to drive plasticity, or there is a single mechanism tuned to climbing-fiber activity that follows activity in vestibular pathways by approximately 100 msec.

Aging affects the balance of neural entrainment and top-down neural modulation in the listening brain

PubMed Central

Henry, Molly J.; Herrmann, Björn; Kunke, Dunja; Obleser, Jonas

2017-01-01

Healthy aging is accompanied by listening difficulties, including decreased speech comprehension, that stem from an ill-understood combination of sensory and cognitive changes. Here, we use electroencephalography to demonstrate that auditory neural oscillations of older adults entrain less firmly and less flexibly to speech-paced (∼3 Hz) rhythms than younger adults’ during attentive listening. These neural entrainment effects are distinct in magnitude and origin from the neural response to sound per se. Non-entrained parieto-occipital alpha (8–12 Hz) oscillations are enhanced in young adults, but suppressed in older participants, during attentive listening. Entrained neural phase and task-induced alpha amplitude exert opposite, complementary effects on listening performance: higher alpha amplitude is associated with reduced entrainment-driven behavioural performance modulation. Thus, alpha amplitude as a task-driven, neuro-modulatory signal can counteract the behavioural corollaries of neural entrainment. Balancing these two neural strategies may present new paths for intervention in age-related listening difficulties. PMID:28654081

A TinyOS-based wireless neural interface.

PubMed

Farshchi, Shahin; Mody, Istvan; Judy, Jack W

2004-01-01

The overlay of a neural interface upon a TinyOS-based sensing and communication platform is described. The system amplifies, digitally encodes, and transmits two EEG channels of neural signals from an un-tethered subject to a remote gateway, which routes the signals to a client PC. This work demonstrates the viability of the TinyOS-based sensor technology as a foundation for chronic remote biological monitoring applications, and thus provides an opportunity to create a system that can leverage from the frequent networking and communications advancements being made by the global TinyOS-development community.

Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients' Consciousness Level Based on Anesthesiologists Experience

PubMed Central

Jiang, George J. A.; Fan, Shou-Zen; Abbod, Maysam F.; Huang, Hui-Hsun; Lan, Jheng-Yan; Tsai, Feng-Fang; Chang, Hung-Chi; Yang, Yea-Wen; Chuang, Fu-Lan; Chiu, Yi-Fang; Jen, Kuo-Kuang; Wu, Jeng-Fu; Shieh, Jiann-Shing

2015-01-01

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully. PMID:25738152

BMP signaling protects telencephalic fate by repressing eye identity and its Cxcr4-dependent morphogenesis.

PubMed

Bielen, Holger; Houart, Corinne

2012-10-16

Depletion of Wnt signaling is a major requirement for the induction of the anterior prosencephalon. However, the molecular events driving the differential regionalization of this area into eye-field and telencephalon fates are still unknown. Here we show that the BMP pathway is active in the anterior neural ectoderm during late blastula to early gastrula stage in zebrafish. Bmp2b mutants and mosaic loss-of-function experiments reveal that BMP acts as a repressor of eye-field fate through inhibition of its key transcription factor Rx3, thereby protecting the future telencephalon from acquiring eye identity. This BMP-driven mechanism initiates the establishment of the telencephalon prior to the involvement of Wnt antagonists from the anterior neural border. Furthermore, we demonstrate that Rx3 and BMP are respectively required to maintain and restrict the chemokine receptor cxcr4a, which in turn contributes to the morphogenetic separation of eye-field and telencephalic cells during early neurulation. Copyright © 2012 Elsevier Inc. All rights reserved.

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Event-driven processing for hardware-efficient neural spike sorting

NASA Astrophysics Data System (ADS)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Neural Correlates of the Use of Psychological Distancing to Regulate Responses to Negative Social Cues: A Study of Patients with Borderline Personality Disorder

PubMed Central

Koenigsberg, Harold W.; Fan, Jin; Ochsner, Kevin; Liu, Xun; Guise, Kevin G.; Pizzarello, Scott; Dorantes, Christine; Guerreri, Stephanie; Tecuta, Lucia; Goodman, Marianne; New, Antonia; Siever, Larry J

