Replication of murine coronavirus requires multiple cysteines in the endodomain of spike protein

International Nuclear Information System (INIS)

Yang, Jinhua; Lv, Jun; Wang, Yuyan; Gao, Shuang; Yao, Qianqian; Qu, Di; Ye, Rong

2012-01-01

A conserved cysteine-rich motif located between the transmembrane domain and the endodomain is essential for membrane fusion and assembly of coronavirus spike (S) protein. Here, we proved that three cysteines within the motif, but not dependent on position, are minimally required for the survival of the recombinant mouse hepatitis virus. When the carboxy termini with these mutated motifs of S proteins were respectively introduced into a heterogeneous protein, both incorporation into lipid rafts and S-palmitoylation of these recombinant proteins showed a similar quantity requirement to cysteine residues. Meanwhile, the redistribution of these proteins on cellular surface indicated that the absence of the positively charged rather than cysteine residues in the motif might lead the dramatic reduction in syncytial formation of some mutants with the deleted motifs. These results suggest that multiple cysteine as well as charged residues concurrently improves the membrane-associated functions of S protein in viral replication and cytopathogenesis.

Replication of murine coronavirus requires multiple cysteines in the endodomain of spike protein

Energy Technology Data Exchange (ETDEWEB)

Yang, Jinhua; Lv, Jun; Wang, Yuyan; Gao, Shuang; Yao, Qianqian; Qu, Di; Ye, Rong, E-mail: yerong24@fudan.edu.cn

2012-06-05

A conserved cysteine-rich motif located between the transmembrane domain and the endodomain is essential for membrane fusion and assembly of coronavirus spike (S) protein. Here, we proved that three cysteines within the motif, but not dependent on position, are minimally required for the survival of the recombinant mouse hepatitis virus. When the carboxy termini with these mutated motifs of S proteins were respectively introduced into a heterogeneous protein, both incorporation into lipid rafts and S-palmitoylation of these recombinant proteins showed a similar quantity requirement to cysteine residues. Meanwhile, the redistribution of these proteins on cellular surface indicated that the absence of the positively charged rather than cysteine residues in the motif might lead the dramatic reduction in syncytial formation of some mutants with the deleted motifs. These results suggest that multiple cysteine as well as charged residues concurrently improves the membrane-associated functions of S protein in viral replication and cytopathogenesis.

Visualization and targeted disruption of protein interactions in living cells

Science.gov (United States)

Herce, Henry D.; Deng, Wen; Helma, Jonas; Leonhardt, Heinrich; Cardoso, M. Cristina

2013-01-01

Protein–protein interactions are the basis of all processes in living cells, but most studies of these interactions rely on biochemical in vitro assays. Here we present a simple and versatile fluorescent-three-hybrid (F3H) strategy to visualize and target protein–protein interactions. A high-affinity nanobody anchors a GFP-fusion protein of interest at a defined cellular structure and the enrichment of red-labelled interacting proteins is measured at these sites. With this approach, we visualize the p53–HDM2 interaction in living cells and directly monitor the disruption of this interaction by Nutlin 3, a drug developed to boost p53 activity in cancer therapy. We further use this approach to develop a cell-permeable vector that releases a highly specific peptide disrupting the p53 and HDM2 interaction. The availability of multiple anchor sites and the simple optical readout of this nanobody-based capture assay enable systematic and versatile analyses of protein–protein interactions in practically any cell type and species. PMID:24154492

Synthesis of Salt Soluble Proteins in Barley. Pulse-Labeling Study of Grain Filling in Liquid-Cultured Detached Spikes

DEFF Research Database (Denmark)

Giese, Nanna Henriette; Hejgaard, Jørn

1984-01-01

The accumulation of salt-soluble proteins in the endosperm of developing barley (Hordeum vulgare L.) grains was examined. Detached spikes of barley were cultured at different levels of nitrogen nutrition and pulse-labeled with [14C] sucrose at specific times after anthesis. Proteins were extracted...... to increased nitrogen nutrition. Two major components, β-amylase and protein Z in particular, had a synthesis profile almost identical to that of the endosperm storage protein, hordein....

Spike Protein Fusion Peptide and Feline Coronavirus Virulence

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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

A camel-derived MERS-CoV with a variant spike protein cleavage site and distinct fusion activation properties

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Millet, Jean Kaoru; Goldstein, Monty E; Labitt, Rachael N; Hsu, Hung-Lun; Daniel, Susan; Whittaker, Gary R

2016-01-01

Middle East respiratory syndrome coronavirus (MERS-CoV) continues to circulate in both humans and camels, and the origin and evolution of the virus remain unclear. Here we characterize the spike protein of a camel-derived MERS-CoV (NRCE-HKU205) identified in 2013, early in the MERS outbreak. NRCE-HKU205 spike protein has a variant cleavage motif with regard to the S2′ fusion activation site—notably, a novel substitution of isoleucine for the otherwise invariant serine at the critical P1′ cleavage site position. The substitutions resulted in a loss of furin-mediated cleavage, as shown by fluorogenic peptide cleavage and western blot assays. Cell–cell fusion and pseudotyped virus infectivity assays demonstrated that the S2′ substitutions decreased spike-mediated fusion and viral entry. However, cathepsin and trypsin-like protease activation were retained, albeit with much reduced efficiency compared with the prototypical EMC/2012 human strain. We show that NRCE-HKU205 has more limited fusion activation properties possibly resulting in more restricted viral tropism and may represent an intermediate in the complex pattern of MERS-CoV ecology and evolution. PMID:27999426

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.

Middle east respiratory syndrome coronavirus spike protein delivered by modified vaccinia virus ankara efficiently induces virus-neutralizing antibodies

NARCIS (Netherlands)

F. Song (Fei); R. Fux (Robert); L.B.V. Provacia (Lisette); A. Volz (Asisa); M. Eickmann; S. Becker (Stephan); A.D.M.E. Osterhaus (Albert); B.L. Haagmans (Bart); G. Suttera (Gerd)

2013-01-01

textabstractMiddle East respiratory syndrome coronavirus (MERS-CoV) has recently emerged as a causative agent of severe respiratory disease in humans. Here, we constructed recombinant modified vaccinia virus Ankara (MVA) expressing full-length MERS-CoV spike (S) protein (MVA-MERS-S). The genetic

Small heat-shock proteins and leaf cooling capacity account for the unusual heat tolerance of the central spike leaves in Agave tequilana var. Weber.

Science.gov (United States)

Luján, Rosario; Lledías, Fernando; Martínez, Luz María; Barreto, Rita; Cassab, Gladys I; Nieto-Sotelo, Jorge

2009-12-01

Agaves are perennial crassulacean acid metabolism (CAM) plants distributed in tropical and subtropical arid environments, features that are attractive for studying the heat-shock response. In agaves, the stress response can be analysed easily during leaf development, as they form a spirally shaped rosette, having the meristem surrounded by folded leaves in the centre (spike) and the unfolded and more mature leaves in the periphery. Here, we report that the spike of Agave tequilana is the most thermotolerant part of the rosette withstanding shocks of up to 55 degrees C. This finding was inconsistent with the patterns of heat-shock protein (Hsp) gene expression, as maximal accumulation of Hsp transcripts was at 44 degrees C in all sectors (spike, inner, middle and outer). However, levels of small HSP (sHSP)-CI and sHSP-CII proteins were conspicuously higher in spike leaves at all temperatures correlating with their thermotolerance. In addition, spike leaves showed a higher stomatal density and abated more efficiently their temperature several degrees below that of air. We propose that the greater capacity for leaf cooling during the day in response to heat stress, and the elevated levels of sHSPs, constitute part of a set of strategies that protect the SAM and folded leaves of A. tequilana from high temperatures.

Disruption of ten protease genes in the filamentous fungus Aspergillus oryzae highly improves production of heterologous proteins.

Science.gov (United States)

Yoon, Jaewoo; Maruyama, Jun-ichi; Kitamoto, Katsuhiko

2011-02-01

Proteolytic degradation by secreted proteases into the culture medium is one of the significant problems to be solved in heterologous protein production by filamentous fungi including Aspergillus oryzae. Double (tppA, and pepE) and quintuple (tppA, pepE, nptB, dppIV, and dppV) disruption of protease genes enhanced human lysozyme (HLY) and bovine chymosin (CHY) production by A. oryzae. In this study, we used a quintuple protease gene disruptant and performed successive rounds of disruption for five additional protease genes (alpA, pepA, AopepAa, AopepAd, and cpI), which were previously investigated by DNA microarray analyses for their expression. Gene disruption was performed by pyrG marker recycling with a highly efficient gene-targeting background (∆ligD) as previously reported. As a result, the maximum yields of recombinant CHY and HLY produced by a decuple protease gene disruptant were approximately 30% and 35%, respectively, higher than those produced by a quintuple protease gene disruptant. Thus, we successfully constructed a decuple protease gene disruptant possessing highly improved capability of heterologous protein production. This is the first report on decuple protease gene disruption that improved the levels of heterologous protein production by the filamentous fungus A. oryzae.

Mutations of 3c and spike protein genes correlate with the occurrence of feline infectious peritonitis.

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Bank-Wolf, Barbara Regina; Stallkamp, Iris; Wiese, Svenja; Moritz, Andreas; Tekes, Gergely; Thiel, Heinz-Jürgen

2014-10-10

The genes encoding accessory proteins 3a, 3b, 3c, 7a and 7b, the S2 domain of the spike (S) protein gene and the membrane (M) protein gene of feline infectious peritonitis virus (FIPV) and feline enteric coronavirus (FECV) samples were amplified, cloned and sequenced. For this faeces and/or ascites samples from 19 cats suffering from feline infectious peritonitis (FIP) as well as from 20 FECV-infected healthy cats were used. Sequence comparisons revealed that 3c genes of animals with FIP were heavily affected by nucleotide deletions and point mutations compared to animals infected with FECV; these alterations resulted either in early termination or destruction of the translation initiation codon. Two ascites-derived samples of cats with FIP which displayed no alterations of ORF3c harboured mutations in the S2 domain of the S protein gene which resulted in amino acid exchanges or deletions. Moreover, changes in 3c were often accompanied by mutations in S2. In contrast, in samples obtained from faeces of healthy cats, the ORF3c was never affected by such mutations. Similarly ORF3c from faecal samples of the cats with FIP was mostly intact and showed only in a few cases the same mutations found in the respective ascites samples. The genes encoding 3a, 3b, 7a and 7b displayed no mutations linked to the feline coronavirus (FCoV) biotype. The M protein gene was found to be conserved between FECV and FIPV samples. Our findings suggest that mutations of 3c and spike protein genes correlate with the occurrence of FIP. Copyright © 2014 Elsevier B.V. All rights reserved.

Evaluation of yolk protein as biomarkers for endocrine disruption in molluscs

DEFF Research Database (Denmark)

Morthorst, Jane Ebsen; Holbech, Henrik; Kinnberg, Karin Lund

is also regulated by estrogens in molluscs even though it still remains unknown if and where vertebrate steroids are synthesized in molluscs and regulation of the endocrine system in molluscs is also unknown. By using our newly developed ELISA the present work investigates if yolk protein is a suitable......During recent years invertebrates and especially molluscs have received increasing attention in the field of endocrine disruption and development of OECD test guidelines to assess the effects of endocrine disrupting compounds (EDCs) in molluscs is under development. The development of standardized...... tests to detect effects of EDCs in molluscs has proved cumbersome due to lack of specific biomarkers and endpoints for endocrine effects. Intersex (presence of oocytes in the testis) and induction of vitellogenin (the yolk protein precursor in oviparous vertebrates) have been used as biomarkers for EDCs...

