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Sample records for sars-cov spike glycoprotein

  1. Orthobunyavirus ultrastructure and the curious tripodal glycoprotein spike.

    Directory of Open Access Journals (Sweden)

    Thomas A Bowden

    Full Text Available The genus Orthobunyavirus within the family Bunyaviridae constitutes an expanding group of emerging viruses, which threaten human and animal health. Despite the medical importance, little is known about orthobunyavirus structure, a prerequisite for understanding virus assembly and entry. Here, using electron cryo-tomography, we report the ultrastructure of Bunyamwera virus, the prototypic member of this genus. Whilst Bunyamwera virions are pleomorphic in shape, they display a locally ordered lattice of glycoprotein spikes. Each spike protrudes 18 nm from the viral membrane and becomes disordered upon introduction to an acidic environment. Using sub-tomogram averaging, we derived a three-dimensional model of the trimeric pre-fusion glycoprotein spike to 3-nm resolution. The glycoprotein spike consists mainly of the putative class-II fusion glycoprotein and exhibits a unique tripod-like arrangement. Protein-protein contacts between neighbouring spikes occur at membrane-proximal regions and intra-spike contacts at membrane-distal regions. This trimeric assembly deviates from previously observed fusion glycoprotein arrangements, suggesting a greater than anticipated repertoire of viral fusion glycoprotein oligomerization. Our study provides evidence of a pH-dependent conformational change that occurs during orthobunyaviral entry into host cells and a blueprint for the structure of this group of emerging pathogens.

  2. Variations in Spike Glycoprotein Gene of MERS-CoV, South Korea, 2015.

    Science.gov (United States)

    Kim, Dae-Won; Kim, You-Jin; Park, Sung Han; Yun, Mi-Ran; Yang, Jeong-Sun; Kang, Hae Ji; Han, Young Woo; Lee, Han Saem; Kim, Heui Man; Kim, Hak; Kim, A-Reum; Heo, Deok Rim; Kim, Su Jin; Jeon, Jun Ho; Park, Deokbum; Kim, Joo Ae; Cheong, Hyang-Min; Nam, Jeong-Gu; Kim, Kisoon; Kim, Sung Soon

    2016-01-01

    An outbreak of nosocomial infections with Middle East respiratory syndrome coronavirus occurred in South Korea in May 2015. Spike glycoprotein genes of virus strains from South Korea were closely related to those of strains from Riyadh, Saudi Arabia. However, virus strains from South Korea showed strain-specific variations.

  3. Glycan shield and fusion activation of a deltacoronavirus spike glycoprotein fine-tuned for enteric infections

    NARCIS (Netherlands)

    Xiong, Xiaoli; Tortorici, M Alejandra; Snijder, Joost|info:eu-repo/dai/nl/338018328; Yoshioka, Craig; Walls, Alexandra C; Li, Wentao|info:eu-repo/dai/nl/411296272; McGuire, Andrew T; Rey, Félix A; Bosch, Berend-Jan|info:eu-repo/dai/nl/273306049; Veesler, David

    2017-01-01

    Coronaviruses recently emerged as major human pathogens causing outbreaks of severe acute respiratory syndrome and Middle-East respiratory syndrome. They utilize the spike (S) glycoprotein anchored in the viral envelope to mediate host attachment and fusion of the viral and cellular membranes to

  4. Ribonucleocapsid Formation of SARS-COV Through Molecular Action of the N-Terminal Domain of N Protein

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    Saikatendu, K.S.; Joseph, J.S.; Subramanian, V.; Neuman, B.W.; Buchmeier, M.J.; Stevens, R.C.; Kuhn, P.; /Scripps Res. Inst.

    2007-07-12

    Conserved amongst all coronaviruses are four structural proteins, the matrix (M), small envelope (E) and spike (S) that are embedded in the viral membrane and the nucleocapsid phosphoprotein (N), which exists in a ribonucleoprotein complex in their lumen. The N terminal domain of coronaviral N proteins (N-NTD) provides a scaffold for RNA binding while the C-terminal domain (N-CTD) mainly acts as oligomerization modules during assembly. The C-terminus of N protein anchors it to the viral membrane by associating with M protein. We characterized the structures of N-NTD from severe acute respiratory syndrome coronavirus (SARS-CoV) in two crystal forms, at 1.17A (monoclinic) and 1.85 A (cubic) respectively, solved by molecular replacement using the homologous avian infectious bronchitis virus (IBV) structure. Flexible loops in the solution structure of SARS-CoV N-NTD are now shown to be well ordered around the beta-sheet core. The functionally important positively charged beta-hairpin protrudes out of the core and is oriented similar to that in the IBV N-NTD and is involved in crystal packing in the monoclinic form. In the cubic form, the monomers form trimeric units that stack in a helical array. Comparison of crystal packing of SARS-CoV and IBV N-NTDs suggest a common mode of RNA recognition, but probably associate differently in vivo during the formation of the ribonucleoprotein complex. Electrostatic potential distribution on the surface of homology models of related coronaviral N-NTDs hints that they employ different modes of both RNA recognition as well as oligomeric assembly, perhaps explaining why their nucleocapsids have different morphologies.

  5. Glycan shield and fusion activation of a deltacoronavirus spike glycoprotein fine-tuned for enteric infections.

    Science.gov (United States)

    Xiong, Xiaoli; Tortorici, M Alejandra; Snijder, Joost; Yoshioka, Craig; Walls, Alexandra C; Li, Wentao; McGuire, Andrew T; Rey, Félix A; Bosch, Berend-Jan; Veesler, David

    2017-11-01

    Coronaviruses recently emerged as major human pathogens causing outbreaks of severe acute respiratory syndrome and Middle-East respiratory syndrome. They utilize the spike (S) glycoprotein anchored in the viral envelope to mediate host attachment and fusion of the viral and cellular membranes to initiate infection. The S protein is a major determinant of the zoonotic potential of coronaviruses and is also the main target of the host humoral immune response. We report here the 3.5 Å resolution cryo-electron microscopy structure of the S glycoprotein trimer from the pathogenic porcine deltacoronavirus (PDCoV), which belongs to the recently identified delta genus. Structural and glycoproteomics data indicate that the glycans of PDCoV S are topologically conserved when compared with the human respiratory coronavirus HCoV-NL63 S, resulting in similar surface areas being shielded from neutralizing antibodies and implying that both viruses are under comparable immune pressure in their respective hosts. The structure further reveals a shortened S 2 ' activation loop, containing a reduced number of basic amino acids, which participates to rendering the spike largely protease-resistant. This property distinguishes PDCoV S from recently characterized betacoronavirus S proteins and suggests that the S protein of enterotropic PDCoV has evolved to tolerate the protease-rich environment of the small intestine and to fine-tune its fusion activation to avoid premature triggering and reduction of infectivity. IMPORTANCE Coronaviruses use transmembrane spike (S) glycoprotein trimers to promote host attachment and fusion of the viral and cellular membranes. We determined a near-atomic resolution cryo-electron microscopy structure of the S ectodomain trimer from the pathogenic porcine deltacoronavirus (PDCoV), which is responsible for diarrhea in piglets and has had devastating consequences for the swine industry worldwide. Structural and glycoproteomics data reveal that PDCoV S is

  6. Palmitoylation of the cysteine-rich endodomain of the SARS-coronavirus spike glycoprotein is important for spike-mediated cell fusion

    International Nuclear Information System (INIS)

    Petit, Chad M.; Chouljenko, Vladimir N.; Iyer, Arun; Colgrove, Robin; Farzan, Michael; Knipe, David M.; Kousoulas, K.G.

    2007-01-01

    The SARS-coronavirus (SARS-CoV) is the etiological agent of the severe acute respiratory syndrome (SARS). The SARS-CoV spike (S) glycoprotein mediates membrane fusion events during virus entry and virus-induced cell-to-cell fusion. The cytoplasmic portion of the S glycoprotein contains four cysteine-rich amino acid clusters. Individual cysteine clusters were altered via cysteine-to-alanine amino acid replacement and the modified S glycoproteins were tested for their transport to cell-surfaces and ability to cause cell fusion in transient transfection assays. Mutagenesis of the cysteine cluster I, located immediately proximal to the predicted transmembrane, domain did not appreciably reduce cell-surface expression, although S-mediated cell fusion was reduced by more than 50% in comparison to the wild-type S. Similarly, mutagenesis of the cysteine cluster II located adjacent to cluster I reduced S-mediated cell fusion by more than 60% compared to the wild-type S, while cell-surface expression was reduced by less than 20%. Mutagenesis of cysteine clusters III and IV did not appreciably affect S cell-surface expression or S-mediated cell fusion. The wild-type S was palmitoylated as evidenced by the efficient incorporation of 3 H-palmitic acid in wild-type S molecules. S glycoprotein palmitoylation was significantly reduced for mutant glycoproteins having cluster I and II cysteine changes, but was largely unaffected for cysteine cluster III and IV mutants. These results show that the S cytoplasmic domain is palmitoylated and that palmitoylation of the membrane proximal cysteine clusters I and II may be important for S-mediated cell fusion

  7. Genetic analysis of the SARS-coronavirus spike glycoprotein functional domains involved in cell-surface expression and cell-to-cell fusion

    International Nuclear Information System (INIS)

    Petit, Chad M.; Melancon, Jeffrey M.; Chouljenko, Vladimir N.; Colgrove, Robin; Farzan, Michael; Knipe, David M.; Kousoulas, K.G.

    2005-01-01

    The SARS-coronavirus (SARS-CoV) is the etiological agent of severe acute respiratory syndrome (SARS). The SARS-CoV spike (S) glycoprotein mediates membrane fusion events during virus entry and virus-induced cell-to-cell fusion. To delineate functional domains of the SARS-CoV S glycoprotein, single point mutations, cluster-to-lysine and cluster-to-alanine mutations, as well as carboxyl-terminal truncations were investigated in transient expression experiments. Mutagenesis of either the coiled-coil domain of the S glycoprotein amino terminal heptad repeat, the predicted fusion peptide, or an adjacent but distinct region, severely compromised S-mediated cell-to-cell fusion, while intracellular transport and cell-surface expression were not adversely affected. Surprisingly, a carboxyl-terminal truncation of 17 amino acids substantially increased S glycoprotein-mediated cell-to-cell fusion suggesting that the terminal 17 amino acids regulated the S fusogenic properties. In contrast, truncation of 26 or 39 amino acids eliminating either one or both of the two endodomain cysteine-rich motifs, respectively, inhibited cell fusion in comparison to the wild-type S. The 17 and 26 amino-acid deletions did not adversely affect S cell-surface expression, while the 39 amino-acid truncation inhibited S cell-surface expression suggesting that the membrane proximal cysteine-rich motif plays an essential role in S cell-surface expression. Mutagenesis of the acidic amino-acid cluster in the carboxyl terminus of the S glycoprotein as well as modification of a predicted phosphorylation site within the acidic cluster revealed that this amino-acid motif may play a functional role in the retention of S at cell surfaces. This genetic analysis reveals that the SARS-CoV S glycoprotein contains extracellular domains that regulate cell fusion as well as distinct endodomains that function in intracellular transport, cell-surface expression, and cell fusion

  8. Central ions and lateral asparagine/glutamine zippers stabilize the post-fusion hairpin conformation of the SARS coronavirus spike glycoprotein

    International Nuclear Information System (INIS)

    Duquerroy, Stephane; Vigouroux, Armelle; Rottier, Peter J.M.; Rey, Felix A.; Jan Bosch, Berend

    2005-01-01

    The coronavirus spike glycoprotein is a class I membrane fusion protein with two characteristic heptad repeat regions (HR1 and HR2) in its ectodomain. Here, we report the X-ray structure of a previously characterized HR1/HR2 complex of the severe acute respiratory syndrome coronavirus spike protein. As expected, the HR1 and HR2 segments are organized in antiparallel orientations within a rod-like molecule. The HR1 helices form an exceptionally long (120 A) internal coiled coil stabilized by hydrophobic and polar interactions. A striking arrangement of conserved asparagine and glutamine residues of HR1 propagates from two central chloride ions, providing hydrogen-bonding 'zippers' that strongly constrain the path of the HR2 main chain, forcing it to adopt an extended conformation at either end of a short HR2 α-helix

  9. Acid-induced movements in the glycoprotein shell of an alphavirus turn the spikes into membrane fusion mode

    OpenAIRE

    Haag, Lars; Garoff, Henrik; Xing, Li; Hammar, Lena; Kan, Sin-Tau; Cheng, R.Holland

    2002-01-01

    In the icosahedral (T = 4) Semliki Forest virus, the envelope protomers, i.e. E1–E2 heterodimers, make one-to-one interactions with capsid proteins below the viral lipid bilayer, transverse the membrane and form an external glycoprotein shell with projections. The shell is organized by protomer domains interacting as hexamers and pentamers around shell openings at icosahedral 2- and 5-fold axes, respectively, and the projections by other domains associating as trimers at 3- and quasi 3-fold a...

  10. Cleavage of a Neuroinvasive Human Respiratory Virus Spike Glycoprotein by Proprotein Convertases Modulates Neurovirulence and Virus Spread within the Central Nervous System.

    Directory of Open Access Journals (Sweden)

    Alain Le Coupanec

    Full Text Available Human coronaviruses (HCoV are respiratory pathogens that may be associated with the development of neurological diseases, in view of their neuroinvasive and neurotropic properties. The viral spike (S glycoprotein is a major virulence factor for several coronavirus species, including the OC43 strain of HCoV (HCoV-OC43. In an attempt to study the role of this protein in virus spread within the central nervous system (CNS and neurovirulence, as well as to identify amino acid residues important for such functions, we compared the sequence of the S gene found in the laboratory reference strain HCoV-OC43 ATCC VR-759 to S sequences of viruses detected in clinical isolates from the human respiratory tract. We identified one predominant mutation at amino acid 758 (from RRSR↓ G758 to RRSR↓R758, which introduces a putative furin-like cleavage (↓ site. Using a molecular cDNA infectious clone to generate a corresponding recombinant virus, we show for the first time that such point mutation in the HCoV-OC43 S glycoprotein creates a functional cleavage site between the S1 and S2 portions of the S protein. While the corresponding recombinant virus retained its neuroinvasive properties, this mutation led to decreased neurovirulence while potentially modifying the mode of virus spread, likely leading to a limited dissemination within the CNS. Taken together, these results are consistent with the adaptation of HCoV-OC43 to the CNS environment, resulting from the selection of quasi-species harboring mutations that lead to amino acid changes in viral genes, like the S gene in HCoV-OC43, which may contribute to a more efficient establishment of a less pathogenic but persistent CNS infection. This adaptative mechanism could potentially be associated with human encephalitis or other neurological degenerative pathologies.

  11. Production and immunogenicity of chimeric virus-like particles containing the spike glycoprotein of infectious bronchitis virus.

    Science.gov (United States)

    Lv, Lishan; Li, Xiaoming; Liu, Genmei; Li, Ran; Liu, Qiliang; Shen, Huifang; Wang, Wei; Xue, Chunyi; Cao, Yongchang

    2014-01-01

    Infectious bronchitis virus (IBV) poses a severe threat to the poultry industry and causes heavy economic losses worldwide. Vaccination is the most effective method of preventing infection and controlling the spread of IBV, but currently available inactivated and attenuated virus vaccines have some disadvantages. We developed a chimeric virus-like particle (VLP)-based candidate vaccine for IBV protection. The chimeric VLP was composed of matrix 1 protein from avian influenza H5N1 virus and a fusion protein neuraminidase (NA)/spike 1 (S1) that was generated by fusing IBV S1 protein to the cytoplasmic and transmembrane domains of NA protein of avian influenza H5N1 virus. The chimeric VLPs elicited significantly higher S1-specific antibody responses in intramuscularly immunized mice and chickens than inactivated IBV viruses. Furthermore, the chimeric VLPs induced significantly higher neutralization antibody levels than inactivated H120 virus in SPF chickens. Finally, the chimeric VLPs induced significantly higher IL-4 production in mice. These results demonstrate that chimeric VLPs have the potential for use in vaccines against IBV infection.

  12. Importance of Neutralizing Monoclonal Antibodies Targeting Multiple Antigenic Sites on the Middle East Respiratory Syndrome Coronavirus Spike Glycoprotein To Avoid Neutralization Escape

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Lingshu; Shi, Wei; Chappell, James D.; Joyce, M. Gordon; Zhang, Yi; Kanekiyo, Masaru; Becker, Michelle M.; van Doremalen, Neeltje; Fischer, Robert; Wang, Nianshuang; Corbett, Kizzmekia S.; Choe, Misook; Mason, Rosemarie D.; Van Galen, Joseph G.; Zhou, Tongqing; Saunders, Kevin O.; Tatti, Kathleen M.; Haynes, Lia M.; Kwong, Peter D.; Modjarrad, Kayvon; Kong, Wing-Pui; McLellan, Jason S.; Denison, Mark R.; Munster, Vincent J.; Mascola, John R.; Graham, Barney S.; Gallagher, Tom

    2018-03-07

    structurally defined probes for the MERS-CoV spike glycoprotein (S), the target for neutralizing antibodies, single B cells were sorted from a convalescent human and immunized nonhuman primates (NHPs). MAbs produced from paired immunoglobulin gene sequences were mapped to multiple epitopes within and outside the receptor-binding domain (RBD) and protected against lethal MERS infection in a murine model following passive immunization. Importantly, combining MAbs targeting distinct epitopes prevented viral neutralization escape from RBD-directed MAbs. These data suggest that antibody responses to multiple domains on CoV spike protein may improve immunity and will guide future vaccine and therapeutic development efforts.

  13. The coronavirus spike protein : mechanisms of membrane fusion and virion incorporation

    NARCIS (Netherlands)

    Bosch, B.J.

    2004-01-01

    The coronavirus spike protein is a membrane-anchored glycoprotein responsible for virus-cell attachment and membrane fusion, prerequisites for a successful virus infection. In this thesis, two aspects are described regarding the molecular biology of the coronavirus spike protein: its membrane fusion

  14. Assembly of spikes into coronavirus particles is mediated by the carboxy-terminal domain of the spike protein

    NARCIS (Netherlands)

    Godeke, G J; de Haan, Cornelis A M; Rossen, J W; Vennema, H; Rottier, P J

    The type I glycoprotein S of coronavirus, trimers of which constitute the typical viral spikes, is assembled into virions through noncovalent interactions with the M protein. Here we demonstrate that incorporation is mediated by the short carboxy-terminal segment comprising the transmembrane and

  15. Deep Spiking Networks

    NARCIS (Netherlands)

    O'Connor, P.; Welling, M.

    2016-01-01

    We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only

  16. Glycoprotein on cell surfaces

    International Nuclear Information System (INIS)

    Muramatsu, T.

    1975-01-01

    There are conjugated polysaccharides in cell membranes and outside of animal cells, and they play important role in the control of cell behavior. In this paper, the studies on the glycoprotein on cell surfaces are reported. It was found that the glycoprotein on cell surfaces have both N-glycoside type and O-glycoside type saccharic chains. Therefore it can be concluded that the basic structure of the saccharic chains in the glycoprotein on cell surfaces is similar to that of blood serum and body fluid. The main glycoprotein in the membranes of red blood corpuscles has been studied most in detail, and it also has both types of saccharic chains. The glycoprotein in liver cell membranes was found to have only the saccharic chains of acid type and to be in different pattern from that in endoplasmic reticula and nuclear membranes, which also has the saccharic chains of neutral type. The structure of the saccharic chains of H-2 antigen, i.e. the peculiar glycoprotein on the surfaces of lymph system cells, has been studied, and it is similar to the saccharic chains of glycoprotein in blood serum. The saccharic chain structures of H-2 antigen and TL antigen are different. TL, H-2 (D), Lna and H-2 (K) are the glycoprotein on cell surfaces, and are independent molecules. The analysis of the saccharic chain patterns on cell surfaces was carried out, and it was shown that the acid type saccharic chains were similar to those of ordinary glycoprotein, because the enzyme of pneumococci hydrolyzed most of the acid type saccharic chains. The change of the saccharic chain patterns of glycoprotein on cell surfaces owing to canceration and multiplication is complex matter. (Kako, I.)

  17. Decoding spikes in a spiking neuronal network

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    Feng Jianfeng [Department of Informatics, University of Sussex, Brighton BN1 9QH (United Kingdom); Ding, Mingzhou [Department of Mathematics, Florida Atlantic University, Boca Raton, FL 33431 (United States)

    2004-06-04

    We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs.

  18. Decoding spikes in a spiking neuronal network

    International Nuclear Information System (INIS)

    Feng Jianfeng; Ding, Mingzhou

    2004-01-01

    We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs

  19. Automatic EEG spike detection.

    Science.gov (United States)

    Harner, Richard

    2009-10-01

    Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

  20. HIV-1 envelope glycoprotein

    Science.gov (United States)

    Caulfield, Michael; Cupo, Albert; Dean, Hansi; Hoffenberg, Simon; King, C. Richter; Klasse, P. J.; Marozsan, Andre; Moore, John P.; Sanders, Rogier W.; Ward, Andrew; Wilson, Ian; Julien, Jean-Philippe

    2017-08-22

    The present application relates to novel HIV-1 envelope glycoproteins, which may be utilized as HIV-1 vaccine immunogens, and antigens for crystallization, electron microscopy and other biophysical, biochemical and immunological studies for the identification of broad neutralizing antibodies. The present invention encompasses the preparation and purification of immunogenic compositions, which are formulated into the vaccines of the present invention.

  1. Glycoprotein and proteoglycan techniques

    International Nuclear Information System (INIS)

    Beeley, J.G.

    1985-01-01

    The aim of this book is to describe techniques which can be used to answer some of the basic questions about glycosylated proteins. Methods are discussed for isolation, compositional analysis, and for determination of the primary structure of carbohydrate units and the nature of protein-carbohydrate linkages of glycoproteins and proteoglycans. High resolution NMR is considered, as well as radioactive labelling techniques. (Auth.)

  2. The variational spiked oscillator

    International Nuclear Information System (INIS)

    Aguilera-Navarro, V.C.; Ullah, N.

    1992-08-01

    A variational analysis of the spiked harmonic oscillator Hamiltonian -d 2 / d x 2 + x 2 + δ/ x 5/2 , δ > 0, is reported in this work. A trial function satisfying Dirichlet boundary conditions is suggested. The results are excellent for a large range of values of the coupling parameter. (author)

  3. The glycoproteins of Marburg and Ebola virus and their potential roles in pathogenesis.

    Science.gov (United States)

    Feldmann, H; Volchkov, V E; Volchkova, V A; Klenk, H D

    1999-01-01

    Filoviruses cause systemic infections that can lead to severe hemorrhagic fever in human and non-human primates. The primary target of the virus appears to be the mononuclear phagocytic system. As the virus spreads through the organism, the spectrum of target cells increases to include endothelial cells, fibroblasts, hepatocytes, and many other cells. There is evidence that the filovirus glycoprotein plays an important role in cell tropism, spread of infection, and pathogenicity. Biosynthesis of the glycoprotein forming the spikes on the virion surface involves cleavage by the host cell protease furin into two disulfide linked subunits GP1 and GP2. GP1 is also shed in soluble form from infected cells. Different strains of Ebola virus show variations in the cleavability of the glycoprotein, that may account for differences in pathogenicity, as has been observed with influenza viruses and paramyxoviruses. Expression of the spike glycoprotein of Ebola virus, but not of Marburg virus, requires transcriptional editing. Unedited GP mRNA yields the nonstructural glycoprotein sGP, which is secreted extensively from infected cells. Whether the soluble glycoproteins GP1 and sGP interfere with the humoral immune response and other defense mechanisms remains to be determined.

  4. Spiking neural network for recognizing spatiotemporal sequences of spikes

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.

    2004-01-01

    Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons

  5. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Zenke, Friedemann; Ganguli, Surya

    2018-04-13

    A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

  6. Coronavirus spike-receptor interactions

    NARCIS (Netherlands)

    Mou, H.

    2015-01-01

    Coronaviruses cause important diseases in humans and animals. Coronavirus infection starts with the virus binding with its spike proteins to molecules present on the surface of host cells that act as receptors. This spike-receptor interaction is highly specific and determines the virus’ cell, tissue

  7. Mapping spikes to sensations

    Directory of Open Access Journals (Sweden)

    Maik Christopher Stüttgen

    2011-11-01

    Full Text Available Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data is observed in parallel with spike trains in sensory neurons (the neurometric data. Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of neural candidate codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research.

  8. Salivary Mucin 19 Glycoproteins

    Science.gov (United States)

    Culp, David J.; Robinson, Bently; Cash, Melanie N.; Bhattacharyya, Indraneel; Stewart, Carol; Cuadra-Saenz, Giancarlo

    2015-01-01

    Saliva functions in innate immunity of the oral cavity, protecting against demineralization of teeth (i.e. dental caries), a highly prevalent infectious disease associated with Streptococcus mutans, a pathogen also linked to endocarditis and atheromatous plaques. Gel-forming mucins are a major constituent of saliva. Because Muc19 is the dominant salivary gel-forming mucin in mice, we studied Muc19−/− mice for changes in innate immune functions of saliva in interactions with S. mutans. When challenged with S. mutans and a cariogenic diet, total smooth and sulcal surface lesions are more than 2- and 1.6-fold higher in Muc19−/− mice compared with wild type, whereas the severity of lesions are up to 6- and 10-fold higher, respectively. Furthermore, the oral microbiota of Muc19−/− mice display higher levels of indigenous streptococci. Results emphasize the importance of a single salivary constituent in the innate immune functions of saliva. In vitro studies of S. mutans and Muc19 interactions (i.e. adherence, aggregation, and biofilm formation) demonstrate Muc19 poorly aggregates S. mutans. Nonetheless, aggregation is enhanced upon adding Muc19 to saliva from Muc19−/− mice, indicating Muc19 assists in bacterial clearance through formation of heterotypic complexes with salivary constituents that bind S. mutans, thus representing a novel innate immune function for salivary gel-forming mucins. In humans, expression of salivary MUC19 is unclear. We find MUC19 transcripts in salivary glands of seven subjects and demonstrate MUC19 glycoproteins in glandular mucous cells and saliva. Similarities and differences between mice and humans in the expression and functions of salivary gel-forming mucins are discussed. PMID:25512380

  9. Wavelet analysis of epileptic spikes

    Science.gov (United States)

    Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-05-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  10. Wavelet analysis of epileptic spikes

    CERN Document Server

    Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-01-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  11. The glycoprotein of measles virus

    International Nuclear Information System (INIS)

    Anttonen, O.; Jokinen, M.; Salmi, A.; Vainionpaeae, R.; Gahmberg, C.G.

    1980-01-01

    Measles virus was propagated in VERO cells and purified from the culture supernatants by two successive tartrate-density-gradient centrifugations. Surface carbohydrates were labelled both in vitro and in vivo with 3 H after treatment with galactose oxidase/NaB 3 H 4 or with [ 3 H]glucosamine. The major labelled glycoprotein in measles virions had a mol.wt. of 79000. After labelling with periodate/NaB 3 H 4 , which would result in specific labelling of sialic acid residues, the 79000-mol.wt. glycoprotein was very weakly labelled. This suggested that there is no or a very low amount of sialic acid in the virions. Further analysis of the glycoprotein showed that galactose is the terminal carbohydrate unit in the oligosaccharide, and the molecular weight of the glycopeptide obtained after Pronase digestion is about 3000. The oligosaccharide is attached to the polypeptide through an alkali-stable bond, indicating a N-glycosidic asparagine linkage. (author)

  12. Training spiking neural networks to associate spatio-temporal input-output spike patterns

    OpenAIRE

    Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

    2013-01-01

    In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the prop...

  13. Recent Progress in Electrochemical Biosensors for Glycoproteins

    Directory of Open Access Journals (Sweden)

    Uichi Akiba

    2016-12-01

    Full Text Available This review provides an overview of recent progress in the development of electrochemical biosensors for glycoproteins. Electrochemical glycoprotein sensors are constructed by combining metal and carbon electrodes with glycoprotein-selective binding elements including antibodies, lectin, phenylboronic acid and molecularly imprinted polymers. A recent trend in the preparation of glycoprotein sensors is the successful use of nanomaterials such as graphene, carbon nanotube, and metal nanoparticles. These nanomaterials are extremely useful for improving the sensitivity of glycoprotein sensors. This review focuses mainly on the protocols for the preparation of glycoprotein sensors and the materials used. Recent improvements in glycoprotein sensors are discussed by grouping the sensors into several categories based on the materials used as recognition elements.

  14. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

    H. Paugam-Moisy; S.M. Bohte (Sander); G. Rozenberg; T.H.W. Baeck (Thomas); J.N. Kok (Joost)

    2012-01-01

    htmlabstractAbstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions

  15. Learning Universal Computations with Spikes

    Science.gov (United States)

    Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

    2016-01-01

    Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

  16. PE2 cleavage mutants of Sindbis virus : Correlation between viral infectivity and pH-dependent membrane fusion activation of the spike heterodimer

    NARCIS (Netherlands)

    Smit, JM; Klimstra, WB; Ryman, KD; Bittman, R; Johnston, RE; Wilschut, J

    2001-01-01

    The spike glycoprotein E2 of Sindbis virus (SIN) is synthesized in the infected cell as a PE2 precursor protein, which matures through cleavage by a cellular furin-like protease. Previous work has shown that SIN mutants impaired in PE2 cleavage are noninfectious on BHK-21 cells, the block in

  17. Automatic spike sorting using tuning information.

    Science.gov (United States)

    Ventura, Valérie

    2009-09-01

    Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

  18. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

  19. Neuronal coding and spiking randomness

    Czech Academy of Sciences Publication Activity Database

    Košťál, Lubomír; Lánský, Petr; Rospars, J. P.

    2007-01-01

    Roč. 26, č. 10 (2007), s. 2693-2988 ISSN 0953-816X R&D Projects: GA MŠk(CZ) LC554; GA AV ČR(CZ) 1ET400110401; GA AV ČR(CZ) KJB100110701 Grant - others:ECO-NET(FR) 112644PF Institutional research plan: CEZ:AV0Z50110509 Keywords : spike train * variability * neurovědy Subject RIV: FH - Neurology Impact factor: 3.673, year: 2007

  20. iSpike: a spiking neural interface for the iCub robot

    International Nuclear Information System (INIS)

    Gamez, D; Fidjeland, A K; Lazdins, E

    2012-01-01

    This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

  1. Information transmission with spiking Bayesian neurons

    International Nuclear Information System (INIS)

    Lochmann, Timm; Deneve, Sophie

    2008-01-01

    Spike trains of cortical neurons resulting from repeatedpresentations of a stimulus are variable and exhibit Poisson-like statistics. Many models of neural coding therefore assumed that sensory information is contained in instantaneous firing rates, not spike times. Here, we ask how much information about time-varying stimuli can be transmitted by spiking neurons with such input and output variability. In particular, does this variability imply spike generation to be intrinsically stochastic? We consider a model neuron that estimates optimally the current state of a time-varying binary variable (e.g. presence of a stimulus) by integrating incoming spikes. The unit signals its current estimate to other units with spikes whenever the estimate increased by a fixed amount. As shown previously, this computation results in integrate and fire dynamics with Poisson-like output spike trains. This output variability is entirely due to the stochastic input rather than noisy spike generation. As a result such a deterministic neuron can transmit most of the information about the time varying stimulus. This contrasts with a standard model of sensory neurons, the linear-nonlinear Poisson (LNP) model which assumes that most variability in output spike trains is due to stochastic spike generation. Although it yields the same firing statistics, we found that such noisy firing results in the loss of most information. Finally, we use this framework to compare potential effects of top-down attention versus bottom-up saliency on information transfer with spiking neurons

  2. Murine Leukemia Virus (MLV)-based Coronavirus Spike-pseudotyped Particle Production and Infection

    Science.gov (United States)

    Millet, Jean Kaoru; Whittaker, Gary R.

    2016-01-01

    Viral pseudotyped particles (pp) are enveloped virus particles, typically derived from retroviruses or rhabdoviruses, that harbor heterologous envelope glycoproteins on their surface and a genome lacking essential genes. These synthetic viral particles are safer surrogates of native viruses and acquire the tropism and host entry pathway characteristics governed by the heterologous envelope glycoprotein used. They have proven to be very useful tools used in research with many applications, such as enabling the study of entry pathways of enveloped viruses and to generate effective gene-delivery vectors. The basis for their generation lies in the capacity of some viruses, such as murine leukemia virus (MLV), to incorporate envelope glycoproteins of other viruses into a pseudotyped virus particle. These can be engineered to contain reporter genes such as luciferase, enabling quantification of virus entry events upon pseudotyped particle infection with susceptible cells. Here, we detail a protocol enabling generation of MLV-based pseudotyped particles, using the Middle East respiratory syndrome coronavirus (MERS-CoV) spike (S) as an example of a heterologous envelope glycoprotein to be incorporated. We also describe how these particles are used to infect susceptible cells and to perform a quantitative infectivity readout by a luciferase assay. PMID:28018942

  3. The SARS coronavirus spike glycoprotein is selectively recognized by lung surfactant protein D and activates macrophages

    DEFF Research Database (Denmark)

    Leth-Larsen, Rikke; Zhong, Fei; Chow, Vincent T K

    2007-01-01

    Da glycosylated protein. It was not secreted in the presence of tunicamycin and was detected as a 130 kDa protein in the cell lysate. The purified S-protein bound to Vero but not 293T cells and was itself recognized by lung surfactant protein D (SP-D), a collectin found in the lung alveoli. The binding required...

  4. Structure of a trimeric variant of the Epstein-Barr virus glycoprotein B

    Energy Technology Data Exchange (ETDEWEB)

    Backovic, Marija [Northwestern Univ., Evanston, IL (United States); Longnecker, Richard [Northwestern Univ., Chicago, IL (United States); Jardetzky, Theodore S [Northwestern Univ., Evanston, IL (United States)

    2009-03-16

    Epstein-Barr virus (EBV) is a herpesvirus that is associated with development of malignancies of lymphoid tissue. EBV infections are life-long and occur in >90% of the population. Herpesviruses enter host cells in a process that involves fusion of viral and cellular membranes. The fusion apparatus is comprised of envelope glycoprotein B (gB) and a heterodimeric complex made of glycoproteins H and L. Glycoprotein B is the most conserved envelope glycoprotein in human herpesviruses, and the structure of gB from Herpes simplex virus 1 (HSV-1) is available. Here, we report the crystal structure of the secreted EBV gB ectodomain, which forms 16-nm long spike-like trimers, structurally homologous to the postfusion trimers of the fusion protein G of vesicular stomatitis virus (VSV). Comparative structural analyses of EBV gB and VSV G, which has been solved in its pre and postfusion states, shed light on gB residues that may be involved in conformational changes and membrane fusion. Also, the EBV gB structure reveals that, despite the high sequence conservation of gB in herpesviruses, the relative orientations of individual domains, the surface charge distributions, and the structural details of EBV gB differ from the HSV-1 protein, indicating regions and residues that may have important roles in virus-specific entry.

  5. The dynamic relationship between cerebellar Purkinje cell simple spikes and the spikelet number of complex spikes.

    Science.gov (United States)

    Burroughs, Amelia; Wise, Andrew K; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y; Lang, Eric J; Apps, Richard; Cerminara, Nadia L

    2017-01-01

    Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes. Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets. The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear. We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number. This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range. Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake cats. In addition

  6. Motor control by precisely timed spike patterns

    DEFF Research Database (Denmark)

    Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel

    2017-01-01

    whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence......A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time...... interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear...

  7. Multineuronal Spike Sequences Repeat with Millisecond Precision

    Directory of Open Access Journals (Sweden)

    Koki eMatsumoto

    2013-06-01

    Full Text Available Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and nonspiking neurons. Multineuronal spike sequences were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

  8. The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

    DEFF Research Database (Denmark)

    Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.

    2016-01-01

    and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states......Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from...... visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary...

  9. Spike voltage topography in temporal lobe epilepsy.

    Science.gov (United States)

    Asadi-Pooya, Ali A; Asadollahi, Marjan; Shimamoto, Shoichi; Lorenzo, Matthew; Sperling, Michael R

    2016-07-15

    We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Bayesian population decoding of spiking neurons.

    Science.gov (United States)

    Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

    2009-01-01

    The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  11. Bayesian population decoding of spiking neurons

    Directory of Open Access Journals (Sweden)

    Sebastian Gerwinn

    2009-10-01

    Full Text Available The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

  12. Statistical properties of superimposed stationary spike trains.

    Science.gov (United States)

    Deger, Moritz; Helias, Moritz; Boucsein, Clemens; Rotter, Stefan

    2012-06-01

    The Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models. The constructed superimposed spike trains reveal strong deviations from the Poisson model. We find that superpositions of model spike trains that take the effective refractoriness of the neurons into account yield a much better description. A minimal model of this kind is the Poisson process with dead-time (PPD). For this process, and for superpositions thereof, we obtain analytical expressions for some second-order statistical quantities-like the count variability, inter-spike interval (ISI) variability and ISI correlations-and demonstrate the match with the in vivo data. We conclude that effective refractoriness is the key property that shapes the statistical properties of the superposition spike trains. We present new, efficient algorithms to generate superpositions of PPDs and of gamma processes that can be used to provide more realistic background input in simulations of networks of spiking neurons. Using these generators, we show in simulations that neurons which receive superimposed spike trains as input are highly sensitive for the statistical effects induced by neuronal refractoriness.

  13. Linking investment spikes and productivity growth

    NARCIS (Netherlands)

    Geylani, P.C.; Stefanou, S.E.

    2013-01-01

    We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing

  14. Mimickers of generalized spike and wave discharges.

    Science.gov (United States)

    Azzam, Raed; Bhatt, Amar B

    2014-06-01

    Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

  15. Role of LAMP1 Binding and pH Sensing by the Spike Complex of Lassa Virus.

    Science.gov (United States)

    Cohen-Dvashi, Hadas; Israeli, Hadar; Shani, Orly; Katz, Aliza; Diskin, Ron

    2016-11-15

    To effectively infect cells, Lassa virus needs to switch in an endosomal compartment from its primary receptor, α-dystroglycan, to a protein termed LAMP1. A unique histidine triad on the surface of the receptor-binding domain from the glycoprotein spike complex of Lassa virus is important for LAMP1 binding. Here we investigate mutated spikes that have an impaired ability to interact with LAMP1 and show that although LAMP1 is important for efficient infectivity, it is not required for spike-mediated membrane fusion per se Our studies reveal important regulatory roles for histidines from the triad in sensing acidic pH and preventing premature spike triggering. We further show that LAMP1 requires a positively charged His230 residue to engage with the spike complex and that LAMP1 binding promotes membrane fusion. These results elucidate the molecular role of LAMP1 binding during Lassa virus cell entry and provide new insights into how pH is sensed by the spike. Lassa virus is a devastating disease-causing agent in West Africa, with a significant yearly death toll and severe long-term complications associated with its infection in survivors. In recent years, we learned that Lassa virus needs to switch receptors in a pH-dependent manner to efficiently infect cells, but neither the molecular mechanisms that allow switching nor the actual effects of switching were known. Here we investigate the activity of the viral spike complex after abrogation of its ability to switch receptors. These studies inform us about the role of switching receptors and provide new insights into how the spike senses acidic pH. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

  16. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  17. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Stochastic variational learning in recurrent spiking networks.

    Science.gov (United States)

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  19. Genetic diversity of ORF3 and spike genes of porcine epidemic diarrhea virus in Thailand.