2009-01-01

Background Emotional instability is a defining feature of borderline personality disorder (BPD), yet little is understood about its underlying neural correlates. One possible contributing factor to emotional instability is a failure to adequately employ adaptive cognitive regulatory strategies such as psychological distancing. Method To determine whether there are differences in neural dynamics underlying this control strategy, between BPD patients and healthy volunteers (HC’s), BOLD fMRI signals were acquired as 18 BPD and 16 HC subjects distanced from or simply looked at negative and neutral pictures depicting social interactions. Contrasts in signal between distance and look condition were compared between groups to identify commonalities and differences in regional activation. Results BPD patients show a different pattern of activation compared to HC subjects when looking at negative vs. neutral pictures. When distancing vs. looking at negative pictures, both groups showed decreased negative affect in rating and increased activation of the dorsolateral prefrontal cortex, areas near/along the intraparietal sulcus (IPS), ventrolateral prefrontal cortex and posterior cingulate/precuneus regions. However, the BPD group showed less BOLD signal change in dorsal anterior cingulate cortex and IPS, less deactivation in the amygdala and greater activation in the superior temporal sulcus and superior frontal gyrus. Conclusion BPD and HC subjects display different neural dynamics while passively viewing social emotional stimuli. In addition, BPD patients do not engage the cognitive control regions to the extent that HC’s do when employing a distancing strategy to regulate emotional reactions, which may be a factor contributing to the affective instability of BPD. PMID:19651401

Serotonergic antidepressants decrease hedonic signals but leave learning signals in the nucleus accumbens unaffected.

PubMed

Graf, Heiko; Metzger, Coraline D; Walter, Martin; Abler, Birgit

2016-01-06

Investigating the effects of serotonergic antidepressants on neural correlates of visual erotic stimulation revealed decreased reactivity within the dopaminergic reward network along with decreased subjective sexual functioning compared with placebo. However, a global dampening of the reward system under serotonergic drugs is not intuitive considering clinical observations of their beneficial effects in the treatment of depression. Particularly, learning signals as coded in prediction error processing within the dopaminergic reward system can be assumed to be rather enhanced as antidepressant drugs have been demonstrated to facilitate the efficacy of psychotherapeutic interventions relying on learning processes. Within the same study sample, we now explored the effects of serotonergic and dopaminergic/noradrenergic antidepressants on prediction error signals compared with placebo by functional MRI. A total of 17 healthy male participants (mean age: 25.4 years) were investigated under the administration of paroxetine, bupropion and placebo for 7 days each within a randomized, double-blind, within-subject cross-over design. During functional MRI, we used an established monetary incentive task to explore neural prediction error signals within the bilateral nucleus accumbens as region of interest within the dopaminergic reward system. In contrast to diminished neural activations and subjective sexual functioning under the serotonergic agent paroxetine under visual erotic stimulation, we revealed unaffected or even enhanced neural prediction error processing within the nucleus accumbens under this antidepressant along with unaffected behavioural processing. Our study provides evidence that serotonergic antidepressants facilitate prediction error signalling and may support suggestions of beneficial effects of these agents on reinforced learning as an essential element in behavioural psychotherapy.

3D silicon neural probe with integrated optical fibers for optogenetic modulation.

PubMed

Kim, Eric G R; Tu, Hongen; Luo, Hao; Liu, Bin; Bao, Shaowen; Zhang, Jinsheng; Xu, Yong

2015-07-21

Optogenetics is a powerful modality for neural modulation that can be useful for a wide array of biomedical studies. Penetrating microelectrode arrays provide a means of recording neural signals with high spatial resolution. It is highly desirable to integrate optics with neural probes to allow for functional study of neural tissue by optogenetics. In this paper, we report the development of a novel 3D neural probe coupled simply and robustly to optical fibers using a hollow parylene tube structure. The device shanks are hollow tubes with rigid silicon tips, allowing the insertion and encasement of optical fibers within the shanks. The position of the fiber tip can be precisely controlled relative to the electrodes on the shank by inherent design features. Preliminary in vivo rat studies indicate that these devices are capable of optogenetic modulation simultaneously with 3D neural signal recording.

Transcriptional Profiling of Hypoxic Neural Stem Cells Identifies Calcineurin-NFATc4 Signaling as a Major Regulator of Neural Stem Cell Biology

PubMed Central

Moreno, Marta; Fernández, Virginia; Monllau, Josep M.; Borrell, Víctor; Lerin, Carles; de la Iglesia, Núria

2015-01-01

Summary Neural stem cells (NSCs) reside in a hypoxic microenvironment within the brain. However, the crucial transcription factors (TFs) that regulate NSC biology under physiologic hypoxia are poorly understood. Here we have performed gene set enrichment analysis (GSEA) of microarray datasets from hypoxic versus normoxic NSCs with the aim of identifying pathways and TFs that are activated under oxygen concentrations mimicking normal brain tissue microenvironment. Integration of TF target (TFT) and pathway enrichment analysis identified the calcium-regulated TF NFATc4 as a major candidate to regulate hypoxic NSC functions. Nfatc4 expression was coordinately upregulated by top hypoxia-activated TFs, while NFATc4 target genes were enriched in hypoxic NSCs. Loss-of-function analyses further revealed that the calcineurin-NFATc4 signaling axis acts as a major regulator of NSC self-renewal and proliferation in vitro and in vivo by promoting the expression of TFs, including Id2, that contribute to the maintenance of the NSC state. PMID:26235896

Orientation Encoding and Viewpoint Invariance in Face Recognition: Inferring Neural Properties from Large-Scale Signals.