Disrupted hippocampal sharp‐wave ripple‐associated spike dynamics in a transgenic mouse model of dementia

Science.gov (United States)

Witton, Jonathan; Staniaszek, Lydia E.; Bartsch, Ullrich; Randall, Andrew D.; Jones, Matthew W.

2015-01-01

Key points High frequency (100–250 Hz) neuronal oscillations in the hippocampus, known as sharp‐wave ripples (SWRs), synchronise the firing behaviour of groups of neurons and play a key role in memory consolidation.Learning and memory are severely compromised in dementias such as Alzheimer's disease; however, the effects of dementia‐related pathology on SWRs are unknown.The frequency and temporal structure of SWRs was disrupted in a transgenic mouse model of tauopathy (one of the major hallmarks of several dementias).Excitatory pyramidal neurons were more likely to fire action potentials in a phase‐locked manner during SWRs in the mouse model of tauopathy; conversely, inhibitory interneurons were less likely to fire phase‐locked spikes during SWRs.These findings indicate there is reduced inhibitory control of hippocampal network events and point to a novel mechanism which may underlie the cognitive impairments in this model of dementia. Abstract Neurons within the CA1 region of the hippocampus are co‐activated during high frequency (100–250 Hz) sharp‐wave ripple (SWR) activity in a manner that probably drives synaptic plasticity and promotes memory consolidation. In this study we have used a transgenic mouse model of dementia (rTg4510 mice), which overexpresses a mutant form of tau protein, to examine the effects of tauopathy on hippocampal SWRs and associated neuronal firing. Tetrodes were used to record simultaneous extracellular action potentials and local field potentials from the dorsal CA1 pyramidal cell layer of 7‐ to 8‐month‐old wild‐type and rTg4510 mice at rest in their home cage. At this age point these mice exhibit neurofibrillary tangles, neurodegeneration and cognitive deficits. Epochs of sleep or quiet restfulness were characterised by minimal locomotor activity and a low theta/delta ratio in the local field potential power spectrum. SWRs detected off‐line were significantly lower in amplitude and had an altered temporal

Cleavage of spike protein of SARS coronavirus by protease factor Xa is associated with viral infectivity

International Nuclear Information System (INIS)

Du, Lanying; Kao, Richard Y.; Zhou, Yusen; He, Yuxian; Zhao, Guangyu; Wong, Charlotte; Jiang, Shibo; Yuen, Kwok-Yung; Jin, Dong-Yan; Zheng, Bo-Jian

2007-01-01

The spike (S) protein of SARS coronavirus (SARS-CoV) has been known to recognize and bind to host receptors, whose conformational changes then facilitate fusion between the viral envelope and host cell membrane, leading to viral entry into target cells. However, other functions of SARS-CoV S protein such as proteolytic cleavage and its implications to viral infection are incompletely understood. In this study, we demonstrated that the infection of SARS-CoV and a pseudovirus bearing the S protein of SARS-CoV was inhibited by a protease inhibitor Ben-HCl. Also, the protease Factor Xa, a target of Ben-HCl abundantly expressed in infected cells, was able to cleave the recombinant and pseudoviral S protein into S1 and S2 subunits, and the cleavage was inhibited by Ben-HCl. Furthermore, this cleavage correlated with the infectivity of the pseudovirus. Taken together, our study suggests a plausible mechanism by which SARS-CoV cleaves its S protein to facilitate viral infection

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.

Current density distribution during disruptions and sawteeth in a simple model of plasma current in a tokamak

International Nuclear Information System (INIS)

Stefanovskii, A. M.

2011-01-01

The processes that are likely to accompany discharge disruptions and sawteeth in a tokamak are considered in a simple plasma current model. The redistribution of the current density in plasma is supposed to be primarily governed by the onset of the MHD-instability-driven turbulent plasma mixing in a finite region of the current column. For different disruption conditions, the variation in the total plasma current (the appearance of a characteristic spike) is also calculated. It is found that the numerical shape and amplitude of the total current spikes during disruptions approximately coincide with those measured in some tokamak experiments. Under the assumptions adopted in the model, the physical mechanism for the formation of the spikes is determined. The mechanism is attributed to the diffusion of the negative current density at the column edge into the zero-conductivity region. The numerical current density distributions in the plasma during the sawteeth differ from the literature data.

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

Generalization of fear inhibition by disrupting hippocampal protein synthesis-dependent reconsolidation process.

Science.gov (United States)

Yang, Chih-Hao; Huang, Chiung-Chun; Hsu, Kuei-Sen

2011-09-01

Repetitive replay of fear memories may precipitate the occurrence of post-traumatic stress disorder and other anxiety disorders. Hence, the suppression of fear memory retrieval may help prevent and treat these disorders. The formation of fear memories is often linked to multiple environmental cues and these interconnected cues may act as reminders for the recall of traumatic experiences. However, as a convenience, a simple paradigm of one cue pairing with the aversive stimulus is usually used in studies of fear conditioning in animals. Here, we built a more complex fear conditioning model by presenting several environmental stimuli during fear conditioning and characterize the effectiveness of extinction training and the disruption of reconsolidation process on the expression of learned fear responses. We demonstrate that extinction training with a single-paired cue resulted in cue-specific attenuation of fear responses but responses to other cures were unchanged. The cue-specific nature of the extinction persisted despite training sessions combined with D-cycloserine treatment reveals a significant weakness in extinction-based treatment. In contrast, the inhibition of the dorsal hippocampus (DH) but not the basolateral amygdala (BLA)-dependent memory reconsolidation process using either protein synthesis inhibitors or genetic disruption of cAMP-response-element-binding protein-mediated transcription comprehensively disrupted the learned connections between fear responses and all paired environmental cues. These findings emphasize the distinct role of the DH and the BLA in the reconsolidation process of fear memories and further indicate that the disruption of memory reconsolidation process in the DH may result in generalization of fear inhibition.

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.

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)

Perceptual consequences of disrupted auditory nerve activity.

Science.gov (United States)

Zeng, Fan-Gang; Kong, Ying-Yee; Michalewski, Henry J; Starr, Arnold

2005-06-01

Perceptual consequences of disrupted auditory nerve activity were systematically studied in 21 subjects who had been clinically diagnosed with auditory neuropathy (AN), a recently defined disorder characterized by normal outer hair cell function but disrupted auditory nerve function. Neurological and electrophysical evidence suggests that disrupted auditory nerve activity is due to desynchronized or reduced neural activity or both. Psychophysical measures showed that the disrupted neural activity has minimal effects on intensity-related perception, such as loudness discrimination, pitch discrimination at high frequencies, and sound localization using interaural level differences. In contrast, the disrupted neural activity significantly impairs timing related perception, such as pitch discrimination at low frequencies, temporal integration, gap detection, temporal modulation detection, backward and forward masking, signal detection in noise, binaural beats, and sound localization using interaural time differences. These perceptual consequences are the opposite of what is typically observed in cochlear-impaired subjects who have impaired intensity perception but relatively normal temporal processing after taking their impaired intensity perception into account. These differences in perceptual consequences between auditory neuropathy and cochlear damage suggest the use of different neural codes in auditory perception: a suboptimal spike count code for intensity processing, a synchronized spike code for temporal processing, and a duplex code for frequency processing. We also proposed two underlying physiological models based on desynchronized and reduced discharge in the auditory nerve to successfully account for the observed neurological and behavioral data. These methods and measures cannot differentiate between these two AN models, but future studies using electric stimulation of the auditory nerve via a cochlear implant might. These results not only show the unique

Unfolded protein response (UPR) signaling regulates arsenic trioxide-mediated macrophage innate immune function disruption

International Nuclear Information System (INIS)

Srivastava, Ritesh K.; Li, Changzhao; Chaudhary, Sandeep C.; Ballestas, Mary E.; Elmets, Craig A.; Robbins, David J.; Matalon, Sadis; Deshane, Jessy S.; Afaq, Farrukh; Bickers, David R.; Athar, Mohammad

2013-01-01

Arsenic exposure is known to disrupt innate immune functions in humans and in experimental animals. In this study, we provide a mechanism by which arsenic trioxide (ATO) disrupts macrophage functions. ATO treatment of murine macrophage cells diminished internalization of FITC-labeled latex beads, impaired clearance of phagocytosed fluorescent bacteria and reduced secretion of pro-inflammatory cytokines. These impairments in macrophage functions are associated with ATO-induced unfolded protein response (UPR) signaling pathway characterized by the enhancement in proteins such as GRP78, p-PERK, p-eIF2α, ATF4 and CHOP. The expression of these proteins is altered both at transcriptional and translational levels. Pretreatment with chemical chaperon, 4-phenylbutyric acid (PBA) attenuated the ATO-induced activation in UPR signaling and afforded protection against ATO-induced disruption of macrophage functions. This treatment also reduced ATO-mediated reactive oxygen species (ROS) generation. Interestingly, treatment with antioxidant N-acetylcysteine (NAC) prior to ATO exposure, not only reduced ROS production and UPR signaling but also improved macrophage functions. These data demonstrate that UPR signaling and ROS generation are interdependent and are involved in the arsenic-induced pathobiology of macrophage. These data also provide a novel strategy to block the ATO-dependent impairment in innate immune responses. - Highlights: • Inorganic arsenic to humans and experimental animals disrupt innate immune responses. • The mechanism underlying arsenic impaired macrophage functions involves UPR signaling. • Chemical chaperon attenuates arsenic-mediated macrophage function impairment. • Antioxidant, NAC blocks impairment in arsenic-treated macrophage functions

Unfolded protein response (UPR) signaling regulates arsenic trioxide-mediated macrophage innate immune function disruption

Energy Technology Data Exchange (ETDEWEB)

Srivastava, Ritesh K.; Li, Changzhao; Chaudhary, Sandeep C. [Department of Dermatology and Skin Diseases Research Center, University of Alabama at Birmingham, Birmingham, AL (United States); Ballestas, Mary E. [Department of Pediatrics Infectious Disease, Children' s of Alabama, School of Medicine, University of Alabama at Birmingham, AL (United States); Elmets, Craig A. [Department of Dermatology and Skin Diseases Research Center, University of Alabama at Birmingham, Birmingham, AL (United States); Robbins, David J. [Department of Surgery, Molecular Oncology Program, Miller School of Medicine, University of Miami, Miami (United States); Matalon, Sadis [Department of Anesthesiology, University of Alabama at Birmingham, Birmingham, AL (United States); Deshane, Jessy S. [Department of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL (United States); Afaq, Farrukh [Department of Dermatology and Skin Diseases Research Center, University of Alabama at Birmingham, Birmingham, AL (United States); Bickers, David R. [Department of Dermatology, Columbia University Medical Center, New York (United States); Athar, Mohammad, E-mail: mathar@uab.edu [Department of Dermatology and Skin Diseases Research Center, University of Alabama at Birmingham, Birmingham, AL (United States)

2013-11-01

Arsenic exposure is known to disrupt innate immune functions in humans and in experimental animals. In this study, we provide a mechanism by which arsenic trioxide (ATO) disrupts macrophage functions. ATO treatment of murine macrophage cells diminished internalization of FITC-labeled latex beads, impaired clearance of phagocytosed fluorescent bacteria and reduced secretion of pro-inflammatory cytokines. These impairments in macrophage functions are associated with ATO-induced unfolded protein response (UPR) signaling pathway characterized by the enhancement in proteins such as GRP78, p-PERK, p-eIF2α, ATF4 and CHOP. The expression of these proteins is altered both at transcriptional and translational levels. Pretreatment with chemical chaperon, 4-phenylbutyric acid (PBA) attenuated the ATO-induced activation in UPR signaling and afforded protection against ATO-induced disruption of macrophage functions. This treatment also reduced ATO-mediated reactive oxygen species (ROS) generation. Interestingly, treatment with antioxidant N-acetylcysteine (NAC) prior to ATO exposure, not only reduced ROS production and UPR signaling but also improved macrophage functions. These data demonstrate that UPR signaling and ROS generation are interdependent and are involved in the arsenic-induced pathobiology of macrophage. These data also provide a novel strategy to block the ATO-dependent impairment in innate immune responses. - Highlights: • Inorganic arsenic to humans and experimental animals disrupt innate immune responses. • The mechanism underlying arsenic impaired macrophage functions involves UPR signaling. • Chemical chaperon attenuates arsenic-mediated macrophage function impairment. • Antioxidant, NAC blocks impairment in arsenic-treated macrophage functions.