    Science.gov (United States)

    Temeeyasen, Gun; Srijangwad, Anchalee; Tripipat, Thitima; Tipsombatboon, Pavita; Piriyapongsa, Jittima; Phoolcharoen, Waranyoo; Chuanasa, Taksina; Tantituvanont, Angkana; Nilubol, Dachrit

    2014-01-01

    Porcine epidemic diarrhea virus (PEDV) has become endemic in the Thai swine industry, causing economic losses and repeated outbreaks since its first emergence in 2007. In the present study, 69 Thai PEDV isolates were obtained from 50 swine herds across Thailand during the period 2008-2012. Both partial and complete nucleotide sequences of the spike (S) glycoprotein and the nucleotide sequences of ORF3 genes were determined to investigate the genetic diversity and molecular epidemiology of Thai PEDV. Based on the analysis of the partial S glycoprotein genes, the Thai PEDV isolates were clustered into 2 groups related to Korean and Chinese field isolates. The results for the complete spike genes, however, demonstrated that both groups were grouped in the same cluster. Interestingly, both groups of Thai PEDV isolates had a 4-aa (GENQ) insertion between positions 55 and 56, a 1-aa insertion between positions 135 and 136, and a 2-aa deletion between positions 155 and 156, making them identical to the Korean KNU series and isolates responsible for outbreaks in China in recent years. In addition to the complete S sequences, the ORF3 gene analyses suggested that the isolates responsible for outbreaks in Thailand are not vaccine related. The results of this study suggest that the PEDV isolates responsible for outbreaks in Thailand since its emergence represent a variant of PEDV that was previously reported in China and Korea. Copyright © 2013 Elsevier B.V. All rights reserved.

  20. Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Cabrerizo, Mercedes; Adjouadi, Malek

    2016-08-01

    This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns. A new encoding method is utilized to convert the EEG signal into spike patterns. The simulation results show that the proposed algorithm identified 69 spikes out of 82 spikes, or 84% detection rate, which is quite high considering the subtleties of interictal spikes and the tediousness of monitoring long EEG records. This CCDS rule is also benchmarked by ReSuMe on the same task.

  1. Decoding spatiotemporal spike sequences via the finite state automata dynamics of spiking neural networks

    International Nuclear Information System (INIS)

    Jin, Dezhe Z

    2008-01-01

    Temporally complex stimuli are encoded into spatiotemporal spike sequences of neurons in many sensory areas. Here, we describe how downstream neurons with dendritic bistable plateau potentials can be connected to decode such spike sequences. Driven by feedforward inputs from the sensory neurons and controlled by feedforward inhibition and lateral excitation, the neurons transit between UP and DOWN states of the membrane potentials. The neurons spike only in the UP states. A decoding neuron spikes at the end of an input to signal the recognition of specific spike sequences. The transition dynamics is equivalent to that of a finite state automaton. A connection rule for the networks guarantees that any finite state automaton can be mapped into the transition dynamics, demonstrating the equivalence in computational power between the networks and finite state automata. The decoding mechanism is capable of recognizing an arbitrary number of spatiotemporal spike sequences, and is insensitive to the variations of the spike timings in the sequences

  2. Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

    Science.gov (United States)

    Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

    2016-01-01

    Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

  3. Reliability of MEG source imaging of anterior temporal spikes: analysis of an intracranially characterized spike focus.

    Science.gov (United States)

    Wennberg, Richard; Cheyne, Douglas

    2014-05-01

    To assess the reliability of MEG source imaging (MSI) of anterior temporal spikes through detailed analysis of the localization and orientation of source solutions obtained for a large number of spikes that were separately confirmed by intracranial EEG to be focally generated within a single, well-characterized spike focus. MSI was performed on 64 identical right anterior temporal spikes from an anterolateral temporal neocortical spike focus. The effects of different volume conductors (sphere and realistic head model), removal of noise with low frequency filters (LFFs) and averaging multiple spikes were assessed in terms of the reliability of the source solutions. MSI of single spikes resulted in scattered dipole source solutions that showed reasonable reliability for localization at the lobar level, but only for solutions with a goodness-of-fit exceeding 80% using a LFF of 3 Hz. Reliability at a finer level of intralobar localization was limited. Spike averaging significantly improved the reliability of source solutions and averaging 8 or more spikes reduced dependency on goodness-of-fit and data filtering. MSI performed on topographically identical individual spikes from an intracranially defined classical anterior temporal lobe spike focus was limited by low reliability (i.e., scattered source solutions) in terms of fine, sublobar localization within the ipsilateral temporal lobe. Spike averaging significantly improved reliability. MSI performed on individual anterior temporal spikes is limited by low reliability. Reduction of background noise through spike averaging significantly improves the reliability of MSI solutions. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  4. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    DEFF Research Database (Denmark)

    Huys, Raoul; Jirsa, Viktor K; Darokhan, Ziauddin

    2016-01-01

    attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization...

  5. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

  6. Spike Bursts from an Excitable Optical System

    Science.gov (United States)

    Rios Leite, Jose R.; Rosero, Edison J.; Barbosa, Wendson A. S.; Tredicce, Jorge R.

    Diode Lasers with double optical feedback are shown to present power drop spikes with statistical distribution controllable by the ratio of the two feedback times. The average time between spikes and the variance within long time series are studied. The system is shown to be excitable and present bursting of spikes created with specific feedback time ratios and strength. A rate equation model, extending the Lang-Kobayashi single feedback for semiconductor lasers proves to match the experimental observations. Potential applications to construct network to mimic neural systems having controlled bursting properties in each unit will be discussed. Brazilian Agency CNPQ.

  7. The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

    Science.gov (United States)

    Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.; Roland, Per E.

    2016-01-01

    Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes. PMID:27582693

  8. Defining glycoprotein cancer biomarkers by MS in conjunction with glycoprotein enrichment.

    Science.gov (United States)

    Song, Ehwang; Mechref, Yehia

    2015-01-01

    Protein glycosylation is an important and common post-translational modification. More than 50% of human proteins are believed to be glycosylated to modulate the functionality of proteins. Aberrant glycosylation has been correlated to several diseases, such as inflammatory skin diseases, diabetes mellitus, cardiovascular disorders, rheumatoid arthritis, Alzheimer's and prion diseases, and cancer. Many approved cancer biomarkers are glycoproteins which are not highly abundant proteins. Therefore, effective qualitative and quantitative assessment of glycoproteins entails enrichment methods. This chapter summarizes glycoprotein enrichment methods, including lectin affinity, immunoaffinity, hydrazide chemistry, hydrophilic interaction liquid chromatography, and click chemistry. The use of these enrichment approaches in assessing the qualitative and quantitative changes of glycoproteins in different types of cancers are presented and discussed. This chapter highlights the importance of glycoprotein enrichment techniques for the identification and characterization of new reliable cancer biomarkers.

  9. Spike persistence and normalization in benign epilepsy with centrotemporal spikes - Implications for management.

    Science.gov (United States)

    Kim, Hunmin; Kim, Soo Yeon; Lim, Byung Chan; Hwang, Hee; Chae, Jong-Hee; Choi, Jieun; Kim, Ki Joong; Dlugos, Dennis J

    2018-05-10

    This study was performed 1) to determine the timing of spike normalization in patients with benign epilepsy with centrotemporal spikes (BECTS); 2) to identify relationships between age of seizure onset, age of spike normalization, years of spike persistence and treatment; and 3) to assess final outcomes between groups of patients with or without spikes at the time of medication tapering. Retrospective analysis of BECTS patients confirmed by clinical data, including age of onset, seizure semiology and serial electroencephalography (EEG) from diagnosis to remission. Age at spike normalization, years of spike persistence, and time of treatment onset to spike normalization were assessed. Final seizure and EEG outcome were compared between the groups with or without spikes at the time of AED tapering. One hundred and thirty-four patients were included. Mean age at seizure onset was 7.52 ± 2.11 years. Mean age at spike normalization was 11.89 ± 2.11 (range: 6.3-16.8) years. Mean time of treatment onset to spike normalization was 4.11 ± 2.13 (range: 0.24-10.08) years. Younger age of seizure onset was correlated with longer duration of spike persistence (r = -0.41, p < 0.001). In treated patients, spikes persisted for 4.1 ± 1.95 years, compared with 2.9 ± 1.97 years in untreated patients. No patients had recurrent seizures after AED was discontinued, regardless of the presence/absence of spikes at time of AED tapering. Years of spike persistence was longer in early onset BECTS patients. Treatment with AEDs did not shorten years of spike persistence. Persistence of spikes at time of treatment withdrawal was not associated with seizure recurrence. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  10. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan; Naous, Rawan; Cauwenberghs, Gert; Salama, Khaled N.

    2015-01-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning

  11. Fitting neuron models to spike trains

    Directory of Open Access Journals (Sweden)

    Cyrille eRossant

    2011-02-01

    Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand flexible enough to easily test different candidate models.We present a generic solution, based on the Brian simulator(a neural network simulator in Python, which allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

  12. Frequency of Rolandic Spikes in ADHD

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-10-01

    Full Text Available The frequency of rolandic spikes in nonepileptic children with attention deficit hyperactivity disorder (ADHD was compared with a control group of normal school-aged children in a study at the University of Frankfurt, Germany.

  13. THE POLITICAL CRITIQUE OF SPIKE Lee's Bamboozled

    African Journals Online (AJOL)

    Admin

    CONTEMPORARY AMERICAN MEDIA: THE POLITICAL. CRITIQUE OF SPIKE ... KEYWORDS: Blackface Minstrelsy, Racist Stereotypes and American Media. INTRODUCTION ..... of a difference that is itself a process of disavowal.” In this ...

  14. Inferring oscillatory modulation in neural spike trains.

    Science.gov (United States)

    Arai, Kensuke; Kass, Robert E

    2017-10-01

    Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

  15. Anticipating Activity in Social Media Spikes

    OpenAIRE

    Higham, Desmond J.; Grindrod, Peter; Mantzaris, Alexander V.; Otley, Amanda; Laflin, Peter

    2014-01-01

    We propose a novel mathematical model for the activity of microbloggers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governments and charities, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions ev...

  16. Building functional networks of spiking model neurons.

    Science.gov (United States)

    Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

    2016-03-01

    Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

  17. Glycoprotein biosynthesis by human normal platelets

    International Nuclear Information System (INIS)

    Rodriguez, P.; Bello, O.; Apitz-Castro, R.

    1987-01-01

    Incorporation of radioactive Man, Gal, Fuc, Glc-N, and NANA into washed human normal platelets and endogenous glycoproteins has been found. Both parameters were time dependent. Analysis of hydrolyzed labeled glycoproteins by paper chromatography revealed that the radioactive monosaccharide incubated with the platelets had not been converted into other sugars. Acid hydrolysis demonstrates the presence of a glycosidic linkage. All the effort directed to the demonstration of the existence of a lipid-sugar intermediate in intact human platelets yielded negative results for Man and Glc-N used as precursors. The incorporation of these sugars into glycoproteins is insensitive to bacitracin, suggesting no involvement of lipid-linked saccharides in the synthesis of glycoproteins in human blood platelets. The absence of inhibition of the glycosylation process in the presence of cycloheximide suggests that the sugars are added to proteins present in the intact platelets. These results support the contention that glycoprotein biosynthesis in human blood platelets observed under our experimental conditions is effected through direct sugar nucleotide glycosylation

  18. Identification of NCAM that interacts with the PHE-CoV spike protein.

    Science.gov (United States)

    Gao, Wei; He, Wenqi; Zhao, Kui; Lu, Huijun; Ren, Wenzhi; Du, Chongtao; Chen, Keyan; Lan, Yungang; Song, Deguang; Gao, Feng

    2010-09-24

    The spike proteins of coronaviruses associate with cellular molecules to mediate infection of their target cells. The characterization of cellular proteins required for virus infection is essential for understanding viral life cycles and may provide cellular targets for antiviral therapies. We identified Neural Cell Adhesion Molecule (NCAM) as a novel interacting partner of the PHE-CoV S protein. A T7 phage display cDNA library from N2a cells was constructed, and the library was screened with the soluble PHE-CoV S glycoproteins. We used a coimmunoprecipitation assay to show that only the NCAM was a binding partner of spike protein. We found that a soluble form of anti-NCAM antibody blocked association of the PHE-CoV with N2a cells. Furthermore, double-stranded siRNA targeted against NCAM inhibited PHE-CoV infection. A novel interaction was identified between NCAM and spike protein and this association is critical during PHE-CoV infection.

  19. Identification of NCAM that interacts with the PHE-CoV spike protein

    Directory of Open Access Journals (Sweden)

    Chen Keyan

    2010-09-01

    Full Text Available Abstract Background The spike proteins of coronaviruses associate with cellular molecules to mediate infection of their target cells. The characterization of cellular proteins required for virus infection is essential for understanding viral life cycles and may provide cellular targets for antiviral therapies. Results We identified Neural Cell Adhesion Molecule (NCAM as a novel interacting partner of the PHE-CoV S protein. A T7 phage display cDNA library from N2a cells was constructed, and the library was screened with the soluble PHE-CoV S glycoproteins. We used a coimmunoprecipitation assay to show that only the NCAM was a binding partner of spike protein. We found that a soluble form of anti-NCAM antibody blocked association of the PHE-CoV with N2a cells. Furthermore, double-stranded siRNA targeted against NCAM inhibited PHE-CoV infection. Conclusions A novel interaction was identified between NCAM and spike protein and this association is critical during PHE-CoV infection.

  20. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  1. Glycoprotein Enrichment Analytical Techniques: Advantages and Disadvantages.

    Science.gov (United States)

    Zhu, R; Zacharias, L; Wooding, K M; Peng, W; Mechref, Y

    2017-01-01

    Protein glycosylation is one of the most important posttranslational modifications. Numerous biological functions are related to protein glycosylation. However, analytical challenges remain in the glycoprotein analysis. To overcome the challenges associated with glycoprotein analysis, many analytical techniques were developed in recent years. Enrichment methods were used to improve the sensitivity of detection, while HPLC and mass spectrometry methods were developed to facilitate the separation of glycopeptides/proteins and enhance detection, respectively. Fragmentation techniques applied in modern mass spectrometers allow the structural interpretation of glycopeptides/proteins, while automated software tools started replacing manual processing to improve the reliability and throughput of the analysis. In this chapter, the current methodologies of glycoprotein analysis were discussed. Multiple analytical techniques are compared, and advantages and disadvantages of each technique are highlighted. © 2017 Elsevier Inc. All rights reserved.

  2. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    Science.gov (United States)

    Huys, Raoul; Jirsa, Viktor K.; Darokhan, Ziauddin; Valentiniene, Sonata; Roland, Per E.

    2016-01-01

    Neurons in the primary visual cortex spontaneously spike even when there are no visual stimuli. It is unknown whether the spiking evoked by visual stimuli is just a modification of the spontaneous ongoing cortical spiking dynamics or whether the spontaneous spiking state disappears and is replaced by evoked spiking. This study of laminar recordings of spontaneous spiking and visually evoked spiking of neurons in the ferret primary visual cortex shows that the spiking dynamics does not change: the spontaneous spiking as well as evoked spiking is controlled by a stable and persisting fixed point attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization is that it avoids the need for a system reorganization following visual stimulation, and impedes the transition of spontaneous spiking to evoked spiking and the propagation of spontaneous spiking from layer 4 to layers 2–3. PMID:26778982

  3. Non-orthogonally transitive G2 spike solution

    International Nuclear Information System (INIS)

    Lim, Woei Chet

    2015-01-01

    We generalize the orthogonally transitive (OT) G 2 spike solution to the non-OT G 2 case. This is achieved by applying Geroch’s transformation on a Kasner seed. The new solution contains two more parameters than the OT G 2 spike solution. Unlike the OT G 2 spike solution, the new solution always resolves its spike. (fast track communication)

  4. Engineered CHO cells for production of diverse, homogeneous glycoproteins

    DEFF Research Database (Denmark)

    Yang, Zhang; Wang, Shengjun; Halim, Adnan

    2015-01-01

    Production of glycoprotein therapeutics in Chinese hamster ovary (CHO) cells is limited by the cells' generic capacity for N-glycosylation, and production of glycoproteins with desirable homogeneous glycoforms remains a challenge. We conducted a comprehensive knockout screen of glycosyltransferas...

  5. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

    Science.gov (United States)

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

  6. Spike rate and spike timing contributions to coding taste quality information in rat periphery

    Directory of Open Access Journals (Sweden)

    Vernon eLawhern

    2011-05-01

    Full Text Available There is emerging evidence that individual sensory neurons in the rodent brain rely on temporal features of the discharge pattern to code differences in taste quality information. In contrast, in-vestigations of individual sensory neurons in the periphery have focused on analysis of spike rate and mostly disregarded spike timing as a taste quality coding mechanism. The purpose of this work was to determine the contribution of spike timing to taste quality coding by rat geniculate ganglion neurons using computational methods that have been applied successfully in other sys-tems. We recorded the discharge patterns of narrowly-tuned and broadly-tuned neurons in the rat geniculate ganglion to representatives of the five basic taste qualities. We used mutual in-formation to determine significant responses and the van Rossum metric to characterize their temporal features. While our findings show that spike timing contributes a significant part of the message, spike rate contributes the largest portion of the message relayed by afferent neurons from rat fungiform taste buds to the brain. Thus, spike rate and spike timing together are more effective than spike rate alone in coding stimulus quality information to a single basic taste in the periphery for both narrowly-tuned specialist and broadly-tuned generalist neurons.

  7. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  8. Spike Code Flow in Cultured Neuronal Networks.

    Science.gov (United States)

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

  9. Spike Code Flow in Cultured Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Shinichi Tamura

    2016-01-01

    Full Text Available We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

  10. Silencing of SARS-CoV spike gene by small interfering RNA in HEK 293T cells

    International Nuclear Information System (INIS)

    Qin Zhaoling; Zhao Ping; Zhang Xiaolian; Yu Jianguo; Cao Mingmei; Zhao Lanjuan; Luan Jie; Qi Zhongtian

    2004-01-01

    Two candidate small interfering RNAs (siRNAs) corresponding to severe acute respiratory syndrome-associated coronavirus (SARS-CoV) spike gene were designed and in vitro transcribed to explore the possibility of silencing SARS-CoV S gene. The plasmid pEGFP-optS, which contains the codon-optimized SARS-CoV S gene and expresses spike-EGFP fusion protein (S-EGFP) as silencing target and expressing reporter, was transfected with siRNAs into HEK 293T cells. At various time points of posttransfection, the levels of S-EGFP expression and amounts of spike mRNA transcript were detected by fluorescence microscopy, flow cytometry, Western blot, and real-time quantitative PCR, respectively. The results showed that the cells transfected with pEGFP-optS expressed S-EGFP fusion protein at a higher level compared with those transfected with pEGFP-S, which contains wildtype SARS-CoV spike gene sequence. The green fluorescence, mean fluorescence intensity, and SARS-CoV S RNA transcripts were found significantly reduced, and the expression of SARS-CoV S glycoprotein was strongly inhibited in those cells co-transfected with either EGFP- or S-specific siRNAs. Our findings demonstrated that the S-specific siRNAs used in this study were able to specifically and effectively inhibit SARS-CoV S glycoprotein expression in cultured cells through blocking the accumulation of S mRNA, which may provide an approach for studies on the functions of SARS-CoV S gene and development of novel prophylactic or therapeutic agents for SARS-CoV

  11. Isolation of glycoproteins from brown algae

    DEFF Research Database (Denmark)

    2015-01-01

    The present invention relates to a novel process for the isolation of unique anti-oxidative glycoproteins from the pH precipitated fractions of enzymatic extracts of brown algae. Two brown seaweeds viz, Fucus serratus and Fucus vesiculosus were hydrolysed by using 3 enzymes viz, Alcalase, Viscozyme...

  12. P-glycoprotein targeted nanoscale drug carriers

    KAUST Repository

    Li, Wengang; Abu Samra, Dina Bashir Kamil; Merzaban, Jasmeen; Khashab, Niveen M.

    2013-01-01

    Multi-drug resistance (MDR) is a trend whereby tumor cells exposed to one cytotoxic agent develop cross-resistance to a range of structurally and functionally unrelated compounds. P -glycoprotein (P -gp) efflux pump is one of the mostly studied drug

  13. Platelet Glycoprotein Ib-IX and Malignancy

    Science.gov (United States)

    2010-09-01

    provide a unique microenvironment supporting the accumulation of more platelets and the elaboration of a fibrin - rich network produced by coagulation...process and can initiate the formation of a platelet - rich thrombus by tethering the platelet to a thrombogenic surface. Several ligands binding to GP Ib... Platelet Glycoprotein Ib-IX and Malignancy PRINCIPAL INVESTIGATOR: Jerry Ware, Ph.D

  14. Involvement of Leishmania donovani major surface glycoprotein ...

    Indian Academy of Sciences (India)

    The major surface glycoprotein gp63 of the kinetoplastid protozoal parasite Leishmania is implicated as a ligand mediating uptake of the parasite into, and survival within, the host macrophage. By expressing gp63 antisense RNA from an episomal vector in L. donovani promastigotes, gp63-deficient transfectants were ...

  15. The electric potential of tripolar spikes

    Energy Technology Data Exchange (ETDEWEB)

    Nocera, L. [Theoretical Plasma Physics, IPCF-CNR, Via Moruzzi 1, I-56124 Pisa (Italy)

    2010-02-22

    We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

  16. The electric potential of tripolar spikes

    International Nuclear Information System (INIS)

    Nocera, L.

    2010-01-01

    We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

  17. Trace element ink spiking for signature authentication

    International Nuclear Information System (INIS)

    Hatzistavros, V.S.; Kallithrakas-Kontos, N.G.

    2008-01-01

    Signature authentication is a critical question in forensic document examination. Last years the evolution of personal computers made signature copying a quite easy task, so the development of new ways for signature authentication is crucial. In the present work a commercial ink was spiked with many trace elements in various concentrations. Inorganic and organometallic ink soluble compounds were used as spiking agents, whilst ink retained its initial properties. The spiked inks were used for paper writing and the documents were analyzed by a non destructive method, the energy dispersive X-ray fluorescence. The thin target model was proved right for quantitative analysis and a very good linear relationship of the intensity (X-ray signal) against concentration was estimated for all used elements. Intensity ratios between different elements in the same ink gave very stable results, independent on the writing alterations. The impact of time both to written document and prepared inks was also investigated. (author)

  18. A Novel and Simple Spike Sorting Implementation.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Poirazi, Panayiota

    2017-04-01

    Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

  19. Spike timing precision of neuronal circuits.

    Science.gov (United States)

    Kilinc, Deniz; Demir, Alper

    2018-04-17

    Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

  20. Evoking prescribed spike times in stochastic neurons

    Science.gov (United States)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

    Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

  1. Temporal Correlations and Neural Spike Train Entropy

    International Nuclear Information System (INIS)

    Schultz, Simon R.; Panzeri, Stefano

    2001-01-01

    Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a 'brute force' approach

  2. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  3. Improved SpikeProp for Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Falah Y. H. Ahmed

    2013-01-01

    Full Text Available A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

  4. Span: spike pattern association neuron for learning spatio-temporal spike patterns.

    Science.gov (United States)

    Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

    2012-08-01

    Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

  5. Mechanisms of Coronavirus Cell Entry Mediated by the Viral Spike Protein

    Directory of Open Access Journals (Sweden)

    Gary R. Whittaker

    2012-06-01

    Full Text Available Coronaviruses are enveloped positive-stranded RNA viruses that replicate in the cytoplasm. To deliver their nucleocapsid into the host cell, they rely on the fusion of their envelope with the host cell membrane. The spike glycoprotein (S mediates virus entry and is a primary determinant of cell tropism and pathogenesis. It is classified as a class I fusion protein, and is responsible for binding to the receptor on the host cell as well as mediating the fusion of host and viral membranes—A process driven by major conformational changes of the S protein. This review discusses coronavirus entry mechanisms focusing on the different triggers used by coronaviruses to initiate the conformational change of the S protein: receptor binding, low pH exposure and proteolytic activation. We also highlight commonalities between coronavirus S proteins and other class I viral fusion proteins, as well as distinctive features that confer distinct tropism, pathogenicity and host interspecies transmission characteristics to coronaviruses.

  6. Physics of volleyball: Spiking with a purpose

    Science.gov (United States)

    Behroozi, F.

    1998-05-01

    A few weeks ago our volleyball coach telephoned me with a problem: How high should a player jump to "spike" a "set" ball so it would clear the net and land at a known distance on the other side of the net?

  7. An Unsupervised Online Spike-Sorting Framework.

    Science.gov (United States)

    Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

    2016-08-01

    Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

  8. Spike-timing theory of working memory.

    Directory of Open Access Journals (Sweden)

    Botond Szatmáry

    Full Text Available Working memory (WM is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds.

  9. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

    Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

    2017-12-01

    Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

  10. Impact of spike train autostructure on probability distribution of joint spike events.

    Science.gov (United States)

    Pipa, Gordon; Grün, Sonja; van Vreeswijk, Carl

    2013-05-01

    The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent non-Poisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a log-normal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate data change the distribution of coincident events and influence the significance estimation. We find, first, that the width of the coincidence count distribution and its FFc depend critically and in a nontrivial way on the detailed properties of the structure of the spike trains as characterized by the coefficient of variation CV. Second, the dependence of the FFc on the CV is complex and mostly nonmonotonic. Third, spike dithering, even if as small as a fraction of the interspike interval, can falsify the inference on coordinated firing.

  11. Labelled antibody techniques in glycoprotein estimation

    International Nuclear Information System (INIS)

    Hazra, D.K.; Ekins, R.P.; Edwards, R.; Williams, E.S.

    1977-01-01

    The problems in the radioimmunoassay of the glycoprotein hormones (pituitary LH, FSH and TSH and human chlorionic gonadotrophin HGG) are reviewed viz: limited specificity and sensitivity in the clinical context, interpretation of disparity between bioassay and radioimmunoassay, and interlaboratory variability. The advantages and limitations of the labelled antibody techniques - classical immonoradiometric methods and 2-site or 125 I-anti-IgG indirect labelling modifications are reviewed in general, and their theoretical potential in glycoprotein assays examined in the light of previous work. Preliminary experiments in the development of coated tube 2-site assay for glycoproteins using 125 I anti-IgG labelling are described, including conditions for maximizing solid phase extraction of the antigen, iodination of anti-IgG, and assay conditions such as effects of temperature of incubation with antigen 'hormonefree serum', heterologous serum and detergent washing. Experiments with extraction and antigen-specific antisera raised in the same or different species are described as exemplified by LH and TSH assay systems, the latter apparently promising greater sensitivity than radioimmunoassay. Proposed experimental and mathematical optimisation and validation of the method as an assay system is outlined, and the areas for further work delineated. (orig.) [de

  12. Detection of bursts in neuronal spike trains by the mean inter-spike interval method

    Institute of Scientific and Technical Information of China (English)

    Lin Chen; Yong Deng; Weihua Luo; Zhen Wang; Shaoqun Zeng

    2009-01-01

    Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgrnent. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

  13. Dimers of beta 2-glycoprotein I mimic the in vitro effects of beta 2-glycoprotein I-anti-beta 2-glycoprotein I antibody complexes

    NARCIS (Netherlands)

    Lutters, B. C.; Meijers, J. C.; Derksen, R. H.; Arnout, J.; de Groot, P. G.

    2001-01-01

    Anti-beta(2)-glycoprotein I antibodies are thought to cause lupus anticoagulant activity by forming bivalent complexes with beta(2)-glycoprotein I (beta(2)GPI). To test this hypothesis, chimeric fusion proteins were constructed of the dimerization domain (apple 4) of factor XI and beta(2)GPI. Both a

  14. Spike protein assembly into the coronavirion: exploring the limits of its sequence requirements

    International Nuclear Information System (INIS)

    Bosch, Berend Jan; Haan, Cornelis A.M. de; Smits, Saskia L.; Rottier, Peter J.M.

    2005-01-01

    The coronavirus spike (S) protein, required for receptor binding and membrane fusion, is incorporated into the assembling virion by interactions with the viral membrane (M) protein. Earlier we showed that the ectodomain of the S protein is not involved in this process. Here we further defined the requirements of the S protein for virion incorporation. We show that the cytoplasmic domain, not the transmembrane domain, determines the association with the M protein and suffices to effect the incorporation into viral particles of chimeric spikes as well as of foreign viral glycoproteins. The essential sequence was mapped to the membrane-proximal region of the cytoplasmic domain, which is also known to be of critical importance for the fusion function of the S protein. Consistently, only short C-terminal truncations of the S protein were tolerated when introduced into the virus by targeted recombination. The important role of the about 38-residues cytoplasmic domain in the assembly of and membrane fusion by this approximately 1300 amino acids long protein is discussed

  15. Grain price spikes and beggar-thy-neighbor policy responses

    DEFF Research Database (Denmark)

    Jensen, Hans Grinsted; Anderson, Kym

    2017-01-01

    When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...

  16. Barbed micro-spikes for micro-scale biopsy

    Science.gov (United States)

    Byun, Sangwon; Lim, Jung-Min; Paik, Seung-Joon; Lee, Ahra; Koo, Kyo-in; Park, Sunkil; Park, Jaehong; Choi, Byoung-Doo; Seo, Jong Mo; Kim, Kyung-ah; Chung, Hum; Song, Si Young; Jeon, Doyoung; Cho, Dongil

    2005-06-01

    Single-crystal silicon planar micro-spikes with protruding barbs are developed for micro-scale biopsy and the feasibility of using the micro-spike as a micro-scale biopsy tool is evaluated for the first time. The fabrication process utilizes a deep silicon etch to define the micro-spike outline, resulting in protruding barbs of various shapes. Shanks of the fabricated micro-spikes are 3 mm long, 100 µm thick and 250 µm wide. Barbs protruding from micro-spike shanks facilitate the biopsy procedure by tearing off and retaining samples from target tissues. Micro-spikes with barbs successfully extracted tissue samples from the small intestines of the anesthetized pig, whereas micro-spikes without barbs failed to obtain a biopsy sample. Parylene coating can be applied to improve the biocompatibility of the micro-spike without deteriorating the biopsy function of the micro-spike. In addition, to show that the biopsy with the micro-spike can be applied to tissue analysis, samples obtained by micro-spikes were examined using immunofluorescent staining. Nuclei and F-actin of cells which are extracted by the micro-spike from a transwell were clearly visualized by immunofluorescent staining.

  17. The Mutation Frequency in Different Spike Categories in Barley

    DEFF Research Database (Denmark)

    Frydenberg, O.; Doll, Hans; Sandfær, J.

    1964-01-01

    After gamma irradiation of barley seeds, a comparison has been made between the chlorophyll-mutant frequencies in X1 spikes that had multicellular bud meristems in the seeds at the time of treatment (denoted as pre-formed spikes) and X1 spikes having no recognizable meristems at the time...

  18. Error-backpropagation in temporally encoded networks of spiking neurons

    NARCIS (Netherlands)

    S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

    2000-01-01

    textabstractFor a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, emph{SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm,

  19. Cell wall O-glycoproteins and N-glycoproteins: biosynthesis and some functional aspects.

    Directory of Open Access Journals (Sweden)

    Eric eNguema-Ona

    2014-10-01

    Full Text Available Cell wall O-glycoproteins and N-glycoproteins are two types of glycomolecules whose glycans are structurally complex. They are both assembled and modified within the endomembrane system, i.e., the endoplasmic reticulum (ER and the Golgi apparatus, before their transport to their final locations within or outside the cell. In contrast to extensin, the O-glycan chains of arabinogalactan proteins are highly heterogeneous consisting mostly of (i a short oligo-arabinoside chain of three to four residues, and (ii a larger -1,3-linked galactan backbone with -1,6-linked side chains containing galactose, arabinose and, often, fucose, rhamnose or glucuronic acid. The fine structure of arabinogalactan chains varies between, and within plant species, and is important for the functional activities of the glycoproteins. With regards to N-glycans, ER-synthesizing events are highly conserved in all eukaryotes studied so far since they are essential for efficient protein folding. In contrast, evolutionary adaptation of N-glycan processing in the Golgi apparatus has given rise to a variety of organism-specific complex structures. Therefore, plant complex-type N-glycans contain specific glyco-epitopes such as core 1,2-xylose, core 1,3-fucose residues and Lewisa substitutions on the terminal position of the antenna. Like O-glycans, N-glycans of proteins are essential for their stability and function. Mutants affected in the glycan metabolic pathways have provided valuable information on the role of N-/O-glycoproteins in the control of growth, morphogenesis and adaptation to biotic and abiotic stresses. With regards to O-glycoproteins only extensin and arabinogalactan proteins are considered herein. The biosynthesis of these glycoproteins and functional aspects are presented and discussed in this review.

  20. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  1. Evolving spiking networks with variable resistive memories.

    Science.gov (United States)

    Howard, Gerard; Bull, Larry; de Lacy Costello, Ben; Gale, Ella; Adamatzky, Andrew

    2014-01-01

    Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

  2. Visualizing spikes in source-space

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Duez, Lene; Scherg, Michael

    2016-01-01

    OBJECTIVE: Reviewing magnetoencephalography (MEG) recordings is time-consuming: signals from the 306 MEG-sensors are typically reviewed divided into six arrays of 51 sensors each, thus browsing each recording six times in order to evaluate all signals. A novel method of reconstructing the MEG...... signals in source-space was developed using a source-montage of 29 brain-regions and two spatial components to remove magnetocardiographic (MKG) artefacts. Our objective was to evaluate the accuracy of reviewing MEG in source-space. METHODS: In 60 consecutive patients with epilepsy, we prospectively...... evaluated the accuracy of reviewing the MEG signals in source-space as compared to the classical method of reviewing them in sensor-space. RESULTS: All 46 spike-clusters identified in sensor-space were also identified in source-space. Two additional spike-clusters were identified in source-space. As 29...

  3. Spiked instantons from intersecting D-branes

    Directory of Open Access Journals (Sweden)

    Nikita Nekrasov

    2017-01-01

    Full Text Available The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence [22] is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  4. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

    We study a stochastic synchronization of spiking activity in feedforward networks of integrate-and-fire model neurons. A stochastic mean field analysis shows that synchronization occurs only when the network size is sufficiently small. This gives evidence that the dynamics, and hence processing, of finite size populations can be drastically different from that observed in the infinite size limit. Our results agree with experimentally observed synchrony in cortical networks, and further strengthen the link between synchrony and propagation in cortical systems.

  5. Non-singular spiked harmonic oscillator

    International Nuclear Information System (INIS)

    Aguilera-Navarro, V.C.; Guardiola, R.

    1990-01-01

    A perturbative study of a class of non-singular spiked harmonic oscillators defined by the hamiltonian H = d sup(2)/dr sup(2) + r sup(2) + λ/r sup(α) in the domain [0,∞] is carried out, in the two extremes of a weak coupling and a strong coupling regimes. A path has been found to connect both expansions for α near 2. (author)

  6. Isolation of allergenically active glycoprotein from Prosopis juliflora pollen.

    Science.gov (United States)

    Thakur, I S

    1989-03-01

    An allergenically active glycoprotein was homogeneously isolated from the aqueous extract of Prosopis juliflora pollen by ConA-Sepharose affinity chromatography. The molecular weight of this glycoprotein was 20,000 dalton, determined by gel filtration and SDS-PAGE. This fraction showed a total carbohydrate concentration of 25%. The purified glycoprotein revealed immunochemically most antigenic or allergenic and demonstrated homogeneous after reaction with P. juliflora pollen antiserum, characterized by gel diffusion, Immunoelectrophoresis and Radioallergosorbent test.

  7. A Fully Automated Approach to Spike Sorting.

    Science.gov (United States)

    Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

    2017-09-13

    Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. The transfer function of neuron spike.

    Science.gov (United States)

    Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

    2015-08-01

    The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Basalt FRP Spike Repairing of Wood Beams

    Directory of Open Access Journals (Sweden)

    Luca Righetti

    2015-08-01

    Full Text Available This article describes aspects within an experimental program aimed at improving the structural performance of cracked solid fir-wood beams repaired with Basalt Fiber Reinforced Polymer (BFRP spikes. Fir wood is characterized by its low density, low compression strength, and high level of defects, and it is likely to distort when dried and tends to fail under tension due to the presence of cracks, knots, or grain deviation. The proposed repair technique consists of the insertion of BFRP spikes into timber beams to restore the continuity of cracked sections. The experimental efforts deal with the evaluation of the bending strength and deformation properties of 24 timber beams. An artificially simulated cracking was produced by cutting the wood beams in half or notching. The obtained results for the repaired beams were compared with those of solid undamaged and damaged beams, and increases of beam capacity, bending strength and of modulus of elasticity, and analysis of failure modes was discussed. For notched beams, the application of the BFRP spikes was able to restore the original bending capacity of undamaged beams, while only a small part of the original capacity was recovered for beams that were cut in half.

  10. Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

    Directory of Open Access Journals (Sweden)

    Qiang Yu

    Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

  11. Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

    Science.gov (United States)

    Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

    2013-01-01

    A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

  12. Nicotine-Mediated ADP to Spike Transition: Double Spiking in Septal Neurons.

    Science.gov (United States)

    Kodirov, Sodikdjon A; Wehrmeister, Michael; Colom, Luis

    2016-04-01

    The majority of neurons in lateral septum (LS) are electrically silent at resting membrane potential. Nicotine transiently excites a subset of neurons and occasionally leads to long lasting bursting activity upon longer applications. We have observed simultaneous changes in frequencies and amplitudes of spontaneous action potentials (AP) in the presence of nicotine. During the prolonged exposure, nicotine increased numbers of spikes within a burst. One of the hallmarks of nicotine effects was the occurrences of double spikes (known also as bursting). Alignment of 51 spontaneous spikes, triggered upon continuous application of nicotine, revealed that the slope of after-depolarizing potential gradually increased (1.4 vs. 3 mV/ms) and neuron fired the second AP, termed as double spiking. A transition from a single AP to double spikes increased the amplitude of after-hyperpolarizing potential. The amplitude of the second (premature) AP was smaller compared to the first one, and this correlation persisted in regard to their duration (half-width). A similar bursting activity in the presence of nicotine, to our knowledge, has not been reported previously in the septal structure in general and in LS in particular.

  13. Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

    Science.gov (United States)

    Mostafa, Hesham

    2017-08-01

    Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

  14. Spikes and matter inhomogeneities in massless scalar field models

    International Nuclear Information System (INIS)

    Coley, A A; Lim, W C

    2016-01-01

    We shall discuss the general relativistic generation of spikes in a massless scalar field or stiff perfect fluid model. We first investigate orthogonally transitive (OT) G 2 stiff fluid spike models both heuristically and numerically, and give a new exact OT G 2 stiff fluid spike solution. We then present a new two-parameter family of non-OT G 2 stiff fluid spike solutions, obtained by the generalization of non-OT G 2 vacuum spike solutions to the stiff fluid case by applying Geroch’s transformation on a Jacobs seed. The dynamics of these new stiff fluid spike solutions is qualitatively different from that of the vacuum spike solutions in that the matter (stiff fluid) feels the spike directly and the stiff fluid spike solution can end up with a permanent spike. We then derive the evolution equations of non-OT G 2 stiff fluid models, including a second perfect fluid, in full generality, and briefly discuss some of their qualitative properties and their potential numerical analysis. Finally, we discuss how a fluid, and especially a stiff fluid or massless scalar field, affects the physics of the generation of spikes. (paper)

  15. Introduction to spiking neural networks: Information processing, learning and applications.

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  16. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  17. Synthesis of Structures Related to Antifreeze Glycoproteins

    OpenAIRE

    Fyrner, Timmy

    2005-01-01

    In this thesis, synthesis of structures related to antifreeze glycoproteins (AFGPs) are presented. Synthetic routes to a protected carbohydrate derivative, 2,3,4,6-tetra-O-benzyl-β-galactopyranosyl-(1→3)-2-deoxy-2-azido-4,6-di-O-benzyl-β-D-thio-1-galactopyranoside, and a tBu-Ala-Thr-Ala-Fmoc tripeptide, are described. These compounds are meant to be used in the assembly of AFGPs and analogues thereof. A Gal-GlcN disaccharide was synthesized via glycosylation between the donor, bromo-2-O-benzo...

  18. Podoplanin - a small glycoprotein with many faces

    OpenAIRE

    Ugorski, Maciej; Dziegiel, Piotr; Suchanski, Jaroslaw

    2016-01-01

    Podoplanin is a small membrane glycoprotein with a large number of O-glycoside chains and therefore it belongs to mucin-type proteins. It can be found on the surface of many types of normal cells originating from various germ layers. It is present primarily on the endothelium of lymphatic vessels, type I pneumocytes and glomerular podocytes. Increased levels of podoplanin or its neo-expression have been found in numerous types of human carcinomas, but it is especially common in squamous cell ...