PubMed

Ramírez, Fernando M

2018-05-01

Viewpoint-invariant face recognition is thought to be subserved by a distributed network of occipitotemporal face-selective areas that, except for the human anterior temporal lobe, have been shown to also contain face-orientation information. This review begins by highlighting the importance of bilateral symmetry for viewpoint-invariant recognition and face-orientation perception. Then, monkey electrophysiological evidence is surveyed describing key tuning properties of face-selective neurons-including neurons bimodally tuned to mirror-symmetric face-views-followed by studies combining functional magnetic resonance imaging (fMRI) and multivariate pattern analyses to probe the representation of face-orientation and identity information in humans. Altogether, neuroimaging studies suggest that face-identity is gradually disentangled from face-orientation information along the ventral visual processing stream. The evidence seems to diverge, however, regarding the prevalent form of tuning of neural populations in human face-selective areas. In this context, caveats possibly leading to erroneous inferences regarding mirror-symmetric coding are exposed, including the need to distinguish angular from Euclidean distances when interpreting multivariate pattern analyses. On this basis, this review argues that evidence from the fusiform face area is best explained by a view-sensitive code reflecting head angular disparity, consistent with a role of this area in face-orientation perception. Finally, the importance is stressed of explicit models relating neural properties to large-scale signals.

Signal Restoration of Non-stationary Acoustic Signals in the Time Domain

NASA Technical Reports Server (NTRS)

Babkin, Alexander S.

1988-01-01

Signal restoration is a method of transforming a nonstationary signal acquired by a ground based microphone to an equivalent stationary signal. The benefit of the signal restoration is a simplification of the flight test requirements because it could dispense with the need to acquire acoustic data with another aircraft flying in concert with the rotorcraft. The data quality is also generally improved because the contamination of the signal by the propeller and wind noise is not present. The restoration methodology can also be combined with other data acquisition methods, such as a multiple linear microphone array for further improvement of the test results. The methodology and software are presented for performing the signal restoration in the time domain. The method has no restrictions on flight path geometry or flight regimes. Only requirement is that the aircraft spatial position be known relative to the microphone location and synchronized with the acoustic data. The restoration process assumes that the moving source radiates a stationary signal, which is then transformed into a nonstationary signal by various modulation processes. The restoration contains only the modulation due to the source motion.

Simultaneous GCaMP6-based fiber photometry and fMRI in rats.

PubMed

Liang, Zhifeng; Ma, Yuncong; Watson, Glenn D R; Zhang, Nanyin

2017-09-01

Understanding the relationship between neural and vascular signals is essential for interpretation of functional MRI (fMRI) results with respect to underlying neuronal activity. Simultaneously measuring neural activity using electrophysiology with fMRI has been highly valuable in elucidating the neural basis of the blood oxygenation-level dependent (BOLD) signal. However, this approach is also technically challenging due to the electromagnetic interference that is observed in electrophysiological recordings during MRI scanning. Recording optical correlates of neural activity, such as calcium signals, avoids this issue, and has opened a new avenue to simultaneously acquire neural and BOLD signals. The present study is the first to demonstrate the feasibility of simultaneously and repeatedly acquiring calcium and BOLD signals in animals using a genetically encoded calcium indicator, GCaMP6. This approach was validated with a visual stimulation experiment, during which robust increases of both calcium and BOLD signals in the superior colliculus were observed. In addition, repeated measurement in the same animal demonstrated reproducible calcium and BOLD responses to the same stimuli. Taken together, simultaneous GCaMP6-based fiber photometry and fMRI recording presents a novel, artifact-free approach to simultaneously measuring neural and fMRI signals. Furthermore, given the cell-type specificity of GCaMP6, this approach has the potential to mechanistically dissect the contributions of individual neuron populations to BOLD signal, and ultimately reveal its underlying neural mechanisms. The current study established the method for simultaneous GCaMP6-based fiber photometry and fMRI in rats. Copyright © 2017 Elsevier B.V. All rights reserved.

Shared motion signals for human perceptual decisions and oculomotor actions

NASA Technical Reports Server (NTRS)

Stone, Leland S.; Krauzlis, Richard J.

2003-01-01

A fundamental question in primate neurobiology is to understand to what extent motor behaviors are driven by shared neural signals that also support conscious perception or by independent subconscious neural signals dedicated to motor control. Although it has clearly been established that cortical areas involved in processing visual motion support both perception and smooth pursuit eye movements, it remains unknown whether the same or different sets of neurons within these structures perform these two functions. Examination of the trial-by-trial variation in human perceptual and pursuit responses during a simultaneous psychophysical and oculomotor task reveals that the direction signals for pursuit and perception are not only similar on average but also co-vary on a trial-by-trial basis, even when performance is at or near chance and the decisions are determined largely by neural noise. We conclude that the neural signal encoding the direction of target motion that drives steady-state pursuit and supports concurrent perceptual judgments emanates from a shared ensemble of cortical neurons.