Peptide-Based Membrane Fusion Inhibitors Targeting HCoV-229E Spike Protein HR1 and HR2 Domains

Directory of Open Access Journals (Sweden)

Shuai Xia

2018-02-01

Full Text Available Human coronavirus 229E (HCoV-229E infection in infants, elderly people, and immunocompromised patients can cause severe disease, thus calling for the development of effective and safe therapeutics to treat it. Here we reported the design, synthesis and characterization of two peptide-based membrane fusion inhibitors targeting HCoV-229E spike protein heptad repeat 1 (HR1 and heptad repeat 2 (HR2 domains, 229E-HR1P and 229E-HR2P, respectively. We found that 229E-HR1P and 229E-HR2P could interact to form a stable six-helix bundle and inhibit HCoV-229E spike protein-mediated cell-cell fusion with IC50 of 5.7 and 0.3 µM, respectively. 229E-HR2P effectively inhibited pseudotyped and live HCoV-229E infection with IC50 of 0.5 and 1.7 µM, respectively. In a mouse model, 229E-HR2P administered intranasally could widely distribute in the upper and lower respiratory tracts and maintain its fusion-inhibitory activity. Therefore, 229E-HR2P is a promising candidate for further development as an antiviral agent for the treatment and prevention of HCoV-229E infection.

Modification of an acetone-sodium dodecyl sulfate disruption method for cellular protein extraction from neuropathogenic Clostridium botulinum

Science.gov (United States)

An acetone-sodium dodecyl sulfate (SDS) disruption method was used for the extraction of cellular proteins from neurotoxigenic Clostridium botulinum. The amount of protein extracted per gram of dry weight and the protein profile as revealed by polyacrylamide gel electrophoresis (PAGE) was comparabl...

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...

Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

International Nuclear Information System (INIS)

Zhe, Sun; Micheletto, Ruggero

2016-01-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh−Rose (H−R) neural networks. (paper)

Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

Science.gov (United States)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

Prediction of density limit disruptions on the J-TEXT tokamak

International Nuclear Information System (INIS)

Wang, S Y; Chen, Z Y; Huang, D W; Tong, R H; Yan, W; Wei, Y N; Ma, T K; Zhang, M; Zhuang, G

2016-01-01

Disruption mitigation is essential for the next generation of tokamaks. The prediction of plasma disruption is the key to disruption mitigation. A neural network combining eight input signals has been developed to predict the density limit disruptions on the J-TEXT tokamak. An optimized training method has been proposed which has improved the prediction performance. The network obtained has been tested on 64 disruption shots and 205 non-disruption shots. A successful alarm rate of 82.8% with a false alarm rate of 12.3% can be achieved at 4.8 ms prior to the current spike of the disruption. It indicates that more physical parameters than the current physical scaling should be considered for predicting the density limit. It was also found that the critical density for disruption can be predicted several tens of milliseconds in advance in most cases. Furthermore, if the network is used for real-time density feedback control, more than 95% of the density limit disruptions can be avoided by setting a proper threshold. (paper)

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.

Improved heterologous protein production by a tripeptidyl peptidase gene (AosedD) disruptant of the filamentous fungus Aspergillus oryzae.

Science.gov (United States)

Zhu, Lin; Nemoto, Takeshi; Yoon, Jaewoo; Maruyama, Jun-ichi; Kitamoto, Katsuhiko

2012-01-01

Proteolytic degradation is one of the serious bottlenecks limiting the yields of heterologous protein production by Aspergillus oryzae. In this study, we selected a tripeptidyl peptidase gene AosedD (AO090166000084) as a candidate potentially degrading the heterologous protein, and performed localization analysis of the fusion protein AoSedD-EGFP in A. oryzae. As a result, the AoSedD-EGFP was observed in the septa and cell walls as well as in the culture medium, suggesting that AoSedD is a secretory enzyme. An AosedD disruptant was constructed to investigate an effect of AoSedD on the production level of heterologous proteins and protease activity. Both of the total protease and tripeptidyl peptidase activities in the culture medium of the AosedD disruptant were decreased as compared to those of the control strain. The maximum yields of recombinant bovine chymosin (CHY) and human lysozyme (HLY) produced by the AosedD disruptants showed approximately 2.9- and 1.7-fold increases, respectively, as compared to their control strains. These results suggest that AoSedD is one of the major proteases involved in the proteolytic degradation of recombinant proteins in A. oryzae.

Membrane Incorporation, Channel Formation, and Disruption of Calcium Homeostasis by Alzheimer's β-Amyloid Protein

Directory of Open Access Journals (Sweden)

Masahiro Kawahara

2011-01-01

Full Text Available Oligomerization, conformational changes, and the consequent neurodegeneration of Alzheimer's β-amyloid protein (AβP play crucial roles in the pathogenesis of Alzheimer's disease (AD. Mounting evidence suggests that oligomeric AβPs cause the disruption of calcium homeostasis, eventually leading to neuronal death. We have demonstrated that oligomeric AβPs directly incorporate into neuronal membranes, form cation-sensitive ion channels (“amyloid channels”, and cause the disruption of calcium homeostasis via the amyloid channels. Other disease-related amyloidogenic proteins, such as prion protein in prion diseases or α-synuclein in dementia with Lewy bodies, exhibit similarities in the incorporation into membranes and the formation of calcium-permeable channels. Here, based on our experimental results and those of numerous other studies, we review the current understanding of the direct binding of AβP into membrane surfaces and the formation of calcium-permeable channels. The implication of composition of membrane lipids and the possible development of new drugs by influencing membrane properties and attenuating amyloid channels for the treatment and prevention of AD is also discussed.

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...

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.

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

Ablation of whirlin long isoform disrupts the USH2 protein complex and causes vision and hearing loss.

Directory of Open Access Journals (Sweden)

Jun Yang

2010-05-01

Full Text Available Mutations in whirlin cause either Usher syndrome type II (USH2, a deafness-blindness disorder, or nonsyndromic deafness. The molecular basis for the variable disease expression is unknown. We show here that only the whirlin long isoform, distinct from a short isoform by virtue of having two N-terminal PDZ domains, is expressed in the retina. Both long and short isoforms are expressed in the inner ear. The N-terminal PDZ domains of the long whirlin isoform mediates the formation of a multi-protein complex that includes usherin and VLGR1, both of which are also implicated in USH2. We localized this USH2 protein complex to the periciliary membrane complex (PMC in mouse photoreceptors that appears analogous to the frog periciliary ridge complex. The latter is proposed to play a role in photoreceptor protein trafficking through the connecting cilium. Mice carrying a targeted disruption near the N-terminus of whirlin manifest retinal and inner ear defects, reproducing the clinical features of human USH2 disease. This is in contrast to mice with mutations affecting the C-terminal portion of whirlin in which the phenotype is restricted to the inner ear. In mice lacking any one of the USH2 proteins, the normal localization of all USH2 proteins is disrupted, and there is evidence of protein destabilization. Taken together, our findings provide new insights into the pathogenic mechanism of Usher syndrome. First, the three USH2 proteins exist as an obligatory functional complex in vivo, and loss of one USH2 protein is functionally close to loss of all three. Second, defects in the three USH2 proteins share a common pathogenic process, i.e., disruption of the PMC. Third, whirlin mutations that ablate the N-terminal PDZ domains lead to Usher syndrome, but non-syndromic hearing loss will result if they are spared.

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

Decoding spikes in a spiking neuronal network

Energy Technology Data Exchange (ETDEWEB)

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.

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

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

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

Amino acid changes in the spike protein of feline coronavirus correlate with systemic spread of virus from the intestine and not with feline infectious peritonitis.

Science.gov (United States)

Porter, Emily; Tasker, Séverine; Day, Michael J; Harley, Ross; Kipar, Anja; Siddell, Stuart G; Helps, Christopher R

2014-04-25

Recent evidence suggests that a mutation in the spike protein gene of feline coronavirus (FCoV), which results in an amino acid change from methionine to leucine at position 1058, may be associated with feline infectious peritonitis (FIP). Tissue and faecal samples collected post mortem from cats diagnosed with or without FIP were subjected to RNA extraction and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) to detect FCoV RNA. In cats with FIP, 95% of tissue, and 81% of faecal samples were PCR-positive, as opposed to 22% of tissue, and 60% of faecal samples in cats without FIP. Relative FCoV copy numbers were significantly higher in the cats with FIP, both in tissues (P < 0.001) and faeces (P = 0.02). PCR-positive samples underwent pyrosequencing encompassing position 1058 of the FCoV spike protein. This identified a methionine codon at position 1058, consistent with the shedding of an enteric form of FCoV, in 77% of the faecal samples from cats with FIP, and in 100% of the samples from cats without FIP. In contrast, 91% of the tissue samples from cats with FIP and 89% from cats without FIP had a leucine codon at position 1058, consistent with a systemic form of FCoV. These results suggest that the methionine to leucine substitution at position 1058 in the FCoV spike protein is indicative of systemic spread of FCoV from the intestine, rather than a virus with the potential to cause FIP.

EBV tegument protein BNRF1 disrupts DAXX-ATRX to activate viral early gene transcription.

Directory of Open Access Journals (Sweden)

Kevin Tsai

2011-11-01

Full Text Available Productive infection by herpesviruses involve the disabling of host-cell intrinsic defenses by viral encoded tegument proteins. Epstein-Barr Virus (EBV typically establishes a non-productive, latent infection and it remains unclear how it confronts the host-cell intrinsic defenses that restrict viral gene expression. Here, we show that the EBV major tegument protein BNRF1 targets host-cell intrinsic defense proteins and promotes viral early gene activation. Specifically, we demonstrate that BNRF1 interacts with the host nuclear protein Daxx at PML nuclear bodies (PML-NBs and disrupts the formation of the Daxx-ATRX chromatin remodeling complex. We mapped the Daxx interaction domain on BNRF1, and show that this domain is important for supporting EBV primary infection. Through reverse transcription PCR and infection assays, we show that BNRF1 supports viral gene expression upon early infection, and that this function is dependent on the Daxx-interaction domain. Lastly, we show that knockdown of Daxx and ATRX induces reactivation of EBV from latently infected lymphoblastoid cell lines (LCLs, suggesting that Daxx and ATRX play a role in the regulation of viral chromatin. Taken together, our data demonstrate an important role of BNRF1 in supporting EBV early infection by interacting with Daxx and ATRX; and suggest that tegument disruption of PML-NB-associated antiviral resistances is a universal requirement for herpesvirus infection in the nucleus.

EBV Tegument Protein BNRF1 Disrupts DAXX-ATRX to Activate Viral Early Gene Transcription

Science.gov (United States)

Tsai, Kevin; Thikmyanova, Nadezhda; Wojcechowskyj, Jason A.; Delecluse, Henri-Jacques; Lieberman, Paul M.