  19. [Research progress on ebola virus glycoprotein].

    Science.gov (United States)

    Ding, Guo-Yong; Wang, Zhi-Yu; Gao, Lu; Jiang, Bao-Fa

    2013-03-01

    Ebola virus (EBOV) causes outbreaks of a highly lethal hemorrhagic fever in humans and there are no effective therapeutic or prophylactic treatments available. The glycoprotein (GP) of EBOV is a transmembrane envelope protein known to play multiple functions including virus attachment and entry, cell rounding and cytotoxicity, down-regulation of host surface proteins, and enhancement of virus assembly and budding. GP is the primary target of protective immunity and the key target for developing neutralizing antibodies. In this paper, the research progress on genetic structure, pathogenesis and immunogenicity of EBOV GP in the last 5 years is reviewed.

  20. Glycoprotein component of plant cell walls

    International Nuclear Information System (INIS)

    Cooper, J.B.; Chen, J.A.; Varner, J.E.

    1984-01-01

    The primary wall surrounding most dicotyledonous plant cells contains a hydroxyproline-rich glycoprotein (HRGP) component named extensin. A small group of glycopeptides solubilized from isolated cell walls by proteolysis contained a repeated pentapeptide glycosylated by tri- and tetraarabinosides linked to hydroxyproline and, by galactose, linked to serine. Recently, two complementary approaches to this problem have provided results which greatly increase the understanding of wall extensin. In this paper the authors describe what is known about the structure of soluble extensin secreted into the walls of the carrot root cells

  1. The Purification of a Blood Group A Glycoprotein: An Affinity Chromatography Experiment.

    Science.gov (United States)

    Estelrich, J.; Pouplana, R.

    1988-01-01

    Describes a purification process through affinity chromatography necessary to obtain specific blood group glycoproteins from erythrocytic membranes. Discusses the preparation of erythrocytic membranes, extraction of glycoprotein from membranes, affinity chromatography purification, determination of glycoproteins, and results. (CW)

  2. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil

    DEFF Research Database (Denmark)

    Ottosen, Lisbeth M.; Lepkova, Katarina; Kubal, Martin

    2006-01-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...

  3. Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons.

    Science.gov (United States)

    Grewe, Jan; Kruscha, Alexandra; Lindner, Benjamin; Benda, Jan

    2017-03-07

    Synchronous activity in populations of neurons potentially encodes special stimulus features. Selective readout of either synchronous or asynchronous activity allows formation of two streams of information processing. Theoretical work predicts that such a synchrony code is a fundamental feature of populations of spiking neurons if they operate in specific noise and stimulus regimes. Here we experimentally test the theoretical predictions by quantifying and comparing neuronal response properties in tuberous and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus These related systems show similar levels of synchronous activity, but only in the more irregularly firing tuberous afferents a synchrony code is established, whereas in the more regularly firing ampullary afferents it is not. The mere existence of synchronous activity is thus not sufficient for a synchrony code. Single-cell features such as the irregularity of spiking and the frequency dependence of the neuron's transfer function determine whether synchronous spikes possess a distinct meaning for the encoding of time-dependent signals.

  4. Structure of Simian Immunodeficiency Virus Envelope Spikes Bound with CD4 and Monoclonal Antibody 36D5.

    Science.gov (United States)

    Hu, Guiqing; Liu, Jun; Roux, Kenneth H; Taylor, Kenneth A

    2017-08-15

    The human immunodeficiency virus type 1 (HIV-1)/simian immunodeficiency virus (SIV) envelope spike (Env) mediates viral entry into host cells. The V3 loop of the gp120 component of the Env trimer contributes to the coreceptor binding site and is a target for neutralizing antibodies. We used cryo-electron tomography to visualize the binding of CD4 and the V3 loop monoclonal antibody (MAb) 36D5 to gp120 of the SIV Env trimer. Our results show that 36D5 binds gp120 at the base of the V3 loop and suggest that the antibody exerts its neutralization effect by blocking the coreceptor binding site. The antibody does this without altering the dynamics of the spike motion between closed and open states when CD4 is bound. The interaction between 36D5 and SIV gp120 is similar to the interaction between some broadly neutralizing anti-V3 loop antibodies and HIV-1 gp120. Two conformations of gp120 bound with CD4 are revealed, suggesting an intrinsic dynamic nature of the liganded Env trimer. CD4 binding substantially increases the binding of 36D5 to gp120 in the intact Env trimer, consistent with CD4-induced changes in the conformation of gp120 and the antibody binding site. Binding by MAb 36D5 does not substantially alter the proportions of the two CD4-bound conformations. The position of MAb 36D5 at the V3 base changes little between conformations, indicating that the V3 base serves as a pivot point during the transition between these two states. IMPORTANCE Glycoprotein spikes on the surfaces of SIV and HIV are the sole targets available to the immune system for antibody neutralization. Spikes evade the immune system by a combination of a thick layer of polysaccharide on the surface (the glycan shield) and movement between spike domains that masks the epitope conformation. Using SIV virions whose spikes were "decorated" with the primary cellular receptor (CD4) and an antibody (36D5) at part of the coreceptor binding site, we visualized multiple conformations trapped by the

  5. Membrane glycoproteins of differentiating skeletal muscle cells

    International Nuclear Information System (INIS)

    Miller, K.R.; Remy, C.N.; Smith, P.B.

    1987-01-01

    The composition of N-linked glycoprotein oligosaccharides was studied in myoblasts and myotubes of the C2 muscle cell line. Oligosaccharides were radioactively labelled for 15 hr with [ 3 H] mannose and plasma membranes isolated. Ten glycopeptides were detected by SDS-PAGE and fluorography. The extent of labelling was 4-6 fold greater in myoblasts vs myotubes. A glycopeptide of Mr > 100,000 was found exclusively in myoblast membranes. Lectin chromatography revealed that the proportion of tri-, tetranntenary, biantennary and high mannose chains was similar throughout differentiation. The high mannose chain fraction was devoid of hybrid chains. The major high mannose chain contained nine mannose residues. The higher level of glycopeptide labelling in myoblasts vs myotubes corresponded to a 5-fold greater rate of protein synthesis. Pulse-chase experiments were used to follow the synthesis of the Dol-oligosaccharides. Myoblasts and myotubes labelled equivalently the glucosylated tetradecasaccharide but myoblasts labelled the smaller intermediates 3-4 greater than myotubes. Myoblasts also exhibited a 2-3 fold higher Dol-P dependent glycosyl transferase activity for chain elongation and Dol-sugar synthesis. Together these results show that the degree of protein synthesis and level of Dol-P are contributing factors in the higher capacity of myoblasts to produce N-glycoproteins compared to myotubes

  6. Annotating Human P-Glycoprotein Bioassay Data.

    Science.gov (United States)

    Zdrazil, Barbara; Pinto, Marta; Vasanthanathan, Poongavanam; Williams, Antony J; Balderud, Linda Zander; Engkvist, Ola; Chichester, Christine; Hersey, Anne; Overington, John P; Ecker, Gerhard F

    2012-08-01

    Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P-glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter-correlations between bioactivity data yielded by different assay setups.

  7. Humanizing recombinant glycoproteins from Chinese hamster ovary cells

    DEFF Research Database (Denmark)

    Hansen, Anders Holmgaard; Amann, Thomas; Kol, Stefan

    With new tools for gene-editing like zinc-fingers, TALENS and CRISPR, it is now feasible totailor-make the N-Glycoforms for therapeutic glycoproteins that have previously been almost impossible. We here demonstrate a case of humanizing a recombinant human glycoprotein that in Wild type (WT) Chinese...

  8. Bioinformatics Analysis of Envelope Glycoprotein E epitopes of ...

    African Journals Online (AJOL)

    The E glycoprotein of dengue virus is responsible for the viral binding to the receptor. The crystal structure of envelope glycoprotein has already been determined. However, where the well-defined Bcell and T-cell epitopes are located is still a question. Because of the large variations among the four dengue genotypes, it is ...

  9. Ammonia transport in the kidney by Rhesus glycoproteins

    Science.gov (United States)

    Verlander, Jill W.

    2014-01-01

    Renal ammonia metabolism is a fundamental element of acid-base homeostasis, comprising a major component of both basal and physiologically altered renal net acid excretion. Over the past several years, a fundamental change in our understanding of the mechanisms of renal epithelial cell ammonia transport has occurred, replacing the previous model which was based upon diffusion equilibrium for NH3 and trapping of NH4+ with a new model in which specific and regulated transport of both NH3 and NH4+ across renal epithelial cell membranes via specific membrane proteins is required for normal ammonia metabolism. A major advance has been the recognition that members of a recently recognized transporter family, the Rhesus glycoprotein family, mediate critical roles in renal and extrarenal ammonia transport. The erythroid-specific Rhesus glycoprotein, Rh A Glycoprotein (Rhag), was the first Rhesus glycoprotein recognized as an ammonia-specific transporter. Subsequently, the nonerythroid Rh glycoproteins, Rh B Glycoprotein (Rhbg) and Rh C Glycoprotein (Rhcg), were cloned and identified as ammonia transporters. They are expressed in specific cell populations and membrane domains in distal renal epithelial cells, where they facilitate ammonia secretion. In this review, we discuss the distribution of Rhbg and Rhcg in the kidney, the regulation of their expression and activity in physiological disturbances, the effects of genetic deletion on renal ammonia metabolism, and the molecular mechanisms of Rh glycoprotein-mediated ammonia transport. PMID:24647713

  10. Solving constraint satisfaction problems with networks of spiking neurons

    Directory of Open Access Journals (Sweden)

    Zeno eJonke

    2016-03-01

    Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.

  11. Spike-based population coding and working memory.

    Directory of Open Access Journals (Sweden)

    Martin Boerlin

    2011-02-01

    Full Text Available Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons.

  12. Consensus-Based Sorting of Neuronal Spike Waveforms.

    Science.gov (United States)

    Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

    2016-01-01

    Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

  13. An improved radioimmunoassay for urinary Tamm-Horsfall glycoprotein

    International Nuclear Information System (INIS)

    Dawnay, A.B. St. J.; Thornley, C.; Cattell, W.R.

    1982-01-01

    A rapid specific radioimmunoassay has been used to measure Tamm-Horsfall glycoprotein (TH glycoprotein) in urine, and the method described. The apparent concentration increased with increasing dilution of urine in water, reaching a plateau at 1 in 20. This increase was greater the higher the osmolality and TH glycoprotein concentration and the lower the pH of the original sample. The apparent concentration of TH glycoprotein in neat or diluted urine was not affected by freezing or by storage at 4 0 C or room temperature for at least 2 days. A physiological range for the urinary excretion rate was established as 22-56 mg/24h, (considerably higher than the amount present in serum) based on samples from 29 individuals with normal renal function, as defined by their creatinine clearance. There was no significant correlation between serum concentrations of TH glycoprotein and its urinary excretion rate, nor between urinary excretion rate and creatinine clearance. (author)

  14. Solubilization of glycoproteins of envelope viruses by detergents

    International Nuclear Information System (INIS)

    Berezin, V.E.; Zaides, V.M.; Artamsnov, A.F.; Isaeva, E.S.; Zhdanov, V.M.

    1986-01-01

    The action of a number of known ionic and nonionic detergents, as well as the new nonionic detergent MESK, on envelope viruses was investigated. It was shown that the nonionic detergents MESK, Triton X-100, and octyl-β-D-glucopyranoside selectively solubilize the outer glycoproteins of the virus particles. The nonionic detergent MESK has the mildest action. Using MESK, purified glycoproteins of influenza, parainfluenza, Venezuelan equine encephalomyelitis, vesicular stomatitis, rabies, and herpes viruses were obtained. The procedure for obtaining glycoproteins includes incubation of the virus suspension with the detergent MESK, removal of subvirus structures by centrifuging, and purification of glycoproteins from detergents by dialysis. Isolated glycoproteins retain a native structure and biological activity and possess high immunogenicity. The detergent MESK is promising for laboratory tests and with respect to the production of subunit vaccines

  15. Eliminating thermal violin spikes from LIGO noise

    Energy Technology Data Exchange (ETDEWEB)

    Santamore, D. H.; Levin, Yuri

    2001-08-15

    We have developed a scheme for reducing LIGO suspension thermal noise close to violin-mode resonances. The idea is to monitor directly the thermally induced motion of a small portion of (a 'point' on) each suspension fiber, thereby recording the random forces driving the test-mass motion close to each violin-mode frequency. One can then suppress the thermal noise by optimally subtracting the recorded fiber motions from the measured motion of the test mass, i.e., from the LIGO output. The proposed method is a modification of an analogous but more technically difficult scheme by Braginsky, Levin and Vyatchanin for reducing broad-band suspension thermal noise. The efficiency of our method is limited by the sensitivity of the sensor used to monitor the fiber motion. If the sensor has no intrinsic noise (i.e. has unlimited sensitivity), then our method allows, in principle, a complete removal of violin spikes from the thermal-noise spectrum. We find that in LIGO-II interferometers, in order to suppress violin spikes below the shot-noise level, the intrinsic noise of the sensor must be less than {approx}2 x 10{sup -13} cm/Hz. This sensitivity is two orders of magnitude greater than that of currently available sensors.

  16. Eliminating thermal violin spikes from LIGO noise

    International Nuclear Information System (INIS)

    Santamore, D. H.; Levin, Yuri

    2001-01-01

    We have developed a scheme for reducing LIGO suspension thermal noise close to violin-mode resonances. The idea is to monitor directly the thermally induced motion of a small portion of (a 'point' on) each suspension fiber, thereby recording the random forces driving the test-mass motion close to each violin-mode frequency. One can then suppress the thermal noise by optimally subtracting the recorded fiber motions from the measured motion of the test mass, i.e., from the LIGO output. The proposed method is a modification of an analogous but more technically difficult scheme by Braginsky, Levin and Vyatchanin for reducing broad-band suspension thermal noise. The efficiency of our method is limited by the sensitivity of the sensor used to monitor the fiber motion. If the sensor has no intrinsic noise (i.e. has unlimited sensitivity), then our method allows, in principle, a complete removal of violin spikes from the thermal-noise spectrum. We find that in LIGO-II interferometers, in order to suppress violin spikes below the shot-noise level, the intrinsic noise of the sensor must be less than ∼2 x 10 -13 cm/Hz. This sensitivity is two orders of magnitude greater than that of currently available sensors

  17. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

    Full Text Available In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probablilty of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations. but here, I take a different perspective, inspired by evolution. I simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable by nature. Networks which are configured according to the common values, have the best dynamic range in response to an impulse and their dynamic range is more robust in respect to synaptic weights. In fact, evolution has favored networks of best dynamic range. I present a phase diagram that shows the dynamic ranges of different networks of different parameteres. This phase diagram gives an insight into the space of parameters -- excitatory to inhibitory ratio, sparseness of connections and synaptic weights. It may serve as a guideline to decide about the values of parameters in a simulation of spiking neural network.

  18. Communication through resonance in spiking neuronal networks.

    Science.gov (United States)

    Hahn, Gerald; Bujan, Alejandro F; Frégnac, Yves; Aertsen, Ad; Kumar, Arvind

    2014-08-01

    The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

  19. Spike Pattern Recognition for Automatic Collimation Alignment

    CERN Document Server

    Azzopardi, Gabriella; Salvachua Ferrando, Belen Maria; Mereghetti, Alessio; Redaelli, Stefano; CERN. Geneva. ATS Department

    2017-01-01

    The LHC makes use of a collimation system to protect its sensitive equipment by intercepting potentially dangerous beam halo particles. The appropriate collimator settings to protect the machine against beam losses relies on a very precise alignment of all the collimators with respect to the beam. The beam center at each collimator is then found by touching the beam halo using an alignment procedure. Until now, in order to determine whether a collimator is aligned with the beam or not, a user is required to follow the collimator’s BLM loss data and detect spikes. A machine learning (ML) model was trained in order to automatically recognize spikes when a collimator is aligned. The model was loosely integrated with the alignment implementation to determine the classification performance and reliability, without effecting the alignment process itself. The model was tested on a number of collimators during this MD and the machine learning was able to output the classifications in real-time.

  20. A spiking neuron circuit based on a carbon nanotube transistor

    International Nuclear Information System (INIS)

    Chen, C-L; Kim, K; Truong, Q; Shen, A; Li, Z; Chen, Y

    2012-01-01

    A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a ‘soma’ circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions. (paper)

  1. The local field potential reflects surplus spike synchrony

    DEFF Research Database (Denmark)

    Denker, Michael; Roux, Sébastien; Lindén, Henrik

    2011-01-01

    While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions...... of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes....... This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations...

  2. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  3. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  4. Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

    Science.gov (United States)

    de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

  5. Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

    Full Text Available Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions cite{Pyragas}, where the slave neuron is able to anticipate in time the behaviour of the master one. In this paper we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI, one of the main features of the neural response associated to the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

  6. Multimodal imaging of spike propagation: a technical case report.

    Science.gov (United States)

    Tanaka, N; Grant, P E; Suzuki, N; Madsen, J R; Bergin, A M; Hämäläinen, M S; Stufflebeam, S M

    2012-06-01

    We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation.

  7. Generalized activity equations for spiking neural network dynamics

    Directory of Open Access Journals (Sweden)

    Michael A Buice

    2013-11-01

    Full Text Available Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

  8. In-reactor creep of zirconium alloys by thermal spikes

    International Nuclear Information System (INIS)

    Ibrahim, E.F.

    1975-01-01

    The size and duration of thermal spikes from fast neutrons have been calculated for zirconium alloys, showing that spikes up to 1.8 nm radius may exist for 2 x 10 -11 s at greater than melting point, at 570K ambient temperature. Creep rates have been calculated assuming that the elastic strain from the applied stress relaxes in the volume of the spikes (by preferential loop alignment or modification of an existing dislocation network). The calculated rates are consistent with strain rates observed in long term tests-in-reactor, if spike lifetimes are 2 to 2.5 x 10 -11 s. (Auth.)

  9. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    Science.gov (United States)

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

  10. Surfing a spike wave down the ventral stream.

    Science.gov (United States)

    VanRullen, Rufin; Thorpe, Simon J

    2002-10-01

    Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

  11. Pumping of drugs by P-glycoprotein

    DEFF Research Database (Denmark)

    Litman, Thomas; Skovsgaard, Torben; Stein, Wilfred D

    2003-01-01

    The apparent inhibition constant, Kapp, for the blockade of P-glycoprotein (P-gp) by four drugs, verapamil, cyclosporin A, XR9576 (tariquidar), and vinblastine, was measured by studying their ability to inhibit daunorubicin and calcein-AM efflux from four strains of Ehrlich cells with different...... levels of drug resistance and P-gp content. For daunorubicin as a transport substrate, Kapp was independent of [P-gp] for verapamil but increased strictly linearly with [P-gp] for vinblastine, cyclosporin A, and XR9576. A theoretical analysis of the kinetics of drug pumping and its reversal shows...... but rather, in serial, i.e., a drug that is pumped from the cytoplasmic phase has to pass the preemptive route upon leaving the cell. Our results are consistent with the Sauna-Ambudkar two-step model for pumping by P-gp. We suggest that the vinblastine/cyclosporin A/XR9576-binding site accepts daunorubicin...

  12. Raman optical activity of proteins and glycoproteins

    International Nuclear Information System (INIS)

    Smyth, E.

    2000-03-01

    Raman optical activity (ROA), measured in this project as a small difference in the intensity of Raman scattering from chiral molecules in right- and left-circularly polarised incident laser light, offers the potential to provide more information about the structure of biological molecules in aqueous solution than conventional spectroscopic techniques. Chapter one contains a general discussion of the relative merits of different spectroscopic techniques for structure determination of biomolecules, as well as a brief introduction to ROA. In Chapter two a theoretical analysis of ROA is developed, which extends the discussion in chapter one. The spectrometer setup and sample preparation is then discussed in chapter three. Instrument and sample conditions are monitored to ensure that the best results are obtained. As with any experimental project problems occur, which may result in a degradation of the spectra obtained. The cause of these problems was explored and remedied whenever possible. Chapter four introduces a brief account of protein, glycoprotein and carbohydrate structure and function, with a particular emphasis on the structure of proteins. In the remaining chapters experimental ROA results on proteins and glycoproteins, with some carbohydrate samples, from a wide range of sources are examined. For example, in chapter five some β-sheet proteins are examined. Structural features in these proteins are examined in the extended amide III region of their ROA spectra, revealing that ROA is sensitive to the rigidity or flexibility inherent in proteins. Chapter six concentrates on a group of proteins (usually glycoproteins) known as the serine proteinase inhibitors (serpins). Medically, the serpins are one of the most important groups of proteins of current interest, with wide-ranging implications in conditions such as Down's syndrome, Alzheimer's disease, and emphysema with associated cirrhosis of the liver. With favourable samples and conditions ROA may offer the

  13. P-glycoprotein in autoimmune rheumatic diseases.

    Science.gov (United States)

    García-Carrasco, M; Mendoza-Pinto, C; Macias Díaz, S; Vera-Recabarren, M; Vázquez de Lara, L; Méndez Martínez, S; Soto-Santillán, P; González-Ramírez, R; Ruiz-Arguelles, A

    2015-07-01

    P-glycoprotein (Pgp) is a transmembrane protein of 170 kD encoded by the multidrug resistance 1 (MDR-1) gene, localized on chromosome 7. More than 50 polymorphisms of the MDR-1 gene have been described; a subset of these has been shown to play a pathophysiological role in the development of inflammatory bowel disease, femoral head osteonecrosis induced by steroids, lung cancer and renal epithelial tumors. Polymorphisms that have a protective effect on the development of conditions such as Parkinson disease have also been identified. P-glycoprotein belongs to the adenosine triphosphate binding cassette transporter superfamily and its structure comprises a chain of approximately 1280 aminoacid residues with an N-C terminal structure, arranged as 2 homologous halves, each of which has 6 transmembrane segments, with a total of 12 segments with 2 cytoplasmic nucleotide binding domains. Many cytokines like interleukin 2 and tumor necrosis factor alpha increase Pgp expression and activity. Pgp functions as an efflux pump for a variety of toxins in order to protect particular organs and tissues as the central nervous system. Pgp transports a variety of substrates including glucocorticoids while other drugs such as tacrolimus and cyclosporine A act as modulators of this protein. The most widely used method to measure Pgp activity is flow cytometry using naturally fluorescent substrates such as anthracyclines or rhodamine 123. The study of drug resistance and its association to Pgp began with the study of resistance to chemotherapy in the treatment of cancer and antiretroviral therapy for human immunodeficiency virus; however, the role of Pgp in the treatment of systemic lupus erythematosus, rheumatoid arthritis and psoriatic arthritis has been a focus of study lately and has emerged as an important mechanism by which treatment failure occurs. The present review analyzes the role of Pgp in these autoimmune diseases. Copyright © 2015 Elsevier B.V. All rights reserved.

  14. AMORE Mo-99 Spike Test Results

    Energy Technology Data Exchange (ETDEWEB)

    Youker, Amanda J. [Argonne National Lab. (ANL), Argonne, IL (United States); Krebs, John F. [Argonne National Lab. (ANL), Argonne, IL (United States); Quigley, Kevin J. [Argonne National Lab. (ANL), Argonne, IL (United States); Byrnes, James P. [Argonne National Lab. (ANL), Argonne, IL (United States); Rotsch, David A [Argonne National Lab. (ANL), Argonne, IL (United States); Brossard, Thomas [Argonne National Lab. (ANL), Argonne, IL (United States); Wesolowski, Kenneth [Argonne National Lab. (ANL), Argonne, IL (United States); Alford, Kurt [Argonne National Lab. (ANL), Argonne, IL (United States); Chemerisov, Sergey [Argonne National Lab. (ANL), Argonne, IL (United States); Vandegrift, George F. [Argonne National Lab. (ANL), Argonne, IL (United States)

    2017-09-27

    With funding from the National Nuclear Security Administrations Material Management and Minimization Office, Argonne National Laboratory (Argonne) is providing technical assistance to help accelerate the U.S. production of Mo-99 using a non-highly enriched uranium (non-HEU) source. A potential Mo-99 production pathway is by accelerator-initiated fissioning in a subcritical uranyl sulfate solution containing low enriched uranium (LEU). As part of the Argonne development effort, we are undertaking the AMORE (Argonne Molybdenum Research Experiment) project, which is essentially a pilot facility for all phases of Mo-99 production, recovery, and purification. Production of Mo-99 and other fission products in the subcritical target solution is initiated by putting an electron beam on a depleted uranium (DU) target; the fast neutrons produced in the DU target are thermalized and lead to fissioning of U-235. At the end of irradiation, Mo is recovered from the target solution and separated from uranium and most of the fission products by using a titania column. The Mo is stripped from the column with an alkaline solution. After acidification of the Mo product solution from the recovery column, the Mo is concentrated (and further purified) in a second titania column. The strip solution from the concentration column is then purified with the LEU Modified Cintichem process. A full description of the process can be found elsewhere [1–3]. The initial commissioning steps for the AMORE project include performing a Mo-99 spike test with pH 1 sulfuric acid in the target vessel without a beam on the target to demonstrate the initial Mo separation-and-recovery process, followed by the concentration column process. All glovebox operations were tested with cold solutions prior to performing the Mo-99 spike tests. Two Mo-99 spike tests with pH 1 sulfuric acid have been performed to date. Figure 1 shows the flow diagram for the remotely operated Mo-recovery system for the AMORE project

  15. Self-control with spiking and non-spiking neural networks playing games.

    Science.gov (United States)

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

    Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

  16. [Wide QRS tachycardia preceded by pacemaker spikes].

    Science.gov (United States)

    Romero, M; Aranda, A; Gómez, F J; Jurado, A

    2014-04-01

    The differential diagnosis and therapeutic management of wide QRS tachycardia preceded by pacemaker spike is presented. The pacemaker-mediated tachycardia, tachycardia fibrillo-flutter in patients with pacemakers, and runaway pacemakers, have a similar surface electrocardiogram, but respond to different therapeutic measures. The tachycardia response to the application of a magnet over the pacemaker could help in the differential diagnosis, and in some cases will be therapeutic, as in the case of a tachycardia-mediated pacemaker. Although these conditions are diagnosed and treated in hospitals with catheterization laboratories using the application programmer over the pacemaker, patients presenting in primary care clinic and emergency forced us to make a diagnosis and treat the haemodynamically unstable patient prior to referral. Copyright © 2012 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España. All rights reserved.

  17. A propane price spike nails users

    International Nuclear Information System (INIS)

    Milke, M.

    1997-01-01

    The increase in price for propane was discussed. In 1993, propane cost about 5 cents per litre; by December 1996, the price has risen to 27 cents wholesale, while retail prices for auto propane reached 40 cents per litre. As a result, farmers and fleet operators are considering switching to an alternative energy supply. The five factors which may have played a role in the propane price spike were described. These included a cold winter which lowered inventories, a Pemex gas plant in Mexico which had been damaged by fire, forcing Mexico to import natural gas and natural gas liquids from the USA, the failure of propane distributors to restock during the summer months in the hope of lower prices, and increased cost of competing fuels in the face of increased demand. It was noted that these factors are transitory, which could mean better prices this summer

  18. Regulation of glycoprotein synthesis in yeast by mating pheromones

    International Nuclear Information System (INIS)

    Tanner, W.

    1984-01-01

    In Saccharomyces cerevisiae, glycosylated proteins amount to less than 2% of the cell protein. Two intensively studied examples of yeast glycoproteins are the external cell wall - associated invertase and the vacuolar carboxypeptidase Y. Recently, it was shown that the mating pheromone, alpha factor, specifically and strongly inhibits the synthesis of N-glycosylated proteins in haploid a cells, whereas O-glycosylated proteins are not affected. In this paper, the pathways of glycoprotein biosynthesis are summarized briefly, and evidence is presented that mating pheomones have a regulatory function in glycoprotein synthesis

  19. Spike sorting for polytrodes: a divide and conquer approach

    Directory of Open Access Journals (Sweden)

    Nicholas V. Swindale

    2014-02-01

    Full Text Available In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 minutes. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis. Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scaleable to larger multi-electrode arrays (MEAs.

  20. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  1. Clustering predicts memory performance in networks of spiking and non-spiking neurons

    Directory of Open Access Journals (Sweden)

    Weiliang eChen

    2011-03-01

    Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

  2. Spikes and memory in (Nord Pool) electricity price spot prices

    DEFF Research Database (Denmark)

    Proietti, Tomasso; Haldrup, Niels; Knapik, Oskar

    Electricity spot prices are subject to transitory sharp movements commonly referred to as spikes. The paper aims at assessing their effects on model based inferences and predictions, with reference to the Nord Pool power exchange. We identify a spike as a price value which deviates substantially...

  3. The Nature of Power Spikes: a regime-switch approach

    NARCIS (Netherlands)

    C.M. de Jong (Cyriel)

    2005-01-01

    textabstractDue to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the

  4. No WIMP mini-spikes in dwarf spheroidal galaxies

    NARCIS (Netherlands)

    Wanders, M.; Bertone, G.; Volonteri, M.; Weniger, C.

    2015-01-01

    The formation of black holes inevitably affects the distribution of dark and baryonic matter in their vicinity, leading to an enhancement of the dark matter density, called spike, and if dark matter is made of WIMPs, to a strong enhancement of the dark matter annihilation rate. Spikes at the center

  5. Spiking and bursting patterns of fractional-order Izhikevich model

    Science.gov (United States)

    Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

    2018-03-01

    Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

  6. Spectral components of cytosolic [Ca2+] spiking in neurons

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

    . Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

  7. Accelerated spike resampling for accurate multiple testing controls.

    Science.gov (United States)

    Harrison, Matthew T

    2013-02-01

    Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

  8. Causal Inference and Explaining Away in a Spiking Network

    Science.gov (United States)

    Moreno-Bote, Rubén; Drugowitsch, Jan

    2015-01-01

    While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

  9. Cytoplasmic tail of coronavirus spike protein has intracellular

    Indian Academy of Sciences (India)

    https://www.ias.ac.in/article/fulltext/jbsc/042/02/0231-0244. Keywords. Coronavirus spike protein trafficking; cytoplasmic tail signal; endoplasmic reticulum–Golgi intermediate complex; lysosome. Abstract. Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is ...

  10. Pressurized water reactor iodine spiking behavior under power transient conditions

    International Nuclear Information System (INIS)

    Ho, J.C.

    1992-01-01

    The most accepted theory explaining the cause of pressurized water reactor iodine spiking is steam formation and condensation in damaged fuel rods. The phase transformation of the primary coolant from water to steam and back again is believed to cause the iodine spiking phenomenon. But due to the complex nature of the phenomenon, a comprehensive model of the behavior has not yet been successfully developed. This paper presents a new model based on an empirical approach, which gives a first-order estimation of the peak iodine spiking magnitude. Based on the proposed iodine spiking model, it is apparent that it is feasible to derive a correlation using the plant operating data base to monitor and control the peak iodine spiking magnitude

  11. Recent progress in multi-electrode spike sorting methods.

    Science.gov (United States)

    Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

    2016-11-01

    In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Unsupervised spike sorting based on discriminative subspace learning.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

  13. A supervised learning rule for classification of spatiotemporal spike patterns.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

    This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

  14. Bioinformatics Analysis of Envelope Glycoprotein E epitopes of ...

    African Journals Online (AJOL)

    User

    2011-05-02

    May 2, 2011 ... 1National Centre of Excellence in Molecular Biology, University of the Punjab Lahore, Pakistan. 2Department of ..... E glycoprotein and its interaction with antibody with the method of molecular dynamics and molecular model ...

  15. Extra-oviductal expression of oviductal glycoprotein 1 in mouse ...

    Indian Academy of Sciences (India)

    2017-01-11

    Jan 11, 2017 ... oestrogen-dependent protein, oviduct-specific glycoprotein. 1 (Ovgp1) also ... the tissues collected were testis, epididymis, prostate gland and seminal vesicle. ... The antigenic sites were unmasked by incuba- tion of sections ...

  16. Herpesvirus glycoproteins undergo multiple antigenic changes before membrane fusion.

    Directory of Open Access Journals (Sweden)

    Daniel L Glauser

    Full Text Available Herpesvirus entry is a complicated process involving multiple virion glycoproteins and culminating in membrane fusion. Glycoprotein conformation changes are likely to play key roles. Studies of recombinant glycoproteins have revealed some structural features of the virion fusion machinery. However, how the virion glycoproteins change during infection remains unclear. Here using conformation-specific monoclonal antibodies we show in situ that each component of the Murid Herpesvirus-4 (MuHV-4 entry machinery--gB, gH/gL and gp150--changes in antigenicity before tegument protein release begins. Further changes then occurred upon actual membrane fusion. Thus virions revealed their final fusogenic form only in late endosomes. The substantial antigenic differences between this form and that of extracellular virions suggested that antibodies have only a limited opportunity to block virion membrane fusion.

  17. Mouse hepatitis virus neurovirulence: evidence of a linkage between S glycoprotein expression and immunopathology

    International Nuclear Information System (INIS)

    Rempel, Julia D.; Murray, Shannon J.; Meisner, Jeffrey; Buchmeier, Michael J.

    2004-01-01

    Differences in disease outcome between the highly neurovirulent MHV-JHM and mildly neurovirulent MHV-A59 have been attributed to variations within the spike (S) glycoprotein. Previously, we found that MHV-JHM neurovirulence was marked by diminished expression of interferon-γ (IFN-γ) mRNA and a reduced presence of CD8 T cells in the CNS concomitant with heightened macrophage inflammatory protein (MIP)-1 transcript levels and greater macrophage infiltration relative to MHV-A59 infection. Here, the ability of the S and non-spike genes to regulate these immune responses was evaluated using chimeric viruses. Chimeric viruses WTR13 and S4R22 were made on MHV-A59 variant backgrounds and, respectively, contained the S gene of MHV-A59 and MHV-JHM. Unexpectedly, genes other than S appeared to modulate events critical to viral replication and survival. Unlike unresolving MHV-JHM infections, the clearance of WTR13 and S4R22 infections coincided with strong IFN-γ transcription and an increase in the number of CD8 T cells infiltrating into the CNS. However, despite the absence of detectable viral titers, approximately 40% of S4R22-infected mice succumbed within 3 weeks, indicating that the enhanced mortality following S4R22 infection was not associated with high viral titers. Instead, similar to the MHV-JHM infection, reduced survival following S4R22 infection was observed in the presence of elevated MIP-1α and MIP-1β mRNA accumulation and enhanced macrophage numbers within infected brains. These observations suggest that the S protein of MHV-JHM influences neurovirulence through the induction of MIP-1α- and MIP-1β-driven macrophage immunopathology

  18. The hydroxyapatite-binding regions of a rat salivary glycoprotein.

    Science.gov (United States)

    Embery, G; Green, D R

    1989-09-01

    The regions of a salivary sulphated glycoprotein which are involved in its attachment to hydroxyapatite (Biogel HTP) have been characterised. The sulphated glycoprotein, a 35S-labelled preparation from mixed palatal and buccal minor gland secretions of the rat was bound onto hydroxyapatite and the resultant glycoprotein-hydroxyapatite complex was sequentially digested with pronase E and alpha-L-fucosidase, a treatment which released 86.8% +/- 1.7% of the radioactivity of the initially bound glycoprotein. The fragments which remained attached to the hydroxyapatite after enzymic digestion were fractionated on Sephadex G-25 and analysed for carbohydrate and amino acid components. A range of amino acids were detected which could reflect both glycosylated and non-glycosylated-binding regions. Sialic acid, although considered to be involved in the attachment process was not detected in any of the fragments remaining after enzymic digestion, a finding which provides indirect evidence that the enzymically liberated products do not subsequently re-attach to the hydroxyapatite surface. The notable feature of the fractions with average Mr estimated at 1000 or less is the high proportion of N-acetylhexosamine and N-acetylgalactosamine. It is apparent that the hexosamine residues, which normally bear the ester sulphate moieties of sulphated glycoproteins, play an important role in the attachment of sulphated glycoproteins to hydroxyapatite.

  19. Nucleic acid-binding glycoproteins which solubilize nucleic acids in dilute acid: re-examination of the Ustilago maydis glycoproteins

    Energy Technology Data Exchange (ETDEWEB)

    Unrau, P.; Champ, D.R.; Young, J.L.; Grant, C.E.

    1980-01-01

    Holloman reported the isolation from Ustilago maydis of a glycoprotein which prevented the precipitation of nucleic acids in cold 5% trichloroacetic acid. Two glycoprotein fractions from U. maydis with this nucleic acid-solubilizing activity were isolated in our laboratory using improved purification procedures. The activity was not due to nuclease contamination. The glycoproteins are distinguished by: their ability to bind to concanavalin A-Sepharose; their differential binding to double- and single-stranded deoxyribonucleic acid, and to ribonucleic acid; their molecular weights (46,000 and 69,000); and the relative amounts present in growing versus nongrowing cells. Both fractions required sulfhydryl-reducing conditions for optimal yields, specific activity, and stability. Nucleic acid binding was cooperative, the minimum number of glycoproteins required to make a native T7 DNA molecule soluble in dilute acid being estimated at 2 and 15, respectively.

  20. Noisy Spiking in Visual Area V2 of Amblyopic Monkeys.

    Science.gov (United States)

    Wang, Ye; Zhang, Bin; Tao, Xiaofeng; Wensveen, Janice M; Smith, Earl L; Chino, Yuzo M

    2017-01-25

    Interocular decorrelation of input signals in developing visual cortex can cause impaired binocular vision and amblyopia. Although increased intrinsic noise is thought to be responsible for a range of perceptual deficits in amblyopic humans, the neural basis for the elevated perceptual noise in amblyopic primates is not known. Here, we tested the idea that perceptual noise is linked to the neuronal spiking noise (variability) resulting from developmental alterations in cortical circuitry. To assess spiking noise, we analyzed the contrast-dependent dynamics of spike counts and spiking irregularity by calculating the square of the coefficient of variation in interspike intervals (CV 2 ) and the trial-to-trial fluctuations in spiking, or mean matched Fano factor (m-FF) in visual area V2 of monkeys reared with chronic monocular defocus. In amblyopic neurons, the contrast versus response functions and the spike count dynamics exhibited significant deviations from comparable data for normal monkeys. The CV 2 was pronounced in amblyopic neurons for high-contrast stimuli and the m-FF was abnormally high in amblyopic neurons for low-contrast gratings. The spike count, CV 2 , and m-FF of spontaneous activity were also elevated in amblyopic neurons. These contrast-dependent spiking irregularities were correlated with the level of binocular suppression in these V2 neurons and with the severity of perceptual loss for individual monkeys. Our results suggest that the developmental alterations in normalization mechanisms resulting from early binocular suppression can explain much of these contrast-dependent spiking abnormalities in V2 neurons and the perceptual performance of our amblyopic monkeys. Amblyopia is a common developmental vision disorder in humans. Despite the extensive animal studies on how amblyopia emerges, we know surprisingly little about the neural basis of amblyopia in humans and nonhuman primates. Although the vision of amblyopic humans is often described as

  1. P-glycoprotein targeted nanoscale drug carriers

    KAUST Repository

    Li, Wengang

    2013-02-01

    Multi-drug resistance (MDR) is a trend whereby tumor cells exposed to one cytotoxic agent develop cross-resistance to a range of structurally and functionally unrelated compounds. P -glycoprotein (P -gp) efflux pump is one of the mostly studied drug carrying processes that shuttle the drugs out of tumor cells. Thus, P -gp inhibitors have attracted a lot of attention as they can stop cancer drugs from being pumped out of target cells with the consumption of ATP. Using quantitive structure activity relationship (QSAR), we have successfully synthesized a series of novel P -gp inhibitors. The obtained dihydropyrroloquinoxalines series were fully characterized and then tested against bacterial and tumor assays with over-expressed P -gps. All compounds were bioactive especially compound 1c that had enhanced antibacterial activity. Furthermore, these compounds were utilized as targeting vectors to direct drug delivery vehicles such as silica nanoparticles (SNPs) to cancerous Hela cells with over expressed P -gps. Cell uptake studies showed a successful accumulation of these decorated SNPs in tumor cells compared to undecorated SNPs. The results obtained show that dihydropyrroloquinoxalines constitute a promising drug candidate for targeting cancers with MDR. Copyright © 2013 American Scientific Publishers All rights reserved.