Model-based Bayesian signal extraction algorithm for peripheral nerves

NASA Astrophysics Data System (ADS)

Eggers, Thomas E.; Dweiri, Yazan M.; McCallum, Grant A.; Durand, Dominique M.

2017-10-01

Objective. Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach. To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main results. The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance. HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of

Hippocampal Mismatch Signals Are Modulated by the Strength of Neural Predictions and Their Similarity to Outcomes.

PubMed

Long, Nicole M; Lee, Hongmi; Kuhl, Brice A

2016-12-14

hippocampal mismatch signals directly to neural measures of prediction strength. Here, we show that hippocampal mismatch signals increase as a function of the strength of predictions in neocortical regions. This increase in hippocampal mismatch signals was particularly robust when outcomes were similar, but not identical, to predictions. These results indicate that hippocampal mismatch signals are driven by both the active generation of predictions and the similarity between predictions and outcomes. Copyright © 2016 the authors 0270-6474/16/3612677-11$15.00/0.

Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

NASA Astrophysics Data System (ADS)

Bakanovskaya, L. N.

2016-08-01

The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

Transient Modulations of Neural Responses to Heartbeats Covary with Bodily Self-Consciousness.

PubMed

Park, Hyeong-Dong; Bernasconi, Fosco; Bello-Ruiz, Javier; Pfeiffer, Christian; Salomon, Roy; Blanke, Olaf

2016-08-10

Recent research has investigated self-consciousness associated with the multisensory processing of bodily signals (e.g., somatosensory, visual, vestibular signals), a notion referred to as bodily self-consciousness, and these studies have shown that the manipulation of bodily inputs induces changes in bodily self-consciousness such as self-identification. Another line of research has highlighted the importance of signals from the inside of the body (e.g., visceral signals) and proposed that neural representations of internal bodily signals underlie self-consciousness, which to date has been based on philosophical inquiry, clinical case studies, and behavioral studies. Here, we investigated the relationship of bodily self-consciousness with the neural processing of internal bodily signals. By combining electrical neuroimaging, analysis of peripheral physiological signals, and virtual reality technology in humans, we show that transient modulations of neural responses to heartbeats in the posterior cingulate cortex covary with changes in bodily self-consciousness induced by the full-body illusion. Additional analyses excluded that measured basic cardiorespiratory parameters or interoceptive sensitivity traits could account for this finding. These neurophysiological data link experimentally the cortical mapping of the internal body to self-consciousness. What are the brain mechanisms of self-consciousness? Prominent views propose that the neural processing associated with signals from the internal organs (such as the heart and the lung) plays a critical role in self-consciousness. Although this hypothesis dates back to influential views in philosophy and psychology (e.g., William James), definitive experimental evidence supporting this idea is lacking despite its recent impact in neuroscience. In the present study, we show that posterior cingulate activities responding to heartbeat signals covary with changes in participants' conscious self-identification with a body

Neurosecurity: security and privacy for neural devices.

PubMed

Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

2009-07-01

An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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

PubMed Central

Dolan, Raymond J.; Kurth-Nelson, Zeb

2018-01-01

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. PMID:29689053

The Basis of Hyperspecificity in Autism: A Preliminary Suggestion Based on Properties of Neural Nets.

ERIC Educational Resources Information Center

McClelland, James L.

2000-01-01

This article discusses representation of information in neural networks and the apparent hyperspecificity that is often seen in the application of previously acquired information by children with autism. Hyperspecificity is seen as reflecting a possible feature of the neural codes used to represent concepts in the autistic brain. (Contains 12…

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function.

PubMed

Yu, Zhe; McKnight, Timothy E; Ericson, M Nance; Melechko, Anatoli V; Simpson, Michael L; Morrison, Barclay

2012-05-01

Neural chips, which are capable of simultaneous multisite neural recording and stimulation, have been used to detect and modulate neural activity for almost thirty years. As neural interfaces, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface may potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single-cell level and even inside the cell. The authors demonstrate the utility of a neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes. The new device can be used to stimulate and/or monitor signals from brain tissue in vitro and for monitoring dynamic information of neuroplasticity both intracellularly and at the single cell level including neuroelectrical and neurochemical activities. Copyright © 2012 Elsevier Inc. All rights reserved.