2011-01-01

Productive infection by herpesviruses involve the disabling of host-cell intrinsic defenses by viral encoded tegument proteins. Epstein-Barr Virus (EBV) typically establishes a non-productive, latent infection and it remains unclear how it confronts the host-cell intrinsic defenses that restrict viral gene expression. Here, we show that the EBV major tegument protein BNRF1 targets host-cell intrinsic defense proteins and promotes viral early gene activation. Specifically, we demonstrate that BNRF1 interacts with the host nuclear protein Daxx at PML nuclear bodies (PML-NBs) and disrupts the formation of the Daxx-ATRX chromatin remodeling complex. We mapped the Daxx interaction domain on BNRF1, and show that this domain is important for supporting EBV primary infection. Through reverse transcription PCR and infection assays, we show that BNRF1 supports viral gene expression upon early infection, and that this function is dependent on the Daxx-interaction domain. Lastly, we show that knockdown of Daxx and ATRX induces reactivation of EBV from latently infected lymphoblastoid cell lines (LCLs), suggesting that Daxx and ATRX play a role in the regulation of viral chromatin. Taken together, our data demonstrate an important role of BNRF1 in supporting EBV early infection by interacting with Daxx and ATRX; and suggest that tegument disruption of PML-NB-associated antiviral resistances is a universal requirement for herpesvirus infection in the nucleus. PMID:22102817

Loosenin, a novel protein with cellulose-disrupting activity from Bjerkandera adusta.

Science.gov (United States)

Quiroz-Castañeda, Rosa E; Martínez-Anaya, Claudia; Cuervo-Soto, Laura I; Segovia, Lorenzo; Folch-Mallol, Jorge L

2011-02-11

Expansins and expansin-like proteins loosen cellulose microfibrils, possibly through the rupture of intramolecular hydrogen bonds. Together with the use of lignocellulolytic enzymes, these proteins are potential molecular tools to treat plant biomass to improve saccharification yields. Here we describe a new type of expansin-related fungal protein that we have called loosenin. Its corresponding gene, loos1, from the basidiomycete Bjerkandera adusta, was cloned and heterologously expressed in Saccharomyces cerevisiae. LOOS1 is distantly related to plant expansins through the shared presence of a DPBB domain, however domain II found in plant expansins is absent. LOOS1 binds tightly to cellulose and chitin, and we demonstrate that cotton fibers become susceptible to the action of a commercial cellulase following treatment with LOOS1. Natural fibers of Agave tequilana also become susceptible to hydrolysis by cellulases after loosenin treatment. LOOS1 is a new type of protein with disrupting activity on cellulose. LOOS1 binds polysaccharides, and given its enhancing properties on the action of hydrolytic enzymes, LOOS1 represents a potential additive in the production of fermentable sugars from lignocellulose.

Modulation of the spike activity of neocortex neurons during a conditioned reflex.

Science.gov (United States)

Storozhuk, V M; Sanzharovskii, A V; Sachenko, V V; Busel, B I

2000-01-01

Experiments were conducted on cats to study the effects of iontophoretic application of glutamate and a number of modulators on the spike activity of neurons in the sensorimotor cortex during a conditioned reflex. These studies showed that glutamate, as well as exerting a direct influence on neuron spike activity, also had a delayed facilitatory action lasting 10-20 min after iontophoresis was finished. Adrenomimetics were found to have a double modulatory effect on intracortical glutamate connections: inhibitory and facilitatory effects were mediated by beta1 and beta2 adrenoceptors respectively. Although dopamine, like glutamate, facilitated neuron spike activity during the period of application, the simultaneous facilitatory actions of glutamate and L-DOPA were accompanied by occlusion of spike activity, and simultaneous application of glutamate and haloperidol suppressed spike activity associated with the conditioned reflex response. Facilitation thus appears to show a significant level of dependence on metabotropic glutamate receptors which, like dopamine receptors, are linked to the intracellular medium via Gi proteins.

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.

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.

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.

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.

Arsenic mediated disruption of promyelocytic leukemia protein nuclear bodies induces ganciclovir susceptibility in Epstein-Barr positive epithelial cells

International Nuclear Information System (INIS)

Sides, Mark D.; Block, Gregory J.; Shan, Bin; Esteves, Kyle C.; Lin, Zhen; Flemington, Erik K.; Lasky, Joseph A.

2011-01-01

Promyelocytic leukemia protein nuclear bodies (PML NBs) have been implicated in host immune response to viral infection. PML NBs are targeted for degradation during reactivation of herpes viruses, suggesting that disruption of PML NB function supports this aspect of the viral life cycle. The Epstein-Barr virus (EBV) Latent Membrane Protein 1 (LMP1) has been shown to suppress EBV reactivation. Our finding that LMP1 induces PML NB immunofluorescence intensity led to the hypothesis that LMP1 may modulate PML NBs as a means of maintaining EBV latency. Increased PML protein and morphometric changes in PML NBs were observed in EBV infected alveolar epithelial cells and nasopharyngeal carcinoma cells. Treatment with low dose arsenic trioxide disrupted PML NBs, induced expression of EBV lytic proteins, and conferred ganciclovir susceptibility. This study introduces an effective modality to induce susceptibility to ganciclovir in epithelial cells with implications for the treatment of EBV associated pathologies.

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.

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

Further enhanced production of heterologous proteins by double-gene disruption (ΔAosedD ΔAovps10) in a hyper-producing mutant of Aspergillus oryzae.

Science.gov (United States)

Zhu, Lin; Maruyama, Jun-ichi; Kitamoto, Katsuhiko

2013-07-01

The filamentous fungus Aspergillus oryzae is used as one of the most favored hosts for heterologous protein production due to its ability to secrete large amounts of proteins into the culture medium. We previously generated a hyper-producing mutant strain of A. oryzae, AUT1, which produced 3.2- and 2.6-fold higher levels of bovine chymosin (CHY) and human lysozyme (HLY), respectively, compared with the wild-type strain. However, further enhancement of heterologous protein production by multiple gene disruption is difficult because of the low gene-targeting efficiency in strain AUT1. Here, we disrupted the ligD gene, which is involved in nonhomologous recombination, and the pyrG gene to create uridine/uracil auxotrophy in strain AUT1, to generate a hyper-producing mutant applicable to pyrG marker recycling with highly efficient gene targeting. We generated single and double disruptants of the tripeptidyl peptidase gene AosedD and vacuolar sorting receptor gene Aovps10 in the hyper-producing mutant background, and found that all disruptants showed significant increases in heterologous protein production. Particularly, double disruption of the Aovps10 and AosedD genes increased the production levels of CHY and HLY by 1.6- and 2.1-fold, respectively, compared with the parental strain. Thus, we successfully generated a fungal host for further enhancing the heterologous protein production ability by combining mutational and molecular breeding techniques.

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.

Loosenin, a novel protein with cellulose-disrupting activity from Bjerkandera adusta

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Segovia Lorenzo

2011-02-01

Full Text Available Abstract Background Expansins and expansin-like proteins loosen cellulose microfibrils, possibly through the rupture of intramolecular hydrogen bonds. Together with the use of lignocellulolytic enzymes, these proteins are potential molecular tools to treat plant biomass to improve saccharification yields. Results Here we describe a new type of expansin-related fungal protein that we have called loosenin. Its corresponding gene, loos1, from the basidiomycete Bjerkandera adusta, was cloned and heterologously expressed in Saccharomyces cerevisiae. LOOS1 is distantly related to plant expansins through the shared presence of a DPBB domain, however domain II found in plant expansins is absent. LOOS1 binds tightly to cellulose and chitin, and we demonstrate that cotton fibers become susceptible to the action of a commercial cellulase following treatment with LOOS1. Natural fibers of Agave tequilana also become susceptible to hydrolysis by cellulases after loosenin treatment. Conclusions LOOS1 is a new type of protein with disrupting activity on cellulose. LOOS1 binds polysaccharides, and given its enhancing properties on the action of hydrolytic enzymes, LOOS1 represents a potential additive in the production of fermentable sugars from lignocellulose.

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

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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.

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

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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.

Petri Net-Based Model of Helicobacter pylori Mediated Disruption of Tight Junction Proteins in Stomach Lining during Gastric Carcinoma

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Anam Naz

2017-09-01

Full Text Available Tight junctions help prevent the passage of digestive enzymes and microorganisms through the space between adjacent epithelial cells lining. However, Helicobacter pylori encoded virulence factors negatively regulate these tight junctions and contribute to dysfunction of gastric mucosa. Here, we have predicted the regulation of important tight junction proteins, such as Zonula occludens-1, Claudin-2 and Connexin32 in the presence of pathogenic proteins. Molecular events such as post translational modifications and crosstalk between phosphorylation, O-glycosylation, palmitoylation and methylation are explored which may compromise the integrity of these tight junction proteins. Furthermore, the signaling pathways disrupted by dysregulated kinases, proteins and post-translational modifications are reviewed to design an abstracted computational model showing the situation-dependent dynamic behaviors of these biological processes and entities. A qualitative hybrid Petri Net model is therefore constructed showing the altered host pathways in the presence of virulence factor cytotoxin-associated gene A, leading to the disruption of tight junction proteins. The model is qualitative logic-based, which does not depend on any kinetic parameter and quantitative data and depends on knowledge derived from experiments. The designed model provides insights into the tight junction disruption and disease progression. Model is then verified by the available experimental data, nevertheless formal in vitro experimentation is a promising way to ensure its validation. The major findings propose that H. pylori activated kinases are responsible to trigger specific post translational modifications within tight junction proteins, at specific sites. These modifications may favor alterations in gastric barrier and provide a route to bacterial invasion into host cells.

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

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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.

Use of non-conventional cell disruption method for extraction of proteins from black yeasts

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Maja eLeitgeb

2016-04-01

Full Text Available The influence of pressure and treatment time on cells disruption of different black yeasts and on activities of extracted proteins using supercritical carbon dioxide process was studied. The cells of three different black yeasts Phaeotheca triangularis, Trimatostroma salinum and Wallemia ichthyophaga were exposed to supercritical carbon dioxide (SC CO2 by varying pressure at fixed temperature (35 °C. The black yeasts cell walls were disrupted and the content of the cells was spilled into the liquid medium. The impact of SC CO2 conditions on secretion of enzymes and proteins from black yeast cells suspension was studied. The residual activity of the enzymes cellulase, β-glucosidase, α-amylase and protease was studied by enzymatic assay. The viability of black yeast cells was determined by measuring the optical density of the cell suspension at 600 nm. The total protein concentration in the suspension was determined on UV-Vis spectrophotometer at 595 nm. The release of intracellular and extracellular products from black yeast cells was achieved. Also, the observation by an environmental scanning electron microscopy shows major morphological changes with SC CO2 treated cells. The advantages of the proposed method are in a simple use which is also possible for heat sensitive materials on one hand and on the other hand integration of the extraction of enzymes and their use in biocatalytical reactions.

Use of Non-Conventional Cell Disruption Method for Extraction of Proteins from Black Yeasts

Science.gov (United States)

Čolnik, Maja; Primožič, Mateja; Knez, Željko; Leitgeb, Maja

2016-01-01

The influence of pressure and treatment time on cells disruption of different black yeasts and on activities of extracted proteins using supercritical carbon dioxide process was studied. The cells of three different black yeasts Phaeotheca triangularis, Trimatostroma salinum, and Wallemia ichthyophaga were exposed to supercritical carbon dioxide (SC CO2) by varying pressure at fixed temperature (35°C). The black yeasts cell walls were disrupted, and the content of the cells was spilled into the liquid medium. The impact of SC CO2 conditions on secretion of enzymes and proteins from black yeast cells suspension was studied. The residual activity of the enzymes cellulase, β-glucosidase, α-amylase, and protease was studied by enzymatic assay. The viability of black yeast cells was determined by measuring the optical density of the cell suspension at 600 nm. The total protein concentration in the suspension was determined on UV–Vis spectrophotometer at 595 nm. The release of intracellular and extracellular products from black yeast cells was achieved. Also, the observation by an environmental scanning electron microscopy shows major morphological changes with SC CO2-treated cells. The advantages of the proposed method are in a simple use, which is also possible for heat-sensitive materials on one hand and on the other hand integration of the extraction of enzymes and their use in biocatalytical reactions. PMID:27148527

Plasma membrane disruption: repair, prevention, adaptation

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McNeil, Paul L.; Steinhardt, Richard A.