  2. Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

    Science.gov (United States)

    Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

    2014-02-05

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    Directory of Open Access Journals (Sweden)

    Omri Harish

    2015-07-01

    Full Text Available The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.

  4. Memory recall and spike-frequency adaptation

    Science.gov (United States)

    Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

    2016-05-01

    The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

  5. Phase diagram of spiking neural networks.

    Science.gov (United States)

    Seyed-Allaei, Hamed

    2015-01-01

    In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters - excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.

  6. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

    The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679

  7. Bursts generate a non-reducible spike-pattern code

    Directory of Open Access Journals (Sweden)

    Hugo G Eyherabide

    2009-05-01

    Full Text Available On the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. Using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the "what" and the "when" of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems.

  8. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

  9. Automated spike preparation system for Isotope Dilution Mass Spectrometry (IDMS)

    International Nuclear Information System (INIS)

    Maxwell, S.L. III; Clark, J.P.

    1990-01-01

    Isotope Dilution Mass Spectrometry (IDMS) is a method frequently employed to measure dissolved, irradiated nuclear materials. A known quantity of a unique isotope of the element to be measured (referred to as the ''spike'') is added to the solution containing the analyte. The resulting solution is chemically purified then analyzed by mass spectrometry. By measuring the magnitude of the response for each isotope and the response for the ''unique spike'' then relating this to the known quantity of the ''spike'', the quantity of the nuclear material can be determined. An automated spike preparation system was developed at the Savannah River Site (SRS) to dispense spikes for use in IDMS analytical methods. Prior to this development, technicians weighed each individual spike manually to achieve the accuracy required. This procedure was time-consuming and subjected the master stock solution to evaporation. The new system employs a high precision SMI Model 300 Unipump dispenser interfaced with an electronic balance and a portable Epson HX-20 notebook computer to automate spike preparation

  10. Predictive coding of dynamical variables in balanced spiking networks.

    Science.gov (United States)

    Boerlin, Martin; Machens, Christian K; Denève, Sophie

    2013-01-01

    Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

  11. A method for decoding the neurophysiological spike-response transform.

    Science.gov (United States)

    Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

    2009-11-15

    Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

  12. Geomagnetic spikes on the core-mantle boundary

    Science.gov (United States)

    Davies, C. J.; Constable, C.

    2017-12-01

    Extreme variations of Earth's magnetic field occurred in the Levantine region around 1000 BC, where the field intensity rose and fell by a factor of 2-3 over a short time and confined spatial region. There is presently no coherent link between this intensity spike and the generating processes in Earth's liquid core. Here we test the attribution of a surface spike to a flux patch visible on the core-mantle boundary (CMB), calculating geometric and energetic bounds on resulting surface geomagnetic features. We show that the Levantine intensity high must span at least 60 degrees in longitude. Models providing the best trade-off between matching surface spike intensity, minimizing L1 and L2 misfit to the available data and satisfying core energy constraints produce CMB spikes 8-22 degrees wide with peak values of O(100) mT. We propose that the Levantine spike grew in place before migrating northward and westward, contributing to the growth of the axial dipole field seen in Holocene field models. Estimates of Ohmic dissipation suggest that diffusive processes, which are often neglected, likely govern the ultimate decay of geomagnetic spikes. Using these results, we search for the presence of spike-like features in geodynamo simulations.

  13. Boobs, Boxing, and Bombs: Problematizing the Entertainment of Spike TV

    OpenAIRE

    Walton, Gerald; Potvin, L.

    2009-01-01

    Spike is the only television network in North America “for men.” Its motto, “Get more action,” is suggestive of pursuits of various forms of violence. We conceptualize Spike not as trivial entertainment, but rather as a form of pop culture that erodes the gains of feminists who have challenged the prevalence of normalized hegemonic masculinity (HM). Our paper highlights themes of Spike content, and connects those themes to the literature on HM. Moreover, we validate the identities and lives ...

  14. P-glycoprotein activity and biological response

    International Nuclear Information System (INIS)

    Vaalburg, W.; Hendrikse, N.H.; Elsinga, P.H.; Bart, J.; Waarde, A. van

    2005-01-01

    P-glycoprotein (P-gp) is a transmembrane drug efflux pump encoded by the MDR-1 gene in humans. Most likely P-gp protects organs against endogenous and exogenous toxins by extruding toxic compounds such as chemotherapeutics and other drugs. Many drugs are substrates for P-gp. Since P-gp is also expressed in the blood-brain barrier, P-gp substrates reach lower concentrations in the brain than in P-gp-negative tissues. Failure of response to chemotherapy of malignancies can be due to intrinsic or acquired drug resistance. Many tumors are multidrug resistant (MDR); resistant to several structurally unrelated chemotherapeutic agents. Several mechanisms are involved in MDR of which P-gp is studied most extensively. P-gp extrudes drugs out of tumor cells resulting in decreased intracellular drug concentrations, leading to the MDR phenotype. Furthermore, the MDR-1 gene exhibits several single nucleotide polymorphisms, some of which result in different transport capabilities. P-gp functionality and the effect of P-gp modulation on the pharmacokinetics of novel and established drugs can be studied in vivo by positron emission tomography (PET) using carbon-11 and fluorine-18-labeled P-gp substrates and modulators. PET may demonstrate the consequences of genetic differences on tissue pharmacokinetics. Inhibitors such as calcium-channel blockers (verapamil), cyclosporin A, ONT-093, and XR9576 can modulate the P-gp functionality. With PET the effect of P-gp modulation on the bioavailability of drugs can be investigated in humans in vivo. PET also allows the measurement of the efficacy of newly developed P-gp modulators

  15. Chimeric Lyssavirus Glycoproteins with Increased Immunological Potential

    Science.gov (United States)

    Jallet, Corinne; Jacob, Yves; Bahloul, Chokri; Drings, Astrid; Desmezieres, Emmanuel; Tordo, Noël; Perrin, Pierre

    1999-01-01

    The rabies virus glycoprotein molecule (G) can be divided into two parts separated by a flexible hinge: the NH2 half (site II part) containing antigenic site II up to the linear region (amino acids [aa] 253 to 275 encompassing epitope VI [aa 264]) and the COOH half (site III part) containing antigenic site III and the transmembrane and cytoplasmic domains. The structural and immunological roles of each part were investigated by cell transfection and mouse DNA-based immunization with homogeneous and chimeric G genes formed by fusion of the site II part of one genotype (GT) with the site III part of the same or another GT. Various site II-site III combinations between G genes of PV (Pasteur virus strain) rabies (GT1), Mokola (GT3), and EBL1 (European bat lyssavirus 1 [GT5]) viruses were tested. Plasmids pGPV-PV, pGMok-Mok, pGMok-PV, and pGEBL1-PV induced transient expression of correctly transported and folded antigens in neuroblastoma cells and virus-neutralizing antibodies against parental viruses in mice, whereas, pG-PVIII (site III part only) and pGPV-Mok did not. The site III part of PV (GT1) was a strong inducer of T helper cells and was very effective at presenting the site II part of various GTs. Both parts are required for correct folding and transport of chimeric G proteins which have a strong potential value for immunological studies and development of multivalent vaccines. Chimeric plasmid pGEBL1-PV broadens the spectrum of protection against European lyssavirus genotypes (GT1, GT5, and GT6). PMID:9847325

  16. Integrated workflows for spiking neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Ján eAntolík

    2013-12-01

    Full Text Available The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeller's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modellers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity.To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualisation into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organised configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualisation stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modelling studies by relieving the user from manual handling of the flow of metadata between the individual

  17. Heterogeneity of Purkinje cell simple spike-complex spike interactions: zebrin- and non-zebrin-related variations.

    Science.gov (United States)

    Tang, Tianyu; Xiao, Jianqiang; Suh, Colleen Y; Burroughs, Amelia; Cerminara, Nadia L; Jia, Linjia; Marshall, Sarah P; Wise, Andrew K; Apps, Richard; Sugihara, Izumi; Lang, Eric J

    2017-08-01

    Cerebellar Purkinje cells (PCs) generate two types of action potentials, simple and complex spikes. Although they are generated by distinct mechanisms, interactions between the two spike types exist. Zebrin staining produces alternating positive and negative stripes of PCs across most of the cerebellar cortex. Thus, here we compared simple spike-complex spike interactions both within and across zebrin populations. Simple spike activity undergoes a complex modulation preceding and following a complex spike. The amplitudes of the pre- and post-complex spike modulation phases were correlated across PCs. On average, the modulation was larger for PCs in zebrin positive regions. Correlations between aspects of the complex spike waveform and simple spike activity were found, some of which varied between zebrin positive and negative PCs. The implications of the results are discussed with regard to hypotheses that complex spikes are triggered by rises in simple spike activity for either motor learning or homeostatic functions. Purkinje cells (PCs) generate two types of action potentials, called simple and complex spikes (SSs and CSs). We first investigated the CS-associated modulation of SS activity and its relationship to the zebrin status of the PC. The modulation pattern consisted of a pre-CS rise in SS activity, and then, following the CS, a pause, a rebound, and finally a late inhibition of SS activity for both zebrin positive (Z+) and negative (Z-) cells, though the amplitudes of the phases were larger in Z+ cells. Moreover, the amplitudes of the pre-CS rise with the late inhibitory phase of the modulation were correlated across PCs. In contrast, correlations between modulation phases across CSs of individual PCs were generally weak. Next, the relationship between CS spikelets and SS activity was investigated. The number of spikelets/CS correlated with the average SS firing rate only for Z+ cells. In contrast, correlations across CSs between spikelet numbers and the

  18. Real-time computing platform for spiking neurons (RT-spike).

    Science.gov (United States)

    Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

    2006-07-01

    A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

  19. Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

    Science.gov (United States)

    D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

    2010-03-09

    It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

  20. Timing intervals using population synchrony and spike timing dependent plasticity

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2016-12-01

    Full Text Available We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model’s output.

  1. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  2. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

    2015-01-01

    . Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

  3. A novel unsupervised spike sorting algorithm for intracranial EEG.

    Science.gov (United States)

    Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

    2011-01-01

    This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

  4. Emergent dynamics of spiking neurons with fluctuating threshold

    Science.gov (United States)

    Bhattacharjee, Anindita; Das, M. K.

    2017-05-01

    Role of fluctuating threshold on neuronal dynamics is investigated. The threshold function is assumed to follow a normal probability distribution. Standard deviation of inter-spike interval of the response is computed as an indicator of irregularity in spike emission. It has been observed that, the irregularity in spiking is more if the threshold variation is more. A significant change in modal characteristics of Inter Spike Intervals (ISI) is seen to occur as a function of fluctuation parameter. Investigation is further carried out for coupled system of neurons. Cooperative dynamics of coupled neurons are discussed in view of synchronization. Total and partial synchronization regimes are depicted with the help of contour plots of synchrony measure under various conditions. Results of this investigation may provide a basis for exploring the complexities of neural communication and brain functioning.

  5. Higher Order Spike Synchrony in Prefrontal Cortex during visual memory

    Directory of Open Access Journals (Sweden)

    Gordon ePipa

    2011-06-01

    Full Text Available Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 µm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to 7 sites exhibit performance dependent modulation of their spike synchronization.

  6. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

    Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

    2015-07-15

    While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

  7. Structures and Functions of Pestivirus Glycoproteins: Not Simply Surface Matters

    Directory of Open Access Journals (Sweden)

    Fun-In Wang

    2015-06-01

    Full Text Available Pestiviruses, which include economically important animal pathogens such as bovine viral diarrhea virus and classical swine fever virus, possess three envelope glycoproteins, namely Erns, E1, and E2. This article discusses the structures and functions of these glycoproteins and their effects on viral pathogenicity in cells in culture and in animal hosts. E2 is the most important structural protein as it interacts with cell surface receptors that determine cell tropism and induces neutralizing antibody and cytotoxic T-lymphocyte responses. All three glycoproteins are involved in virus attachment and entry into target cells. E1-E2 heterodimers are essential for viral entry and infectivity. Erns is unique because it possesses intrinsic ribonuclease (RNase activity that can inhibit the production of type I interferons and assist in the development of persistent infections. These glycoproteins are localized to the virion surface; however, variations in amino acids and antigenic structures, disulfide bond formation, glycosylation, and RNase activity can ultimately affect the virulence of pestiviruses in animals. Along with mutations that are driven by selection pressure, antigenic differences in glycoproteins influence the efficacy of vaccines and determine the appropriateness of the vaccines that are currently being used in the field.

  8. Structures and Functions of Pestivirus Glycoproteins: Not Simply Surface Matters.

    Science.gov (United States)

    Wang, Fun-In; Deng, Ming-Chung; Huang, Yu-Liang; Chang, Chia-Yi

    2015-06-29

    Pestiviruses, which include economically important animal pathogens such as bovine viral diarrhea virus and classical swine fever virus, possess three envelope glycoproteins, namely Erns, E1, and E2. This article discusses the structures and functions of these glycoproteins and their effects on viral pathogenicity in cells in culture and in animal hosts. E2 is the most important structural protein as it interacts with cell surface receptors that determine cell tropism and induces neutralizing antibody and cytotoxic T-lymphocyte responses. All three glycoproteins are involved in virus attachment and entry into target cells. E1-E2 heterodimers are essential for viral entry and infectivity. Erns is unique because it possesses intrinsic ribonuclease (RNase) activity that can inhibit the production of type I interferons and assist in the development of persistent infections. These glycoproteins are localized to the virion surface; however, variations in amino acids and antigenic structures, disulfide bond formation, glycosylation, and RNase activity can ultimately affect the virulence of pestiviruses in animals. Along with mutations that are driven by selection pressure, antigenic differences in glycoproteins influence the efficacy of vaccines and determine the appropriateness of the vaccines that are currently being used in the field.

  9. A novel automated spike sorting algorithm with adaptable feature extraction.

    Science.gov (United States)

    Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

    2012-10-15

    To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  11. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-06

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  12. SPIKY: a graphical user interface for monitoring spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

    2015-05-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. Copyright © 2015 the American Physiological Society.

  13. Nonlinear evolution of single spike in Richtmyer-Meshkov instability

    International Nuclear Information System (INIS)

    Fukuda, Y.; Nishihara, K.; Wouchuk, J.G.

    2000-01-01

    Nonlinear evolution of single spike structure and vortex in the Richtmyer-Meshkov instability is investigated with the use of a two-dimensional hydrodynamic code. It is shown that singularity appears in the vorticity left by transmitted and reflected shocks at a corrugated interface. This singularity results in opposite sign of vorticity along the interface that causes double spiral structure of the spike. (authors)

  14. Coincidence Detection Using Spiking Neurons with Application to Face Recognition

    Directory of Open Access Journals (Sweden)

    Fadhlan Kamaruzaman

    2015-01-01

    Full Text Available We elucidate the practical implementation of Spiking Neural Network (SNN as local ensembles of classifiers. Synaptic time constant τs is used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD strategy trained in supervised manner using a novel supervised learning method called τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. This paper also discusses the approximation of spike timing in Spike Response Model (SRM for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP. We also found that the proposed method delivers better classification accuracy than k nearest neighbor, ensembles of kNN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.

  15. The Omega-Infinity Limit of Single Spikes

    CERN Document Server

    Axenides, Minos; Linardopoulos, Georgios

    A new infinite-size limit of strings in RxS2 is presented. The limit is obtained from single spike strings by letting their angular velocity omega become infinite. We derive the energy-momenta relation of omega-infinity single spikes as their linear velocity v-->1 and their angular momentum J-->1. Generally, the v-->1, J-->1 limit of single spikes is singular and has to be excluded from the spectrum and be studied separately. We discover that the dispersion relation of omega-infinity single spikes contains logarithms in the limit J-->1. This result is somewhat surprising, since the logarithmic behavior in the string spectra is typically associated with their motion in non-compact spaces such as AdS. Omega-infinity single spikes seem to completely cover the surface of the 2-sphere they occupy, so that they may essentially be viewed as some sort of "brany strings". A proof of the sphere-filling property of omega-infinity single spikes is given in the appendix.

  16. Comparison of Classifier Architectures for Online Neural Spike Sorting.

    Science.gov (United States)

    Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

    2017-04-01

    High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

  17. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  18. Absolute Ca Isotopic Measurement Using an Improved Double Spike Technique

    Directory of Open Access Journals (Sweden)

    Jason Jiun-San Shen

    2009-01-01

    Full Text Available A new vector analytical method has been developed in order to obtain the true isotopic composition of the 42Ca-48Ca double spike. This is achieved by using two different sample-spike mixtures combined with the double spike and natural Ca data. Be cause the natural sample (two mixtures and the spike should all lie on a single mixing line, we are able to con strain the true isotopic composition of our double spike using this new approach. Once the isotopic composition of the Ca double spike is established, we are able to obtain the true Ca isotopic composition of the NIST Ca standard SRM915a, 40Ca/44Ca = 46.537 ± 2 (2sm, n = 55, 42Ca/44Ca = 0.31031 ± 1, 43Ca/44Ca = 0.06474 ± 1, and 48Ca/44Ca = 0.08956 ± 1. De spite an off set of 1.3% in 40Ca/44Ca between our result and the previously re ported value (Russell et al. 1978, our data indicate an off set of 1.89__in 40Ca/44Ca between SRM915a and seawater, entirely consistent with the published results.

  19. Synthetic glycopeptides and glycoproteins with applications in biological research

    Directory of Open Access Journals (Sweden)

    Ulrika Westerlind

    2012-05-01

    Full Text Available Over the past few years, synthetic methods for the preparation of complex glycopeptides have been drastically improved. The need for homogenous glycopeptides and glycoproteins with defined chemical structures to study diverse biological phenomena further enhances the development of methodologies. Selected recent advances in synthesis and applications, in which glycopeptides or glycoproteins serve as tools for biological studies, are reviewed. The importance of specific antibodies directed to the glycan part, as well as the peptide backbone has been realized during the development of synthetic glycopeptide-based anti-tumor vaccines. The fine-tuning of native chemical ligation (NCL, expressed protein ligation (EPL, and chemoenzymatic glycosylation techniques have all together enabled the synthesis of functional glycoproteins. The synthesis of structurally defined, complex glycopeptides or glyco-clusters presented on natural peptide backbones, or mimics thereof, offer further possibilities to study protein-binding events.

  20. Multiple genes encode the major surface glycoprotein of Pneumocystis carinii

    DEFF Research Database (Denmark)

    Kovacs, J A; Powell, F; Edman, J C

    1993-01-01

    hydrophobic region at the carboxyl terminus. The presence of multiple related msg genes encoding the major surface glycoprotein of P. carinii suggests that antigenic variation is a possible mechanism for evading host defenses. Further characterization of this family of genes should allow the development......The major surface antigen of Pneumocystis carinii, a life-threatening opportunistic pathogen in human immunodeficiency virus-infected patients, is an abundant glycoprotein that functions in host-organism interactions. A monoclonal antibody to this antigen is protective in animals, and thus...... blot studies using chromosomal or restricted DNA, the major surface glycoproteins are the products of a multicopy family of genes. The predicted protein has an M(r) of approximately 123,000, is relatively rich in cysteine residues (5.5%) that are very strongly conserved, and contains a well conserved...

  1. Intracellular localization of hydroxyproline-rich glycoprotein biosynthesis

    International Nuclear Information System (INIS)

    Robinson, D.G.; Andreae, M.; Glas, A.R.; Sauer, A.

    1984-01-01

    The structural proteins of plant cell walls are glycoproteins characterized by O-glucosidic linkages to hydroxyproline or serine. Proline, not hydroxyproline, is the translatable amino acid in hydroxyproline-rich glycoproteins (HRGP). Hydroxylation and arabinosylation of proline are sequential, post-translational events. Because of this, there is no a priori reason for expecting HRGP synthesis to follow the well-established route for secretory and plasma membrane (PM) glycoproteins, i.e., from endoplasmic reticulum (ER) via the Golgi apparatus (GA) to the PM. In this paper, two plausible alternatives for HRGO secretion are examined. Because a feature of the majority of dicotyledons is overlapping GA and PM regions in sucrose density gradients, the authors have used two monocotyledonous systems to determine the distribution of HRGP and enzyme activity

  2. Australine, a pyrrolizidine alkaloid that inhibits amyloglucosidase and glycoprotein processing

    International Nuclear Information System (INIS)

    Tropea, J.E.; Molyneux, R.J.; Kaushal, G.P.; Pan, Y.T.; Mitchell, M.; Elbein, A.D.

    1989-01-01

    Australine is a polyhydroxylated pyrrolizidine alkaloid that was isolated from the seeds of the Australian tree Castanospermum australe and characterized by NMR and X-ray diffraction analysis. Since swainsonine and catanospermine are polyhydroxylated indolizidine alkaloids that inhibit specific glycosidases, the authors tested australine against a variety of exoglycosidases to determine whether it would inhibit any of these enzymes. This alkaloid proved to be a good inhibitor of the α-glucosidase amyloglucosidase (50% inhibition at 5.8 μM), but it did not inhibit β-glucosidase, α- or β-mannosidase, or α- or β-galactosidase. The inhibition of amyloglucosidase was of a competitive nature. Australine also inhibited the glycoprotein processing enzyme glucosidase I, but had only slight activity toward glucosidase II. When incubated with cultured cells, this alkaloid inhibited glycoprotein processing at the glucosidase I step and caused the accumulation of glycoproteins with Glc 3 Man 7-9 (GlcNAc) 2 -oligosaccharides

  3. Stress-Induced Impairment of a Working Memory Task: Role of Spiking Rate and Spiking History Predicted Discharge

    Science.gov (United States)

    Devilbiss, David M.; Jenison, Rick L.; Berridge, Craig W.

    2012-01-01

    Stress, pervasive in society, contributes to over half of all work place accidents a year and over time can contribute to a variety of psychiatric disorders including depression, schizophrenia, and post-traumatic stress disorder. Stress impairs higher cognitive processes, dependent on the prefrontal cortex (PFC) and that involve maintenance and integration of information over extended periods, including working memory and attention. Substantial evidence has demonstrated a relationship between patterns of PFC neuron spiking activity (action-potential discharge) and components of delayed-response tasks used to probe PFC-dependent cognitive function in rats and monkeys. During delay periods of these tasks, persistent spiking activity is posited to be essential for the maintenance of information for working memory and attention. However, the degree to which stress-induced impairment in PFC-dependent cognition involves changes in task-related spiking rates or the ability for PFC neurons to retain information over time remains unknown. In the current study, spiking activity was recorded from the medial PFC of rats performing a delayed-response task of working memory during acute noise stress (93 db). Spike history-predicted discharge (SHPD) for PFC neurons was quantified as a measure of the degree to which ongoing neuronal discharge can be predicted by past spiking activity and reflects the degree to which past information is retained by these neurons over time. We found that PFC neuron discharge is predicted by their past spiking patterns for nearly one second. Acute stress impaired SHPD, selectively during delay intervals of the task, and simultaneously impaired task performance. Despite the reduction in delay-related SHPD, stress increased delay-related spiking rates. These findings suggest that neural codes utilizing SHPD within PFC networks likely reflects an additional important neurophysiological mechanism for maintenance of past information over time. Stress

  4. Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

    Directory of Open Access Journals (Sweden)

    Zedong Bi

    2016-08-01

    Full Text Available Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded, by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy.

  5. A Functional Henipavirus Envelope Glycoprotein Pseudotyped Lentivirus Assay System

    Directory of Open Access Journals (Sweden)

    Broder Christopher C

    2010-11-01

    Full Text Available Abstract Background Hendra virus (HeV and Nipah virus (NiV are newly emerged zoonotic paramyxoviruses discovered during outbreaks in Queensland, Australia in 1994 and peninsular Malaysia in 1998/9 respectively and classified within the new Henipavirus genus. Both viruses can infect a broad range of mammalian species causing severe and often-lethal disease in humans and animals, and repeated outbreaks continue to occur. Extensive laboratory studies on the host cell infection stage of HeV and NiV and the roles of their envelope glycoproteins have been hampered by their highly pathogenic nature and restriction to biosafety level-4 (BSL-4 containment. To circumvent this problem, we have developed a henipavirus envelope glycoprotein pseudotyped lentivirus assay system using either a luciferase gene or green fluorescent protein (GFP gene encoding human immunodeficiency virus type-1 (HIV-1 genome in conjunction with the HeV and NiV fusion (F and attachment (G glycoproteins. Results Functional retrovirus particles pseudotyped with henipavirus F and G glycoproteins displayed proper target cell tropism and entry and infection was dependent on the presence of the HeV and NiV receptors ephrinB2 or B3 on target cells. The functional specificity of the assay was confirmed by the lack of reporter-gene signals when particles bearing either only the F or only G glycoprotein were prepared and assayed. Virus entry could be specifically blocked when infection was carried out in the presence of a fusion inhibiting C-terminal heptad (HR-2 peptide, a well-characterized, cross-reactive, neutralizing human mAb specific for the henipavirus G glycoprotein, and soluble ephrinB2 and B3 receptors. In addition, the utility of the assay was also demonstrated by an examination of the influence of the cytoplasmic tail of F in its fusion activity and incorporation into pseudotyped virus particles by generating and testing a panel of truncation mutants of NiV and HeV F

  6. Preparation of 131I-asialo-α1-acid glycoprotein

    International Nuclear Information System (INIS)

    Rijk, P.P. van

    1975-01-01

    α 1 -Acid glycoprotein (orosomucoid) was prepared from a byproduct of the ethanol plasma fractionation by means of ion-exchange procedures. Immunoelectrophoresis suggested a high degree of purity; the purified protein contained 13.5% sialic acid and 17.8% hexose. The α 1 -acid glycoprotein was modified by removal of sialic acid with neurominidase (E.C. 3.2.1.18) followed by iodination with 131 I. The purpose of the preparation, its potential use as a pharmacon for liver-function studies in nuclear medicine, is the subject of further study

  7. Intestinal mucus and juice glycoproteins have a liquid crystalline structure

    International Nuclear Information System (INIS)

    Denisova, E.A.; Lazarev, P.I.; Vazina, A.A.; Zheleznaya, L.A.

    1985-01-01

    X-ray diffraction patterns have been obtained from the following components of canine gastrointestinal tract: (1) native small intestine mucus layer; (2) the precipitate of the flocks formed in the duodenal juice with decreasing pH; (3) concentrated solutions of glycoproteins isolated from the duodenal juice. The X-ray patterns consist of a large number of sharp reflections of spacings between about 100 and 4 A. Some reflections are common for all components studied. All the patterns are interpreted as arising from the glycoprotein molecules ordered into a liquid crystalline structure. (author)

  8. Three-Dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation.

    Science.gov (United States)

    Pan, Li; Aguilar, Hillary Andaluz; Wang, Linna; Iliuk, Anton; Tao, W Andy

    2016-11-30

    Glycoproteins have vast structural diversity that plays an important role in many biological processes and have great potential as disease biomarkers. Here, we report a novel functionalized reverse phase protein array (RPPA), termed polymer-based reverse phase glycoprotein array (polyGPA), to capture and profile glycoproteomes specifically, and validate glycoproteins. Nitrocellulose membrane functionalized with globular hydroxyaminodendrimers was used to covalently capture preoxidized glycans on glycoproteins from complex protein samples such as biofluids. The captured glycoproteins were subsequently detected using the same validated antibodies as in RPPA. We demonstrated the outstanding specificity, sensitivity, and quantitative capabilities of polyGPA by capturing and detecting purified as well as endogenous α-1-acid glycoprotein (AGP) in human plasma. We further applied quantitative N-glycoproteomics and the strategy to validate a panel of glycoproteins identified as potential biomarkers for bladder cancer by analyzing urine glycoproteins from bladder cancer patients or matched healthy individuals.

  9. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

    NARCIS (Netherlands)

    S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

    2000-01-01

    textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

  10. Joint Probability-Based Neuronal Spike Train Classification

    Directory of Open Access Journals (Sweden)

    Yan Chen

    2009-01-01

    Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

  11. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

  12. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

    Full Text Available 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 post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  13. Monoclonal antibody to an external epitope of the human mdr1 P-glycoprotein

    NARCIS (Netherlands)

    Arceci, R. J.; Stieglitz, K.; Bras, J.; Schinkel, A.; Baas, F.; Croop, J.

    1993-01-01

    A membrane glycoprotein, termed P-glycoprotein, has been shown to be responsible for cross-resistance to a broad range of structurally and functionally distinct cytotoxic agents. P-glycoprotein, encoded in humans by the mdr1 gene, functions as an energy-dependent efflux pump to exclude these

  14. A metric space approach to the information capacity of spike trains

    OpenAIRE

    HOUGHTON, CONOR JAMES; GILLESPIE, JAMES

    2010-01-01

    PUBLISHED Classical information theory can be either discrete or continuous, corresponding to discrete or continuous random variables. However, although spike times in a spike train are described by continuous variables, the information content is usually calculated using discrete information theory. This is because the number of spikes, and hence, the number of variables, varies from spike train to spike train, making the continuous theory difficult to apply.It is possible to avoid ...

  15. Noise-enhanced coding in phasic neuron spike trains.

    Science.gov (United States)

    Ly, Cheng; Doiron, Brent

    2017-01-01

    The stochastic nature of neuronal response has lead to conjectures about the impact of input fluctuations on the neural coding. For the most part, low pass membrane integration and spike threshold dynamics have been the primary features assumed in the transfer from synaptic input to output spiking. Phasic neurons are a common, but understudied, neuron class that are characterized by a subthreshold negative feedback that suppresses spike train responses to low frequency signals. Past work has shown that when a low frequency signal is accompanied by moderate intensity broadband noise, phasic neurons spike trains are well locked to the signal. We extend these results with a simple, reduced model of phasic activity that demonstrates that a non-Markovian spike train structure caused by the negative feedback produces a noise-enhanced coding. Further, this enhancement is sensitive to the timescales, as opposed to the intensity, of a driving signal. Reduced hazard function models show that noise-enhanced phasic codes are both novel and separate from classical stochastic resonance reported in non-phasic neurons. The general features of our theory suggest that noise-enhanced codes in excitable systems with subthreshold negative feedback are a particularly rich framework to study.

  16. Spike morphology in blast-wave-driven instability experiments

    International Nuclear Information System (INIS)

    Kuranz, C. C.; Drake, R. P.; Grosskopf, M. J.; Fryxell, B.; Budde, A.; Hansen, J. F.; Miles, A. R.; Plewa, T.; Hearn, N.; Knauer, J.

    2010-01-01

    The laboratory experiments described in the present paper observe the blast-wave-driven Rayleigh-Taylor instability with three-dimensional (3D) initial conditions. About 5 kJ of energy from the Omega laser creates conditions similar to those of the He-H interface during the explosion phase of a supernova. The experimental target is a 150 μm thick plastic disk followed by a low-density foam. The plastic piece has an embedded, 3D perturbation. The basic structure of the pattern is two orthogonal sine waves where each sine wave has an amplitude of 2.5 μm and a wavelength of 71 μm. In some experiments, an additional wavelength is added to explore the interaction of modes. In experiments with 3D initial conditions the spike morphology differs from what has been observed in other Rayleigh-Taylor experiments and simulations. Under certain conditions, experimental radiographs show some mass extending from the interface to the shock front. Current simulations show neither the spike morphology nor the spike penetration observed in the experiments. The amount of mass reaching the shock front is analyzed and potential causes for the spike morphology and the spikes reaching the shock are discussed. One such hypothesis is that these phenomena may be caused by magnetic pressure, generated by an azimuthal magnetic field produced by the plasma dynamics.

  17. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  18. Multiplexed Spike Coding and Adaptation in the Thalamus

    Directory of Open Access Journals (Sweden)

    Rebecca A. Mease

    2017-05-01

    Full Text Available High-frequency “burst” clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ “multiplexing” to differentially encode low- and high-frequency stimulus features associated with either T-type calcium “low-threshold” or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1 burst size, (2 burst onset time, and (3 precise spike timing within bursts. Strikingly, this latter “intraburst” encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.

  19. Scaling of spiking and humping in keyhole welding

    Energy Technology Data Exchange (ETDEWEB)

    Wei, P S; Chuang, K C [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); DebRoy, T [Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802 (United States); Ku, J S, E-mail: pswei@mail.nsysu.edu.tw, E-mail: cielo.zhuang@gmail.com, E-mail: rtd1@psu.edu, E-mail: jsku@mail.nsysu.edu.tw [Institute of Materials Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China)

    2011-06-22

    Spiking, rippling and humping seriously reduce the strength of welds. The effects of beam focusing, volatile alloying element concentration and welding velocity on spiking, coarse rippling and humping in keyhole mode electron-beam welding are examined through scale analysis. Although these defects have been studied in the past, the mechanisms for their formation are not fully understood. This work relates the average amplitudes of spikes to fusion zone depth for the welding of Al 6061, SS 304 and carbon steel, and Al 5083. The scale analysis introduces welding and melting efficiencies and an appropriate power distribution to account for the focusing effects, and the energy which is reflected and escapes through the keyhole opening to the surroundings. The frequency of humping and spiking can also be predicted from the scale analysis. The analysis also reveals the interrelation between coarse rippling and humping. The data and the mechanistic findings reported in this study are useful for understanding and preventing spiking and humping during keyhole mode electron and laser beam welding.

  20. Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

    Science.gov (United States)

    Halliday, D M; Rosenberg, J R

    2017-04-24

    A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

  1. Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs

    Directory of Open Access Journals (Sweden)

    Zedong eBi

    2016-02-01

    Full Text Available In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis. Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e. synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons. Neurons (including the post-synaptic neuron in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1 synchronous firing and burstiness tend to increase DiffV, (2 heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3 heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our

  2. Rat macrophages: membrane glycoproteins in differentiation and function

    NARCIS (Netherlands)

    van den Berg, T. K.; Döpp, E. A.; Dijkstra, C. D.

    2001-01-01

    Macrophages (mphi) play a crucial role in the immune system. The rat offers unique advantages for studying the biology of mphi. Firstly, monoclonal antibodies (mAb) against many rat mphi surface glycoproteins have become available. These have not only demonstrated a considerable heterogeneity among

  3. Multiple genes encode the major surface glycoprotein of Pneumocystis carinii

    DEFF Research Database (Denmark)

    Kovacs, J A; Powell, F; Edman, J C

    1993-01-01

    The major surface antigen of Pneumocystis carinii, a life-threatening opportunistic pathogen in human immunodeficiency virus-infected patients, is an abundant glycoprotein that functions in host-organism interactions. A monoclonal antibody to this antigen is protective in animals, and thus this a...

  4. Structural and functional analysis of bovine herpesvirus 1 minor glycoproteins

    NARCIS (Netherlands)

    Baranowski, E.; Keil, G.; Lyaku, J.; Rijsewijk, F.A.M.; Oirschot, van J.T.; Pastoret, P.P.; Thiry, E.

    1996-01-01

    This paper focuses on the structure and functions of bovine herpesvirus 1 minor glycoproteins gH, gE, gG and gp42. It reviews the progress which has been made in their identification and characterization, in the study of their temporal expression and processing in infected cells, and finally in the

  5. Separation and identification of carp pituitary proteins and glycoproteins

    Czech Academy of Sciences Publication Activity Database

    Ryšlavá, H.; Janatová, M.; Čalounová, G.; Selicharová, Irena; Barthová, J.; Barth, Tomislav

    2005-01-01

    Roč. 50, č. 9 (2005), 430-437 ISSN 1212-1819 R&D Projects: GA MZe(CZ) QF3028 Institutional research plan: CEZ:AV0Z4055905 Keywords : carp hormones * glycoproteins * oligosaccharide chains Subject RIV: CE - Biochemistry Impact factor: 0.254, year: 2005

  6. Glycoprotein of the wall of sycamore tissue-culture cells.

    Science.gov (United States)

    Heath, M F; Northcote, D H

    1971-12-01

    1. A glycoprotein containing a large amount of hydroxyproline is present in the cell walls of sycamore callus cells. This protein is insoluble and remained in the alpha-cellulose when a mild separation procedure was used to obtain the polysaccharide fractions of the wall. The glycoprotein contained a high proportion of arabinose and galactose. 2. Soluble glycopeptides were prepared from the alpha-cellulose fraction when peptide bonds were broken by hydrazinolysis. The soluble material was fractionated by gel filtration and one glycopeptide was further purified by electrophoresis; it had a composition of 10% hydroxyproline, 35% arabinose and 55% galactose, and each hydroxyproline residue carried a glycosyl radical so that the oligosaccharides on the glycopeptide had an average degree of polymerization of 9. 3. The extraction of the glycopeptides was achieved without cleavage of glycosyl bonds, so that the glycoprotein cannot act as a covalent cross-link between the major polysaccharides of the wall. 4. The wall protein approximates in conformation to polyhydroxyproline and therefore it probably has similar physicochemical properties to polyhydroxyproline. This is discussed in relation to the function of the glycoprotein and its effect on the physical and chemical nature of the wall.

  7. Cancer Biomarker Discovery: Lectin-Based Strategies Targeting Glycoproteins

    Directory of Open Access Journals (Sweden)

    David Clark

    2012-01-01

    Full Text Available Biomarker discovery can identify molecular markers in various cancers that can be used for detection, screening, diagnosis, and monitoring of disease progression. Lectin-affinity is a technique that can be used for the enrichment of glycoproteins from a complex sample, facilitating the discovery of novel cancer biomarkers associated with a disease state.

  8. Glycoprotein expression by adenomatous polyps of the colon

    Science.gov (United States)

    Roney, Celeste A.; Xie, Jianwu; Xu, Biying; Jabour, Paul; Griffiths, Gary; Summers, Ronald M.

    2008-03-01

    Colon cancer is the second leading cause of cancer related deaths in the United States. Specificity in diagnostic imaging for detecting colorectal adenomas, which have a propensity towards malignancy, is desired. Adenomatous polyp specimens of the colon were obtained from the mouse model of colorectal cancer called adenomatous polyposis coli-multiple intestinal neoplasia (APC Min). Histological evaluation, by the legume protein Ulex europaeus agglutinin I (UEA-1), determined expression of the glycoprotein α-L-fucose. FITC-labelled UEA-1 confirmed overexpression of the glycoprotein by the polyps on fluorescence microscopy in 17/17 cases, of which 13/17 included paraffin-fixed mouse polyp specimens. In addition, FITC-UEA-1 ex vivo multispectral optical imaging of 4/17 colonic specimens displayed over-expression of the glycoprotein by the polyps, as compared to non-neoplastic mucosa. Here, we report the surface expression of α-L-fucosyl terminal residues by neoplastic mucosal cells of APC specimens of the mouse. Glycoprotein expression was validated by the carbohydrate binding protein UEA-1. Future applications of this method are the development of agents used to diagnose cancers by biomedical imaging modalities, including computed tomographic colonography (CTC). UEA-1 targeting to colonic adenomas may provide a new avenue for the diagnosis of colorectal carcinoma by CT imaging.

  9. Glycoprotein Ibalpha signalling in platelet apoptosis and clearance

    NARCIS (Netherlands)

    van der Wal, E.

    2010-01-01

    Storage of platelets at low temperature reduces bacterial growth and might better preserve the haemostatic function of platelets than current procedures. Incubation at 0C is known to expose ?-N-acetyl-D-glucosamine-residues on glycoprotein (GP)Ibalpha inducing receptor-clustering and platelet

  10. Glycoprotein Ibα clustering in platelet storage and function

    NARCIS (Netherlands)

    Gitz, E.

    2013-01-01

    Platelets are anucleated, discoid-shaped cells that play an essential role in the formation of a hemostatic plug to prevent blood loss from injured vessels. Initial platelet arrest at the damaged arterial vessel wall is mediated through the interaction between the platelet receptor glycoprotein (GP)

  11. Glycoproteins and sialyl transferase of human B lymphoblastoid cell lines

    International Nuclear Information System (INIS)

    Lui, S.W.L.; Ng, M.H.