Proteomic Signatures of Acquired Letrozole Resistance in Breast Cancer: Suppressed Estrogen Signaling and Increased Cell Motility and Invasiveness*

PubMed Central

Tilghman, Syreeta L.; Townley, Ian; Zhong, Qiu; Carriere, Patrick P.; Zou, Jin; Llopis, Shawn D.; Preyan, Lynez C.; Williams, Christopher C.; Skripnikova, Elena; Bratton, Melyssa R.; Zhang, Qiang; Wang, Guangdi

2013-01-01

Aromatase inhibitors, such as letrozole, have become the first-line treatment for postmenopausal women with estrogen-dependent breast cancer. However, acquired resistance remains a major clinical obstacle. Previous studies demonstrated constitutive activation of the MAPK signaling, overexpression of HER2, and down-regulation of aromatase and ERα in letrozole-resistant breast cancer cells. Given the complex signaling network involved in letrozole-refractory breast cancer and the lack of effective treatment for hormone resistance, further investigation of aromatase inhibitor resistance by a novel systems biology approach may reveal previously unconsidered molecular changes that could be utilized as therapeutic targets. This study was undertaken to characterize for the first time global proteomic alterations occurring in a letrozole-resistant cell line. A quantitative proteomic analysis of the whole cell lysates of LTLT-Ca (resistant) versus AC-1 cells (sensitive) was performed to identify significant protein expression changes. A total of 1743 proteins were identified and quantified, of which 411 were significantly up-regulated and 452 significantly down-regulated (p < 0.05, fold change > 1.20). Bioinformatics analysis revealed that acquired letrozole resistance is associated with a hormone-independent, more aggressive phenotype. LTLT-Ca cells exhibited 84% and 138% increase in migration and invasion compared with the control cells. The ROCK inhibitor partially abrogated the enhanced migration and invasion of the letrozole-resistant cells. Flow cytometric analyses also demonstrated an increase in vimentin and twist expression in letrozole-resistance cells, suggesting an onset of epithelial to mesenchymal transition (EMT). Moreover, targeted gene expression arrays confirmed a 28-fold and sixfold up-regulation of EGFR and HER2, respectively, whereas ERα and pS2 were dramatically reduced by 28-fold and 1100-fold, respectively. Taken together, our study revealed global

Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System Following Neural Injury

DTIC Science & Technology

2017-08-01

AWARD NUMBER: W81XWH-12-1-0051 TITLE: Activation of mTor Signaling by Gene Transduction to Induce Axon Regeneration in the Central Nervous System ...Central Nervous System Following Neural Injury 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0051 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Robert...induces re- growth of dopaminergic axons at 3 to 6 weeks after destruction by a neurotoxin. However, this approach cannot be used in humans because

Mate call as reward: Acoustic communication signals can acquire positive reinforcing values during adulthood in female zebra finches (Taeniopygia guttata).

PubMed

Hernandez, Alexandra M; Perez, Emilie C; Mulard, Hervé; Mathevon, Nicolas; Vignal, Clémentine

2016-02-01

Social stimuli can have rewarding properties and promote learning. In birds, conspecific vocalizations like song can act as a reinforcer, and specific song variants can acquire particular rewarding values during early life exposure. Here we ask if, during adulthood, an acoustic signal simpler and shorter than song can become a reward for a female songbird because of its particular social value. Using an operant choice apparatus, we showed that female zebra finches display a preferential response toward their mate's calls. This reinforcing value of mate's calls could be involved in the maintenance of the monogamous pair-bond of the zebra finch. (c) 2016 APA, all rights reserved).

Nuclear Calcium Signaling Controls Expression of a Large Gene Pool: Identification of a Gene Program for Acquired Neuroprotection Induced by Synaptic Activity

PubMed Central

Zhang, Sheng-Jia; Zou, Ming; Lu, Li; Lau, David; Ditzel, Désirée A. W.; Delucinge-Vivier, Celine; Aso, Yoshinori; Descombes, Patrick; Bading, Hilmar

2009-01-01

Synaptic activity can boost neuroprotection through a mechanism that requires synapse-to-nucleus communication and calcium signals in the cell nucleus. Here we show that in hippocampal neurons nuclear calcium is one of the most potent signals in neuronal gene expression. The induction or repression of 185 neuronal activity-regulated genes is dependent upon nuclear calcium signaling. The nuclear calcium-regulated gene pool contains a genomic program that mediates synaptic activity-induced, acquired neuroprotection. The core set of neuroprotective genes consists of 9 principal components, termed Activity-regulated Inhibitor of Death (AID) genes, and includes Atf3, Btg2, GADD45β, GADD45γ, Inhibin β-A, Interferon activated gene 202B, Npas4, Nr4a1, and Serpinb2, which strongly promote survival of cultured hippocampal neurons. Several AID genes provide neuroprotection through a common process that renders mitochondria more resistant to cellular stress and toxic insults. Stereotaxic delivery of AID gene-expressing recombinant adeno-associated viruses to the hippocampus confers protection in vivo against seizure-induced brain damage. Thus, treatments that enhance nuclear calcium signaling or supplement AID genes represent novel therapies to combat neurodegenerative conditions and neuronal cell loss caused by synaptic dysfunction, which may be accompanied by a deregulation of calcium signal initiation and/or propagation to the cell nucleus. PMID:19680447