2003-01-01

Many metazoan cells inhabit mechanically stressful environments and, consequently, their plasma membranes are frequently disrupted. Survival requires that the cell rapidly repair or reseal the disruption. Rapid resealing is an active and complex structural modification that employs endomembrane as its primary building block, and cytoskeletal and membrane fusion proteins as its catalysts. Endomembrane is delivered to the damaged plasma membrane through exocytosis, a ubiquitous Ca2+-triggered response to disruption. Tissue and cell level architecture prevent disruptions from occurring, either by shielding cells from damaging levels of force, or, when this is not possible, by promoting safe force transmission through the plasma membrane via protein-based cables and linkages. Prevention of disruption also can be a dynamic cell or tissue level adaptation triggered when a damaging level of mechanical stress is imposed. Disease results from failure of either the preventive or resealing mechanisms.

The structure of the bacteriophage PRD1 spike sheds light on the evolution of viral capsid architecture.

Science.gov (United States)

Merckel, Michael C; Huiskonen, Juha T; Bamford, Dennis H; Goldman, Adrian; Tuma, Roman

2005-04-15

Comparisons of bacteriophage PRD1 and adenovirus protein structures and virion architectures have been instrumental in unraveling an evolutionary relationship and have led to a proposal of a phylogeny-based virus classification. The structure of the PRD1 spike protein P5 provides further insight into the evolution of viral proteins. The crystallized P5 fragment comprises two structural domains: a globular knob and a fibrous shaft. The head folds into a ten-stranded jelly roll beta barrel, which is structurally related to the tumor necrosis factor (TNF) and the PRD1 coat protein domains. The shaft domain is a structural counterpart to the adenovirus spike shaft. The structural relationships between PRD1, TNF, and adenovirus proteins suggest that the vertex proteins may have originated from an ancestral TNF-like jelly roll coat protein via a combination of gene duplication and deletion.

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.

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

Proteolytic activation of the SARS-coronavirus spike protein: cutting enzymes at the cutting edge of antiviral research.

Science.gov (United States)

Simmons, Graham; Zmora, Pawel; Gierer, Stefanie; Heurich, Adeline; Pöhlmann, Stefan

2013-12-01

The severe acute respiratory syndrome (SARS) pandemic revealed that zoonotic transmission of animal coronaviruses (CoV) to humans poses a significant threat to public health and warrants surveillance and the development of countermeasures. The activity of host cell proteases, which cleave and activate the SARS-CoV spike (S) protein, is essential for viral infectivity and constitutes a target for intervention. However, the identities of the proteases involved have been unclear. Pioneer studies identified cathepsins and type II transmembrane serine proteases as cellular activators of SARS-CoV and demonstrated that several emerging viruses might exploit these enzymes to promote their spread. Here, we will review the proteolytic systems hijacked by SARS-CoV for S protein activation, we will discuss their contribution to viral spread in the host and we will outline antiviral strategies targeting these enzymes. This paper forms part of a series of invited articles in Antiviral Research on "From SARS to MERS: 10years of research on highly pathogenic human coronaviruses.'' Copyright © 2013 Elsevier B.V. All rights reserved.

Immune responses induced by recombinant Bacillus subtilis expressing the spike protein of transmissible gastroenteritis virus in pigs.

Science.gov (United States)

Mou, Chunxiao; Zhu, Liqi; Xing, Xianping; Lin, Jian; Yang, Qian

2016-07-01

Transmissible gastroenteritis (TGE) causes severe diarrhea in suckling piglets, results in enormous economic loss in swine-producing areas of the world. To develop an effective, safe, and convenient vaccine for the prevention of TGE, we have constructed a recombinant Bacillus subtilis strain (B. subtilis CotGSG) displaying the transmissible gastroenteritis virus (TGEV) spike (S) protein and discussed its immune function to intestinal submucosal dendritic cells (DCs). Our results showed that the recombinant B. subtilis had the ability to recruit more DCs to sample B. subtilis CotGSG, migrate to MLNs, and induce immune responses. Immunized piglets with B. subtilis CotGSG could significantly elevate the specific SIgA titers in feces, IgG titers and neutralizing antibodies in serum. Collectively, our results suggested that recombinant B. subtilis CotGSG expressing the TGEV S protein could effectively induce immune responses via DCs, and provided a perspective on potential novel strategy and approach that may be applicable to the development of the next generation of TGEV vaccines. Copyright © 2016 Elsevier B.V. All rights reserved.

A Peptidomimetic Antibiotic Targets Outer Membrane Proteins and Disrupts Selectively the Outer Membrane in Escherichia coli.

Science.gov (United States)

Urfer, Matthias; Bogdanovic, Jasmina; Lo Monte, Fabio; Moehle, Kerstin; Zerbe, Katja; Omasits, Ulrich; Ahrens, Christian H; Pessi, Gabriella; Eberl, Leo; Robinson, John A

2016-01-22

Increasing antibacterial resistance presents a major challenge in antibiotic discovery. One attractive target in Gram-negative bacteria is the unique asymmetric outer membrane (OM), which acts as a permeability barrier that protects the cell from external stresses, such as the presence of antibiotics. We describe a novel β-hairpin macrocyclic peptide JB-95 with potent antimicrobial activity against Escherichia coli. This peptide exhibits no cellular lytic activity, but electron microscopy and fluorescence studies reveal an ability to selectively disrupt the OM but not the inner membrane of E. coli. The selective targeting of the OM probably occurs through interactions of JB-95 with selected β-barrel OM proteins, including BamA and LptD as shown by photolabeling experiments. Membrane proteomic studies reveal rapid depletion of many β-barrel OM proteins from JB-95-treated E. coli, consistent with induction of a membrane stress response and/or direct inhibition of the Bam folding machine. The results suggest that lethal disruption of the OM by JB-95 occurs through a novel mechanism of action at key interaction sites within clusters of β-barrel proteins in the OM. These findings open new avenues for developing antibiotics that specifically target β-barrel proteins and the integrity of the Gram-negative OM. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

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.

Adverse effects of fullerenes (nC{sub 60}) spiked to sediments on Lumbriculus variegatus (Oligochaeta)

Energy Technology Data Exchange (ETDEWEB)

Pakarinen, K., E-mail: kukka.tervonen@uef.fi [Department of Biology, University of Eastern Finland, 80101 Joensuu (Finland); Petersen, E.J. [Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD (United States); Leppaenen, M.T.; Akkanen, J.; Kukkonen, J.V.K. [Department of Biology, University of Eastern Finland, 80101 Joensuu (Finland)

2011-12-15

Effects of fullerene-spiked sediment on a benthic organism, Lumbriculus variegatus (Oligochaeta), were investigated. Survival, growth, reproduction, and feeding rates were measured to assess possible adverse effects of fullerene agglomerates produced by water stirring and then spiked to a natural sediment. L. variegatus were exposed to 10 and 50 mg fullerenes/kg sediment dry mass for 28 d. These concentrations did not impact worm survival or reproduction compared to the control. Feeding activities were slightly decreased for both concentrations indicating fullerenes' disruptive effect on feeding. Depuration efficiency decreased in the high concentration only. Electron and light microscopy and extraction of the worm fecal pellets revealed fullerene agglomerates in the gut tract but not absorption into gut epithelial cells. Micrographs also indicated that 16% of the epidermal cuticle fibers of the worms were not present in the 50 mg/kg exposures, which may make worms susceptible to other contaminants. - Highlights: > Effects of fullerene-spiked sediment on black worms were investigated. > Survival, growth, reproduction, and feeding rates were measured. > Exposure did not impact worm survival or reproduction. > Feeding rates and depuration efficiency were decreased. > Worms transferred fullerenes from the sediment to the sediment surface. - Exposure to fullerene-spiked sediment decreased black worms' feeding and depuration efficiency, but fullerenes did not appear to be absorbed into the microvilli.

Kinetic Modeling of Methionine Oxidation in Monoclonal Antibodies from Hydrogen Peroxide Spiking Studies.

Science.gov (United States)

Hui, Ada; Lam, Xanthe M; Kuehl, Christopher; Grauschopf, Ulla; Wang, Y John

2015-01-01

When isolator technology is applied to biotechnology drug product fill-finish process, hydrogen peroxide (H2O2) spiking studies for the determination of the sensitivity of protein to residual peroxide in the isolator can be useful for assessing a maximum vapor phase hydrogen peroxide (VPHP) level. When monoclonal antibody (mAb) drug products were spiked with H2O2, an increase in methionine (Met 252 and Met 428) oxidation in the Fc region of the mAbs with a decrease in H2O2 concentration was observed for various levels of spiked-in peroxide. The reaction between Fc-Met and H2O2 was stoichiometric (i.e., 1:1 molar ratio), and the reaction rate was dependent on the concentrations of mAb and H2O2. The consumption of H2O2 by Fc-Met oxidation in the mAb followed pseudo first-order kinetics, and the rate was proportional to mAb concentration. The extent of Met 428 oxidation was half of that of Met 252, supporting that Met 252 is twice as reactive as Met 428. Similar results were observed for free L-methionine when spiked with H2O2. However, mAb formulation excipients may affect the rate of H2O2 consumption. mAb formulations containing trehalose or sucrose had faster H2O2 consumption rates than formulations without the sugars, which could be the result of impurities (e.g., metal ions) present in the excipients that may act as catalysts. Based on the H2O2 spiking study results, we can predict the amount Fc-Met oxidation for a given protein concentration and H2O2 level. Our kinetic modeling of the reaction between Fc-Met oxidation and H2O2 provides an outline to design a H2O2 spiking study to support the use of VPHP isolator for antibody drug product manufacture. Isolator technology is increasing used in drug product manufacturing of biotherapeutics. In order to understand the impact of residual vapor phase hydrogen peroxide (VPHP) levels on protein product quality, hydrogen peroxide (H2O2) spiking studies may be performed to determine the sensitivity of monoclonal antibody

Deficient incorporation of spike protein into virions contributes to the lack of infectivity following establishment of a persistent, non-productive infection in oligodendroglial cell culture by murine coronavirus

International Nuclear Information System (INIS)

Liu Yin; Herbst, Werner; Cao Jianzhong; Zhang Xuming

2011-01-01

Infection of mouse oligodendrocytes with a recombinant mouse hepatitis virus (MHV) expressing a green fluorescence protein facilitated specific selection of virus-infected cells and subsequent establishment of persistence. Interestingly, while viral genomic RNAs persisted in infected cells over 14 subsequent passages with concomitant synthesis of viral subgenomic mRNAs and structural proteins, no infectious virus was isolated beyond passage 2. Further biochemical and electron microscopic analyses revealed that virions, while assembled, contained little spike in the envelope, indicating that lack of infectivity during persistence was likely due to deficiency in spike incorporation. This type of non-lytic, non-productive persistence in oligodendrocytes is unique among animal viruses and resembles MHV persistence previously observed in the mouse central nervous system. Thus, establishment of such a culture system that can recapitulate the in vivo phenomenon will provide a powerful approach for elucidating the mechanisms of coronavirus persistence in glial cells at the cellular and molecular levels.

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.

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.

Evidence for a neurophysiologic auditory deficit in children with benign epilepsy with centro-temporal spikes.