    1980-01-01

    We used two radiolabeling methods to study glycoproteins on the surface of lymphoblastoid cells. One of the methods affects tritiation of residues which are oxidized with galactose oxidase and the other causes tritiation of neuraminic acid residues. This approach was shown to allow a better resolution of cell surface glycoproteins than if either method were used alone. Glycoproteins of B 1 - 19 cells which harbor the Epstein-Barr virus genomes were compared with those of its parental cell line, BJAB, which does not harbor the viral genomes. These studies did not reveal a unique viral protein. A 28,000 mol. wt. glycoprotein was found to be the most prominent neuraminic acidlabeled product of B 1 - 19 cells and also of the two other cell lines, Raji and Ly38, which harbor the EBV genomes. A similar molecular weight species from BJAB cells identified by galactose oxidase labeling might be deficient in neuraminic acid residues as it was poorly labeled by the periodate oxidation method. The neuraminic acid content and level of sialyl transferase of BJAB cells were found to be lower than those of the other cell lines studied. (auth.)

  12. Direct chemical modification and voltammetric detection of glycans in glycoproteins

    Czech Academy of Sciences Publication Activity Database

    Trefulka, Mojmír; Paleček, Emil

    2014-01-01

    Roč. 48, NOV2014 (2014), s. 52-55 ISSN 1388-2481 R&D Projects: GA ČR(CZ) GAP301/11/2055 Institutional support: RVO:68081707 Keywords : Glycoproteins * Chemical modification * Os(VI)L complexes Subject RIV: BO - Biophysics Impact factor: 4.847, year: 2014

  13. MDR1 P-glycoprotein transports endogenous opioid peptides

    NARCIS (Netherlands)

    Oude Elferink, R. P.; Zadina, J.

    2001-01-01

    MDR1 P-glycoprotein is generally regarded as an efflux pump for amphipathic toxic compounds. The question remains, however, whether certain endogenous compounds are also substrates for this transporter. Certain peptides have been shown to interact with MDR1 Pgp as well and we have therefore

  14. Quantitative mass spectrometric analysis of glycoproteins combined with enrichment methods.

    Science.gov (United States)

    Ahn, Yeong Hee; Kim, Jin Young; Yoo, Jong Shin

    2015-01-01

    Mass spectrometry (MS) has been a core technology for high sensitive and high-throughput analysis of the enriched glycoproteome in aspects of quantitative assays as well as qualitative profiling of glycoproteins. Because it has been widely recognized that aberrant glycosylation in a glycoprotein may involve in progression of a certain disease, the development of efficient analysis tool for the aberrant glycoproteins is very important for deep understanding about pathological function of the glycoprotein and new biomarker development. This review first describes the protein glycosylation-targeting enrichment technologies mainly employing solid-phase extraction methods such as hydrizide-capturing, lectin-specific capturing, and affinity separation techniques based on porous graphitized carbon, hydrophilic interaction chromatography, or immobilized boronic acid. Second, MS-based quantitative analysis strategies coupled with the protein glycosylation-targeting enrichment technologies, by using a label-free MS, stable isotope-labeling, or targeted multiple reaction monitoring (MRM) MS, are summarized with recent published studies. © 2014 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc.

  15. Cereal n-glycoproteins enrichment by lectin affinity monolithic chromatography

    Czech Academy of Sciences Publication Activity Database

    Flodrová, Dana; Bobálová, Janette; Laštovičková, Markéta

    2016-01-01

    Roč. 44, č. 2 (2016), s. 286-297 ISSN 0133-3720 R&D Projects: GA ČR(CZ) GPP503/12/P395 Institutional support: RVO:68081715 Keywords : barley * wheat * glycoprotein * mass spectrometry * lectin chromatography Subject RIV: CB - Analytical Chemistry, Separation Impact factor: 0.496, year: 2016

  16. Molecular cloning of S1 glycoprotein gene of infectious bronchitis ...

    African Journals Online (AJOL)

    In vitro protein expression is an important method of obtaining large amounts of viral proteins to investigate their biological properties. The S1 glycoprotein of infectious bronchitis virus, due to its effective immune-dominant role is an appropriate candidate for production of recombinant vaccine against infectious bronchitis ...

  17. The peanut lectin-binding glycoproteins of human epidermal keratinocytes

    International Nuclear Information System (INIS)

    Morrison, A.I.; Keeble, S.; Watt, F.M.

    1988-01-01

    The peanut lectin (PNA) is known to bind more strongly to keratinocytes that are undergoing terminal differentiation than to proliferating keratinocytes. In order to investigate the significance of this change in cell-surface carbohydrate authors have identified the PNA-binding glycoproteins of cultured human keratinocytes and antibodies against them. Two heavily glycosylated bands of 110 and 250 kDa were resolved by PAGE of [ 14 C]galactose- or [ 14 C]mannose- and [ 14 C]glucosamine-labeled cell extracts eluted with galactose from PNA affinity columns. The higher molecular weight band was also detected on PNA blots of unlabeled cell extracts transferred to nitrocellulose. Both bands were sensitive to pronase digestion, but only the 250-kDa band was digested with trypsin. A rabbit antiserum that we prepared (anti-PNA-gp) immunoprecipitated both bands from cell extracts. In contrast to PNA, anti-PNA-gp bound equally to proliferating and terminally differentiating cells, indicating that some epitope(s) of the PNA-binding glycoproteins is present on the cell surface prior to terminal differentiation. When keratinocytes grown as a monolayer in low-calcium medium were switched to medium containing 2 mM calcium ions in order to induce desmosome formation and stratification, there was a dramatic redistribution of the PNA-binding glycoproteins, which became concentrated at the boundaries between cells. This may suggest a role for the glycoproteins in cell-cell interactions during stratification

  18. Magnetic enzyme reactors for isolation and study of heterogeneous glycoproteins

    International Nuclear Information System (INIS)

    Korecka, Lucie; Jezova, Jana; Bilkova, Zuzana; Benes, Milan; Horak, Daniel; Hradcova, Olga; Slovakova, Marcela; Viovy, Jean-Louis

    2005-01-01

    The newly developed magnetic micro- and nanoparticles with defined hydrophobicity and porosity were used for the preparation of magnetic enzyme reactors. Magnetic particles with immobilized proteolytic enzymes trypsin, chymotrypsin and papain and with enzyme neuraminidase were used to study the structure of heterogeneous glycoproteins. Factors such as the type of carrier, immobilization procedure, operational and storage stability, and experimental conditions were optimized

  19. Comparison of spike-sorting algorithms for future hardware implementation.

    Science.gov (United States)

    Gibson, Sarah; Judy, Jack W; Markovic, Dejan

    2008-01-01

    Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

  20. Grain price spikes and beggar-thy-neighbor policy responses

    DEFF Research Database (Denmark)

    Jensen, Hans Grinsted; Anderson, Kym

    When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...... have assessed the extent to which those policies contributed to the 2006-08 international price rise, but only by focusing on one commodity or using a back-of-the envelope (BOTE) method. This paper provides a more-comprehensive analysis using a global economy-wide model that is able to take account...... of the interactions between markets for farm products that are closely related in production and/or consumption, and able to estimate the impacts of those insulating policies on grain prices and on the grain trade and economic welfare of the world’s various countries. Our results support the conclusion from earlier...

  1. Character recognition from trajectory by recurrent spiking neural networks.

    Science.gov (United States)

    Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

    2017-07-01

    Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

  2. Evaluation of the uranium double spike technique for environmental monitoring

    International Nuclear Information System (INIS)

    Hemberger, P.H.; Rokop, D.J.; Efurd, D.W.; Roensch, F.R.; Smith, D.H.; Turner, M.L.; Barshick, C.M.; Bayne, C.K.

    1998-01-01

    Use of a uranium double spike in analysis of environmental samples showed that a 235 U enrichment of 1% ( 235 U/ 238 U = 0.00732) can be distinguished from natural ( 235 U/ 238 U = 0.00725). Experiments performed jointly at Los Alamos National Laboratory (LANL) and Oak Ridge National Laboratory (ORNL) used a carefully calibrated double spike of 233 U and 236 U to obtain much better precision than is possible using conventional analytical techniques. A variety of different sampling media (vegetation and swipes) showed that, provided sufficient care is exercised in choice of sample type, relative standard deviations of less than ± 0.5% can be routinely obtained. This ability, unavailable without use of the double spike, has enormous potential significance in the detection of undeclared nuclear facilities

  3. A Hybrid Setarx Model for Spikes in Tight Electricity Markets

    Directory of Open Access Journals (Sweden)

    Carlo Lucheroni

    2012-01-01

    Full Text Available The paper discusses a simple looking but highly nonlinear regime-switching, self-excited threshold model for hourly electricity prices in continuous and discrete time. The regime structure of the model is linked to organizational features of the market. In continuous time, the model can include spikes without using jumps, by defining stochastic orbits. In passing from continuous time to discrete time, the stochastic orbits survive discretization and can be identified again as spikes. A calibration technique suitable for the discrete version of this model, which does not need deseasonalization or spike filtering, is developed, tested and applied to market data. The discussion of the properties of the model uses phase-space analysis, an approach uncommon in econometrics. (original abstract

  4. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  5. Statins Suppress Ebola Virus Infectivity by Interfering with Glycoprotein Processing.

    Science.gov (United States)

    Shrivastava-Ranjan, Punya; Flint, Mike; Bergeron, Éric; McElroy, Anita K; Chatterjee, Payel; Albariño, César G; Nichol, Stuart T; Spiropoulou, Christina F

    2018-05-01

    Ebola virus (EBOV) infection is a major public health concern due to high fatality rates and limited effective treatments. Statins, widely used cholesterol-lowering drugs, have pleiotropic mechanisms of action and were suggested as potential adjunct therapy for Ebola virus disease (EVD) during the 2013-2016 outbreak in West Africa. Here, we evaluated the antiviral effects of statin (lovastatin) on EBOV infection in vitro Statin treatment decreased infectious EBOV production in primary human monocyte-derived macrophages and in the hepatic cell line Huh7. Statin treatment did not interfere with viral entry, but the viral particles released from treated cells showed reduced infectivity due to inhibition of viral glycoprotein processing, as evidenced by decreased ratios of the mature glycoprotein form to precursor form. Statin-induced inhibition of infectious virus production and glycoprotein processing was reversed by exogenous mevalonate, the rate-limiting product of the cholesterol biosynthesis pathway, but not by low-density lipoprotein. Finally, statin-treated cells produced EBOV particles devoid of the surface glycoproteins required for virus infectivity. Our findings demonstrate that statin treatment inhibits EBOV infection and suggest that the efficacy of statin treatment should be evaluated in appropriate animal models of EVD. IMPORTANCE Treatments targeting Ebola virus disease (EVD) are experimental, expensive, and scarce. Statins are inexpensive generic drugs that have been used for many years for the treatment of hypercholesterolemia and have a favorable safety profile. Here, we show the antiviral effects of statins on infectious Ebola virus (EBOV) production. Our study reveals a novel molecular mechanism in which statin regulates EBOV particle infectivity by preventing glycoprotein processing and incorporation into virus particles. Additionally, statins have anti-inflammatory and immunomodulatory effects. Since inflammation and dysregulation of the immune

  6. Development of glycoprotein capture-based label-free method for the high-throughput screening of differential glycoproteins in hepatocellular carcinoma.

    Science.gov (United States)

    Chen, Rui; Tan, Yexiong; Wang, Min; Wang, Fangjun; Yao, Zhenzhen; Dong, Liwei; Ye, Mingliang; Wang, Hongyang; Zou, Hanfa

    2011-07-01

    A robust, reproducible, and high throughput method was developed for the relative quantitative analysis of glycoprotein abundances in human serum. Instead of quantifying glycoproteins by glycopeptides in conventional quantitative glycoproteomics, glycoproteins were quantified by nonglycosylated peptides derived from the glycoprotein digest, which consists of the capture of glycoproteins in serum samples and the release of nonglycopeptides by trypsin digestion of captured glycoproteins followed by two-dimensional liquid chromatography-tandem MS analysis of released peptides. Protein quantification was achieved by comparing the spectrum counts of identified nonglycosylated peptides of glycoproteins between different samples. This method was demonstrated to have almost the same specificity and sensitivity in glycoproteins quantification as capture at glycopeptides level. The differential abundance of proteins present at as low as nanogram per milliliter levels was quantified with high confidence. The established method was applied to the analysis of human serum samples from healthy people and patients with hepatocellular carcinoma (HCC) to screen differential glycoproteins in HCC. Thirty eight glycoproteins were found with substantial concentration changes between normal and HCC serum samples, including α-fetoprotein, the only clinically used marker for HCC diagnosis. The abundance changes of three glycoproteins, i.e. galectin-3 binding protein, insulin-like growth factor binding protein 3, and thrombospondin 1, which were associated with the development of HCC, were further confirmed by enzyme-linked immunosorbent assay. In conclusion, the developed method was an effective approach to quantitatively analyze glycoproteins in human serum and could be further applied in the biomarker discovery for HCC and other cancers.

  7. A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

    Science.gov (United States)

    Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

    2017-05-01

    This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

  8. Glycoprotein H of herpes simplex virus type 1 requires glycoprotein L for transport to the surfaces of insect cells

    NARCIS (Netherlands)

    Westra, DF; Glazenburg, KL; Harmsen, MC; Tiran, A; Scheffer, AJ; Welling, GW; The, TH; WellingWester, S

    In mammalian cells, formation of heterooligomers consisting of the glycoproteins H and L (gH and gL) of herpes simplex virus type 1 is essential for the cell-to-cell spread of virions and for the penetration of virions into cells. We examined whether formation of gH1/gL1 heterooligomers and cell

  9. Spike and burst coding in thalamocortical relay cells.

    Directory of Open Access Journals (Sweden)

    Fleur Zeldenrust

    2018-02-01

    Full Text Available Mammalian thalamocortical relay (TCR neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA and the Event-Triggered Covariance (ETC. This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms and showed a clear distinction between spikes (selective for fluctuations and bursts (selective for integration. The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT and the cyclic nucleotide modulated h current (Ih. The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two

  10. Local Variation of Hashtag Spike Trains and Popularity in Twitter

    Science.gov (United States)

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

    We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. PMID:26161650

  11. Sleep deprivation and spike-wave discharges in epileptic rats

    OpenAIRE

    Drinkenburg, W.H.I.M.; Coenen, A.M.L.; Vossen, J.M.H.; Luijtelaar, E.L.J.M. van

    1995-01-01

    The effects of sleep deprivation were studied on the occurrence of spike-wave discharges in the electroencephalogram of rats of the epileptic WAG/Rij strain, a model for absence epilepsy. This was done before, during and after a period of 12 hours of near total sleep deprivation. A substantial increase in the number of spike-wave discharges was found during the first 4 hours of the deprivation period, whereas in the following deprivation hours epileptic activity returned to baseline values. I...

  12. Spike propagation in driven chain networks with dominant global inhibition

    International Nuclear Information System (INIS)

    Chang Wonil; Jin, Dezhe Z.

    2009-01-01

    Spike propagation in chain networks is usually studied in the synfire regime, in which successive groups of neurons are synaptically activated sequentially through the unidirectional excitatory connections. Here we study the dynamics of chain networks with dominant global feedback inhibition that prevents the synfire activity. Neural activity is driven by suprathreshold external inputs. We analytically and numerically demonstrate that spike propagation along the chain is a unique dynamical attractor in a wide parameter regime. The strong inhibition permits a robust winner-take-all propagation in the case of multiple chains competing via the inhibition.

  13. Spiking neuron devices consisting of single-flux-quantum circuits

    International Nuclear Information System (INIS)

    Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

    2006-01-01

    Single-flux-quantum (SFQ) circuits can be used for making spiking neuron devices, which are useful elements for constructing intelligent, brain-like computers. The device we propose is based on the leaky integrate-and-fire neuron (IFN) model and uses a SFQ pulse as an action signal or a spike of neurons. The operation of the neuron device is confirmed by computer simulator. It can operate with a short delay of 100 ps or less and is the highest-speed neuron device ever reported

  14. Ezrin interacts with the SARS coronavirus Spike protein and restrains infection at the entry stage.

    Directory of Open Access Journals (Sweden)

    Jean Kaoru Millet

    Full Text Available BACKGROUND: Entry of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV and its envelope fusion with host cell membrane are controlled by a series of complex molecular mechanisms, largely dependent on the viral envelope glycoprotein Spike (S. There are still many unknowns on the implication of cellular factors that regulate the entry process. METHODOLOGY/PRINCIPAL FINDINGS: We performed a yeast two-hybrid screen using as bait the carboxy-terminal endodomain of S, which faces the cytosol during and after opening of the fusion pore at early stages of the virus life cycle. Here we show that the ezrin membrane-actin linker interacts with S endodomain through the F1 lobe of its FERM domain and that both the eight carboxy-terminal amino-acids and a membrane-proximal cysteine cluster of S endodomain are important for this interaction in vitro. Interestingly, we found that ezrin is present at the site of entry of S-pseudotyped lentiviral particles in Vero E6 cells. Targeting ezrin function by small interfering RNA increased S-mediated entry of pseudotyped particles in epithelial cells. Furthermore, deletion of the eight carboxy-terminal amino acids of S enhanced S-pseudotyped particles infection. Expression of the ezrin dominant negative FERM domain enhanced cell susceptibility to infection by SARS-CoV and S-pseudotyped particles and potentiated S-dependent membrane fusion. CONCLUSIONS/SIGNIFICANCE: Ezrin interacts with SARS-CoV S endodomain and limits virus entry and fusion. Our data present a novel mechanism involving a cellular factor in the regulation of S-dependent early events of infection.

  15. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

  16. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

    Science.gov (United States)

    Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

    2013-01-01

    We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

  17. Toxicity of nickel-spiked freshwater sediments to benthic invertebrates-Spiking methodology, species sensitivity, and nickel bioavailability

    Science.gov (United States)

    Besser, John M.; Brumbaugh, William G.; Kemble, Nile E.; Ivey, Chris D.; Kunz, James L.; Ingersoll, Christopher G.; Rudel, David

    2011-01-01

    This report summarizes data from studies of the toxicity and bioavailability of nickel in nickel-spiked freshwater sediments. The goal of these studies was to generate toxicity and chemistry data to support development of broadly applicable sediment quality guidelines for nickel. The studies were conducted as three tasks, which are presented here as three chapters: Task 1, Development of methods for preparation and toxicity testing of nickel-spiked freshwater sediments; Task 2, Sensitivity of benthic invertebrates to toxicity of nickel-spiked freshwater sediments; and Task 3, Effect of sediment characteristics on nickel bioavailability. Appendices with additional methodological details and raw chemistry and toxicity data for the three tasks are available online at http://pubs.usgs.gov/sir/2011/5225/downloads/.

  18. Google Searches for "Cheap Cigarettes" Spike at Tax Increases: Evidence from an Algorithm to Detect Spikes in Time Series Data.

    Science.gov (United States)

    Caputi, Theodore L

    2018-05-03

    Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status.1-8 Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase.9,10 However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes". Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. • Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing power • Contributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.

  19. Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

    Directory of Open Access Journals (Sweden)

    Pietro Quaglio

    2017-05-01

    Full Text Available Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs. STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons. In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST. We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE analysis.

  20. A mechanistic understanding of allosteric immune escape pathways in the HIV-1 envelope glycoprotein.

    Directory of Open Access Journals (Sweden)

    Anurag Sethi

    Full Text Available The HIV-1 envelope (Env spike, which consists of a compact, heterodimeric trimer of the glycoproteins gp120 and gp41, is the target of neutralizing antibodies. However, the high mutation rate of HIV-1 and plasticity of Env facilitates viral evasion from neutralizing antibodies through various mechanisms. Mutations that are distant from the antibody binding site can lead to escape, probably by changing the conformation or dynamics of Env; however, these changes are difficult to identify and define mechanistically. Here we describe a network analysis-based approach to identify potential allosteric immune evasion mechanisms using three known HIV-1 Env gp120 protein structures from two different clades, B and C. First, correlation and principal component analyses of molecular dynamics (MD simulations identified a high degree of long-distance coupled motions that exist between functionally distant regions within the intrinsic dynamics of the gp120 core, supporting the presence of long-distance communication in the protein. Then, by integrating MD simulations with network theory, we identified the optimal and suboptimal communication pathways and modules within the gp120 core. The results unveil both strain-dependent and -independent characteristics of the communication pathways in gp120. We show that within the context of three structurally homologous gp120 cores, the optimal pathway for communication is sequence sensitive, i.e. a suboptimal pathway in one strain becomes the optimal pathway in another strain. Yet the identification of conserved elements within these communication pathways, termed inter-modular hotspots, could present a new opportunity for immunogen design, as this could be an additional mechanism that HIV-1 uses to shield vulnerable antibody targets in Env that induce neutralizing antibody breadth.

  1. Vaccine efficacy in senescent mice challenged with recombinant SARS-CoV bearing epidemic and zoonotic spike variants.

    Directory of Open Access Journals (Sweden)

    Damon Deming

    2006-12-01

    Full Text Available In 2003, severe acute respiratory syndrome coronavirus (SARS-CoV was identified as the etiological agent of severe acute respiratory syndrome, a disease characterized by severe pneumonia that sometimes results in death. SARS-CoV is a zoonotic virus that crossed the species barrier, most likely originating from bats or from other species including civets, raccoon dogs, domestic cats, swine, and rodents. A SARS-CoV vaccine should confer long-term protection, especially in vulnerable senescent populations, against both the 2003 epidemic strains and zoonotic strains that may yet emerge from animal reservoirs. We report the comprehensive investigation of SARS vaccine efficacy in young and senescent mice following homologous and heterologous challenge.Using Venezuelan equine encephalitis virus replicon particles (VRP expressing the 2003 epidemic Urbani SARS-CoV strain spike (S glycoprotein (VRP-S or the nucleocapsid (N protein from the same strain (VRP-N, we demonstrate that VRP-S, but not VRP-N vaccines provide complete short- and long-term protection against homologous strain challenge in young and senescent mice. To test VRP vaccine efficacy against a heterologous SARS-CoV, we used phylogenetic analyses, synthetic biology, and reverse genetics to construct a chimeric virus (icGDO3-S encoding a synthetic S glycoprotein gene of the most genetically divergent human strain, GDO3, which clusters among the zoonotic SARS-CoV. icGD03-S replicated efficiently in human airway epithelial cells and in the lungs of young and senescent mice, and was highly resistant to neutralization with antisera directed against the Urbani strain. Although VRP-S vaccines provided complete short-term protection against heterologous icGD03-S challenge in young mice, only limited protection was seen in vaccinated senescent animals. VRP-N vaccines not only failed to protect from homologous or heterologous challenge, but resulted in enhanced immunopathology with eosinophilic

  2. A memristive spiking neuron with firing rate coding

    Directory of Open Access Journals (Sweden)

    Marina eIgnatov

    2015-10-01

    Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

  3. Cochlear spike synchronization and neuron coincidence detection model

    Science.gov (United States)

    Bader, Rolf

    2018-02-01

    Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

  4. Proficiency test on incurred and spiked pesticide residues in cereals

    DEFF Research Database (Denmark)

    Poulsen, Mette Erecius; Christensen, Hanne Bjerre; Herrmann, Susan Strange

    2009-01-01

    A proficiency test on incurred and spiked pesticide residues in wheat was organised in 2008. The test material was grown in 2007 and treated in the field with 14 pesticides formulations containing the active substances, alpha-cypermethrin, bifentrin, carbendazim, chlormequat, chlorpyrifos...

  5. Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

    Directory of Open Access Journals (Sweden)

    Emre eNeftci

    2014-01-01

    Full Text Available Restricted Boltzmann Machines (RBMs and Deep Belief Networks have been demonstrated to perform efficiently in variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The reverberating activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP carries out the weight updates in an online, asynchronous fashion.We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

  6. Spike sorting based upon machine learning algorithms (SOMA).

    Science.gov (United States)

    Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

    2007-02-15

    We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

  7. Spike-timing-based computation in sound localization.

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2010-11-01

    Full Text Available Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal.

  8. Thermal spike analysis of highly charged ion tracks

    International Nuclear Information System (INIS)

    Karlušić, M.; Jakšić, M.

    2012-01-01

    The irradiation of material using swift heavy ion or highly charged ion causes excitation of the electron subsystem at nanometer scale along the ion trajectory. According to the thermal spike model, energy deposited into the electron subsystem leads to temperature increase due to electron–phonon coupling. If ion-induced excitation is sufficiently intensive, then melting of the material can occur, and permanent damage (i.e., ion track) can be formed upon rapid cooling. We present an extension of the analytical thermal spike model of Szenes for the analysis of surface ion track produced after the impact of highly charged ion. By applying the model to existing experimental data, more than 60% of the potential energy of the highly charged ion was shown to be retained in the material during the impact and transformed into the energy of the thermal spike. This value is much higher than 20–40% of the transferred energy into the thermal spike by swift heavy ion. Thresholds for formation of highly charged ion track in different materials show uniform behavior depending only on few material parameters.

  9. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

    Andersen, Michael Riis; Winther, Ole; Hansen, Lars Kai

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...

  10. Effect of Rolandic Spikes on ADHD Impulsive Behavior

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2007-01-01

    Full Text Available The association of Rolandic spikes with the neuropsychological profile of children with attention deficit hyperactivity disorder (ADHD was studied in a total of 48 patients at JW Goethe-University, Frankfurt/Main; and Central Institute of Mental Health, Mannheim, Germany.

  11. Sleep deprivation and spike-wave discharges in epileptic rats

    NARCIS (Netherlands)

    Drinkenburg, W.H.I.M.; Coenen, A.M.L.; Vossen, J.M.H.; Luijtelaar, E.L.J.M. van

    1995-01-01

    The effects of sleep deprivation were studied on the occurrence of spike-wave discharges in the electroencephalogram of rats of the epileptic WAG/Rij strain, a model for absence epilepsy. This was done before, during and after a period of 12 hours of near total sleep deprivation. A substantial

  12. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

    Science.gov (United States)

    Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

    2017-03-01

    Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

  13. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  14. Fast computation with spikes in a recurrent neural network

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.; Seung, H. Sebastian

    2002-01-01

    Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner

  15. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  16. Dynamics of directional coupling underlying spike-wave discharges

    NARCIS (Netherlands)

    Sysoeva, M.V.; Luttjohann, A.K.; Luijtelaar, E.L.J.M. van; Sysoev, I.V.

    2016-01-01

    Purpose: Spike and wave discharges (SWDs), generated within cortico-thalamo-cortical networks, are the electroencephalographic biomarker of absence epilepsy. The current work aims to identify mechanisms of SWD initiation, maintenance and termination by the analyses of dynamics and directionality of

  17. Breathing, spiking and chaos in a laser with injected signal

    Energy Technology Data Exchange (ETDEWEB)

    Lugiato, L A; Narducci, L M

    1983-06-01

    The behavior of a laser driven by an injected cw field detuned from the operating laser frequency is considered. The analysis covers the entire range of incident power levels from zero to the injection locking threshold. In this domain, the output intensity exhibits regular and chaotic oscillations, a period doubling cascade in reverse order, envelope breathing and spiking.

  18. Inhibitory Synaptic Plasticity - Spike timing dependence and putative network function.

    Directory of Open Access Journals (Sweden)

    Tim P Vogels

    2013-07-01

    Full Text Available While the plasticity of excitatory synaptic connections in the brain has been widely studied, the plasticity of inhibitory connections is much less understood. Here, we present recent experimental and theoretical □ndings concerning the rules of spike timing-dependent inhibitory plasticity and their putative network function. This is a summary of a workshop at the COSYNE conference 2012.

  19. Event-driven contrastive divergence for spiking neuromorphic systems.

    Science.gov (United States)

    Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

    2013-01-01

    Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

  20. Spiking Activity of a LIF Neuron in Distributed Delay Framework

    Directory of Open Access Journals (Sweden)

    Saket Kumar Choudhary

    2016-06-01

    Full Text Available Evolution of membrane potential and spiking activity for a single leaky integrate-and-fire (LIF neuron in distributed delay framework (DDF is investigated. DDF provides a mechanism to incorporate memory element in terms of delay (kernel function into a single neuron models. This investigation includes LIF neuron model with two different kinds of delay kernel functions, namely, gamma distributed delay kernel function and hypo-exponential distributed delay kernel function. Evolution of membrane potential for considered models is studied in terms of stationary state probability distribution (SPD. Stationary state probability distribution of membrane potential (SPDV for considered neuron models are found asymptotically similar which is Gaussian distributed. In order to investigate the effect of membrane potential delay, rate code scheme for neuronal information processing is applied. Firing rate and Fano-factor for considered neuron models are calculated and standard LIF model is used for comparative study. It is noticed that distributed delay increases the spiking activity of a neuron. Increase in spiking activity of neuron in DDF is larger for hypo-exponential distributed delay function than gamma distributed delay function. Moreover, in case of hypo-exponential delay function, a LIF neuron generates spikes with Fano-factor less than 1.

  1. Glycoprotein cytoplasmic domain sequences required for rescue of a vesicular stomatitis virus glycoprotein mutant

    International Nuclear Information System (INIS)

    Whitt, M.A.; Chong, L.; Rose, J.K.

    1989-01-01

    The authors have used transient expression of the wild-type vesicular stomatitis virus (VSV) glycoprotein (G protein) from cloned cDNA to rescue a temperature-sensitive G protein mutant of VSV in cells at the nonpermissive temperature. Using cDNAs encoding G proteins with deletions in the normal 29-amino-acid cytoplasmic domain, they determined that the presence of either the membrane-proximal 9 amino acids or the membrane-distal 12 amino acids was sufficient for rescue of the temperature-sensitive mutant. G proteins with cytoplasmic domains derived from other cellular or viral G proteins did not rescue the mutant, nor did G proteins with one or three amino acids of the normal cytoplasmic domain. Rescue correlated directly with the ability of the G proteins to be incorporated into virus particles. This was shown by analysis of radiolabeled particles separated on sucrose gradients as well as by electron microscopy of rescued virus after immunogold labeling. Quantitation of surface expression showed that all of the mutated G proteins were expressed less efficiently on the cell surface than was wild-type G protein. However, they were able to correct for differences in rescue efficiency resulting from differences in the level of surface expression by reducing wild-type G protein expression to levels equivalent to those observed for the mutated G proteins. The results provide evidence that at least a portion of the cytoplasmic domain is required for efficient assembly of the VSV G protein into virions during virus budding

  2. A Novel Method for Detection of Glycoproteins on Sodium Dodecyl Sulphate Polyacrylamide Gel Using Radio-Iodinated Tyrosine

    DEFF Research Database (Denmark)

    Nalla, Amarnadh; Draz, Hossam M.; Dole, Anita

    2009-01-01

    The aim of this study is to develop a novel method for detection of glycoproteins on polyacrylamide gel. In this method, radio-iodinated-tyrosine (125I-tyrosine) was conjugated to glycoprotein by schiff's base mechanism on the sodium dodecyl sulfate- polyacrylamide gel. Ovalbumin and Concanavalin...... of glycoproteins using 125I-tyrosine selectively detected ovalbumin. Present results showed that MPD enhanced glycoprotein detection method can be used as a sensitive tool for the detection of glycoproteins on polyacrylamide gel...

  3. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    Science.gov (United States)

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

  4. Determinants of foamy virus envelope glycoprotein mediated resistance to superinfection

    International Nuclear Information System (INIS)

    Berg, Angelika; Pietschmann, Thomas; Rethwilm, Axel; Lindemann, Dirk

    2003-01-01

    Little is known about the nature of foamy virus (FV) receptor molecules on target cells and their interaction with the viral glycoproteins. Similar to other viruses, cellular expression of the FV Env protein is sufficient to induce resistance to exogenous FV, a phenomenon called superinfection resistance (SIR). In this study we define determinants of the FV Env protein essential for mediating SIR. FV Env requires the extracellular domains of the SU and the TM subunits as well as membrane anchorage, efficient cell surface transport, and most probably correct subunit processing. This is in contrast to murine leukemia virus where secreted proteins comprising the receptor-binding domain in SU are sufficient to induce SIR. Furthermore, we demonstrate that cellular expression of the prototype FV envelope proteins induces SIR against pseudotypes with glycoproteins of other FV species, including of simian, feline, bovine, and equine origin. This implies that all of them use the same receptor molecules for viral entry

  5. TROPHOBLASTIC β1 – GLYCOPROTEIN SYNTHESIS IN SEROPOSITIVE PREGNANT WOMEN

    Directory of Open Access Journals (Sweden)

    R. N. Bogdanovich

    2005-01-01

    Full Text Available Abstract. The level of trophoblastic β1 – glycoprotein (SP–1 was determined in the blood sera of 200 healthy pregnant women and 184 women with threatened abortions in term till 20 weeks of pregnancy. In group of women experiencing recurrent abortions in 38 % cases antibodies to chorionic gonadotropin, in 39,5 % cases antibodies to phospholipids, in 25,5 % – antibodies to tireoglobulin were revealed in significant amounts. In 20,65 % lupus anticoagulant was found. The majority of women in this group had changes in homeostasis. The presence of autoantibodies during pregnancy is the unfavourable factor in the development of placental insufficiency. This is proved by the decreased secretion of trophoblastic β1 – glycoprotein – a marker of the fetal part of placenta. (Med. Immunol., 2005, vol.7, № 1, pp. 85588

  6. Comparison of glycoprotein expression between ovarian and colon adenocarcinomas

    DEFF Research Database (Denmark)

    Multhaupt, H A; Arenas-Elliott, C P; Warhol, M J

    1999-01-01

    , carcinoembryonic antigen, and cytokeratins 7 and 20 to detect tumor-associated glycoproteins and keratin proteins in ovarian and colonic carcinomas. RESULTS: CA125, carcinoembryonic antigen, and cytokeratins 7 and 20 can distinguish between colonic and serous or endometrioid adenocarcinomas of the ovary in both...... primary and metastatic lesions. Mucinous ovarian adenocarcinomas differed in that they express carcinoembryonic antigen and cytokeratins 7 and 20 and weakly express CA125. The other glycoprotein antigens were equally expressed by ovarian and colonic adenocarcinomas and therefore were of no use...... in distinguishing between these 2 entities. CONCLUSION: A panel of monoclonal antibodies against cytokeratins 7 and 20 antigens, CA125, and carcinoembryonic antigen is useful in differentiating serous and endometrioid adenocarcinomas of the ovary from colonic adenocarcinomas. Mucinous ovarian adenocarcinomas cannot...

  7. Antigiardial activity of glycoproteins and glycopeptides from Ziziphus honey.

    Science.gov (United States)

    Mohammed, Seif Eldin A; Kabashi, Ahmed S; Koko, Waleed S; Azim, M Kamran

    2015-01-01

    Natural honey contains an array of glycoproteins, proteoglycans and glycopeptides. Size-exclusion chromatography fractionated Ziziphus honey proteins into five peaks with molecular masses in the range from 10 to >200 kDa. The fractionated proteins exhibited in vitro activities against Giardia lamblia with IC50 values ≤ 25 μg/mL. Results indicated that honey proteins were more active as antiprotozoal agents than metronidazole. This study indicated the potential of honey proteins and peptides as novel antigiardial agents.

  8. A Simplified Model of Glycoprotein Production within Cell Culture

    OpenAIRE

    Lambert, A. B.; Smith, F. T.; Velayudhan, A.

    2017-01-01

    Complex biological products, such as those used to treat various forms of cancer, are typically produced by mammalian cells in bioreactors. The most important class of such biological medicines is proteins. These typically bind to sugars (glycans) in a process known as glycosylation, creating glycoproteins, which are more stable and effective medicines. The glycans are large polymers that are formed by a long sequence of enzyme catalysed reactions. This sequence is not always completed, thus ...

  9. Tumor specific glycoproteins and method for detecting tumorigenic cancers

    International Nuclear Information System (INIS)

    Davidson, E.A.; Bolmer, S.D.

    1981-01-01

    The detection of tumour specific glycoproteins (TSGP) in human sera often indicates the presence of a malignant tumour in a patient. The distinguishing characteristics of TSGP isolated from the blood sera of cancer patients are described in detail together with methods of TSGP isolation and purification. Details are also given of radioimmunoassay techniques capable of detecting very low levels of serum TSGP with high specificity. (U.K.)

  10. Mucus glycoprotein secretion by tracheal explants: effects of pollutants

    International Nuclear Information System (INIS)

    Last, J.A.; Kaizu, T.

    1980-01-01

    Tracheal slices incubated with radioactive precursors in tissue culture medium secrete labeled mucus glycoproteins into the culture medium. We have used an in vivtro approach, a combined method utilizing exposure to pneumotoxins in vivo coupled with quantitation of mucus secretion rates in vitro, to study the effects of inhaled pollutants on mucus biosynthesis by rat airways. In addition, we have purified the mucus glycoproteins secreted by rat tracheal explants in order to determine putative structural changes that might by the basis for the observed augmented secretion rates after exposure of rats to H2SO4 aerosols in combination with high ambient levels of ozone. After digestion with papain, mucus glycoproteins secreted by tracheal explants may be separated into five fractions by ion-exchange chromatography, with recovery in high yield, on columns of DEAE-cellulose. Each of these five fractions, one neutral and four acidic, migrates as a single unique spot upon cellulose acetate electrophoresis at pH values of 8.6 and 1.2. The neutral fraction, which is labeled with [3H] glucosamine, does not contain radioactivity when Na2 35SO4 is used as the precursor. Acidic fractions I to IV are all labeled with either 3H-glucosamine or Na2 35SO4 as precursor. Acidic fraction II contains sialic acid as the terminal sugar on its oligosaccharide side chains, based upon its chromatographic behavior on columns of wheat-germ agglutinin-Agarose. Treatment of this fraction with neuraminidase shifts its elution position in the gradient to a lower salt concentration, coincident with acidic fraction I. After removal of terminal sialic acid residues with either neuraminidase or low pH treatment, the resultant terminal sugar on the oligosaccharide side chains is fucose. These results are identical with those observed with mucus glycoproteins secreted by cultured human tracheal explants and purified by these same techniques

  11. Prediction of conserved sites and domains in glycoproteins B, C and D of herpes viruses.

    Science.gov (United States)

    Rasheed, Muhammad Asif; Ansari, Abdur Rahman; Ihsan, Awais; Navid, Muhammad Tariq; Ur-Rehman, Shahid; Raza, Sohail

    2018-03-01

    Glycoprotein B (gB), C (gC) and D (gD) of herpes simplex virus are implicated in virus adsorption and penetration. The gB, gC and gD are glycoproteins for different processes of virus binding and attachment to the host cells. Moreover, their expression is necessary and sufficient to induce cell fusion in the absence of other glycoproteins. Egress of herpes simplex virus (HSV) and other herpes viruses from cells involves extensive modification of cellular membranes and sequential envelopment, de-envelopment and re-envelopment steps. Viral glycoproteins are important in these processes, and frequently two or more glycoproteins can largely suffice in any step. Hence, we target the 3 important glycoproteins (B, C and D) of eight different herpes viruses of different species. These species include human (HSV1 and 2), bovine (BHV1), equine (EHV1 and 4), chicken (ILT1 and MDV2) and pig (PRV1). By applying different bioinformatics tools, we highlighted the conserved sites in these glycoproteins which might be most significant regarding attachment and infection of the viruses. Moreover the conserved domains in these glycoproteins are also highlighted. From this study, we will able to analyze the role of different viral glycoproteins of different species during herpes virus adsorption and penetration. Moreover, this study will help to construct the antivirals that target the glycoproteins of different herpes viruses. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. Extracellular Glycoproteins in Embryogenic Culture of Pumpkin (Cucurbita pepo L.