Two Pore Channel 2 Differentially Modulates Neural Differentiation of Mouse Embryonic Stem Cells

PubMed Central

Zhang, Zhe-Hao; Lu, Ying-Ying; Yue, Jianbo

2013-01-01

Nicotinic acid adenine dinucleotide phosphate (NAADP) is an endogenous Ca2+ mobilizing nucleotide presented in various species. NAADP mobilizes Ca2+ from acidic organelles through two pore channel 2 (TPC2) in many cell types and it has been previously shown that NAADP can potently induce neuronal differentiation in PC12 cells. Here we examined the role of TPC2 signaling in the neural differentiation of mouse embryonic stem (ES) cells. We found that the expression of TPC2 was markedly decreased during the initial ES cell entry into neural progenitors, and the levels of TPC2 gradually rebounded during the late stages of neurogenesis. Correspondingly, TPC2 knockdown accelerated mouse ES cell differentiation into neural progenitors but inhibited these neural progenitors from committing to neurons. Overexpression of TPC2, on the other hand, inhibited mouse ES cell from entering the early neural lineage. Interestingly, TPC2 knockdown had no effect on the differentiation of astrocytes and oligodendrocytes of mouse ES cells. Taken together, our data indicate that TPC2 signaling plays a temporal and differential role in modulating the neural lineage entry of mouse ES cells, in that TPC2 signaling inhibits ES cell entry to early neural progenitors, but is required for late neuronal differentiation. PMID:23776607

Railway obstacle detection algorithm using neural network

NASA Astrophysics Data System (ADS)

Yu, Mingyang; Yang, Peng; Wei, Sen

2018-05-01

Aiming at the difficulty of detection of obstacle in outdoor railway scene, a data-oriented method based on neural network to obtain image objects is proposed. First, we mark objects of images(such as people, trains, animals) acquired on the Internet. and then use the residual learning units to build Fast R-CNN framework. Then, the neural network is trained to get the target image characteristics by using stochastic gradient descent algorithm. Finally, a well-trained model is used to identify an outdoor railway image. if it includes trains and other objects, it will issue an alert. Experiments show that the correct rate of warning reached 94.85%.

IGF1R signalling in testicular germ cell tumour cells impacts on cell survival and acquired cisplatin resistance.

PubMed

Selfe, Joanna; Goddard, Neil C; McIntyre, Alan; Taylor, Kathryn R; Renshaw, Jane; Popov, Sergey D; Thway, Khin; Summersgill, Brenda; Huddart, Robert A; Gilbert, Duncan C; Shipley, Janet M

2018-02-01

Testicular germ cell tumours (TGCTs) are the most frequent malignancy and cause of death from solid tumours in the 20- to 40-year age group. Although most cases show sensitivity to cis-platinum-based chemotherapy, this is associated with long-term toxicities and chemo-resistance. Roles for receptor tyrosine kinases other than KIT are largely unknown in TGCT. We therefore conducted a phosphoproteomic screen and identified the insulin growth factor receptor-1 (IGF1R) as both highly expressed and activated in TGCT cell lines representing the nonseminomatous subtype. IGF1R was also frequently expressed in tumour samples from patients with nonseminomas. Functional analysis of cell line models showed that long-term shRNA-mediated IGF1R silencing leads to apoptosis and complete ablation of nonseminoma cells with active IGF1R signalling. Cell lines with high levels of IGF1R activity also showed reduced AKT signalling in response to decreased IGF1R expression as well as sensitivity to the small-molecule IGF1R inhibitor NVP-AEW541. These results were in contrast to those in the seminoma cell line TCAM2 that lacked IGF1R signalling via AKT and was one of the two cell lines least sensitive to the IGF1R inhibitor. The dependence on IGF1R activity in the majority of nonseminomas parallels the known role of IGF signalling in the proliferation, migration, and survival of primordial germ cells, the putative cell of origin for TGCT. Upregulation of IGF1R expression and signalling was also found to contribute to acquired cisplatin resistance in an in vitro nonseminoma model, providing a rationale for targeting IGF1R in cisplatin-resistant disease. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Conducting polymer coated neural recording electrodes

NASA Astrophysics Data System (ADS)

Harris, Alexander R.; Morgan, Simeon J.; Chen, Jun; Kapsa, Robert M. I.; Wallace, Gordon G.; Paolini, Antonio G.