Science.gov (United States)

Liasis, A; Bamiou, D E; Boyd, S; Towell, A

2006-07-01

Benign focal epilepsy in childhood with centro-temporal spikes (BECTS) is one of the most common forms of epilepsy. Recent studies have questioned the benign nature of BECTS, as they have revealed neuropsychological deficits in many domains including language. The aim of this study was to investigate whether the epileptic discharges during the night have long-term effects on auditory processing, as reflected on electrophysiological measures, during the day, which could underline the language deficits. In order to address these questions we recorded base line electroencephalograms (EEG), sleep EEG and auditory event related potentials in 12 children with BECTS and in age- and gender-matched controls. In the children with BECTS, 5 had unilateral and 3 had bilateral spikes. In the 5 patients with unilateral spikes present during sleep, an asymmetry of the auditory event related component (P85-120) was observed contralateral to the side of epileptiform activity compared to the normal symmetrical vertex distribution that was noted in all controls and in 3 the children with bilateral spikes. In all patients the peak to peak amplitude of this event related potential component was statistically greater compared to the controls. Analysis of subtraction waveforms (deviant - standard) revealed no evidence of a mismatch negativity component in any of the children with BECTS. We propose that the abnormality of P85-120 and the absence of mismatch negativity during wake recordings in this group may arise in response to the long-term effects of spikes occurring during sleep, resulting in disruption of the evolution and maintenance of echoic memory traces. These results may indicate that patients with BECTS have abnormal processing of auditory information at a sensory level ipsilateral to the hemisphere evoking spikes during sleep.

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

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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

The host-binding domain of the P2 phage tail spike reveals a trimeric iron-binding structure

International Nuclear Information System (INIS)

Yamashita, Eiki; Nakagawa, Atsushi; Takahashi, Junichi; Tsunoda, Kin-ichi; Yamada, Seiko; Takeda, Shigeki

2011-01-01

The C-terminal domain of a bacteriophage P2 tail-spike protein, gpV, was crystallized and its structure was solved at 1.27 Å resolution. The refined model showed a triple β-helix structure and the presence of iron, calcium and chloride ions. The adsorption and infection of bacteriophage P2 is mediated by tail fibres and tail spikes. The tail spikes on the tail baseplate are used to irreversibly adsorb to the host cells. Recently, a P2 phage tail-spike protein, gpV, was purified and it was shown that a C-terminal domain, Ser87–Leu211, is sufficient for the binding of gpV to host Escherichia coli membranes [Kageyama et al. (2009 ▶), Biochemistry, 48, 10129–10135]. In this paper, the crystal structure of the C-terminal domain of P2 gpV is reported. The structure is a triangular pyramid and looks like a spearhead composed of an intertwined β-sheet, a triple β-helix and a metal-binding region containing iron, calcium and chloride ions

Human coronavirus 229E encodes a single ORF4 protein between the spike and the envelope genes

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Berkhout Ben

2006-12-01

Full Text Available Abstract Background The genome of coronaviruses contains structural and non-structural genes, including several so-called accessory genes. All group 1b coronaviruses encode a single accessory protein between the spike and envelope genes, except for human coronavirus (HCoV 229E. The prototype virus has a split gene, encoding the putative ORF4a and ORF4b proteins. To determine whether primary HCoV-229E isolates exhibit this unusual genome organization, we analyzed the ORF4a/b region of five current clinical isolates from The Netherlands and three early isolates collected at the Common Cold Unit (CCU in Salisbury, UK. Results All Dutch isolates were identical in the ORF4a/b region at amino acid level. All CCU isolates are only 98% identical to the Dutch isolates at the nucleotide level, but more closely related to the prototype HCoV-229E (>98%. Remarkably, our analyses revealed that the laboratory adapted, prototype HCoV-229E has a 2-nucleotide deletion in the ORF4a/b region, whereas all clinical isolates carry a single ORF, 660 nt in size, encoding a single protein of 219 amino acids, which is a homologue of the ORF3 proteins encoded by HCoV-NL63 and PEDV. Conclusion Thus, the genome organization of the group 1b coronaviruses HCoV-NL63, PEDV and HCoV-229E is identical. It is possible that extensive culturing of the HCoV-229E laboratory strain resulted in truncation of ORF4. This may indicate that the protein is not essential in cell culture, but the highly conserved amino acid sequence of the ORF4 protein among clinical isolates suggests that the protein plays an important role in vivo.

Improved SpikeProp for Using Particle Swarm Optimization

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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.

Spiked proteomic standard dataset for testing label-free quantitative software and statistical methods

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Claire Ramus

2016-03-01

Full Text Available This data article describes a controlled, spiked proteomic dataset for which the “ground truth” of variant proteins is known. It is based on the LC-MS analysis of samples composed of a fixed background of yeast lysate and different spiked amounts of the UPS1 mixture of 48 recombinant proteins. It can be used to objectively evaluate bioinformatic pipelines for label-free quantitative analysis, and their ability to detect variant proteins with good sensitivity and low false discovery rate in large-scale proteomic studies. More specifically, it can be useful for tuning software tools parameters, but also testing new algorithms for label-free quantitative analysis, or for evaluation of downstream statistical methods. The raw MS files can be downloaded from ProteomeXchange with identifier http://www.ebi.ac.uk/pride/archive/projects/PXD001819. Starting from some raw files of this dataset, we also provide here some processed data obtained through various bioinformatics tools (including MaxQuant, Skyline, MFPaQ, IRMa-hEIDI and Scaffold in different workflows, to exemplify the use of such data in the context of software benchmarking, as discussed in details in the accompanying manuscript [1]. The experimental design used here for data processing takes advantage of the different spike levels introduced in the samples composing the dataset, and processed data are merged in a single file to facilitate the evaluation and illustration of software tools results for the detection of variant proteins with different absolute expression levels and fold change values.

Receptor-binding domain of SARS-CoV spike protein induces highly potent neutralizing antibodies: implication for developing subunit vaccine

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He Yuxian; Zhou Yusen; Liu Shuwen; Kou Zhihua; Li Wenhui; Farzan, Michael; Jiang Shibo

2004-01-01

The spike (S) protein of severe acute respiratory syndrome (SARS) coronavirus (CoV), a type I transmembrane envelope glycoprotein, consists of S1 and S2 domains responsible for virus binding and fusion, respectively. The S1 contains a receptor-binding domain (RBD) that can specifically bind to angiotensin-converting enzyme 2 (ACE2), the receptor on target cells. Here we show that a recombinant fusion protein (designated RBD-Fc) containing 193-amino acid RBD (residues 318-510) and a human IgG1 Fc fragment can induce highly potent antibody responses in the immunized rabbits. The antibodies recognized RBD on S1 domain and completely inhibited SARS-CoV infection at a serum dilution of 1:10,240. Rabbit antisera effectively blocked binding of S1, which contains RBD, to ACE2. This suggests that RBD can induce highly potent neutralizing antibody responses and has potential to be developed as an effective and safe subunit vaccine for prevention of SARS

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

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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.

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

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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.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

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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.

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

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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.

Endothelium-targeted overexpression of heat shock protein 27 ameliorates blood–brain barrier disruption after ischemic brain injury

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Jiang, Xiaoyan; Zhang, Lili; Pu, Hongjian; Hu, Xiaoming; Zhang, Wenting; Cai, Wei; Gao, Yanqin; Leak, Rehana K.; Keep, Richard F.; Bennett, Michael V. L.; Chen, Jun

2017-01-01

The damage borne by the endothelial cells (ECs) forming the blood–brain barrier (BBB) during ischemic stroke and other neurological conditions disrupts the structure and function of the neurovascular unit and contributes to poor patient outcomes. We recently reported that structural aberrations in brain microvascular ECs—namely, uncontrolled actin polymerization and subsequent disassembly of junctional proteins, are a possible cause of the early onset BBB breach that arises within 30–60 min of reperfusion after transient focal ischemia. Here, we investigated the role of heat shock protein 27 (HSP27) as a direct inhibitor of actin polymerization and protectant against BBB disruption after ischemia/reperfusion (I/R). Using in vivo and in vitro models, we found that targeted overexpression of HSP27 specifically within ECs—but not within neurons—ameliorated BBB impairment 1–24 h after I/R. Mechanistically, HSP27 suppressed I/R-induced aberrant actin polymerization, stress fiber formation, and junctional protein translocation in brain microvascular ECs, independent of its protective actions against cell death. By preserving BBB integrity after I/R, EC-targeted HSP27 overexpression attenuated the infiltration of potentially destructive neutrophils and macrophages into brain parenchyma, thereby improving long-term stroke outcome. Notably, early poststroke administration of HSP27 attached to a cell-penetrating transduction domain (TAT-HSP27) rapidly elevated HSP27 levels in brain microvessels and ameliorated I/R-induced BBB disruption and subsequent neurological deficits. Thus, the present study demonstrates that HSP27 can function at the EC level to preserve BBB integrity after I/R brain injury. HSP27 may be a therapeutic agent for ischemic stroke and other neurological conditions involving BBB breakdown. PMID:28137866

Spike-based population coding and working memory.

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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.

Consensus-Based Sorting of Neuronal Spike Waveforms.

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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.

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

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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

The thermal-spike model description of the ion-irradiated polyimide

International Nuclear Information System (INIS)

Sun Youmei; Zhang Chonghong; Zhu Zhiyong; Wang Zhiguang; Jin Yunfan; Liu Jie; Wang Ying

2004-01-01

To describe the role of electronic energy loss (dE/dX) e for chemical modification of polyimide (PI), multi-layer stacks (corresponding to different dE/dX) were irradiated by different swift heavy ions (1.158 GeV Fe 56 and 1.755 GeV Xe 136 ) under vacuum and at room temperature. Chemical changes of modified PI films were studied by Fourier transform infrared (FTIR) spectroscopy. The chain disruption rate of PI was investigated in the fluence range from 1 x 10 11 to 6 x 10 12 ions/cm 2 and a wider energy stopping power range (2.2-5.1 keV/nm for Fe 56 ions and 8.6-11.5 keV/nm for Xe 136 ions). Alkyne formation was observed over the electronic energy loss range of interest. By applying the saturated track model assumption (the damage process only occur in a cylinder of area σ), the mean degradation and alkyne formation radii in tracks were induced for Fe and Xe ion irradiation, respectively. The results were validated by the thermal-spike model. The analysis of the irradiated PI films shows that the predictions of the thermal-spike model of Szenes are in qualitative agreement with the curve shape of experimental results

Disruption of a Ciliary B9 Protein Complex Causes Meckel Syndrome

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Dowdle, William E.; Robinson, Jon F.; Kneist, Andreas; Sirerol-Piquer, M. Salomé; Frints, Suzanna G.M.; Corbit, Kevin C.; Zaghloul, Norran A.; van Lijnschoten, Gesina; Mulders, Leon; Verver, Dideke E.; Zerres, Klaus; Reed, Randall R.; Attié-Bitach, Tania; Johnson, Colin A.; García-Verdugo, José Manuel; Katsanis, Nicholas; Bergmann, Carsten; Reiter, Jeremy F.