    Directory of Open Access Journals (Sweden)

    Hana Čipčić Paljetak

    2011-01-01

    Full Text Available The extracellular proteins in three distinctly induced embryogenic lines of pumpkin (Cucurbita pepo L. cultivated in four MS media modified regarding the nitrogen composition or auxin presence/absence have been analyzed. Extracellular glycoproteins containing α-D-mannose were specifically detected by the lectine concavalin A. During the cultivation of embryogenic tissue in the medium supplemented with reduced nitrogen, the embryos were mostly arrested at preglobular and globular developmental stages, which coincide with the absence of protein secretion. Secreted glycoproteins of 76, 68, 37 and 34 kDa were detected only if any of the three lines were cultivated in the medium that stimulates embryo development, irrespectively of the addition of 2,4-dichlorophenoxyacetic acid or tunicamycin. The glycoprotein of 64 kDa was detected in all lines cultivated in hormone-free MS medium with conventional nitrogen sources and it appears to be associated with embryo maturation. Tunicamycin treatment did not influence embryogenesis, although it specifically affected glycosylation of proteins in the investigated lines. Our results show that besides auxin, the source of nitrate is of great importance for proper protein glycosylation, excretion and developmental transition of pumpkin somatic embryos.

  13. Requirements within the Ebola Viral Glycoprotein for Tetherin Antagonism

    Directory of Open Access Journals (Sweden)

    Nathan H. Vande Burgt

    2015-10-01

    Full Text Available Tetherin is an interferon-induced, intrinsic cellular response factor that blocks release of numerous viruses, including Ebola virus, from infected cells. As with many viruses targeted by host factors, Ebola virus employs a tetherin antagonist, the viral glycoprotein (EboGP, to counteract restriction and promote virus release. Unlike other tetherin antagonists such as HIV-1 Vpu or KSHV K5, the features within EboGP needed to overcome tetherin are not well characterized. Here, we describe sequences within the EboGP ectodomain and membrane spanning domain (msd as necessary to relieve tetherin restriction of viral particle budding. Fusing the EboGP msd to a normally secreted form of the glycoprotein effectively promotes Ebola virus particle release. Cellular protein or lipid anchors could not substitute for the EboGP msd. The requirement for the EboGP msd was not specific for filovirus budding, as similar results were seen with HIV particles. Furthermore trafficking of chimeric proteins to budding sites did not correlate with an ability to counter tetherin. Additionally, we find that a glycoprotein construct, which mimics the cathepsin-activated species by proteolytic removal of the EboGP glycan cap and mucin domains, is unable to counteract tetherin. Combining these results suggests an important role for the EboGP glycan cap and msd in tetherin antagonism.

  14. Glycoprotein fucosylation is increased in seminal plasma of subfertile men

    Directory of Open Access Journals (Sweden)

    Beata Olejnik

    2015-04-01

    Full Text Available Fucose, the monosaccharide frequent in N- and O-glycans, is a part of Lewis-type antigens that are known to mediate direct sperm binding to the zona pellucida. Such interaction was found to be inhibited in vitroby fucose-containing oligo- and polysaccharides, as well as neoglycoproteins. The objective of this study was to screen seminal plasma proteins of infertile/subfertile men for the content and density of fucosylated glycoepitopes, and compare them to samples of fertile normozoospermic subjects. Seminal proteins were separated in polyacrylamide gel electrophoresis and blotted onto nitrocellulose membrane and probed with fucose-specific Aleuria aurantia lectin (AAL. Twelve electrophoretic bands were selected for quantitative densitometric analysis. It was found that the content, and especially the density of fucosylated glycans, were higher in glycoproteins present in seminal plasma of subfertile men. No profound differences in fucosylation density were found among the groups of normozoospermic, oligozoospermic, asthenozoospermic, and oligoasthenozoospermic subfertile men. According to the antibody probing, AAL-reactive bands can be attributed to male reproductive tract glycoproteins, including prostate-specific antigen, prostatic acid phosphatase, glycodelin and chorionic gonadotropin. Fibronectin, α1 -acid glycoprotein, α1 -antitrypsin, immunoglobulin G and antithrombin III may also contribute to this high fucosylation. It is suggested that the abundant fucosylated glycans in the sperm environment could interfere with the sperm surface and disturb the normal course of the fertilization cascade.

  15. The haemagglutination activity of equine herpesvirus type 1 glycoprotein C.

    Science.gov (United States)

    Andoh, Kiyohiko; Hattori, Shiho; Mahmoud, Hassan Y A H; Takasugi, Maaya; Shimoda, Hiroshi; Bannai, Hiroshi; Tsujimura, Koji; Matsumura, Tomio; Kondo, Takashi; Kirisawa, Rikio; Mochizuki, Masami; Maeda, Ken

    2015-01-02

    Equine herpesvirus type 1 (EHV-1) has haemagglutination (HA) activity toward equine red blood cells (RBCs), but the identity of its haemagglutinin is unknown. To identify the haemagglutinin of EHV-1, the major glycoproteins of EHV-1 were expressed in 293T cells, and the cells or cell lysates were mixed with equine RBCs. The results showed that only EHV-1 glycoprotein C (gC)-producing cells adsorbed equine RBCs, and that the lysate of EHV-1 gC-expressing cells agglutinated equine RBCs. EHV-1 lacking gC did not show HA activity. HA activity was inhibited by monoclonal antibodies (MAbs) specific for gC, but not by antibodies directed against other glycoproteins. In addition, HA activity was not inhibited by the addition of heparin. These results indicate that EHV-1 gC can bind equine RBCs irrespective of heparin, in contrast to other herpesvirus gC proteins. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. Australine, a pyrrolizidine alkaloid that inhibits amyloglucosidase and glycoprotein processing

    Energy Technology Data Exchange (ETDEWEB)

    Tropea, J.E.; Molyneux, R.J.; Kaushal, G.P.; Pan, Y.T.; Mitchell, M.; Elbein, A.D. (Univ. of Texas Health Science Center, San Antonio (USA))

    1989-03-07

    Australine is a polyhydroxylated pyrrolizidine alkaloid that was isolated from the seeds of the Australian tree Castanospermum australe and characterized by NMR and X-ray diffraction analysis. Since swainsonine and catanospermine are polyhydroxylated indolizidine alkaloids that inhibit specific glycosidases, the authors tested australine against a variety of exoglycosidases to determine whether it would inhibit any of these enzymes. This alkaloid proved to be a good inhibitor of the {alpha}-glucosidase amyloglucosidase (50% inhibition at 5.8 {mu}M), but it did not inhibit {beta}-glucosidase, {alpha}- or {beta}-mannosidase, or {alpha}- or {beta}-galactosidase. The inhibition of amyloglucosidase was of a competitive nature. Australine also inhibited the glycoprotein processing enzyme glucosidase I, but had only slight activity toward glucosidase II. When incubated with cultured cells, this alkaloid inhibited glycoprotein processing at the glucosidase I step and caused the accumulation of glycoproteins with Glc{sub 3}Man{sub 7-9}(GlcNAc){sub 2}-oligosaccharides.

  17. On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

    Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

  18. Diallel analysis to study the genetic makeup of spike and yield ...

    African Journals Online (AJOL)

    African Journal of Biotechnology ... Five wheat genotypes were crossed in complete diallel fashion for gene ... by pursuing pedigree method while heterosis can be exploited for spike length, grain weight per spike and grain yield per plant.

  19. Nipah virus infection and glycoprotein targeting in endothelial cells

    Directory of Open Access Journals (Sweden)

    Maisner Andrea

    2010-11-01

    Full Text Available Abstract Background The highly pathogenic Nipah virus (NiV causes fatal respiratory and brain infections in animals and humans. The major hallmark of the infection is a systemic endothelial infection, predominantly in the CNS. Infection of brain endothelial cells allows the virus to overcome the blood-brain-barrier (BBB and to subsequently infect the brain parenchyma. However, the mechanisms of NiV replication in endothelial cells are poorly elucidated. We have shown recently that the bipolar or basolateral expression of the NiV surface glycoproteins F and G in polarized epithelial cell layers is involved in lateral virus spread via cell-to-cell fusion and that correct sorting depends on tyrosine-dependent targeting signals in the cytoplasmic tails of the glycoproteins. Since endothelial cells share many characteristics with epithelial cells in terms of polarization and protein sorting, we wanted to elucidate the role of the NiV glycoprotein targeting signals in endothelial cells. Results As observed in vivo, NiV infection of endothelial cells induced syncytia formation. The further finding that infection increased the transendothelial permeability supports the idea of spread of infection via cell-to-cell fusion and endothelial cell damage as a mechanism to overcome the BBB. We then revealed that both glycoproteins are expressed at lateral cell junctions (bipolar, not only in NiV-infected primary endothelial cells but also upon stable expression in immortalized endothelial cells. Interestingly, mutation of tyrosines 525 and 542/543 in the cytoplasmic tail of the F protein led to an apical redistribution of the protein in endothelial cells whereas tyrosine mutations in the G protein had no effect at all. This fully contrasts the previous results in epithelial cells where tyrosine 525 in the F, and tyrosines 28/29 in the G protein were required for correct targeting. Conclusion We conclude that the NiV glycoprotein distribution is responsible for

  20. Measures of spike train synchrony for data with multiple time scales

    NARCIS (Netherlands)

    Satuvuori, Eero; Mulansky, Mario; Bozanic, Nebojsa; Malvestio, Irene; Zeldenrust, Fleur; Lenk, Kerstin; Kreuz, Thomas

    2017-01-01

    Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by

  1. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  2. Isolation and characterization of two antigenic glycoproteins from the pollen of Prosopis juliflora.

    Science.gov (United States)

    Thakur, I S

    1991-03-01

    Two antigenically active glycoprotein fractions were isolated from crude extract of the pollen of Prosopis juliflora using DEAE-cellulose ion exchange chromatography. The glycoproteins gave single band on polyacrylamide gel electrophoresis. The molecular weight of these two glycoprotein was 20,000 and 10,000 as determined by gel filtration on Sephadex G-75. With the help of crossed immunoelectrophoresis and gel diffusion crude extract exhibited twelve and three precipitating antigens suggesting its heterogeneous nature; and the purified glycoprotein fractions however formed single precipitin band on gel diffusion test and immunoelectrophoresis. As tested by ELISA the polyclonal antisera raised in rabbit showed strong binding affinity with glycoprotein of MW 20,000. These result indicates that the two glycoprotein fractions are not antigenically identical.

  3. Spikes matter for phase-locked bursting in inhibitory neurons

    Science.gov (United States)

    Jalil, Sajiya; Belykh, Igor; Shilnikov, Andrey

    2012-03-01

    We show that inhibitory networks composed of two endogenously bursting neurons can robustly display several coexistent phase-locked states in addition to stable antiphase and in-phase bursting. This work complements and enhances our recent result [Jalil, Belykh, and Shilnikov, Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.81.045201 81, 045201(R) (2010)] that fast reciprocal inhibition can synchronize bursting neurons due to spike interactions. We reveal the role of spikes in generating multiple phase-locked states and demonstrate that this multistability is generic by analyzing diverse models of bursting networks with various fast inhibitory synapses; the individual cell models include the reduced leech heart interneuron, the Sherman model for pancreatic beta cells, and the Purkinje neuron model.

  4. Reflex reading epilepsy: effect of linguistic characteristics on spike frequency.

    Science.gov (United States)

    Safi, Dima; Lassonde, Maryse; Nguyen, Dang Khoa; Denault, Carole; Macoir, Joël; Rouleau, Isabelle; Béland, Renée

    2011-04-01

    Reading epilepsy is a rare reflex epilepsy in which seizures are provoked by reading. Several cases have been described in the literature, but the pathophysiological processes vary widely and remain unclear. We describe a 42-year-old male patient with reading epilepsy evaluated using clinical assessments and continuous video/EEG recordings. We administered verbal, nonverbal, and reading tasks to determine factors precipitating seizures. Linguistic characteristics of the words were manipulated. Results indicated that reading-induced seizures were significantly more numerous than those observed during verbal and nonverbal tasks. In reading tasks, spike frequency significantly increased with involvement of the phonological reading route. Spikes were recorded predominantly in left parasagittal regions. Future cerebral imaging studies will enable us to visualize the spatial localization and temporal course of reading-induced seizures and brain activity involved in reading. A better understanding of reading epilepsy is crucial for reading rehabilitation in these patients. Copyright © 2011 Elsevier Inc. All rights reserved.

  5. Negotiating Multicollinearity with Spike-and-Slab Priors.

    Science.gov (United States)

    Ročková, Veronika; George, Edward I

    2014-08-01

    In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout.

  6. Adrenalectomy eliminates the extinction spike in autoshaping with rats.

    Science.gov (United States)

    Thomas, B L; Papini, M R

    2001-03-01

    Experiment 1, using rats, investigated the effect of adrenalectomy (ADX) on the invigoration of lever-contact performance that occurs in the autoshaping situation after a shift from acquisition to extinction (called the extinction spike). Groups of rats with ADX or sham operations were trained under spaced and massed conditions [average intertrial intervals (ITI) of either 15 or 90 s] for 10 sessions and then shifted to extinction. ADX did not affect acquisition training but it eliminated the extinction spike. Plasma corticosterone levels during acquisition were shown in Experiment 2 to be similar in rats trained under spaced or massed conditions. Adrenal participation in the emotional arousal induced by conditions of surprising nonreward (e.g., extinction) is discussed.

  7. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

    Brinch, Ulla C; Ekelund, Flemming; Jacobsen, Carsten S

    2002-01-01

    We examined the harmful side effects on indigenous soil microorganisms of two organic solvents, acetone and dichloromethane, that are normally used for spiking of soil with polycyclic aromatic hydrocarbons for experimental purposes. The solvents were applied in two contamination protocols to either...... higher than in control soil, probably due mainly to release of predation from indigenous protozoa. In order to minimize solvent effects on indigenous soil microorganisms when spiking native soil samples with compounds having a low water solubility, we propose a common protocol in which the contaminant...... tagged with luxAB::Tn5. For both solvents, application to the whole sample resulted in severe side effects on both indigenous protozoa and bacteria. Application of dichloromethane to the whole soil volume immediately reduced the number of protozoa to below the detection limit. In one of the soils...

  8. Voltage spike detection in high field superconducting accelerator magnets

    Energy Technology Data Exchange (ETDEWEB)

    Orris, D.F.; Carcagno, R.; Feher, S.; Makulski, A.; Pischalnikov, Y.M.; /Fermilab

    2004-12-01

    A measurement system for the detection of small magnetic flux changes in superconducting magnets, which are due to either mechanical motion of the conductor or flux jump, has been developed at Fermilab. These flux changes are detected as small amplitude, short duration voltage spikes, which are {approx}15mV in magnitude and lasts for {approx}30 {micro}sec. The detection system combines an analog circuit for the signal conditioning of two coil segments and a fast data acquisition system for digitizing the results, performing threshold detection, and storing the resultant data. The design of the spike detection system along with the modeling results and noise analysis will be presented. Data from tests of high field Nb{sub 3}Sn magnets at currents up to {approx}20KA will also be shown.

  9. Voltage spike detection in high field superconducting accelerator magnets

    International Nuclear Information System (INIS)

    Orris, D.F.; Carcagno, R.; Feher, S.; Makulski, A.; Pischalnikov, Y.M.

    2004-01-01

    A measurement system for the detection of small magnetic flux changes in superconducting magnets, which are due to either mechanical motion of the conductor or flux jump, has been developed at Fermilab. These flux changes are detected as small amplitude, short duration voltage spikes, which are ∼15mV in magnitude and lasts for ∼30(micro)sec. The detection system combines an analog circuit for the signal conditioning of two coil segments and a fast data acquisition system for digitizing the results, performing threshold detection, and storing the resultant data. The design of the spike detection system along with the modeling results and noise analysis will be presented. Data from tests of high field Nb3Sn magnets at currents up to ∼20KA will also be shown

  10. Past, present and future of spike sorting techniques.

    Science.gov (United States)

    Rey, Hernan Gonzalo; Pedreira, Carlos; Quian Quiroga, Rodrigo

    2015-10-01

    Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  11. Note on the coefficient of variations of neuronal spike trains.

    Science.gov (United States)

    Lengler, Johannes; Steger, Angelika

    2017-08-01

    It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

  12. Analysis of voltage spikes in superconducting Nb3Sn magnets

    International Nuclear Information System (INIS)

    Rahimzadeh-Kalaleh, S.; Ambrosio, G.; Chlachidze, G.; Donnelly, C.

    2008-01-01

    Fermi National Accelerator Laboratory has been developing a new generation of superconducting accelerator magnets based on Niobium Tin (Nb 3 Sn). The performance of these magnets is influenced by thermo-magnetic instabilities, known as flux jumps, which can lead to premature trips of the quench detection system due to large voltage transients or quenches at low current. In an effort to better characterize and understand these instabilities, a system for capturing fast voltage transients was developed and used in recent tests of R and D model magnets. A new automated voltage spike analysis program was developed for the analysis of large amount of voltage-spike data. We report results from the analysis of large statistics data samples for short model magnets that were constructed using MJR and RRP strands having different sub-element size and structure. We then assess the implications for quench protection of Nb 3 Sn magnets

  13. EPILEPTIC ENCEPHALOPATHY WITH CONTINUOUS SPIKES-WAVES ACTIVITY DURING SLEEP

    Directory of Open Access Journals (Sweden)

    E. D. Belousova

    2012-01-01

    Full Text Available The author represents the review and discussion of current scientific literature devoted to epileptic encephalopathy with continuous spikes-waves activity during sleep — the special form of partly reversible age-dependent epileptic encephalopathy, characterized by triad of symptoms: continuous prolonged epileptiform (spike-wave activity on EEG in sleep, epileptic seizures and cognitive disorders. The author describes the aspects of classification, pathogenesis and etiology, prevalence, clinical picture and diagnostics of this disorder, including the peculiar anomalies on EEG. The especial attention is given to approaches to the treatment of epileptic encephalopathy with continuous spikeswaves activity during sleep. Efficacy of valproates, corticosteroid hormones and antiepileptic drugs of other groups is considered. The author represents own experience of treatment this disorder with corticosteroids, scheme of therapy and assessment of efficacy.

  14. Supervised learning in spiking neural networks with FORCE training.

    Science.gov (United States)

    Nicola, Wilten; Clopath, Claudia

    2017-12-20

    Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

  15. The Ripple Pond: Enabling Spiking Networks to See

    Directory of Open Access Journals (Sweden)

    Saeed eAfshar

    2013-11-01

    Full Text Available We present the biologically inspired Ripple Pond Network (RPN, a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilising the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable temporal patterns and the use of asynchronous frames for information binding.

  16. The ripple pond: enabling spiking networks to see.

    Science.gov (United States)

    Afshar, Saeed; Cohen, Gregory K; Wang, Runchun M; Van Schaik, André; Tapson, Jonathan; Lehmann, Torsten; Hamilton, Tara J

    2013-01-01

    We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns (TP) suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.

  17. Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware

    Directory of Open Access Journals (Sweden)

    Andreas Stöckel

    2017-08-01

    Full Text Available Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP. Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output.

  18. A Theory of Material Spike Formation in Flow Separation

    Science.gov (United States)

    Serra, Mattia; Haller, George

    2017-11-01

    We develop a frame-invariant theory of material spike formation during flow separation over a no-slip boundary in two-dimensional flows with arbitrary time dependence. This theory identifies both fixed and moving separation, is effective also over short-time intervals, and admits a rigorous instantaneous limit. Our theory is based on topological properties of material lines, combining objectively stretching- and rotation-based kinematic quantities. The separation profile identified here serves as the theoretical backbone for the material spike from its birth to its fully developed shape, and remains hidden to existing approaches. Finally, our theory can be used to rigorously explain the perception of off-wall separation in unsteady flows, and more importantly, provide the conditions under which such a perception is justified. We illustrate our results in several examples including steady, time-periodic and unsteady analytic velocity fields with flat and curved boundaries, and an experimental dataset.

  19. Planning Annuaulised hours when spike in demand exists

    Directory of Open Access Journals (Sweden)

    MR Sureshkumar

    2012-04-01

    Full Text Available Manpower planning using annualised hours is an effective tool where seasonal demand for staff in industry exists. In annualised hours (AH workers are contracted to work for a certain number of hours per year. The workers are associated with relative efficiency for different types of tasks. This paper proposes a Mixed Integer linear Programming (MILP model to solve an annualised working hours planning problem when spike in demand exists. The holiday weeks for the workers are considered as partially individualised. If a worker has been assigned with more than one type of working week in a week, this will be compensated with one or more holiday week. The performance of the model is demonstrated with an example. It can be seen that this type of modelling helps to meet the spikes in demand with less capacity shortage compared with one working week in a week.

  20. Asymptotics of empirical eigenstructure for high dimensional spiked covariance.

    Science.gov (United States)

    Wang, Weichen; Fan, Jianqing

    2017-06-01

    We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies.

  1. Supervised learning with decision margins in pools of spiking neurons.

    Science.gov (United States)

    Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

    2014-10-01

    Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

  2. Three-dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation

    OpenAIRE

    Pan, Li; Aguilar, Hillary Andaluz; Wang, Linna; Iliuk, Anton; Tao, W. Andy

    2016-01-01

    Glycoproteins have vast structural diversity which plays an important role in many biological processes and have great potential as disease biomarkers. Here we report a novel functionalized reverse phase protein array (RPPA), termed polymer-based reverse phase GlycoProtein Array (polyGPA), to specifically capture and profile glycoproteomes, and validate glycoproteins. Nitrocellulose membrane functionalized with globular hydroxyaminodendrimers was used to covalently capture pre-oxidized glycan...

  3. Development of a radioimmunoassay for 'Tamm-Horsfall-like' glycoprotein in serum and cerebrospinal fluid

    International Nuclear Information System (INIS)

    Hartmann, L.; Bringuier, A.-F.; Schuller, E.

    1983-01-01

    Affinity chromatography purification was combined with a radioimmunoassay for 'Tamm-Horsfall-like' glycoprotein. This enabled serum comcentrations to be established and to demonstrate its presence in cerebrospinal fluid for the first time. This assay method used in different circumstances suggests a multifocal synthesis. Nevertheless, urinary Tamm-Horsfall glycoprotein so far must be distinguished from the serum or cerebrospinal fluid Tamm-Horsfall-like glycoprotein. (Auth.)

  4. Archeomagnetic Intensity Spikes: Global or Regional Geomagnetic Field Features?

    Directory of Open Access Journals (Sweden)

    Monika Korte

    2018-03-01

    Full Text Available Variations of the geomagnetic field prior to direct observations are inferred from archeo- and paleomagnetic experiments. Seemingly unusual variations not seen in the present-day and historical field are of particular interest to constrain the full range of core dynamics. Recently, archeomagnetic intensity spikes, characterized by very high field values that appear to be associated with rapid secular variation rates, have been reported from several parts of the world. They were first noted in data from the Levant at around 900 BCE. A recent re-assessment of previous and new Levantine data, involving a rigorous quality assessment, interprets the observations as an extreme local geomagnetic high with at least two intensity spikes between the 11th and 8th centuries BCE. Subsequent reports of similar features from Asia, the Canary Islands and Texas raise the question of whether such features might be common occurrences, or whether they might even be part of a global magnetic field feature. Here we use spherical harmonic modeling to test two hypotheses: firstly, whether the Levantine and other potential spikes might be associated with higher dipole field intensity than shown by existing global field models around 1,000 BCE, and secondly, whether the observations from different parts of the world are compatible with a westward drifting intense flux patch. Our results suggest that the spikes originate from intense flux patches growing and decaying mostly in situ, combined with stronger and more variable dipole moment than shown by previous global field models. Axial dipole variations no more than 60% higher than observed in the present field, probably within the range of normal geodynamo behavior, seem sufficient to explain the observations.

  5. Discriminating Sea Spikes in Incoherent Radar Measurements of Sea Clutter

    Science.gov (United States)

    2008-03-01

    het detecteren echter niet te verwachten dat bet gebruik van sea spikes te onderzoeken. Een van deze modellen zal leiden tot een Auteur (s) dergelijk...report I TNO-DV 2008 A067 6/33 Abbreviations CFAR Constant False-Alarm Rate CST Composite Surface Theory FFT Fast Fourier Transform PDF Probability Density...described by the composite surface theory (CST). This theory describes the sea surface as small Bragg-resonant capillary waves riding on top of

  6. Simulating large-scale spiking neuronal networks with NEST

    OpenAIRE

    Schücker, Jannis; Eppler, Jochen Martin

    2014-01-01

    The Neural Simulation Tool NEST [1, www.nest-simulator.org] is the simulator for spiking neural networkmodels of the HBP that focuses on the dynamics, size and structure of neural systems rather than on theexact morphology of individual neurons. Its simulation kernel is written in C++ and it runs on computinghardware ranging from simple laptops to clusters and supercomputers with thousands of processor cores.The development of NEST is coordinated by the NEST Initiative [www.nest-initiative.or...

  7. Poisson-Like Spiking in Circuits with Probabilistic Synapses

    Science.gov (United States)

    Moreno-Bote, Rubén

    2014-01-01

    Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

  8. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    Stefano eCardanobile

    2011-12-01

    Full Text Available Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations.

  9. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

  10. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Sheng-Ying Lai

    2013-11-01

    Full Text Available This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA and fuzzy C-means (FCM algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA. It is embedded in a System-on-Chip (SOC platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

  11. Efficient computation in networks of spiking neurons: simulations and theory

    International Nuclear Information System (INIS)

    Natschlaeger, T.

    1999-01-01

    One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so called action potentials or spikes. In this thesis we investigate possible mechanisms which can in principle explain how complex computations in spiking neural networks (SNN) can be performed very fast, i.e. within a few 10 milliseconds. Some of these models are based on the assumption that relevant information is encoded by the timing of individual spikes (temporal coding). We will also discuss a model which is based on a population code and still is able to perform fast complex computations. In their natural environment biological neural systems have to process signals with a rich temporal structure. Hence it is an interesting question how neural systems process time series. In this context we explore possible links between biophysical characteristics of single neurons (refractory behavior, connectivity, time course of postsynaptic potentials) and synapses (unreliability, dynamics) on the one hand and possible computations on times series on the other hand. Furthermore we describe a general model of computation that exploits dynamic synapses. This model provides a general framework for understanding how neural systems process time-varying signals. (author)

  12. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Science.gov (United States)

    Hwang, Wen-Jyi; Lee, Wei-Hao; Lin, Shiow-Jyu; Lai, Sheng-Ying

    2013-01-01

    This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation. PMID:24189331

  13. Spike: Artificial intelligence scheduling for Hubble space telescope

    Science.gov (United States)

    Johnston, Mark; Miller, Glenn; Sponsler, Jeff; Vick, Shon; Jackson, Robert

    1990-01-01

    Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

  14. Linking structure and activity in nonlinear spiking networks.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2017-06-01

    Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

  15. A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

    Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

  16. How adaptation shapes spike rate oscillations in recurrent neuronal networks

    Directory of Open Access Journals (Sweden)

    Moritz eAugustin

    2013-02-01

    Full Text Available Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 Hz to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks.

  17. Enhanced polychronisation in a spiking network with metaplasticity

    Directory of Open Access Journals (Sweden)

    Mira eGuise

    2015-02-01

    Full Text Available Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002. In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004; Izhikevich, 2006a. Polychronous groups (PNGs develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP, but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.

  18. Linking structure and activity in nonlinear spiking networks.

    Science.gov (United States)

    Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

    2017-06-01

    Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

  19. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

    1 Some basic neurophysiology 4 The neuron 1. 1 4 1. 1. 1 The axon 7 1. 1. 2 The synapse 9 12 1. 1. 3 The soma 1. 1. 4 The dendrites 13 13 1. 2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2. 2 Spontaneous activi~ in neurons 3 Stochastic modelling of single neuron spike trains 19 3. 1 Characteristics of a neuron spike train 19 3. 2 The mathematical neuron 23 4 Superposition models 26 4. 1 superposition of renewal processes 26 4. 2 Superposition of stationary point processe- limiting behaviour 34 4. 2. 1 Palm functions 35 4. 2. 2 Asymptotic behaviour of n stationary point processes superposed 36 4. 3 Superposition models of neuron spike trains 37 4. 3. 1 Model 4. 1 39 4. 3. 2 Model 4. 2 - A superposition model with 40 two input channels 40 4. 3. 3 Model 4. 3 4. 4 Discussion 41 43 5 Deletion models 5. 1 Deletion models with 1nd~endent interaction of excitatory and inhibitory sequences 44 VI 5. 1. 1 Model 5. 1 The basic de...

  20. The Use Of Spikes Protocol In Cancer: An Integrative Review

    Directory of Open Access Journals (Sweden)

    Fernando Henrique de Sousa

    2017-03-01

    Full Text Available This is an integrative review which aimed to evaluate the use of the SPIKES protocol in Oncology. We selected articles published in Medline and CINAHL databases between 2005-2015, in English, with the descriptors defined by the Medical Subject Headings (MeSH:cancer, neoplasms, plus the uncontrolled descriptor: protocol spikes.  Six articles met the inclusion criteria and were analyzed in full, three thematic categories were established: aspects inherent to the health care professional; Aspects related to the patient and aspects related to the protocol. The main effects of the steps of SPIKES protocol can provide the strengthening of ties between health professionals and patients, and ensure the maintenance and quality of this relationship.  The results indicate an important limiting factor for effective doctor-patient relationship, the little training provided to medical professionals communication of bad news, verified by the difficulty reported in this moment through interviews in the analyzed studies.

  1. Economic impact on the Florida economy of energy price spikes

    International Nuclear Information System (INIS)

    Mory, J.F.

    1992-01-01

    A substantial disturbance in oil supplies is likely to generate a large price upsurge and a downturn in the level of economic activity. Each of these two effects diminishes demand by a certain amount. The specific price surge required to reduce demand to the lower level of supply can be calculated with an oil demand function and with empirical estimations of the association between price spikes and declines in economic activity. The first section presents an energy demand model for Florida, which provides the price and income elasticities needed. The second section includes theoretical explanations and empirical estimations of the relationship between price spikes and recessions. Based on historical evidence, it seems that Florida's and the nation's economic systems are very sensitive to oil price surges. As price spikes appear damaging to the economy, it could be expected that reductions in the price of oil are beneficial to the system. That is likely to be the case in the long run, but no empirical evidence of favorable short-term effects of oil price decreases was found. Several possible explanations and theoretical reasons are offered to explain this lack of association. The final section presents estimates of the effect of oil disruptions upon specific industries in Florida and the nation

  2. Spike latency and response properties of an excitable micropillar laser

    Science.gov (United States)

    Selmi, F.; Braive, R.; Beaudoin, G.; Sagnes, I.; Kuszelewicz, R.; Erneux, T.; Barbay, S.

    2016-10-01

    We present experimental measurements concerning the response of an excitable micropillar laser with saturable absorber to incoherent as well as coherent perturbations. The excitable response is similar to the behavior of spiking neurons but with much faster time scales. It is accompanied by a subnanosecond nonlinear delay that is measured for different bias pump values. This mechanism provides a natural scheme for encoding the strength of an ultrafast stimulus in the response delay of excitable spikes (temporal coding). Moreover, we demonstrate coherent and incoherent perturbations techniques applied to the micropillar with perturbation thresholds in the range of a few femtojoules. Responses to coherent perturbations assess the cascadability of the system. We discuss the physical origin of the responses to single and double perturbations with the help of numerical simulations of the Yamada model and, in particular, unveil possibilities to control the relative refractory period that we recently evidenced in this system. Experimental measurements are compared to both numerical simulations of the Yamada model and analytic expressions obtained in the framework of singular perturbation techniques. This system is thus a good candidate to perform photonic spike processing tasks in the framework of novel neuroinspired computing systems.

  3. Developing baculovirus-insect cell expression systems for humanized recombinant glycoprotein production

    International Nuclear Information System (INIS)

    Jarvis, Donald L.

    2003-01-01

    The baculovirus-insect cell expression system is widely used to produce recombinant glycoproteins for many different biomedical applications. However, due to the fundamental nature of insect glycoprotein processing pathways, this system is typically unable to produce recombinant mammalian glycoproteins with authentic oligosaccharide side chains. This minireview summarizes our current understanding of insect protein glycosylation pathways and our recent efforts to address this problem. These efforts have yielded new insect cell lines and baculoviral vectors that can produce recombinant glycoproteins with humanized oligosaccharide side chains

  4. Correlations decrease with propagation of spiking activity in the mouse barrel cortex

    Directory of Open Access Journals (Sweden)

    Gayathri Nattar Ranganathan

    2011-05-01

    Full Text Available Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex.

  5. Fast convergence of spike sequences to periodic patterns in recurrent networks

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.

    2002-01-01

    The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks

  6. Transient reduction in theta power caused by interictal spikes in human temporal lobe epilepsy.

    Science.gov (United States)

    Manling Ge; Jundan Guo; Yangyang Xing; Zhiguo Feng; Weide Lu; Xinxin Ma; Yuehua Geng; Xin Zhang

    2017-07-01

    The inhibitory impacts of spikes on LFP theta rhythms(4-8Hz) are investigated around sporadic spikes(SSs) based on intracerebral EEG of 4 REM sleep patients with temporal lobe epilepsy(TLE) under the pre-surgical monitoring. Sequential interictal spikes in both genesis area and extended propagation pathway are collected, that, SSs genesis only in anterior hippocampus(aH)(possible propagation pathway in Entorhinal cortex(EC)), only in EC(possible propagation pathway in aH), and in both aH and EC synchronously. Instantaneous theta power was estimated by using Gabor wavelet transform, and theta power level was estimated by averaged over time and frequency before SSs(350ms pre-spike) and after SSs(350ms post-spike). The inhibitory effect around spikes was evaluated by the ratio of theta power level difference between pre-spike and post-spike to pre-spike theta power level. The findings were that theta power level was reduced across SSs, and the effects were more sever in the case of SSs in both aH and EC synchronously than either SSs only in EC or SSs only in aH. It is concluded that interictal spikes impair LFP theta rhythms transiently and directly. The work suggests that the reduction of theta power after the interictal spike might be an evaluation indicator of damage of epilepsy to human cognitive rhythms.

  7. Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

    Science.gov (United States)

    Gong, Yubing; Xu, Bo; Wu, Ya'nan

    2013-09-01

    In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

  8. Contamination spike simulation and measurement in a clean metal vapor laser

    International Nuclear Information System (INIS)

    Lin, C.E.; Yang, C.Y.

    1990-01-01

    This paper describes a new method for the generation of contamination-induced voltage spikes in a clean metal vapor laser. The method facilitates the study of the characteristics of this troublesome phenomenon in laser systems. Analysis of these artificially generated dirt spikes shows that the breakdown time of the laser tube is increased when these spike appear. The concept of a Townsend discharge is used to identify the parameter which changes the breakdown time of the discharges. The residual ionization control method is proposed to generate dirt spikes in a clean laser. Experimental results show that a wide range of dirt spike magnitudes can be obtained by using the proposed method. The method provides easy and accurate control of the magnitude of the dirt spike, and the laser tube does not become polluted. Results based on the measurements can be used in actual laser systems to monitor the appearance of dirt spikes and thus avoid the danger of thyratron failure

  9. Monoclonal antibodies directed to E1 glycoprotein of rubella virus

    International Nuclear Information System (INIS)

    Umino, Y.; Sato, A.; Katow, S.; Matsuno, T.; Sugiura, A.

    1985-01-01

    We have prepared four monoclonal antibodies to rubella virus E1 glycoprotein. Three nonoverlapping antigenic sites were delineated on E1 protein by competitive binding assays. Antibodies binding to one site were characterized by high hemagglutination inhibition (HI) titer but poor neutralizing activity. The addition of antiglobulin conferred neutralizing activity. Antibodies directed to two other antigenic sites had modest hemolysis inhibition but little or no HI and neutralizing activities. The addition of antiglobulin markedly augmented HI activity but had little effect on neutralizing activity. Epitopes defined by three antibodies were conserved among four rubella virus strains examined. (Author)

  10. High-performance liquid chromatography of human glycoprotein hormones.

    Science.gov (United States)

    Chlenov, M A; Kandyba, E I; Nagornaya, L V; Orlova, I L; Volgin, Y V

    1993-02-12

    The chromatographic behavior of the glycoprotein hormones from human pituitary glands and of placental origin [thyroid-stimulating hormone, luteinizing hormone and chorionic gonadotropin (CG)] was studied. It was shown that hydrophobic interaction chromatography on a microparticulate packing and anion-exchange HPLC can be applied for the purification of these hormones. Reversed-phase HPLC on wide-pore C4-bonded silica at neutral pH can be applied for the determination of the above hormones and for the isolation of pure CG and its subunits.

  11. Gamma-radiolysis of some glycoproteins in dilute aqueous solutions

    Energy Technology Data Exchange (ETDEWEB)

    Nagrani, S

    1981-01-01

    A study has been made of the radiation-induced damage of some glycoproteins in dilute aqueous solutions. By use of specific radical scavengers, the roles of the individual free radicals, formed by ..gamma..-radiolysis, in causing damage has been assessed. The most effective radical in causing damage to human and porcine glycopolypeptide is the OH radical. The structure of the different blood group glycopolypeptides determines the sensitivity towards the free radical attack. The glycopolypeptide shows depolymerization and a characteristic absorption at approximately 270 nm due to the formation of additional products on irradiation. Chemical changes of the irradiated glycopolypeptide solutions revealed significant damage to the oligosaccharide chain and the polypeptide core of the glycopolypeptide. The radiation-induced inactivation of another glycoprotein, external yeast invertase, due to different radical species at pH 7.0 decreases in the following order: ea-barq > OH radical > (SCN) radical/sub 2//sup -/ > Br radical/sub 2//sup -/. The structure of this enzyme, accounts for the mechanism of enzyme inactivation and the relative damage of carbohydrate and amino acid residues. The irradiated enzyme solutions show significant changes in their electrophoretic behaviour on cellogel electrophoresis due to the formation of radiolysis products, which also show characteristic absorption maxima at approximately 275 nm. (author).

  12. Characterization of monomeric intermediates during VSV glycoprotein structural transition.

    Directory of Open Access Journals (Sweden)

    Aurélie A Albertini

    2012-02-01

    Full Text Available Entry of enveloped viruses requires fusion of viral and cellular membranes, driven by conformational changes of viral glycoproteins. Crystal structures provide static pictures of pre- and post-fusion conformations of these proteins but the transition pathway remains elusive. Here, using several biophysical techniques, including analytical ultracentrifugation, circular dichroïsm, electron microscopy and small angle X-ray scattering, we have characterized the low-pH-induced fusogenic structural transition of a soluble form of vesicular stomatitis virus (VSV glycoprotein G ectodomain (G(th, aa residues 1-422, the fragment that was previously crystallized. While the post-fusion trimer is the major species detected at low pH, the pre-fusion trimer is not detected in solution. Rather, at high pH, G(th is a flexible monomer that explores a large conformational space. The monomeric population exhibits a marked pH-dependence and adopts more elongated conformations when pH decreases. Furthermore, large relative movements of domains are detected in absence of significant secondary structure modification. Solution studies are complemented by electron micrographs of negatively stained viral particles in which monomeric ectodomains of G are observed at the viral surface at both pH 7.5 and pH 6.7. We propose that the monomers are intermediates during the conformational change and thus that VSV G trimers dissociate at the viral surface during the structural transition.