2013-02-01

Objective. Neural recording electrodes suffer from poor signal to noise ratio, charge density, biostability and biocompatibility. This paper investigates the ability of conducting polymer coated electrodes to record acute neural response in a systematic manner, allowing in depth comparison of electrochemical and electrophysiological response. Approach. Polypyrrole (Ppy) and poly-3,4-ethylenedioxythiophene (PEDOT) doped with sulphate (SO4) or para-toluene sulfonate (pTS) were used to coat iridium neural recording electrodes. Detailed electrochemical and electrophysiological investigations were undertaken to compare the effect of these materials on acute in vivo recording. Main results. A range of charge density and impedance responses were seen with each respectively doped conducting polymer. All coatings produced greater charge density than uncoated electrodes, while PEDOT-pTS, PEDOT-SO4 and Ppy-SO4 possessed lower impedance values at 1 kHz than uncoated electrodes. Charge density increased with PEDOT-pTS thickness and impedance at 1 kHz was reduced with deposition times up to 45 s. Stable electrochemical response after acute implantation inferred biostability of PEDOT-pTS coated electrodes while other electrode materials had variable impedance and/or charge density after implantation indicative of a protein fouling layer forming on the electrode surface. Recording of neural response to white noise bursts after implantation of conducting polymer-coated electrodes into a rat model inferior colliculus showed a general decrease in background noise and increase in signal to noise ratio and spike count with reduced impedance at 1 kHz, regardless of the specific electrode coating, compared to uncoated electrodes. A 45 s PEDOT-pTS deposition time yielded the highest signal to noise ratio and spike count. Significance. A method for comparing recording electrode materials has been demonstrated with doped conducting polymers. PEDOT-pTS showed remarkable low fouling during

Brain Signal Variability is Parametrically Modifiable

PubMed Central

Garrett, Douglas D.; McIntosh, Anthony R.; Grady, Cheryl L.

2014-01-01

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture. PMID:23749875

Computational neural learning formalisms for manipulator inverse kinematics

NASA Technical Reports Server (NTRS)

Gulati, Sandeep; Barhen, Jacob; Iyengar, S. Sitharama

1989-01-01

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints.

Neuropilin-1 modulates TGFβ signaling to drive glioblastoma growth and recurrence after anti-angiogenic therapy

PubMed Central

Kwiatkowski, Sam C.; Guerrero, Paola A.; Hirota, Shinya; Chen, Zhihua; Morales, John E.; Aghi, Manish

2017-01-01

Glioblastoma (GBM) is a rapidly progressive brain cancer that exploits the neural microenvironment, and particularly blood vessels, for selective growth and survival. Anti-angiogenic agents such as the vascular endothelial growth factor-A (VEGF-A) blocking antibody bevacizumab yield short-term benefits to patients due to blood vessel regression and stabilization of vascular permeability. However, tumor recurrence is common, and this is associated with acquired resistance to bevacizumab. The mechanisms that drive acquired resistance and tumor recurrence in response to anti-angiogenic therapy remain largely unknown. Here, we report that Neuropilin-1 (Nrp1) regulates GBM growth and invasion by balancing tumor cell responses to VEGF-A and transforming growth factor βs (TGFβs). Nrp1 is expressed in GBM cells where it promotes TGFβ receptor internalization and signaling via Smad transcription factors. GBM that recur after bevacizumab treatment show down-regulation of Nrp1 expression, indicating that altering the balance between VEGF-A and TGFβ signaling is one mechanism that promotes resistance to anti-angiogenic agents. Collectively, these data reveal that Nrp1 plays a critical role in balancing responsiveness to VEGF-A versus TGFβ to regulate GBM growth, progression, and recurrence after anti-vascular therapy. PMID:28938007

Neural Representations of Physics Concepts.

PubMed

Mason, Robert A; Just, Marcel Adam

2016-06-01

We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems. © The Author(s) 2016.

Time to address the problems at the neural interface

NASA Astrophysics Data System (ADS)