2011-01-01

Nearly every ciliated organism possesses three B9 domain-containing proteins: MKS1, B9D1, and B9D2. Mutations in human MKS1 cause Meckel syndrome (MKS), a severe ciliopathy characterized by occipital encephalocele, liver ductal plate malformations, polydactyly, and kidney cysts. Mouse mutations in either Mks1 or B9d2 compromise ciliogenesis and result in phenotypes similar to those of MKS. Given the importance of these two B9 proteins to ciliogenesis, we examined the role of the third B9 protein, B9d1. Mice lacking B9d1 displayed polydactyly, kidney cysts, ductal plate malformations, and abnormal patterning of the neural tube, concomitant with compromised ciliogenesis, ciliary protein localization, and Hedgehog (Hh) signal transduction. These data prompted us to screen MKS patients for mutations in B9D1 and B9D2. We identified a homozygous c.301A>C (p.Ser101Arg) B9D2 mutation that segregates with MKS, affects an evolutionarily conserved residue, and is absent from controls. Unlike wild-type B9D2 mRNA, the p.Ser101Arg mutation failed to rescue zebrafish phenotypes induced by the suppression of b9d2. With coimmunoprecipitation and mass spectrometric analyses, we found that Mks1, B9d1, and B9d2 interact physically, but that the p.Ser101Arg mutation abrogates the ability of B9d2 to interact with Mks1, further suggesting that the mutation compromises B9d2 function. Our data indicate that B9d1 is required for normal Hh signaling, ciliogenesis, and ciliary protein localization and that B9d1 and B9d2 are essential components of a B9 protein complex, disruption of which causes MKS. PMID:21763481

Spike voltage topography in temporal lobe epilepsy.

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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.

Conditioned taste aversion memory and c-Fos induction are disrupted in RIIbeta-protein kinase A mutant mice.

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Koh, Ming Teng; Clarke, Sharon N D A; Spray, Kristina J; Thiele, Todd E; Bernstein, Ilene L

2003-07-14

The cAMP-dependent protein kinase (PKA) signaling pathway has been implicated in many forms of learning. The present studies examined conditioned taste aversion (CTA) learning, an amygdala-dependent task, in mice with a targeted disruption of a gene for a specific regulatory subunit of PKA (RIIbeta), which is selectively expressed in amygdala. Null mutant (RIIbeta(-/-)) mice and littermate controls (RIIbeta(+/+)) were tested for protein synthesis-independent short-term memory (STM) and protein synthesis-dependent long-term memory (LTM) for CTAs. The ability of the unconditioned stimulus (US) drug, LiCl, to induce c-Fos in regions thought to be important in this learning was also determined. RIIbeta(-/-) mice showed significant impairment in CTA memory when tested 24h after training (LTM). In contrast, STM was normal. With regard to the c-Fos response to LiCl, the US drug, significant elevations were evident in brainstem (nucleus of the solitary tract) and pontine (parabrachial nucleus) regions, in mutants as well as wild-type controls. However, in amygdala, elevations were seen in controls but were absent in the mutants. These findings suggest that disruption of PKA signaling interferes with LTM consolidation of CTA and that a possible mediator of this effect is interference with c-Fos expression in amygdala which may be necessary for CTA memory.

Multineuronal Spike Sequences Repeat with Millisecond Precision

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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.

Spiking Neural P Systems with Communication on Request.

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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.

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

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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

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.

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)

α-Synuclein-induced lysosomal dysfunction occurs through disruptions in protein trafficking in human midbrain synucleinopathy models.

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Mazzulli, Joseph R; Zunke, Friederike; Isacson, Ole; Studer, Lorenz; Krainc, Dimitri

2016-02-16

Parkinson's disease (PD) is an age-related neurodegenerative disorder characterized by the accumulation of protein aggregates comprised of α-synuclein (α-syn). A major barrier in treatment discovery for PD is the lack of identifiable therapeutic pathways capable of reducing aggregates in human neuronal model systems. Mutations in key components of protein trafficking and cellular degradation machinery represent important risk factors for PD; however, their precise role in disease progression and interaction with α-syn remains unclear. Here, we find that α-syn accumulation reduced lysosomal degradation capacity in human midbrain dopamine models of synucleinopathies through disrupting hydrolase trafficking. Accumulation of α-syn at the cell body resulted in aberrant association with cis-Golgi-tethering factor GM130 and disrupted the endoplasmic reticulum-Golgi localization of rab1a, a key mediator of vesicular transport. Overexpression of rab1a restored Golgi structure, improved hydrolase trafficking and activity, and reduced pathological α-syn in patient neurons. Our work suggests that enhancement of lysosomal hydrolase trafficking may prove beneficial in synucleinopathies and indicates that human midbrain disease models may be useful for identifying critical therapeutic pathways in PD and related disorders.

Wavelet analysis of epileptic spikes

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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.

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.

Particulate matter air pollution disrupts endothelial cell barrier via calpain-mediated tight junction protein degradation

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Wang Ting

2012-08-01

Full Text Available Abstract Background Exposure to particulate matter (PM is a significant risk factor for increased cardiopulmonary morbidity and mortality. The mechanism of PM-mediated pathophysiology remains unknown. However, PM is proinflammatory to the endothelium and increases vascular permeability in vitro and in vivo via ROS generation. Objectives We explored the role of tight junction proteins as targets for PM-induced loss of lung endothelial cell (EC barrier integrity and enhanced cardiopulmonary dysfunction. Methods Changes in human lung EC monolayer permeability were assessed by Transendothelial Electrical Resistance (TER in response to PM challenge (collected from Ft. McHenry Tunnel, Baltimore, MD, particle size >0.1 μm. Biochemical assessment of ROS generation and Ca2+ mobilization were also measured. Results PM exposure induced tight junction protein Zona occludens-1 (ZO-1 relocation from the cell periphery, which was accompanied by significant reductions in ZO-1 protein levels but not in adherens junction proteins (VE-cadherin and β-catenin. N-acetyl-cysteine (NAC, 5 mM reduced PM-induced ROS generation in ECs, which further prevented TER decreases and atteneuated ZO-1 degradation. PM also mediated intracellular calcium mobilization via the transient receptor potential cation channel M2 (TRPM2, in a ROS-dependent manner with subsequent activation of the Ca2+-dependent protease calpain. PM-activated calpain is responsible for ZO-1 degradation and EC barrier disruption. Overexpression of ZO-1 attenuated PM-induced endothelial barrier disruption and vascular hyperpermeability in vivo and in vitro. Conclusions These results demonstrate that PM induces marked increases in vascular permeability via ROS-mediated calcium leakage via activated TRPM2, and via ZO-1 degradation by activated calpain. These findings support a novel mechanism for PM-induced lung damage and adverse cardiovascular outcomes.

Characterization of novel monoclonal antibodies against the MERS-coronavirus spike protein and their application in species-independent antibody detection by competitive ELISA.

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Fukushi, Shuetsu; Fukuma, Aiko; Kurosu, Takeshi; Watanabe, Shumpei; Shimojima, Masayuki; Shirato, Kazuya; Iwata-Yoshikawa, Naoko; Nagata, Noriyo; Ohnishi, Kazuo; Ato, Manabu; Melaku, Simenew Keskes; Sentsui, Hiroshi; Saijo, Masayuki

2018-01-01

Since discovering the Middle East respiratory syndrome coronavirus (MERS-CoV) as a causative agent of severe respiratory illness in the Middle East in 2012, serological testing has been conducted to assess antibody responses in patients and to investigate the zoonotic reservoir of the virus. Although the virus neutralization test is the gold standard assay for MERS diagnosis and for investigating the zoonotic reservoir, it uses live virus and so must be performed in high containment laboratories. Competitive ELISA (cELISA), in which a labeled monoclonal antibody (MAb) competes with test serum antibodies for target epitopes, may be a suitable alternative because it detects antibodies in a species-independent manner. In this study, novel MAbs against the spike protein of MERS-CoV were produced and characterized. One of these MAbs was used to develop a cELISA. The cELISA detected MERS-CoV-specific antibodies in sera from MERS-CoV-infected rats and rabbits immunized with the spike protein of MERS-CoV. The MAb-based cELISA was validated using sera from Ethiopian dromedary camels. Relative to the neutralization test, the cELISA detected MERS-CoV-specific antibodies in 66 Ethiopian dromedary camels with a sensitivity and specificity of 98% and 100%, respectively. The cELISA and neutralization test results correlated well (Pearson's correlation coefficients=0.71-0.76, depending on the cELISA serum dilution). This cELISA may be useful for MERS epidemiological investigations on MERS-CoV infection. Copyright © 2017 Elsevier B.V. All rights reserved.

Vented spikes improve delivery from intravenous bags with no air headspace.

Science.gov (United States)

Galush, William J; Horst, Travis A

2015-07-01

Flexible plastic bags are the container of choice for most intravenous (i.v.) infusions. Under certain circumstances, however, the air-liquid interface present in these i.v. bags can lead to physical instability of protein biopharmaceuticals, resulting in product aggregation. In principle, the air headspace present in the bags can be removed to increase drug stability, but experiments described here show that this can result in incomplete draining of solution from the bag using gravity delivery, or generation of negative pressure in the bag when an infusion pump is used. It is expected that these issues could lead to incomplete delivery of medication to patients or pump-related problems, respectively. However, here it is shown that contrary to the standard pharmacy practice of using nonvented spikes with i.v. bags, the use of vented spikes with i.v. bags that lack air headspace allows complete delivery of the dose solution without impacting the physical stability of a protein-based drug. © 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.

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...

HLA-A*0201 T-cell epitopes in severe acute respiratory syndrome (SARS) coronavirus nucleocapsid and spike proteins

International Nuclear Information System (INIS)

Tsao, Y.-P.; Lin, J.-Y.; Jan, J.-T.; Leng, C.-H.; Chu, C.-C.; Yang, Y.-C.; Chen, S.-L.

2006-01-01

The immunogenicity of HLA-A*0201-restricted cytotoxic T lymphocyte (CTL) peptide in severe acute respiratory syndrome coronavirus (SARS-CoV) nuclear capsid (N) and spike (S) proteins was determined by testing the proteins' ability to elicit a specific cellular immune response after immunization of HLA-A2.1 transgenic mice and in vitro vaccination of HLA-A2.1 positive human peripheral blood mononuclearcytes (PBMCs). First, we screened SARS N and S amino acid sequences for allele-specific motif matching those in human HLA-A2.1 MHC-I molecules. From HLA peptide binding predictions (http://thr.cit.nih.gov/molbio/hla_bind/), ten each potential N- and S-specific HLA-A2.1-binding peptides were synthesized. The high affinity HLA-A2.1 peptides were validated by T2-cell stabilization assays, with immunogenicity assays revealing peptides N223-231, N227-235, and N317-325 to be First identified HLA-A*0201-restricted CTL epitopes of SARS-CoV N protein. In addition, previous reports identified three HLA-A*0201-restricted CTL epitopes of S protein (S978-986, S1203-1211, and S1167-1175), here we found two novel peptides S787-795 and S1042-1050 as S-specific CTL epitopes. Moreover, our identified N317-325 and S1042-1050 CTL epitopes could induce recall responses when IFN-γ stimulation of blood CD8 + T-cells revealed significant difference between normal healthy donors and SARS-recovered patients after those PBMCs were in vitro vaccinated with their cognate antigen. Our results would provide a new insight into the development of therapeutic vaccine in SARS

Prophylactic effect of rebamipide on aspirin-induced gastric lesions and disruption of tight junctional protein zonula occludens-1 distribution.