  13. The Lyssavirus glycoprotein: A key to cross-immunity.

    Science.gov (United States)

    Buthelezi, Sindisiwe G; Dirr, Heini W; Chakauya, Ereck; Chikwamba, Rachel; Martens, Lennart; Tsekoa, Tsepo L; Stoychev, Stoyan H; Vandermarliere, Elien

    2016-11-01

    Rabies is an acute viral encephalomyelitis in warm-blooded vertebrates, caused by viruses belonging to Rhabdovirus family and genus Lyssavirus. Although rabies is categorised as a neglected disease, the rabies virus (RABV) is the most studied amongst Lyssaviruses which show nearly identical infection patterns. In efforts to improving post-exposure prophylaxis, several anti-rabies monoclonal antibodies (mAbs) targeting the glycoprotein (G protein) sites I, II, III and G5 have been characterized. To explore cross-neutralization capacity of available mAbs and discover new possible B-cell epitopes, we have analyzed all available glycoprotein sequences from Lyssaviruses with a focus on sequence variation and conservation. This information was mapped on the structure of a representative G protein. We proposed several possible cross-neutralizing B-cell epitopes (GUVTTTF, WLRTV, REECLD and EHLVVEEL) in complement to the already well-characterized antigenic sites. The research could facilitate development of novel cross-reactive mAbs against RABV and even more broad, against possibly all Lyssavirus members. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. The Lyssavirus glycoprotein: A key to cross-immunity

    International Nuclear Information System (INIS)

    Buthelezi, Sindisiwe G.; Dirr, Heini W.; Chakauya, Ereck; Chikwamba, Rachel; Martens, Lennart; Tsekoa, Tsepo L.; Stoychev, Stoyan H.; Vandermarliere, Elien

    2016-01-01

    Rabies is an acute viral encephalomyelitis in warm-blooded vertebrates, caused by viruses belonging to Rhabdovirus family and genus Lyssavirus. Although rabies is categorised as a neglected disease, the rabies virus (RABV) is the most studied amongst Lyssaviruses which show nearly identical infection patterns. In efforts to improving post-exposure prophylaxis, several anti-rabies monoclonal antibodies (mAbs) targeting the glycoprotein (G protein) sites I, II, III and G5 have been characterized. To explore cross-neutralization capacity of available mAbs and discover new possible B-cell epitopes, we have analyzed all available glycoprotein sequences from Lyssaviruses with a focus on sequence variation and conservation. This information was mapped on the structure of a representative G protein. We proposed several possible cross-neutralizing B-cell epitopes (GUVTTTF, WLRTV, REECLD and EHLVVEEL) in complement to the already well-characterized antigenic sites. The research could facilitate development of novel cross-reactive mAbs against RABV and even more broad, against possibly all Lyssavirus members. -- Highlights: •The current PEP has raised safety and availability concerns. •Cocktails of mAbs have been proposed as alternative treatment. •Amino acid conservation amongst Lyssavirus G proteins was studied. •Possible cross-neutralizing B-cell epitopes were proposed.

  15. The Lyssavirus glycoprotein: A key to cross-immunity

    Energy Technology Data Exchange (ETDEWEB)

    Buthelezi, Sindisiwe G. [Council for Scientific and Industrial Research, Biosciences Unit, Pretoria (South Africa); Protein Structure-Function Research Unit, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg (South Africa); Dirr, Heini W. [Protein Structure-Function Research Unit, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg (South Africa); Chakauya, Ereck; Chikwamba, Rachel [Council for Scientific and Industrial Research, Biosciences Unit, Pretoria (South Africa); Martens, Lennart [Unit for Computational Omics and Systems Biology, Medical Biotechnology Center, VIB, Ghent (Belgium); Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent (Belgium); Tsekoa, Tsepo L. [Council for Scientific and Industrial Research, Biosciences Unit, Pretoria (South Africa); Stoychev, Stoyan H., E-mail: Sstoychev@csir.co.za [Council for Scientific and Industrial Research, Biosciences Unit, Pretoria (South Africa); Vandermarliere, Elien [Unit for Computational Omics and Systems Biology, Medical Biotechnology Center, VIB, Ghent (Belgium); Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent (Belgium); Bioinformatics Institute Gent, Ghent University, Ghent (Belgium)

    2016-11-15

    Rabies is an acute viral encephalomyelitis in warm-blooded vertebrates, caused by viruses belonging to Rhabdovirus family and genus Lyssavirus. Although rabies is categorised as a neglected disease, the rabies virus (RABV) is the most studied amongst Lyssaviruses which show nearly identical infection patterns. In efforts to improving post-exposure prophylaxis, several anti-rabies monoclonal antibodies (mAbs) targeting the glycoprotein (G protein) sites I, II, III and G5 have been characterized. To explore cross-neutralization capacity of available mAbs and discover new possible B-cell epitopes, we have analyzed all available glycoprotein sequences from Lyssaviruses with a focus on sequence variation and conservation. This information was mapped on the structure of a representative G protein. We proposed several possible cross-neutralizing B-cell epitopes (GUVTTTF, WLRTV, REECLD and EHLVVEEL) in complement to the already well-characterized antigenic sites. The research could facilitate development of novel cross-reactive mAbs against RABV and even more broad, against possibly all Lyssavirus members. -- Highlights: •The current PEP has raised safety and availability concerns. •Cocktails of mAbs have been proposed as alternative treatment. •Amino acid conservation amongst Lyssavirus G proteins was studied. •Possible cross-neutralizing B-cell epitopes were proposed.

  16. Internalization and Axonal Transport of the HIV Glycoprotein gp120

    Science.gov (United States)

    Berth, Sarah; Caicedo, Hector Hugo; Sarma, Tulika; Morfini, Gerardo

    2015-01-01

    The HIV glycoprotein gp120, a neurotoxic HIV glycoprotein that is overproduced and shed by HIV-infected macrophages, is associated with neurological complications of HIV such as distal sensory polyneuropathy, but interactions of gp120 in the peripheral nervous system remain to be characterized. Here, we demonstrate internalization of extracellular gp120 in a manner partially independent of binding to its coreceptor CXCR4 by F11 neuroblastoma cells and cultured dorsal root ganglion neurons. Immunocytochemical and pharmacological experiments indicate that gp120 does not undergo trafficking through the endolysosomal pathway. Instead, gp120 is mainly internalized through lipid rafts in a cholesterol-dependent manner, with a minor fraction being internalized by fluid phase pinocytosis. Experiments using compartmentalized microfluidic chambers further indicate that, after internalization, endocytosed gp120 selectively undergoes retrograde but not anterograde axonal transport from axons to neuronal cell bodies. Collectively, these studies illuminate mechanisms of gp120 internalization and axonal transport in peripheral nervous system neurons, providing a novel framework for mechanisms for gp120 neurotoxicity. PMID:25636314

  17. Identification of a mouse synaptic glycoprotein gene in cultured neurons.

    Science.gov (United States)

    Yu, Albert Cheung-Hoi; Sun, Chun Xiao; Li, Qiang; Liu, Hua Dong; Wang, Chen Ran; Zhao, Guo Ping; Jin, Meilei; Lau, Lok Ting; Fung, Yin-Wan Wendy; Liu, Shuang

    2005-10-01

    Neuronal differentiation and aging are known to involve many genes, which may also be differentially expressed during these developmental processes. From primary cultured cerebral cortical neurons, we have previously identified various differentially expressed gene transcripts from cultured cortical neurons using the technique of arbitrarily primed PCR (RAP-PCR). Among these transcripts, clone 0-2 was found to have high homology to rat and human synaptic glycoprotein. By in silico analysis using an EST database and the FACTURA software, the full-length sequence of 0-2 was assembled and the clone was named as mouse synaptic glycoprotein homolog 2 (mSC2). DNA sequencing revealed transcript size of mSC2 being smaller than the human and rat homologs. RT-PCR indicated that mSC2 was expressed differentially at various culture days. The mSC2 gene was located in various tissues with higher expression in brain, lung, and liver. Functions of mSC2 in neurons and other tissues remain elusive and will require more investigation.

  18. Mechanism of feline immunodeficiency virus envelope glycoprotein-mediated fusion

    International Nuclear Information System (INIS)

    Garg, Himanshu; Fuller, Frederick J.; Tompkins, Wayne A.F.

    2004-01-01

    Feline immunodeficiency virus (FIV) shares remarkable homology to primate lentiviruses, human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV). The process of lentiviral env glycoprotein-mediated fusion of membranes is essential for viral entry and syncytia formation. A detailed understanding of this phenomenon has helped identify new targets for antiviral drug development. Using a model based on syncytia formation between FIV env-expressing cells and a feline CD4+ T cell line we have studied the mechanism of FIV env-mediated fusion. Using this model we show that FIV env-mediated fusion mechanism and kinetics are similar to HIV env. Syncytia formation could be blocked by CXCR4 antagonist AMD3100, establishing the importance of this receptor in FIV gp120 binding. Interestingly, CXCR4 alone was not sufficient to allow fusion by a primary isolate of FIV, as env glycoprotein from FIV-NCSU 1 failed to induce syncytia in several feline cell lines expressing CXCR4. Syncytia formation could be inhibited at a post-CXCR4 binding step by synthetic peptide T1971, which inhibits interaction of heptad repeat regions of gp41 and formation of the hairpin structure. Finally, using site-directed mutagenesis, we also show that a conserved tryptophan-rich region in the membrane proximal ectodomain of gp41 is critical for fusion, possibly at steps post hairpin structure formation

  19. Filamentous fungi as production organisms for glycoproteins of bio-medical interest

    NARCIS (Netherlands)

    Maras, M.; Die, I. van; Contreras, R.; Hondel, C.A.M.J.J. van den

    1999-01-01

    Filamentous fungi are commonly used in the fermentation industry for large scale production of glycoproteins. Several of these proteins can be produced in concentrations up to 20-40 g per litre. The production of heterologous glycoproteins is at least one or two orders of magnitude lower but

  20. Histochemical and structural analysis of mucous glycoprotein secreted by the gill of Mytilus edulis

    International Nuclear Information System (INIS)

    Ahn, Hae-Young.

    1988-01-01

    Studies were carried out to characterized various mucous cells in the gill filament, to ascertain structural characteristics of the secreted mucous glycoproteins, and to determine the ability of the gill epithelium to incorporate [ 14 C]glucosamine as a precursor in the biosynthesis and secretion of mucous glycoproteins. Using histochemical staining techniques, mucous cells containing neutral and acidic mucins were found in the lateral region, whereas mucous cells containing primarily neutral or sulfated mucins were found in the postlateral region. Serotonin, but not dopamine, stimulated the mucous secretion. In tissues pretreated with [ 14 C]glucosamine, the secreted glycoproteins contain incorporated radiolabel. Analysis by column chromatography using Bio-Gel P-2 and P-6 shows that the secretion contains two glycoprotein populations. Glycoprotein II has a molecular weight of 2.3 x 10 4 daltons. Upon alkaline reductive borohydride cleavage of the O-glycosidic linkages of glycoprotein I, about 70% of the radiolabel was removed from the protein. Gas chromatographic analysis of the carbohydrate composition shows that the glycoproteins contains N-acetylglucosamine (GluNAc), N-acetylgalactosamine (GalNAc), and galactose, fucose and mannose. Amino acid analysis shows that the glycoproteins are rich in serine, threonine and proline

  1. Systematic Regional Variations in Purkinje Cell Spiking Patterns

    Science.gov (United States)

    Xiao, Jianqiang; Cerminara, Nadia L.; Kotsurovskyy, Yuriy; Aoki, Hanako; Burroughs, Amelia; Wise, Andrew K.; Luo, Yuanjun; Marshall, Sarah P.; Sugihara, Izumi; Apps, Richard; Lang, Eric J.

    2014-01-01

    In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs) in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+) and negative (Z−) bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z− PCs, respectively. In both datasets significant differences in simple spike (SS) activity were observed between cortical regions. Specifically, Z− and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution) was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS) activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions. PMID:25144311

  2. From spiking neuron models to linear-nonlinear models.

    Science.gov (United States)

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  3. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. Spike-timing dependent plasticity in the striatum

    Directory of Open Access Journals (Sweden)

    Elodie Fino

    2010-06-01

    Full Text Available The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs, are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, the NO synthase and cholinergic interneurons also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.

  5. Systematic regional variations in Purkinje cell spiking patterns.

    Directory of Open Access Journals (Sweden)

    Jianqiang Xiao

    Full Text Available In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+ and negative (Z- bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z- PCs, respectively. In both datasets significant differences in simple spike (SS activity were observed between cortical regions. Specifically, Z- and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions.

  6. An online supervised learning method based on gradient descent for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Yang, Jing; Zhong, Shuiming

    2017-09-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Independent component analysis separates spikes of different origin in the EEG.

    Science.gov (United States)

    Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

    2006-02-01

    Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

  8. Routes to Chaos Induced by a Discontinuous Resetting Process in a Hybrid Spiking Neuron Model.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya

    2018-01-10

    Several hybrid spiking neuron models combining continuous spike generation mechanisms and discontinuous resetting processes following spiking have been proposed. The Izhikevich neuron model, for example, can reproduce many spiking patterns. This model clearly possesses various types of bifurcations and routes to chaos under the effect of a state-dependent jump in the resetting process. In this study, we focus further on the relation between chaotic behaviour and the state-dependent jump, approaching the subject by comparing spiking neuron model versions with and without the resetting process. We first adopt a continuous two-dimensional spiking neuron model in which the orbit in the spiking state does not exhibit divergent behaviour. We then insert the resetting process into the model. An evaluation using the Lyapunov exponent with a saltation matrix and a characteristic multiplier of the Poincar'e map reveals that two types of chaotic behaviour (i.e. bursting chaotic spikes and near-period-two chaotic spikes) are induced by the resetting process. In addition, we confirm that this chaotic bursting state is generated from the periodic spiking state because of the slow- and fast-scale dynamics that arise when jumping to the hyperpolarization and depolarization regions, respectively.

  9. A matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.

    Science.gov (United States)

    Balaji Ramachandran, Supriya; Gillis, Kevin D

    2018-01-01

    Electrochemical microelectrodes located immediately adjacent to the cell surface can detect spikes of amperometric current during exocytosis as the transmitter released from a single vesicle is oxidized on the electrode surface. Automated techniques to detect spikes are needed in order to quantify the spike rate as a measure of the rate of exocytosis. We have developed a Matched Filter (MF) detection algorithm that scans the data set with a library of prototype spike templates while performing a least-squares fit to determine the amplitude and standard error. The ratio of the fit amplitude to the standard error constitutes a criterion score that is assigned for each time point and for each template. A spike is detected when the criterion score exceeds a threshold and the highest-scoring template and the time of peak score is identified. The search for the next spike commences only after the score falls below a second, lower threshold to reduce false positives. The approach was extended to detect spikes with double-exponential decays with the sum of two templates. Receiver Operating Characteristic plots (ROCs) demonstrate that the algorithm detects >95% of manually identified spikes with a false-positive rate of ∼2%. ROCs demonstrate that the MF algorithm performs better than algorithms that detect spikes based on a derivative-threshold approach. The MF approach performs well and leads into approaches to identify spike parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Stimulus Sensitivity of a Spiking Neural Network Model

    Science.gov (United States)

    Chevallier, Julien

    2018-02-01

    Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.

  11. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

    DEFF Research Database (Denmark)

    Tully, Philip J; Lindén, Henrik; Hennig, Matthias H

    2016-01-01

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...

  12. Impact of substance P on the correlation of spike train evoked by electro acupuncture

    International Nuclear Information System (INIS)

    Jin, Chen; Zhang, Xuan; Wang, Jiang; Guo, Yi; Zhao, Xue; Guo, Yong-Ming

    2016-01-01

    Highlights: • We analyze spike trains induced by EA before and after inhibiting SP in PC6 area. • Inhibiting SP leads to an increase of spiking rate of median nerve. • SP may modulate membrane potential to affect the spiking rate. • SP has an influence on long-range correlation of spike train evoked by EA. • SP play an important role in EA-induced neural spiking and encoding. - Abstract: Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.

  13. Purification and characterization of a soluble glycoprotein from garlic (Allium sativum) and its in vitro bioactivity.

    Science.gov (United States)

    Wang, Yan; Zou, Tingting; Xiang, Minghui; Jin, Chenzhong; Zhang, Xuejiao; Chen, Yong; Jiang, Qiuqing; Hu, Yihong

    2016-10-02

    A soluble glycoprotein was purified to homogeneity from ripe garlic (Allium sativum) bulbs using ammonium sulfate precipitation, Sephadex G-100 gel filtration, and diethylaminoethyl-52 cellulose anion-exchange chromatography. A native mass of 55.7 kDa estimated on gel permeation chromatography and a molecular weight of 13.2 kDa observed on sodium dodecyl sulfate-polyacrylamide gel electrophoresis supported that the glycoprotein is a homotetramer. β-Elimination reaction result suggested that the glycoprotein is an N-linked type. Fourier-transform infrared spectroscopy proved that it contains sugar. Gas chromatography-mass spectrometer analysis showed that its sugar component was galactose. The glycoprotein has 1,1-diphenyl-2-picrylhydrazil free radical scavenging activity and the peroxidation inhibition ability to polyunsaturated fatty acid. These results indicated that the glycoprotein has potential for food additives, functional foods, and even biotechnological and medical applications.

  14. Improved radioimmunoassay for urinary Tamm-Horsfall glycoprotein. Investigation and resolution of factors affecting its quantification

    Energy Technology Data Exchange (ETDEWEB)

    Dawney, A B.St.J.; Thornley, C; Cattell, W R [Saint Bartholomew' s Hospital, London (UK)

    1982-09-15

    A rapid specific radioimmunoassay has been used to measure Tamm-Horsfall glycoprotein (TH glycoprotein) in urine, and the method described. The apparent concentration increased with increasing dilution of urine in water, reaching a plateau at 1 in 20. This increase was greater the higher the osmolality and TH glycoprotein concentration and the lower the pH of the original sample. The apparent concentration of TH glycoprotein in neat or diluted urine was not affected by freezing or by storage at 4/sup 0/C or room temperature for at least 2 days. A physiological range for the urinary excretion rate was established as 22-56 mg/24h, (considerably higher than the amount present in serum) based on samples from 29 individuals with normal renal function, as defined by their creatinine clearance. There was no significant correlation between serum concentrations of TH glycoprotein and its urinary excretion rate, nor between urinary excretion rate and creatinine clearance.

  15. Glycoproteins of mouse vaginal epithelium: differential expression related to estrous cyclicity

    DEFF Research Database (Denmark)

    Horvat, B; Multhaupt, H A; Damjanov, I

    1993-01-01

    We used lectin overlay blotting and SDS-PAGE to analyze the estrous cycle-specific expression of mouse vaginal epithelial glycoproteins. Seven lectins chosen for their differential carbohydrate-binding specificity revealed 15 glycoproteins that showed cycle-related expression. Each lectin had...... in proestrus, coincident with the transformation of two superficial layers of vaginal squamous epithelium into mucinous cuboidal cells. Electron microscopic lectin histochemistry revealed the glycoproteins in the mucinous granules of surface cuboidal cells and in the lumen of the vagina. Our results illustrate...... the complexity of glycoconjugate synthesis in mouse vagina and reveal the distinct cycle-specific patterns of individual glycoprotein expression. These cyclic glycoproteins could serve as vaginal biochemical markers for the specific phases of the estrous cycle....

  16. Purification and characterization of the glycoprotein allergen from Prosopis juliflora pollen.

    Science.gov (United States)

    Thakur, I S

    1991-02-01

    Highly active glycoprotein allergens have been isolated from pollen of Prosopis juliflora by a combination of Sephadex G-100 gel filtration and Sodium dodecyl sulphate-Poly-acrylamide gel electrophoresis. The glycoprotein fraction was homogeneous, and had molecular weight 20,000. The purified glycoprotein allergen contained 20% carbohydrate, mainly arabinose and galactose. Enzymatic digestion of glycoprotein with protease released glycopeptides of molecular weight ranging from less than 1,000 to more than 5,000 on Sephadex G-25 gel filtration. Antigenicity or allergenicity testing of these glycopeptides by immunodiffusion, immunoelectrophoresis, and radioallergosorbent test indicated complete loss of allergenic activity after digestion with protease whereas incubation with beta-D-galactosidase and periodate oxidation had little affect on the allergenic activity of the glycoprotein fraction. But incubation with alpha-D-glucosidase did not affect the allergenic activity significantly. All these tests indicated that protein played significant role in allergenicity of P. juliflora pollen.

  17. Adipokine zinc-α2-glycoprotein regulated by growth hormone and linked to insulin sensitivity.

    Science.gov (United States)

    Balaz, Miroslav; Ukropcova, Barbara; Kurdiova, Timea; Gajdosechova, Lucia; Vlcek, Miroslav; Janakova, Zuzana; Fedeles, Jozef; Pura, Mikulas; Gasperikova, Daniela; Smith, Steven R; Tkacova, Ruzena; Klimes, Iwar; Payer, Juraj; Wolfrum, Christian; Ukropec, Jozef

    2015-02-01

    Hypertrophic obesity is associated with impaired insulin sensitivity and lipid-mobilizing activity of zinc-α2-glycoprotein. Adipose tissue (AT) of growth hormone (GH) -deficient patients is characterized by extreme adipocyte hypertrophy due to defects in AT lipid metabolism. It was hypothesized that zinc-α2-glycoprotein is regulated by GH and mediates some of its beneficial effects in AT. AT from patients with GH deficiency and individuals with obesity-related GH deficit was obtained before and after 5-year and 24-month GH supplementation therapy. GH action was tested in primary human adipocytes. Relationships of GH and zinc-α2-glycoprotein with adipocyte size and insulin sensitivity were evaluated in nondiabetic patients with noncancerous cachexia and hypertrophic obesity. AT in GH-deficient adults displayed a substantial reduction of zinc-α2-glycoprotein. GH therapy normalized AT zinc-α2-glycoprotein. Obesity-related relative GH deficit was associated with almost 80% reduction of zinc-α2-glycoprotein mRNA in AT. GH increased zinc-α2-glycoprotein mRNA in both AT of obese men and primary human adipocytes. Interdependence of GH and zinc-α2-glycoprotein in regulating AT morphology and metabolic phenotype was evident from their relationship with adipocyte size and AT-specific and whole-body insulin sensitivity. The results demonstrate that GH is involved in regulation of AT zinc-α2-glycoprotein; however, the molecular mechanism linking GH and zinc-α2-glycoprotein in AT is yet unknown. © 2014 The Obesity Society.

  18. Determination of P-Glycoprotein Expression by Flow Cytometry in Hematological Malignancies

    Directory of Open Access Journals (Sweden)

    Berkay Saraymen

    2016-03-01

    Full Text Available Objective: Determination the expression of P-glycoprotein is especially problematic for normal tissues because immuno­logical methods are limited in terms of sensitivity. We aimed to determine the expression of P-glycoprotein and CD34 by flow cytometry, and to evaluate the level of expression of P-glycoprotein and CD34 with unresponsive to treatment in pa­tients diagnosed with hematologic malignancy. Methods: Our study included fifty patients diagnosed with acute myeloblastic leukemia and acute lymphoblastic leuke­mia, and twenty healthy controls who were admitted to Erci­yes University Hematology-Oncology Hospital. The suspend­ed cells from bone marrow samples of patients and the pe­ripheral blood samples of healthy people were marked with P-glycoprotein phycoerythrin and CD34 FITC or PerCP Cy 5.5; and then surface expression was measured by means of flow cytometry. Results: In 6 of 30 acute myeloblastic leukemia patients P-glycoprotein and CD34 expression, in 6 of 20 acute lympho­blastic leukemia patients P-glycoprotein, in 5 of them CD34 expression were determined. A significant relation between P-glycoprotein and CD34 expressions in acute myeloblas­tic leukemia and acute lymphoblastic leukemia bone marrow samples was reported. Conclusion: Our data indicate that flow cytometry is more reliable, precise and faster than molecular methods for mea­suring P-glycoprotein expression and suggests the pos­sibility of a significant relationship between P-glycoprotein and CD34 expressions in acute myeloblastic leukemia and acute lymphoblastic leukemia bone marrow samples. The blast cells expressing CD34 on their surface along with P-glycoprotein simultaneously show that multi drug resistance 1 gene is mostly active in immature cells.

  19. Human platelet glycoprotein IX: An adhesive prototype of leucine-rich glycoproteins with flank-center-flank structures

    International Nuclear Information System (INIS)

    Hickey, M.J.; Williams, S.A.; Roth, G.J.

    1989-01-01

    The glycoprotein (GP) Ib-IX complex on the surface of human platelets functions as the von Willebrand factor receptor and mediates von Willebrand factor-dependent platelet adhesion to blood vessels. GPIX is a relatively small (M r , 17,000) protein that may provide for membrane insertion and orientation of the larger component of the complex. GPIb (M r , 165,000). Using antibody screening, the authors cloned a cDNA encoding GPIX from a human erythroleukemia cell cDNA library constructed in phage λgt11. Lacking a 5' untranslated region and start codon, the cDNA sequence includes 604 nucleotides, beginning with 495 bases at the 5' end coding for 165 amino acids, followed by a stop codon and 106 noncoding bases at the 3' end. By Northern blot analysis, the GPIX cDNA hybridizes with a single 1.0-kilobase species of platelet poly(A) + RNA. Translation of the cDNA sequence gives a predicted protein sequence beginning with a truncated putative signal sequence of 5 amino acids followed by a sequence of 17 amino acids matching that determined directly by Edman degradation of intact GPIX. GPIX contains a leucine-rich glycoprotein (LRG) sequence of 24 amino acids similar to conserved LRG sequences in GPIb and other proteins from humans, Drosophila, and yeast. The role of the flank-LRG center-flank structure in the evolution and function of the LRG proteins remains to be defined

  20. Effect of heavy metals on pH buffering capacity and solubility of Ca, Mg, K, and P in non-spiked and heavy metal-spiked soils.

    Science.gov (United States)

    Najafi, Sarvenaz; Jalali, Mohsen

    2016-06-01

    In many parts of the world, soil acidification and heavy metal contamination has become a serious concern due to the adverse effects on chemical properties of soil and crop yield. The aim of this study was to investigate the effect of pH (in the range of 1 to 3 units above and below the native pH of soils) on calcium (Ca), magnesium (Mg), potassium (K), and phosphorus (P) solubility in non-spiked and heavy metal-spiked soil samples. Spiked samples were prepared by cadmium (Cd), copper (Cu), nickel (Ni), and zinc (Zn) as chloride salts and incubating soils for 40 days. The pH buffering capacity (pHBC) of each sample was determined by plotting the amount of H(+) or OH(-) added (mmol kg(-1)) versus the related pH value. The pHBC of soils ranged from 47.1 to 1302.5 mmol kg(-1) for non-spiked samples and from 45.0 to 1187.4 mmol kg(-1) for spiked soil samples. The pHBC values were higher in soil 2 (non-spiked and spiked) which had higher calcium carbonate content. The results indicated the presence of heavy metals in soils generally decreased the solution pH and pHBC values in spiked samples. In general, solubility of Ca, Mg, and K decreased with increasing equilibrium pH of non-spiked and spiked soil samples. In the case of P, increasing the pH to about 7, decreased the solubility in all soils but further increase of pH from 7, enhanced P solubility. The solubility trends and values for Ca, Mg, and K did not differed significantly in non-spiked and spiked samples. But in the case of P, a reduction in solubility was observed in heavy metal-spiked soils. The information obtained in this study can be useful to make better estimation of the effects of soil pollutants on anion and cation solubility from agricultural and environmental viewpoints.

  1. A structural model for electricity prices with spikes: measurement of spike risk and optimal policies for hydropower plant operation

    International Nuclear Information System (INIS)

    Kanamura, Takashi

    2007-01-01

    This paper proposes a new model for electricity prices based on demand and supply, which we call a structural model. We show that the structural model can generate price spikes that fits the observed data better than those generated by other preceding models such as the jump diffusion model and the Box-Cox transformation model. We apply the structural model to obtain the optimal operation policy for a pumped-storage hydropower generator, and show that the structural model can provide more realistic optimal policies than the jump diffusion model. (author)

  2. A structural model for electricity prices with spikes: measurement of spike risk and optimal policies for hydropower plant operation

    Energy Technology Data Exchange (ETDEWEB)

    Kanamura, Takashi [Hitotsubashi University, Tokyo (Japan). Graduate School of International Corporate Strategy; Ohashi, Azuhiko [J-Power, Tokyo (Japan)

    2007-09-15

    This paper proposes a new model for electricity prices based on demand and supply, which we call a structural model. We show that the structural model can generate price spikes that fits the observed data better than those generated by other preceding models such as the jump diffusion model and the Box-Cox transformation model. We apply the structural model to obtain the optimal operation policy for a pumped-storage hydropower generator, and show that the structural model can provide more realistic optimal policies than the jump diffusion model. (author)

  3. Spiked natural matrix materials as quality assessment samples

    International Nuclear Information System (INIS)

    Feiner, M.S.; Sanderson, C.G.

    1988-01-01

    The Environmental Measurements Laboratory has conducted the Quality Assessment Program since 1976 to evaluate the quality of the environmental radioactivity data, which is reported to the Department of Energy by as many as 42 commercial contractors involved in nuclear work. In this program, matrix materials of known radionuclide concentrations are distributed routinely to the contractors and the reported results are compared. The five matrices used are: soil, vegetation, animal tissue, water and filter paper. Environmental soil, vegetation and animal tissue are used, but the water and filter paper samples are prepared by spiking with known amounts of standard solutions traceable to the National Bureau of Standards. A summary of results is given to illustrate the successful operation of the program. Because of the difficulty and high cost of collecting large samples of natural matrix material and to increase the versatility of the program, an attempt was recently made to prepare the soil, vegetation and animal tissue samples with spiked solutions. A description of the preparation of these reference samples and the results of analyses are presented along with a discussion of the pitfalls and advantages of this approach. 19 refs.; 6 tabs

  4. Motif statistics and spike correlations in neuronal networks

    International Nuclear Information System (INIS)

    Hu, Yu; Shea-Brown, Eric; Trousdale, James; Josić, Krešimir

    2013-01-01

    Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state. (paper)

  5. Dynamics of Monoterpene Formation in Spike Lavender Plants

    Directory of Open Access Journals (Sweden)

    Isabel Mendoza-Poudereux

    2017-12-01

    Full Text Available The metabolic cross-talk between the mevalonate (MVA and the methylerythritol phosphate (MEP pathways was analyzed in spike lavender (Lavandula latifolia Med on the basis of 13CO2-labelling experiments using wildtype and transgenic plants overexpressing the 3-hydroxy-3-methylglutaryl CoA reductase (HMGR, the first and key enzyme of the MVA pathway. The plants were labelled in the presence of 13CO2 in a gas chamber for controlled pulse and chase periods of time. GC/MS and NMR analysis of 1,8-cineole and camphor, the major monoterpenes present in their essential oil, indicated that the C5-precursors, isopentenyl diphosphate (IPP and dimethylallyl diphosphate (DMAPP of both monoterpenes are predominantly biosynthesized via the MEP pathway. Surprisingly, overexpression of HMGR did not have significant impact upon the crosstalk between the MVA and MEP pathways indicating that the MEP route is the preferred pathway for the synthesis of C5 monoterpene precursors in spike lavender.

  6. Performance evaluation of PCA-based spike sorting algorithms.

    Science.gov (United States)

    Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

    2008-09-01

    Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

  7. Physical implementation of pair-based spike timing dependent plasticity

    International Nuclear Information System (INIS)

    Azghadi, M.R.; Al-Sarawi, S.; Iannella, N.; Abbott, D.

    2011-01-01

    Full text: Objective Spike-timing-dependent plasticity (STOP) is one of several plasticity rules which leads to learning and memory in the brain. STOP induces synaptic weight changes based on the timing of the pre- and post-synaptic neurons. A neural network which can mimic the adaptive capability of biological brains in the temporal domain, requires the weight of single connections to be altered by spike timing. To physically realise this network into silicon, a large number of interconnected STOP circuits on the same substrate is required. This imposes two significant limitations in terms of power and area. To cover these limitations, very large scale integrated circuit (VLSI) technology provides attractive features in terms of low power and small area requirements. An example is demonstrated by (lndiveli et al. 2006). The objective of this paper is to present a new implementation of the STOP circuit which demonstrates better power and area in comparison to previous implementations. Methods The proposed circuit uses complementary metal oxide semiconductor (CMOS) technology as depicted in Fig. I. The synaptic weight can be stored on a capacitor and charging/discharging current can lead to potentiation and depression. HSpice simulation results demonstrate that the average power, peak power, and area of the proposed circuit have been reduced by 6, 8 and 15%, respectively, in comparison with Indiveri's implementation. These improvements naturally lead to packing more STOP circuits onto the same substrate, when compared to previous proposals. Hence, this new implementation is quite interesting for real-world large neural networks.

  8. Spike timing analysis in neural networks with unsupervised synaptic plasticity

    Science.gov (United States)

    Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

    2013-01-01

    The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

  9. Macroscopic phase-resetting curves for spiking neural networks

    Science.gov (United States)

    Dumont, Grégory; Ermentrout, G. Bard; Gutkin, Boris

    2017-10-01

    The study of brain rhythms is an open-ended, and challenging, subject of interest in neuroscience. One of the best tools for the understanding of oscillations at the single neuron level is the phase-resetting curve (PRC). Synchronization in networks of neurons, effects of noise on the rhythms, effects of transient stimuli on the ongoing rhythmic activity, and many other features can be understood by the PRC. However, most macroscopic brain rhythms are generated by large populations of neurons, and so far it has been unclear how the PRC formulation can be extended to these more common rhythms. In this paper, we describe a framework to determine a macroscopic PRC (mPRC) for a network of spiking excitatory and inhibitory neurons that generate a macroscopic rhythm. We take advantage of a thermodynamic approach combined with a reduction method to simplify the network description to a small number of ordinary differential equations. From this simplified but exact reduction, we can compute the mPRC via the standard adjoint method. Our theoretical findings are illustrated with and supported by numerical simulations of the full spiking network. Notably our mPRC framework allows us to predict the difference between effects of transient inputs to the excitatory versus the inhibitory neurons in the network.

  10. Inverse stochastic resonance in networks of spiking neurons.

    Science.gov (United States)

    Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J

    2017-07-01

    Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

  11. Evolving Spiking Neural Networks for Recognition of Aged Voices.

    Science.gov (United States)

    Silva, Marco; Vellasco, Marley M B R; Cataldo, Edson

    2017-01-01

    The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help determine a suitable treatment to avoid its progress or even to eliminate the problem. This work focuses on the development of a new model for the identification of aging voices (independently of their chronological age), using as input attributes parameters extracted from the voice and glottal signals. The proposed model, named Quantum binary-real evolving Spiking Neural Network (QbrSNN), is based on spiking neural networks (SNNs), with an unsupervised training algorithm, and a Quantum-Inspired Evolutionary Algorithm that automatically determines the most relevant attributes and the optimal parameters that configure the SNN. The QbrSNN model was evaluated in a database composed of 120 records, containing samples from three groups of speakers. The results obtained indicate that the proposed model provides better accuracy than other approaches, with fewer input attributes. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

  12. Macroscopic phase-resetting curves for spiking neural networks.

    Science.gov (United States)

    Dumont, Grégory; Ermentrout, G Bard; Gutkin, Boris

    2017-10-01

    The study of brain rhythms is an open-ended, and challenging, subject of interest in neuroscience. One of the best tools for the understanding of oscillations at the single neuron level is the phase-resetting curve (PRC). Synchronization in networks of neurons, effects of noise on the rhythms, effects of transient stimuli on the ongoing rhythmic activity, and many other features can be understood by the PRC. However, most macroscopic brain rhythms are generated by large populations of neurons, and so far it has been unclear how the PRC formulation can be extended to these more common rhythms. In this paper, we describe a framework to determine a macroscopic PRC (mPRC) for a network of spiking excitatory and inhibitory neurons that generate a macroscopic rhythm. We take advantage of a thermodynamic approach combined with a reduction method to simplify the network description to a small number of ordinary differential equations. From this simplified but exact reduction, we can compute the mPRC via the standard adjoint method. Our theoretical findings are illustrated with and supported by numerical simulations of the full spiking network. Notably our mPRC framework allows us to predict the difference between effects of transient inputs to the excitatory versus the inhibitory neurons in the network.

  13. Training Spiking Neural Models Using Artificial Bee Colony

    Science.gov (United States)

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  14. Spike neural models (part I: The Hodgkin-Huxley model

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2017-05-01

    Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

  15. Chaos and reliability in balanced spiking networks with temporal drive.

    Science.gov (United States)

    Lajoie, Guillaume; Lin, Kevin K; Shea-Brown, Eric

    2013-05-01

    Biological information processing is often carried out by complex networks of interconnected dynamical units. A basic question about such networks is that of reliability: If the same signal is presented many times with the network in different initial states, will the system entrain to the signal in a repeatable way? Reliability is of particular interest in neuroscience, where large, complex networks of excitatory and inhibitory cells are ubiquitous. These networks are known to autonomously produce strongly chaotic dynamics-an obvious threat to reliability. Here, we show that such chaos persists in the presence of weak and strong stimuli, but that even in the presence of chaos, intermittent periods of highly reliable spiking often coexist with unreliable activity. We elucidate the local dynamical mechanisms involved in this intermittent reliability, and investigate the relationship between this phenomenon and certain time-dependent attractors arising from the dynamics. A conclusion is that chaotic dynamics do not have to be an obstacle to precise spike responses, a fact with implications for signal coding in large networks.

  16. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.

    Directory of Open Access Journals (Sweden)

    Philip J Tully

    2016-05-01

    Full Text Available Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx. We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  17. Fuel switching? Demand destruction? Gas market responses to price spikes

    International Nuclear Information System (INIS)

    Lippe, D.

    2004-01-01

    This presentation defined fuel switching and addressed the issue regarding which consumers have the capability to switch fuels. In response to short term price aberrations, consumers with fuel switching capabilities reduce their use of one fuel and increase consumption of an alternative fuel. For example, natural gas consumption by some consumers declines in response to price spikes relative to prices of alternative fuels. This presentation also addressed the issue of differentiating between fuel switching and demand destruction. It also demonstrated how to compare gas prices versus alternative fuel prices and how to determine when consumers will likely switch fuels. Price spikes have implications for long term trends in natural gas demand, supply/demand balances and prices. The power generating sector represents a particular class of gas consumers that reduce operating rates of gas fired plants and increase operating rates of other plants. Some gas consumers even shut down plants until gas prices declines and relative economies improve. Some practical considerations for fuel switching include storage tank capacity, domestic refinery production, winter heating season, and decline in working gas storage. tabs., figs

  18. Hg stable isotope analysis by the double-spike method.

    Science.gov (United States)

    Mead, Chris; Johnson, Thomas M

    2010-06-01

    Recent publications suggest great potential for analysis of Hg stable isotope abundances to elucidate sources and/or chemical processes that control the environmental impact of mercury. We have developed a new MC-ICP-MS method for analysis of mercury isotope ratios using the double-spike approach, in which a solution containing enriched (196)Hg and (204)Hg is mixed with samples and provides a means to correct for instrumental mass bias and most isotopic fractionation that may occur during sample preparation and introduction into the instrument. Large amounts of isotopic fractionation induced by sample preparation and introduction into the instrument (e.g., by batch reactors) are corrected for. This may greatly enhance various Hg pre-concentration methods by correcting for minor fractionation that may occur during preparation and removing the need to demonstrate 100% recovery. Current precision, when ratios are normalized to the daily average, is 0.06 per thousand, 0.06 per thousand, 0.05 per thousand, and 0.05 per thousand (2sigma) for (202)Hg/(198)Hg, (201)Hg/(198)Hg, (200)Hg/(198)Hg, and (199)Hg/(198)Hg, respectively. This is slightly better than previously published methods. Additionally, this precision was attained despite the presence of large amounts of other Hg isotopes (e.g., 5.0% atom percent (198)Hg) in the spike solution; substantially better precision could be achieved if purer (196)Hg were used.

  19. Bistability induces episodic spike communication by inhibitory neurons in neuronal networks.

    Science.gov (United States)

    Kazantsev, V B; Asatryan, S Yu

    2011-09-01

    Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

  20. Upregulation of transmitter release probability improves a conversion of synaptic analogue signals into neuronal digital spikes

    Science.gov (United States)

    2012-01-01

    Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca2+. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners. PMID:22852823

  1. On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

    Science.gov (United States)

    Koyama, Shinsuke

    2015-07-01

    We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

  2. The Mechanisms of Repetitive Spike Generation in an Axonless Retinal Interneuron

    Directory of Open Access Journals (Sweden)

    Mark S. Cembrowski

    2012-02-01

    Full Text Available Several types of retinal interneurons exhibit spikes but lack axons. One such neuron is the AII amacrine cell, in which spikes recorded at the soma exhibit small amplitudes (5 ms. Here, we used electrophysiological recordings and computational analysis to examine the mechanisms underlying this atypical spiking. We found that somatic spikes likely represent large, brief action potential-like events initiated in a single, electrotonically distal dendritic compartment. In this same compartment, spiking undergoes slow modulation, likely by an M-type K conductance. The structural correlate of this compartment is a thin neurite that extends from the primary dendritic tree: local application of TTX to this neurite, or excision of it, eliminates spiking. Thus, the physiology of the axonless AII is much more complex than would be anticipated from morphological descriptions and somatic recordings; in particular, the AII possesses a single dendritic structure that controls its firing pattern.