Durand, Dominique M.; Ghovanloo, Maysam; Krames, Elliot

2014-04-01

Neural engineers have made significant, if not remarkable, progress in interfacing with the nervous system in the last ten years. In particular, neuromodulation of the brain has generated significant therapeutic benefits [1-5]. EEG electrodes can be used to communicate with patients with locked-in syndrome [6]. In the central nervous system (CNS), electrode arrays placed directly over or within the cortex can record neural signals related to the intent of the subject or patient [7, 8]. A similar technology has allowed paralyzed patients to control an otherwise normal skeletal system with brain signals [9, 10]. This technology has significant potential to restore function in these and other patients with neural disorders such as stroke [11]. Although there are several multichannel arrays described in the literature, the workhorse for these cortical interfaces has been the Utah array [12]. This 100-channel electrode array has been used in most studies on animals and humans since the 1990s and is commercially available. This array and other similar microelectrode arrays can record neural signals with high quality (high signal-to-noise ratio), but these signals fade and disappear after a few months and therefore the current technology is not reliable for extended periods of time. Therefore, despite these major advances in communicating with the brain, clinical translation cannot be implemented. The reasons for this failure are not known but clearly involve the interface between the electrode and the neural tissue. The Defense Advanced Research Project Agency (DARPA) as well as other federal funding agencies such as the National Science Foundation (NSF) and the National Institutes of Health have provided significant financial support to investigate this problem without much success. A recent funding program from DARPA was designed to establish the failure modes in order to generate a reliable neural interface technology and again was unsuccessful at producing a robust

Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

PubMed

Glasser, Matthew F; Coalson, Timothy S; Bijsterbosch, Janine D; Harrison, Samuel J; Harms, Michael P; Anticevic, Alan; Van Essen, David C; Smith, Stephen M

2018-06-02

Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data. Copyright © 2018 Elsevier Inc. All rights reserved.

Programmable Analog Memory Resistors For Electronic Neural Networks

NASA Technical Reports Server (NTRS)

Ramesham, Rajeshuni; Thakoor, Sarita; Daud, Taher; Thakoor, Anilkumar P.

1990-01-01

Electrical resistance of new solid-state device altered repeatedly by suitable control signals, yet remains at steady value when control signal removed. Resistance set at low value ("on" state), high value ("off" state), or at any convenient intermediate value and left there until new value desired. Circuits of this type particularly useful in nonvolatile, associative electronic memories based on models of neural networks. Such programmable analog memory resistors ideally suited as synaptic interconnects in "self-learning" neural nets. Operation of device depends on electrochromic property of WO3, which when pure is insulator. Potential uses include nonvolatile, erasable, electronically programmable read-only memories.

Augmented BMP signaling in the neural crest inhibits nasal cartilage morphogenesis by inducing p53-mediated apoptosis.

PubMed

Hayano, Satoru; Komatsu, Yoshihiro; Pan, Haichun; Mishina, Yuji

2015-04-01

Bone morphogenetic protein (BMP) signaling plays many roles in skull morphogenesis. We have previously reported that enhanced BMP signaling through the BMP type IA receptor (BMPR1A) in cranial neural crest cells causes craniosynostosis during postnatal development. Additionally, we observed that 55% of Bmpr1a mutant mice show neonatal lethality characterized by a distended gastrointestinal tract. Here, we show that severely affected mutants exhibit defective nasal cartilage, failure of fusion between the nasal septum and the secondary palate, and higher levels of phosphorylated SMAD1 and SMAD5 in the nasal tissue. TUNEL demonstrated an increase in apoptosis in both condensing mesenchymal tissues and cartilage of the nasal region in mutants. The levels of p53 (TRP53) tumor suppressor protein were also increased in the same tissue. Injection of pifithrin-α, a chemical inhibitor of p53, into pregnant mice prevented neonatal lethality while concomitantly reducing apoptosis in nasal cartilage primordia, suggesting that enhanced BMP signaling induces p53-mediated apoptosis in the nasal cartilage. The expression of Bax and caspase 3, downstream targets of p53, was increased in the mutants; however, the p53 expression level was unchanged. It has been reported that MDM2 interacts with p53 to promote degradation. We found that the amount of MDM2-p53 complex was decreased in all mutants, and the most severely affected mutants had the largest decrease. Our previous finding that the BMP signaling component SMAD1 prevents MDM2-mediated p53 degradation coupled with our new data indicate that augmented BMP signaling induces p53-mediated apoptosis by prevention of p53 degradation in developing nasal cartilage. Thus, an appropriate level of BMP signaling is required for proper craniofacial morphogenesis. © 2015. Published by The Company of Biologists Ltd.

The challenges of neural mind-reading paradigms.

PubMed

Vilarroya, Oscar

2013-01-01

Neural mind-reading studies, based on multivariate pattern analysis (MVPA) methods, are providing exciting new studies. Some of the results obtained with these paradigms have raised high expectations, such as the possibility of creating brain reading devices. However, such hopes are based on the assumptions that: (a) the BOLD signal is a marker of neural activity; (b) the BOLD pattern identified by a MVPA is a neurally sound pattern; (c) the MVPA's feature space is a good mapping of the neural representation of a stimulus, and (d) the pattern identified by a MVPA corresponds to a representation. I examine here the challenges that still have to be met before fully accepting such assumptions.

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

NASA Technical Reports Server (NTRS)

Decker, Arthur J.; Fite, E. Brian; Mehmed, Oral; Thorp, Scott A.

1997-01-01

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.