  3. Rotational Angles and Velocities During Down the Line and Diagonal Across Court Volleyball Spikes

    OpenAIRE

    Justin R. Brown; Bader J. Alsarraf; Mike Waller; Patricia Eisenman; Charlie A. Hicks-Little

    2014-01-01

    The volleyball spike is an explosive movement that is frequently used to end a rally and earn a point. High velocity spikes are an important skill for a successful volleyball offense. Although the influence of vertical jump height and arm velocity on spiked ball velocity (SBV) have been investigated, little is known about the relationship of shoulder and hip angular kinematics with SBV. Other sport skills, like the baseball pitch share similar movement patterns and suggest trunk rotation is i...

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

    Science.gov (United States)

    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.

  5. SPAN: spike pattern association neuron for learning spatio-temporal sequences

    OpenAIRE

    Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

    2012-01-01

    Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

  6. Genes with a spike expression are clustered in chromosome (sub)bands and spike (sub)bands have a powerful prognostic value in patients with multiple myeloma

    Science.gov (United States)

    Kassambara, Alboukadel; Hose, Dirk; Moreaux, Jérôme; Walker, Brian A.; Protopopov, Alexei; Reme, Thierry; Pellestor, Franck; Pantesco, Véronique; Jauch, Anna; Morgan, Gareth; Goldschmidt, Hartmut; Klein, Bernard

    2012-01-01

    Background Genetic abnormalities are common in patients with multiple myeloma, and may deregulate gene products involved in tumor survival, proliferation, metabolism and drug resistance. In particular, translocations may result in a high expression of targeted genes (termed spike expression) in tumor cells. We identified spike genes in multiple myeloma cells of patients with newly-diagnosed myeloma and investigated their prognostic value. Design and Methods Genes with a spike expression in multiple myeloma cells were picked up using box plot probe set signal distribution and two selection filters. Results In a cohort of 206 newly diagnosed patients with multiple myeloma, 2587 genes/expressed sequence tags with a spike expression were identified. Some spike genes were associated with some transcription factors such as MAF or MMSET and with known recurrent translocations as expected. Spike genes were not associated with increased DNA copy number and for a majority of them, involved unknown mechanisms. Of spiked genes, 36.7% clustered significantly in 149 out of 862 documented chromosome (sub)bands, of which 53 had prognostic value (35 bad, 18 good). Their prognostic value was summarized with a spike band score that delineated 23.8% of patients with a poor median overall survival (27.4 months versus not reached, Pband score was independent of other gene expression profiling-based risk scores, t(4;14), or del17p in an independent validation cohort of 345 patients. Conclusions We present a new approach to identify spike genes and their relationship to patients’ survival. PMID:22102711

  7. Small-angle scattering study of Aspergillus awamori glycoprotein glucoamylase

    International Nuclear Information System (INIS)

    Schmidt, A. E.; Shvetsov, A. V.; Kuklin, A. I.; Lebedev, D. V.; Surzhik, M. A.; Sergeev, V. R.; Isaev-Ivanov, V. V.

    2016-01-01

    Glucoamylase from fungus Aspergillus awamori is glycoside hydrolase that catalyzes the hydrolysis of α-1,4- and α-1,6-glucosidic bonds in glucose polymers and oligomers. This glycoprotein consists of a catalytic domain and a starch-binding domain connected by an O-glycosylated polypeptide chain. The conformation of the linker, the relative arrangement of the domains, and the structure of the full-length enzyme are unknown. The structure of the recombinant glucoamylase GA1 was studied by molecular modelling and small-angle neutron scattering (SANS) methods. The experimental SANS data provide evidence that glucoamylase exists as a monomer in solution and contains a glycoside component, which makes a substantial contribution to the scattering. The model of full-length glucoamylase, which was calculated without taking into account the effect of glycosylation, is consistent with the experimental data and has a radius of gyration of 33.4 ± 0.6 Å

  8. Small-angle scattering study of Aspergillus awamori glycoprotein glucoamylase

    Science.gov (United States)

    Schmidt, A. E.; Shvetsov, A. V.; Kuklin, A. I.; Lebedev, D. V.; Surzhik, M. A.; Sergeev, V. R.; Isaev-Ivanov, V. V.

    2016-01-01

    Glucoamylase from fungus Aspergillus awamori is glycoside hydrolase that catalyzes the hydrolysis of α-1,4- and α-1,6-glucosidic bonds in glucose polymers and oligomers. This glycoprotein consists of a catalytic domain and a starch-binding domain connected by an O-glycosylated polypeptide chain. The conformation of the linker, the relative arrangement of the domains, and the structure of the full-length enzyme are unknown. The structure of the recombinant glucoamylase GA1 was studied by molecular modelling and small-angle neutron scattering (SANS) methods. The experimental SANS data provide evidence that glucoamylase exists as a monomer in solution and contains a glycoside component, which makes a substantial contribution to the scattering. The model of full-length glucoamylase, which was calculated without taking into account the effect of glycosylation, is consistent with the experimental data and has a radius of gyration of 33.4 ± 0.6 Å.

  9. Hepatitis C Virus E2 Envelope Glycoprotein Core Structure

    Energy Technology Data Exchange (ETDEWEB)

    Kong, Leopold; Giang, Erick; Nieusma, Travis; Kadam, Rameshwar U.; Cogburn, Kristin E.; Hua, Yuanzi; Dai, Xiaoping; Stanfield, Robyn L.; Burton, Dennis R.; Ward, Andrew B.; Wilson, Ian A.; Law, Mansun

    2014-08-26

    Hepatitis C virus (HCV), a Hepacivirus, is a major cause of viral hepatitis, liver cirrhosis, and hepatocellular carcinoma. HCV envelope glycoproteins E1 and E2 mediate fusion and entry into host cells and are the primary targets of the humoral immune response. The crystal structure of the E2 core bound to broadly neutralizing antibody AR3C at 2.65 angstroms reveals a compact architecture composed of a central immunoglobulin-fold β sandwich flanked by two additional protein layers. The CD81 receptor binding site was identified by electron microscopy and site-directed mutagenesis and overlaps with the AR3C epitope. The x-ray and electron microscopy E2 structures differ markedly from predictions of an extended, three-domain, class II fusion protein fold and therefore provide valuable information for HCV drug and vaccine design.

  10. Human Milk Glycoproteins Protect Infants Against Human Pathogens

    Science.gov (United States)

    Liu, Bo

    2013-01-01

    Abstract Breastfeeding protects the neonate against pathogen infection. Major mechanisms of protection include human milk glycoconjugates functioning as soluble receptor mimetics that inhibit pathogen binding to the mucosal cell surface, prebiotic stimulation of gut colonization by favorable microbiota, immunomodulation, and as a substrate for bacterial fermentation products in the gut. Human milk proteins are predominantly glycosylated, and some biological functions of these human milk glycoproteins (HMGPs) have been reported. HMGPs range in size from 14 kDa to 2,000 kDa and include mucins, secretory immunoglobulin A, bile salt-stimulated lipase, lactoferrin, butyrophilin, lactadherin, leptin, and adiponectin. This review summarizes known biological roles of HMGPs that may contribute to the ability of human milk to protect neonates from disease. PMID:23697737

  11. Small-angle scattering study of Aspergillus awamori glycoprotein glucoamylase

    Energy Technology Data Exchange (ETDEWEB)

    Schmidt, A. E., E-mail: schmidt@omrb.pnpi.spb.ru; Shvetsov, A. V. [National Research Center “Kurchatov Institute”, Konstantinov Petersburg Nuclear Physics Institute (Russian Federation); Kuklin, A. I. [Joint Institute for Nuclear Research (Russian Federation); Lebedev, D. V.; Surzhik, M. A.; Sergeev, V. R.; Isaev-Ivanov, V. V. [National Research Center “Kurchatov Institute”, Konstantinov Petersburg Nuclear Physics Institute (Russian Federation)

    2016-01-15

    Glucoamylase from fungus Aspergillus awamori is glycoside hydrolase that catalyzes the hydrolysis of α-1,4- and α-1,6-glucosidic bonds in glucose polymers and oligomers. This glycoprotein consists of a catalytic domain and a starch-binding domain connected by an O-glycosylated polypeptide chain. The conformation of the linker, the relative arrangement of the domains, and the structure of the full-length enzyme are unknown. The structure of the recombinant glucoamylase GA1 was studied by molecular modelling and small-angle neutron scattering (SANS) methods. The experimental SANS data provide evidence that glucoamylase exists as a monomer in solution and contains a glycoside component, which makes a substantial contribution to the scattering. The model of full-length glucoamylase, which was calculated without taking into account the effect of glycosylation, is consistent with the experimental data and has a radius of gyration of 33.4 ± 0.6 Å.

  12. The chaperone BAG6 captures dislocated glycoproteins in the cytosol.

    Directory of Open Access Journals (Sweden)

    Jasper H L Claessen

    Full Text Available Secretory and membrane (glycoproteins are subject to quality control in the endoplasmic reticulum (ER to ensure that only functional proteins reach their destination. Proteins deemed terminally misfolded and hence functionally defective may be dislocated to the cytosol, where the proteasome degrades them. What we know about this process stems mostly from overexpression of tagged misfolded proteins, or from situations where viruses have hijacked the quality control machinery to their advantage. We know of only very few endogenous substrates of ER quality control, most of which are degraded as part of a signaling pathway, such as Insig-1, but such examples do not necessarily represent terminally misfolded proteins. Here we show that endogenous dislocation clients are captured specifically in association with the cytosolic chaperone BAG6, or retrieved en masse via their glycan handle.

  13. Immunoglobulin-E reactivity to wine glycoproteins in heavy drinkers

    DEFF Research Database (Denmark)

    Gonzalez-Quintela, Arturo; Gomez-Rial, Jose; Valcarcel, Catalina

    2011-01-01

    and biological significance of IgE antibodies to N-glycans from wine glycoproteins in heavy drinkers. A structured questionnaire, skin prick tests, serum IgE levels, IgE-immunoblotting to wine extracts, and basophil activation tests were used to characterize 20 heavy drinkers and 10 control subjects. Eleven...... heavy drinkers (55%) showed IgE binding to proteins in wine extracts. The proteins were identified by mass spectrometry as grape-derived vacuolar invertase and thaumatin-like protein. Immunoblot reactivity was closely associated with the presence of IgE to CCDs and was inhibited by preincubation...... with a glycoconjugate containing bromelain-type N-glycans. The same conjugate, CCD-bearing allergens, and wine extracts activated basophils in patients with high-titer CCD-specific IgE but not in healthy controls. There was no relationship between immunoblot reactivity and consumption of any specific type of wine...

  14. Multichannel interictal spike activity detection using time-frequency entropy measure.

    Science.gov (United States)

    Thanaraj, Palani; Parvathavarthini, B

    2017-06-01

    Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

  15. Origin of frontal lobe spikes in the early onset benign occipital lobe epilepsy (Panayiotopoulos syndrome).

    Science.gov (United States)

    Leal, Alberto J R; Ferreira, José C; Dias, Ana I; Calado, Eulália

    2008-09-01

    Early onset benign occipital lobe epilepsy (Panayiotopoulos syndrome [PS]) is a common and easily recognizable epilepsy. Interictal EEG spike activity is often multifocal but most frequently localized in the occipital lobes. The origin and clinical significance of the extra-occipital spikes remain poorly understood. Three patients with the PS and interictal EEG spikes with frontal lobe topography were studied using high-resolution EEG. Independent component analysis (ICA) was used to decompose the spikes in components with distinct temporal dynamics. The components were mapped in the scalp with a spline-laplacian algorithm. The change in scalp potential topography from spike onset to peak, suggests the contribution of several intracranial generators, with different kinetics of activation and significant overlap. ICA was able to separate the major contributors to frontal spikes and consistently revealed an early activating group of components over the occipital areas in all the patients. The local origin of these early potentials was established by the spline-laplacian montage. Frontal spikes in PS are consistently associated with early and unilateral occipital lobe activation, suggesting a postero-anterior spike propagation. Frontal spikes in the PS represent a secondary activation triggered by occipital interictal discharges and do not represent an independent focus.

  16. Voltage-spike analysis for a free-running parallel inverter

    Science.gov (United States)

    Lee, F. C. Y.; Wilson, T. G.

    1974-01-01

    Unwanted and sometimes damaging high-amplitude voltage spikes occur during each half cycle in many transistor saturable-core inverters at the moment when the core saturates and the transistors switch. The analysis shows that spikes are an intrinsic characteristic of certain types of inverters even with negligible leakage inductance and purely resistive load. The small but unavoidable after-saturation inductance of the saturable-core transformer plays an essential role in creating these undesired thigh-voltage spikes. State-plane analysis provides insight into the complex interaction between core and transistors, and shows the circuit parameters upon which the magnitude of these spikes depends.

  17. Multineuron spike train analysis with R-convolution linear combination kernel.

    Science.gov (United States)

    Tezuka, Taro

    2018-06-01

    A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. In silico-based vaccine design against Ebola virus glycoprotein

    Directory of Open Access Journals (Sweden)

    Dash R

    2017-03-01

    Full Text Available Raju Dash,1 Rasel Das,2 Md Junaid,3 Md Forhad Chowdhury Akash,4 Ashekul Islam,5 SM Zahid Hosen1 1Molecular Modeling and Drug Design Laboratory (MMDDL, Pharmacology Research Division, Bangladesh Council of Scientific and Industrial Research (BCSIR, Chittagong, Bangladesh; 2Nanotechnology and Catalysis Research Center, University of Malaya, Kuala Lumpur, Malaysia; 3Department of Pharmaceutical Sciences, North South University, Dhaka, Bangladesh; 4Department of Pharmacy, BGC Trust University Bangladesh, Chittagong, Bangladesh; 5Department of Biochemistry and Molecular Biology, University of Chittagong, Chittagong, Bangladesh Abstract: Ebola virus (EBOV is one of the lethal viruses, causing more than 24 epidemic outbreaks to date. Despite having available molecular knowledge of this virus, no definite vaccine or other remedial agents have been developed yet for the management and avoidance of EBOV infections in humans. Disclosing this, the present study described an epitope-based peptide vaccine against EBOV, using a combination of B-cell and T-cell epitope predictions, followed by molecular docking and molecular dynamics simulation approach. Here, protein sequences of all glycoproteins of EBOV were collected and examined via in silico methods to determine the most immunogenic protein. From the identified antigenic protein, the peptide region ranging from 186 to 220 and the sequence HKEGAFFLY from the positions of 154–162 were considered the most potential B-cell and T-cell epitopes, correspondingly. Moreover, this peptide (HKEGAFFLY interacted with HLA-A*32:15 with the highest binding energy and stability, and also a good conservancy of 83.85% with maximum population coverage. The results imply that the designed epitopes could manifest vigorous enduring defensive immunity against EBOV. Keywords: Ebola virus, epitope, glycoprotein, vaccine design

  19. THE ROLE OF P-GLYCOPROTEIN IN RATIONAL PHARMACOTHERAPY IN CARDIOLOGY

    Directory of Open Access Journals (Sweden)

    A. V. Shulkin

    2015-09-01

    Full Text Available On the basis of the analysis of published data the role of P-glycoprotein, carrier protein, in rational pharmacotherapy in cardiology was shown on the example of its substrates – digoxin, antiplatelet agents and anticoagulants. Determination of C3435T polymorphism of multidrug resistance gene (MDR1, encoding P-glycoprotein, in pharmacotherapy with digoxin, antiplatelet drugs (clopidogrel tikagrelol, prasugrel and anticoagulants (dabigatran etexilate, rivaroxaban, edoxaban is not feasible in routine practice. Drug in- teractions have clinical implications for the efficacy and safety of pharmacotherapy in coadministration of these drugs with P-glycoprotein substrates, inducers and inhibitors.

  20. Homologous and heterologous antibody responses of mice immunized with purified feline herpesvirus type 1 and canine herpesvirus glycoproteins.

    Science.gov (United States)

    Limcumpao, J A; Horimoto, T; Xuan, X N; Tohya, Y; Azetaka, M; Takahashi, E; Mikami, T

    1991-06-01

    The three glycoproteins each of feline herpesvirus type 1 (FHV-1) and canine herpesvirus (CHV) were purified by affinity chromatography using glycoprotein-specific monoclonal antibodies and used individually or in combination in immunizing mice to determine their relative immunogenicity. All the glycoproteins induced detectable virus neutralizing antibodies to the homologous virus but FHV-1 gp143/108 and its cross-reacting counterpart, CHV gp145/112, elicited the highest titers not only to the homologous virus but to the heterologous virus as well. The production of ELISA antibodies after glycoprotein immunization was variable, while hemagglutination-inhibiting antibodies were produced by only 1 out of 10 FHV-1 gp60-inoculated mice. In general, the antibody titers induced by CHV glycoproteins were lower than those by FHV-1 glycoproteins. These results indicate that these glycoproteins may be useful as subunit vaccines against FHV-1 and CHV infections.

  1. Mechanical circulatory support is associated with loss of platelet receptors glycoprotein Ibα and glycoprotein VI.

    Science.gov (United States)

    Lukito, P; Wong, A; Jing, J; Arthur, J F; Marasco, S F; Murphy, D A; Bergin, P J; Shaw, J A; Collecutt, M; Andrews, R K; Gardiner, E E; Davis, A K

    2016-11-01

    Essentials Relationship of acquired von Willebrand disease (VWD) and platelet dysfunction is explored. Patients with ventricular assist devices and on extracorporeal membrane oxygenation are investigated. Acquired VWD and platelet receptor shedding is demonstrated in the majority of patients. Loss of platelet adhesion receptors glycoprotein (GP) Ibα and GPVI may increase bleeding risk. Background Ventricular assist devices (VADs) and extracorporeal membrane oxygenation (ECMO) are associated with bleeding that is not fully explained by anticoagulant or antiplatelet use. Exposure of platelets to elevated shear in vitro leads to increased shedding. Objectives To investigate whether loss of platelet receptors occurs in vivo, and the relationship with acquired von Willebrand syndrome (AVWS). Methods Platelet counts, coagulation tests and von Willebrand factor (VWF) analyses were performed on samples from 21 continuous flow VAD (CF-VAD), 20 ECMO, 12 heart failure and seven aortic stenosis patients. Levels of platelet receptors were measured by flow cytometry or ELISA. Results The loss of high molecular weight VWF multimers was observed in 18 of 19 CF-VAD and 14 of 20 ECMO patients, consistent with AVWS. Platelet receptor shedding was demonstrated by elevated soluble glycoprotein (GP) VI levels in plasma and significantly reduced surface GPIbα and GPVI levels in CF-VAD and ECMO patients as compared with healthy donors. Platelet receptor levels were also significantly reduced in heart failure patients. Conclusions These data link AVWS and increased platelet receptor shedding in patients with CF-VADs or ECMO for the first time. Loss of the platelet surface receptors GPIbα and GPVI in heart failure, CF-VAD and ECMO patients may contribute to ablated platelet adhesion/activation, and limit thrombus formation under high/pathologic shear conditions. © 2016 International Society on Thrombosis and Haemostasis.

  2. Grain price spikes and beggar-thy-neighbor policy responses

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

    on the agenda of various international policy fora, including the annual meetings of G20 countries in recent years. For that reason, recent studies have attempted to quantify the extent to which such policy actions contributed to the rise in food prices. A study by Jensen & Anderson (2014) uses the global AGE...... model GTAP and the corresponding database to quantify the global policy actions contributions to the raise in food prices by modeling the changes in distortions to agricultural incentives in the period 2006 to 2008. We link the results from this global model into a national AGE model, highlighting how...... global "Beggar-thy-Neighbor Policy Responses" impacted on poor households in Uganda. More specifically we examine the following research questions: What were the Ugandan economy-wide and poverty impacts of the price spikes? What was the impact of other countries "Beggar-thy-Neighbor Policy Responses...

  3. Shock initiation of explosives: Temperature spikes and growth spurts

    Science.gov (United States)

    Bassett, Will P.; Dlott, Dana D.

    2016-08-01

    When energetic materials are subjected to high-velocity impacts, the first steps in the shock-to-detonation transition are the creation, ignition, and growth of hot spots. We used 1-3.2 km s-1 laser-launched flyer plates to impact powdered octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine, a powerful explosive, and monitored hundreds of emission bursts with an apparatus that determined temperature and emissivity at all times. The time-dependent volume fraction of hot spots was determined by measuring the time-dependent emissivity. After the shock, most hot spots extinguished, but the survivors smoldered for hundreds of nanoseconds until their temperatures spiked, causing a hot spot growth spurt. Depending on the impact duration, the growth spurts could be as fast as 300 ns and as slow as 13 μs.

  4. Brian: a simulator for spiking neural networks in Python

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2008-11-01

    Full Text Available Brian is a new simulator for spiking neural networks, written in Python (http://brian.di.ens.fr. It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

  5. Importance of vesicle release stochasticity in neuro-spike communication.

    Science.gov (United States)

    Ramezani, Hamideh; Akan, Ozgur B

    2017-07-01

    Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.

  6. A self-resetting spiking phase-change neuron

    Science.gov (United States)

    Cobley, R. A.; Hayat, H.; Wright, C. D.

    2018-05-01

    Neuromorphic, or brain-inspired, computing applications of phase-change devices have to date concentrated primarily on the implementation of phase-change synapses. However, the so-called accumulation mode of operation inherent in phase-change materials and devices can also be used to mimic the integrative properties of a biological neuron. Here we demonstrate, using physical modelling of nanoscale devices and SPICE modelling of associated circuits, that a single phase-change memory cell integrated into a comparator type circuit can deliver a basic hardware mimic of an integrate-and-fire spiking neuron with self-resetting capabilities. Such phase-change neurons, in combination with phase-change synapses, can potentially open a new route for the realisation of all-phase-change neuromorphic computing.

  7. A 244Pu spike reference material CBNM IRM-042A

    International Nuclear Information System (INIS)

    Verbruggen, A.; Gallet, M.; Hendrickx, F.; Bievre, P. de

    1991-01-01

    An highly enriched 244 Pu isotopic reference material (CBNM IRM-042a) has been prepared and certified for 244 Pu isotope concentration. The certified value of (2.257 7 ± 0.004 4).10 18 atoms 242 Pu.kg -1 of solution has been established by isotope dilution mass spectrometry. The plutonium isotopic composition has been determined by thermal ionization mass spectrometry and calibration of these measurements by means of synthetic 242 Pu/ 239 Pu mixtures. The isotopic reference material is supplied in a sealed glass ampoule containing approximately 10 g of a 5M nitric acid solution at an approximate concentration of 1 μg Pu per g solution. This isotopic reference material is part of a systematic CBNM programme to supply spike isotopic reference materials of various isotopes at different concentrations

  8. Quantum square-well with logarithmic central spike

    Science.gov (United States)

    Znojil, Miloslav; Semorádová, Iveta

    2018-01-01

    Singular repulsive barrier V (x) = -gln(|x|) inside a square-well is interpreted and studied as a linear analog of the state-dependent interaction ℒeff(x) = -gln[ψ∗(x)ψ(x)] in nonlinear Schrödinger equation. In the linearized case, Rayleigh-Schrödinger perturbation theory is shown to provide a closed-form spectrum at sufficiently small g or after an amendment of the unperturbed Hamiltonian. At any spike strength g, the model remains solvable numerically, by the matching of wave functions. Analytically, the singularity is shown regularized via the change of variables x = expy which interchanges the roles of the asymptotic and central boundary conditions.

  9. Brian: a simulator for spiking neural networks in python.

    Science.gov (United States)

    Goodman, Dan; Brette, Romain

    2008-01-01

    "Brian" is a new simulator for spiking neural networks, written in Python (http://brian. di.ens.fr). It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

  10. Spiking neural networks on high performance computer clusters

    Science.gov (United States)

    Chen, Chong; Taha, Tarek M.

    2011-09-01

    In this paper we examine the acceleration of two spiking neural network models on three clusters of multicore processors representing three categories of processors: x86, STI Cell, and NVIDIA GPGPUs. The x86 cluster utilized consists of 352 dualcore AMD Opterons, the Cell cluster consists of 320 Sony Playstation 3s, while the GPGPU cluster contains 32 NVIDIA Tesla S1070 systems. The results indicate that the GPGPU platform can dominate in performance compared to the Cell and x86 platforms examined. From a cost perspective, the GPGPU is more expensive in terms of neuron/s throughput. If the cost of GPGPUs go down in the future, this platform will become very cost effective for these models.

  11. Spike Protein Fusion Peptide and Feline Coronavirus Virulence

    Science.gov (United States)

    Chang, Hui-Wen; Egberink, Herman F.; Halpin, Rebecca; Spiro, David J.

    2012-01-01

    Coronaviruses are well known for their potential to change their host or tissue tropism, resulting in unpredictable new diseases and changes in pathogenicity; severe acute respiratory syndrome and feline coronaviruses, respectively, are the most recognized examples. Feline coronaviruses occur as 2 pathotypes: nonvirulent feline enteric coronaviruses (FECVs), which replicate in intestinal epithelium cells, and lethal feline infectious peritonitis viruses (FIPVs), which replicate in macrophages. Evidence indicates that FIPV originates from FECV by mutation, but consistent distinguishing differences have not been established. We sequenced the full genome of 11 viruses of each pathotype and then focused on the single most distinctive site by additionally sequencing hundreds of viruses in that region. As a result, we identified 2 alternative amino acid differences in the putative fusion peptide of the spike protein that together distinguish FIPV from FECV in >95% of cases. By these and perhaps other mutations, the virus apparently acquires its macrophage tropism and spreads systemically. PMID:22709821

  12. Variational and perturbative schemes for a spiked harmonic oscillator

    International Nuclear Information System (INIS)

    Aguilera-Navarro, V.C.; Estevez, G.A.; Guardiola, R.

    1989-01-01

    A variational analysis of the spiked harmonic-oscillator Hamiltonian operator -d 2 /dx 2 + x 2 + l(l+1)/x 2 + λ |x| -α , where α is a real positive parameter, is reported in this work. The formalism makes use of the functional space spanned by the solutions of the Schroedinger equation for the linear harmonic-oscillator Hamiltonian supplemented by a Dirichlet boundary condition, and a standard procedure for diagonalizing symmetric matrices. The eigenvalues obtained by increasing the dimension of the basis set provides accurate approximations for the ground-state energy of the model system, valid for positive and relatively large values of the coupling parameter λ. Additionally, a large-coupling pertubative-expansion is carried out and the contributions up to fourth order to the ground-state energy are explicitly evaluated. Numerical results are compared for the special case α=5/2. (author) [pt

  13. A self-resetting spiking phase-change neuron.

    Science.gov (United States)

    Cobley, R A; Hayat, H; Wright, C D

    2018-05-11

    Neuromorphic, or brain-inspired, computing applications of phase-change devices have to date concentrated primarily on the implementation of phase-change synapses. However, the so-called accumulation mode of operation inherent in phase-change materials and devices can also be used to mimic the integrative properties of a biological neuron. Here we demonstrate, using physical modelling of nanoscale devices and SPICE modelling of associated circuits, that a single phase-change memory cell integrated into a comparator type circuit can deliver a basic hardware mimic of an integrate-and-fire spiking neuron with self-resetting capabilities. Such phase-change neurons, in combination with phase-change synapses, can potentially open a new route for the realisation of all-phase-change neuromorphic computing.

  14. Double spike with isotope pattern deconvolution for mercury speciation

    International Nuclear Information System (INIS)

    Castillo, A.; Rodriguez-Gonzalez, P.; Centineo, G.; Roig-Navarro, A.F.; Garcia Alonso, J.I.

    2009-01-01

    Full text: A double-spiking approach, based on an isotope pattern deconvolution numerical methodology, has been developed and applied for the accurate and simultaneous determination of inorganic mercury (IHg) and methylmercury (MeHg). Isotopically enriched mercury species ( 199 IHg and 201 MeHg) are added before sample preparation to quantify the extent of methylation and demethylation processes. Focused microwave digestion was evaluated to perform the quantitative extraction of such compounds from solid matrices of environmental interest. Satisfactory results were obtained in different certificated reference materials (dogfish liver DOLT-4 and tuna fish CRM-464) both by using GC-ICPMS and GC-MS, demonstrating the suitability of the proposed analytical method. (author)

  15. Analysis of glycoprotein-derived oligosaccharides in glycoproteins detected on two-dimensional gel by capillary electrophoresis using on-line concentration method.

    Science.gov (United States)

    Kamoda, Satoru; Nakanishi, Yasuharu; Kinoshita, Mitsuhiro; Ishikawa, Rika; Kakehi, Kazuaki

    2006-02-17

    Capillary electrophoresis (CE) is an effective tool to analyze carbohydrate mixture derived from glycoproteins with high resolution. However, CE has a disadvantage that a few nanoliters of a sample solution are injected to a narrow capillary. Therefore, we have to prepare a sample solution of high concentration for CE analysis. In the present study, we applied head column field-amplified sample stacking method to the analysis of N-linked oligosaccharides derived from glycoprotein separated by two-dimensional gel electrophoresis. Model studies demonstrated that we achieved 60-360 times concentration effect on the analysis of carbohydrate chains labeled with 3-aminobenzoic acid (3-AA). The method was applied to the analysis of N-linked oligosaccharides from glycoproteins separated and detected on PAGE gel. Heterogeneity of alpha1-acid glycoprotein (AGP), i.e. glycoforms, was examined by 2D-PAGE and N-linked oligosaccharides were released by in-gel digestion with PNGase F. The released oligosaccharides were derivatized with 3-AA and analyzed by CE. The results showed that glycoforms having lower pI values contained a larger amount of tetra- and tri-antennary oligosaccharides. In contrast, glycoforms having higher pI values contained bi-antennary oligosaccharides abundantly. The result clearly indicated that the spot of a glycoprotein glycoform detected by Coomassie brilliant blue staining on 2D-PAGE gel is sufficient for quantitative profiling of oligosaccharides.

  16. EFFECTS OF DIFFERENT GROWING CONDITIONS ON THE MORPHOLOGICAL FEATURES OF THE SPIKE OF HEXAPLOID TRITICALE

    Directory of Open Access Journals (Sweden)

    K. U. Kurkiev

    2016-01-01

    Full Text Available Aim. The aim is to study the effect of different environmental conditions on the morphological traits of the spike of hexaploid triticale varieties.Methods. We analyzed 507 samples of triticale of various eco-geographical origins, in different years of study and at different seeding times. To investigate the influence of environmental conditions on the phenotypic expression of the studied traits we held a comparative analysis of the spike of two years and, in addition, of spring triticale during winter and spring crops. Analysis on the features was carried out on the main spikes. We studied the following morphological characteristics of the spike: length, number of spikelets and density.Results and discussion. The study of differences in individual variety samples showed that more than 60% triticale samples had significant differences in the length of the spike, depending on the weather conditions of the year – with the winter crops number of spikelets per spike was significantly higher than with the spring crops. A comparative analysis of the impact of the weather conditions of the year on triticale showed that significant differences in the density of the spike were observed in less than 30%.Conclusion. Study of the influence of conditions of the year and sowing dates on the main features of the spike of triticale showed that the density of the spike is the least affected by the external environment. The length of the spikes and the number of spikelets per spike differed significantly when growing in a various conditions.

  17. Rebound spiking in layer II medial entorhinal cortex stellate cells: Possible mechanism of grid cell function

    Science.gov (United States)

    Shay, Christopher F.; Ferrante, Michele; Chapman, G. William; Hasselmo, Michael E.

    2015-01-01

    Rebound spiking properties of medial entorhinal cortex (mEC) stellate cells induced by inhibition may underlie their functional properties in awake behaving rats, including the temporal phase separation of distinct grid cells and differences in grid cell firing properties. We investigated rebound spiking properties using whole cell patch recording in entorhinal slices, holding cells near spiking threshold and delivering sinusoidal inputs, superimposed with realistic inhibitory synaptic inputs to test the capacity of cells to selectively respond to specific phases of inhibitory input. Stellate cells showed a specific phase range of hyperpolarizing inputs that elicited spiking, but non-stellate cells did not show phase specificity. In both cell types, the phase range of spiking output occurred between the peak and subsequent descending zero crossing of the sinusoid. The phases of inhibitory inputs that induced spikes shifted earlier as the baseline sinusoid frequency increased, while spiking output shifted to later phases. Increases in magnitude of the inhibitory inputs shifted the spiking output to earlier phases. Pharmacological blockade of h-current abolished the phase selectivity of hyperpolarizing inputs eliciting spikes. A network computational model using cells possessing similar rebound properties as found in vitro produces spatially periodic firing properties resembling grid cell firing when a simulated animal moves along a linear track. These results suggest that the ability of mEC stellate cells to fire rebound spikes in response to a specific range of phases of inhibition could support complex attractor dynamics that provide completion and separation to maintain spiking activity of specific grid cell populations. PMID:26385258

  18. Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

    Science.gov (United States)

    Maisel, Brenton; Lindenberg, Katja

    2017-02-01

    While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.

  19. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  20. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  1. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Directory of Open Access Journals (Sweden)

    Claudia Casellato

    Full Text Available The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning, a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  2. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Science.gov (United States)

    Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio

    2014-01-01

    The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  3. Stimulus-dependent spiking relationships with the EEG

    Science.gov (United States)

    Snyder, Adam C.

    2015-01-01

    The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

  4. Spike-timing dependent plasticity and the cognitive map

    Directory of Open Access Journals (Sweden)

    Daniel eBush

    2010-10-01

    Full Text Available Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesised that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modelled using auto-associative networks, which utilise rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighbouring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post- synaptic firing according to a spike-timing dependent plasticity (STDP rule. Furthermore, electrophysiology studies have identified persistent ‘theta-coded’ temporal correlations in place cell activity in vivo, characterised by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post- synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilises this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

  5. Analog memristive synapse in spiking networks implementing unsupervised learning

    Directory of Open Access Journals (Sweden)

    Erika Covi

    2016-10-01

    Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

  6. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning.

    Science.gov (United States)

    Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

    2016-01-01

    Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO 2 -based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.

  7. Spike-timing dependent plasticity and the cognitive map.

    Science.gov (United States)

    Bush, Daniel; Philippides, Andrew; Husbands, Phil; O'Shea, Michael

    2010-01-01

    Since the discovery of place cells - single pyramidal neurons that encode spatial location - it has been hypothesized that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modeled using auto-associative networks, which utilize rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighboring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post-synaptic firing according to a spike-timing dependent plasticity (STDP) rule. Furthermore, electrophysiology studies have identified persistent "theta-coded" temporal correlations in place cell activity in vivo, characterized by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post-synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilizes this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

  8. Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

    Directory of Open Access Journals (Sweden)

    Fernando Perez-Peña

    2013-11-01

    Full Text Available In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM. All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6 and power requirements (3.4 W to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6. It also evidences the suitable use of AER as a communication protocol between processing and actuation.

  9. Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

    Science.gov (United States)

    Perez-Peña, Fernando; Morgado-Estevez, Arturo; Linares-Barranco, Alejandro; Jimenez-Fernandez, Angel; Gomez-Rodriguez, Francisco; Jimenez-Moreno, Gabriel; Lopez-Coronado, Juan

    2013-01-01

    In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation. PMID:24264330

  10. Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

    International Nuclear Information System (INIS)

    Shimazaki, Hideaki

    2013-01-01

    Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

  11. Learning of spiking networks with different forms of long-term synaptic plasticity

    International Nuclear Information System (INIS)

    Vlasov, D.S.; Sboev, A.G.; Serenko, A.V.; Rybka, R.B.; Moloshnikov, I.A.

    2016-01-01

    The possibility of modeling the learning process based on different forms of spike timing-dependent plasticity (STDP) has been studied. It has been shown that the learnability depends on the choice of the spike pairing scheme in the STDP rule and the type of the input signal used during learning [ru

  12. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    International Nuclear Information System (INIS)

    Xue Ming; Wang Jiang; Deng Bin; Wei Xi-Le; Yu Hai-Tao; Chen Ying-Yuan

    2013-01-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture. (interdisciplinary physics and related areas of science and technology)

  13. Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

    Directory of Open Access Journals (Sweden)

    Laureline Logiaco

    2015-08-01

    Full Text Available The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

  14. Detecting dependencies between spike trains of pairs of neurons through copulas

    DEFF Research Database (Denmark)

    Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

    2011-01-01

    The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously...... the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation....

  15. Neuronal Networks in Children with Continuous Spikes and Waves during Slow Sleep

    Science.gov (United States)

    Siniatchkin, Michael; Groening, Kristina; Moehring, Jan; Moeller, Friederike; Boor, Rainer; Brodbeck, Verena; Michel, Christoph M.; Rodionov, Roman; Lemieux, Louis; Stephani, Ulrich

    2010-01-01

    Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least greater than 85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and…

  16. Calcium spikes and calcium plateaux evoked by differential polarization in dendrites of turtle motoneurones in vitro

    DEFF Research Database (Denmark)

    Hounsgaard, J; Kiehn, O

    1993-01-01

    The ability of dendrites in turtle motoneurones to support calcium spikes and calcium plateaux was investigated using differential polarization by applied electric fields. 2. Electric fields were generated by passing current through transverse slices of the turtle spinal cord between two plate......+ spikes and Ca2+ plateaux are present in dendrites of spinal motoneurones of the turtle....

  17. A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

    Science.gov (United States)

    Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

    2017-02-10

    Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

  18. Development of on-off spiking in superior paraolivary nucleus neurons of the mouse

    Science.gov (United States)

    Felix, Richard A.; Vonderschen, Katrin; Berrebi, Albert S.

    2013-01-01

    The superior paraolivary nucleus (SPON) is a prominent cell group in the auditory brain stem that has been increasingly implicated in representing temporal sound structure. Although SPON neurons selectively respond to acoustic signals important for sound periodicity, the underlying physiological specializations enabling these responses are poorly understood. We used in vitro and in vivo recordings to investigate how SPON neurons develop intrinsic cellular properties that make them well suited for encoding temporal sound features. In addition to their hallmark rebound spiking at the stimulus offset, SPON neurons were characterized by spiking patterns termed onset, adapting, and burst in response to depolarizing stimuli in vitro. Cells with burst spiking had some morphological differences compared with other SPON neurons and were localized to the dorsolateral region of the nucleus. Both membrane and spiking properties underwent strong developmental regulation, becoming more temporally precise with age for both onset and offset spiking. Single-unit recordings obtained in young mice demonstrated that SPON neurons respond with temporally precise onset spiking upon tone stimulation in vivo, in addition to the typical offset spiking. Taken together, the results of the present study demonstrate that SPON neurons develop sharp on-off spiking, which may confer sensitivity to sound amplitude modulations or abrupt sound transients. These findings are consistent with the proposed involvement of the SPON in the processing of temporal sound structure, relevant for encoding communication cues. PMID:23515791

  19. Remifentanil-induced spike activity as a diagnostic tool in epilepsy surgery

    DEFF Research Database (Denmark)

    Grønlykke, L; Knudsen, M L; Høgenhaven, H

    2008-01-01

    To assess the value of remifentanil in intraoperative evaluation of spike activity in patients undergoing surgery for mesial temporal lobe epilepsy (MTLE).......To assess the value of remifentanil in intraoperative evaluation of spike activity in patients undergoing surgery for mesial temporal lobe epilepsy (MTLE)....

  20. A unified approach to linking experimental, statistical and computational analysis of spike train data.

    Directory of Open Access Journals (Sweden)

    Liang Meng

    Full Text Available A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data, but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach--linking statistical, computational, and experimental neuroscience--provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.