Science.gov (United States)

Suzuki, Takahiro; Yoshida, Norimasa; Nakabe, Nami; Isozaki, Yutaka; Kajikawa, Hirokazu; Takagi, Tomohisa; Handa, Osamu; Kokura, Satoshi; Ichikawa, Hiroshi; Naito, Yuji; Matsui, Hirofumi; Yoshikawa, Toshikazu

2008-03-01

Aspirin and nonsteroidal anti-inflammatory agents are known to induce gastroduodenal complications such as ulcer, bleeding, and dyspepsia. In this study, we examined the prophylactic effect of rebamipide, an anti-ulcer agent with free-radical scavenging and anti-inflammatory effect, on acidified aspirin-induced gastric mucosal injury in rats. In addition, we investigated the mucosal barrier functions disrupted by aspirin. Oral administration of acidified aspirin resulted in linear hemorrhagic erosions with increasing myeloperoxidase activity and thiobarbituric acid-reactive substance concentrations in the gastric mucosa. Rebamipide suppressed these acidified aspirin-induced gastric lesions and inflammatory changes significantly, and its protective effect was more potent in the case of repeated (twice daily for 3 days) treatment than single treatment before aspirin administration. Immunostaining of zonula occludens (ZO)-1, one of the tight junctional proteins, was strengthened in rat gastric mucosa after repeated administration of rebamipide. In addition, aspirin induced the increasing transport of fluorescine isothiocyanate-labeled dextrans with localized disruption and decreased expression of ZO-1 protein on rat gastric mucosal cell line RGM-1. Rebamipide effectively prevented aspirin-induced permeability changes and disruption of ZO-1 distribution. These results suggest that rebamipide protects against aspirin-induced gastric mucosal lesions by preserving gastric epithelial cell-to cell integrity in addition to the anti-inflammatory effects.

Generalized activity equations for spiking neural network dynamics

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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.

Liposome Disruption Assay to Examine Lytic Properties of Biomolecules.

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Jimah, John R; Schlesinger, Paul H; Tolia, Niraj H

2017-08-05

Proteins may have three dimensional structural or amino acid features that suggest a role in targeting and disrupting lipids within cell membranes. It is often necessary to experimentally investigate if these proteins and biomolecules are able to disrupt membranes in order to conclusively characterize the function of these biomolecules. Here, we describe an in vitro assay to evaluate the membrane lytic properties of proteins and biomolecules. Large unilamellar vesicles (liposomes) containing carboxyfluorescein at fluorescence-quenching concentrations are treated with the biomolecule of interest. A resulting increase in fluorescence due to leakage of the dye from liposomes and subsequent dilution in the buffer demonstrates that the biomolecule is sufficient for disrupting liposomes and membranes. Additionally, since liposome disruption may occur via pore-formation or via general solubilization of lipids similar to detergents, we provide a method to distinguish between these two mechanisms. Pore-formation can be identified and evaluated by examining the blockade of carboxyfluorescein release with dextran molecules that fit the pore. The methods described here were used to determine that the malaria vaccine candidate CelTOS and proapoptotic Bax disrupt liposomes by pore formation (Saito et al. , 2000; Jimah et al. , 2016). Since membrane lipid binding by a biomolecule precedes membrane disruption, we recommend the companion protocol: Jimah et al. , 2017.

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...

Dopamine D4 receptor activation increases hippocampal gamma oscillations by enhancing synchronization of fast-spiking interneurons.

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Richard Andersson

Full Text Available BACKGROUND: Gamma oscillations are electric activity patterns of the mammalian brain hypothesized to serve attention, sensory perception, working memory and memory encoding. They are disrupted or altered in schizophrenic patients with associated cognitive deficits, which persist in spite of treatment with antipsychotics. Because cognitive symptoms are a core feature of schizophrenia it is relevant to explore signaling pathways that potentially regulate gamma oscillations. Dopamine has been reported to decrease gamma oscillation power via D1-like receptors. Based on the expression pattern of D4 receptors (D4R in hippocampus, and pharmacological effects of D4R ligands in animals, we hypothesize that they are in a position to regulate gamma oscillations as well. METHODOLOGY/PRINCIPAL FINDINGS: To address this hypothesis we use rat hippocampal slices and kainate-induced gamma oscillations. Local field potential recordings as well as intracellular recordings of pyramidal cells, fast-spiking and non-fast-spiking interneurons were carried out. We show that D4R activation with the selective ligand PD168077 increases gamma oscillation power, which can be blocked by the D4R-specific antagonist L745,870 as well as by the antipsychotic drug Clozapine. Pyramidal cells did not exhibit changes in excitatory or inhibitory synaptic current amplitudes, but inhibitory currents became more coherent with the oscillations after application of PD168077. Fast-spiking, but not non-fast spiking, interneurons, increase their action potential phase-coupling and coherence with regard to ongoing gamma oscillations in response to D4R activation. Among several possible mechanisms we found that the NMDA receptor antagonist AP5 also blocks the D4R mediated increase in gamma oscillation power. CONCLUSIONS/SIGNIFICANCE: We conclude that D4R activation affects fast-spiking interneuron synchronization and thereby increases gamma power by an NMDA receptor-dependent mechanism. This

The Parkinson’s disease-associated protein α-synuclein disrupts stress signaling – a possible implication for methamphetamine use?

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Shaoxiao Wang

2014-03-01

Full Text Available The human neuronal protein α-synuclein (α-syn has been linked by a plethora of studies as a causative factor in sporadic Parkinson’s disease (PD. To speed the pace of discovery about the biology and pathobiology of α-syn, organisms such as yeast, worms, and flies have been used to investigate the mechanisms by which elevated levels of α-syn are toxic to cells and to screen for drugs and genes that suppress this toxicity. We recently reported [Wang et al. Proc. Natl. Acad. Sci.(2012 109: 16119–16124] that human α-syn, at high expression levels, disrupts stress-activated signal transduction pathways in both yeast and human neuroblastoma cells. Disruption of these signaling pathways ultimately leads to vulnerability to stress and to cell death. Here we discuss how the disruption of cell signaling by α-syn may have relevance to the parkinsonism that is associated with the abuse of the drug methamphetamine (meth.

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.

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

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

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.

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

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)

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.

Granzyme B Disrupts Central Metabolism and Protein Synthesis in Bacteria to Promote an Immune Cell Death Program.

Science.gov (United States)

Dotiwala, Farokh; Sen Santara, Sumit; Binker-Cosen, Andres Ariel; Li, Bo; Chandrasekaran, Sriram; Lieberman, Judy

2017-11-16

Human cytotoxic lymphocytes kill intracellular microbes. The cytotoxic granule granzyme proteases released by cytotoxic lymphocytes trigger oxidative bacterial death by disrupting electron transport, generating superoxide anion and inactivating bacterial oxidative defenses. However, they also cause non-oxidative cell death because anaerobic bacteria are also killed. Here, we use differential proteomics to identify granzyme B substrates in three unrelated bacteria: Escherichia coli, Listeria monocytogenes, and Mycobacteria tuberculosis. Granzyme B cleaves a highly conserved set of proteins in all three bacteria, which function in vital biosynthetic and metabolic pathways that are critical for bacterial survival under diverse environmental conditions. Key proteins required for protein synthesis, folding, and degradation are also substrates, including multiple aminoacyl tRNA synthetases, ribosomal proteins, protein chaperones, and the Clp system. Because killer cells use a multipronged strategy to target vital pathways, bacteria may not easily become resistant to killer cell attack. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

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.

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.

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.

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.

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...

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.

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

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.

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.

Surfing a spike wave down the ventral stream.

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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.

Protein/lipid coaggregates are formed during α-synuclein-induced disruption of lipid bilayers

DEFF Research Database (Denmark)

van Maarschalkerweerd, Andreas; Vetri, Valeria; Langkilde, Annette Eva

2014-01-01

Amyloid formation is associated with neurodegenerative diseases such as Parkinson's disease (PD). Significant α-synuclein (αSN) deposition in lipid-rich Lewy bodies is a hallmark of PD. Nonetheless, an unraveling of the connection between neurodegeneration and amyloid fibrils, including the molec......Amyloid formation is associated with neurodegenerative diseases such as Parkinson's disease (PD). Significant α-synuclein (αSN) deposition in lipid-rich Lewy bodies is a hallmark of PD. Nonetheless, an unraveling of the connection between neurodegeneration and amyloid fibrils, including...... the molecular mechanisms behind potential amyloid-mediated toxic effects, is still missing. Interaction between amyloid aggregates and the lipid cell membrane is expected to play a key role in the disease progress. Here, we present experimental data based on hybrid analysis of two-photon-microscopy, solution...... small-angle X-ray scattering and circular dichroism data. Data show in real time changes in liposome morphology and stability upon protein addition and reveal that membrane disruption mediated by amyloidogenic αSN is associated with dehydration of anionic lipid membranes and stimulation of protein...

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

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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.

Spike timing precision of neuronal circuits.

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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.

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

Lipid rafts regulate PCB153-induced disruption of occludin and brain endothelial barrier function through protein phosphatase 2A and matrix metalloproteinase-2

Energy Technology Data Exchange (ETDEWEB)

Eum, Sung Yong, E-mail: seum@miami.edu; Jaraki, Dima; András, Ibolya E.; Toborek, Michal

2015-09-15

Occludin is an essential integral transmembrane protein regulating tight junction (TJ) integrity in brain endothelial cells. Phosphorylation of occludin is associated with its localization to TJ sites and incorporation into intact TJ assembly. The present study is focused on the role of lipid rafts in polychlorinated biphenyl (PCB)-induced disruption of occludin and endothelial barrier function. Exposure of human brain endothelial cells to 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB153) induced dephosphorylation of threonine residues of occludin and displacement of occludin from detergent-resistant membrane (DRM)/lipid raft fractions within 1 h. Moreover, lipid rafts modulated the reduction of occludin level through activation of matrix metalloproteinase 2 (MMP-2) after 24 h PCB153 treatment. Inhibition of protein phosphatase 2A (PP2A) activity by okadaic acid or fostriecin markedly protected against PCB153-induced displacement of occludin and increased permeability of endothelial cells. The implication of lipid rafts and PP2A signaling in these processes was further defined by co-immunoprecipitation of occludin with PP2A and caveolin-1, a marker protein of lipid rafts. Indeed, a significant MMP-2 activity was observed in lipid rafts and was increased by exposure to PCB153. The pretreatment of MMP-2 inhibitors protected against PCB153-induced loss of occludin and disruption of lipid raft structure prevented the increase of endothelial permeability. Overall, these results indicate that lipid raft-associated processes, such as PP2A and MMP-2 activation, participate in PCB153-induced disruption of occludin function in brain endothelial barrier. This study contributes to a better understanding of the mechanisms leading to brain endothelial barrier dysfunction in response to exposure to environmental pollutants, such as ortho-substituted PCBs. - Highlights: • PCB153 disturbed human brain endothelial barrier through disruption of occludin. • Lipid raft-associated PP

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.

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

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

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.

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.

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.

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

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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.

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

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.

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.

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.

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.

A study on antigenicity and receptor-binding ability of fragment 450-650 of the spike protein of SARS coronavirus

International Nuclear Information System (INIS)

Zhao Jincun; Wang Wei; Yuan Zhihong; Jia Rujing; Zhao Zhendong; Xu Xiaojun; Lv Ping; Zhang Yan; Jiang Chengyu; Gao Xiaoming

2007-01-01

The spike (S) protein of SARS coronavirus (SARS-CoV) is responsible for viral binding with ACE2 molecules. Its receptor-binding motif (S-RBM) is located between residues 424 and 494, which folds into 2 anti-parallel β-sheets, β5 and β6. We have previously demonstrated that fragment 450-650 of the S protein (S450-650) is predominantly recognized by convalescent sera of SARS patients. The N-terminal 60 residues (450-510) of the S450-650 fragment covers the entire β6 strand of S-RBM. In the present study, we demonstrate that patient sera predominantly recognized 2 linear epitopes outside the β6 fragment, while the mouse antisera, induced by immunization of BALB/c mice with recombinant S450-650, mainly recognized the β6 strand-containing region. Unlike patient sera, however, the mouse antisera were unable to inhibit the infectivity of S protein-expressing (SARS-CoV-S) pseudovirus. Fusion protein between green fluorescence protein (GFP) and S450-650 (S450-650-GFP) was able to stain Vero E6 cells and deletion of the β6 fragment rendered the fusion product (S511-650-GFP) unable to do so. Similarly, recombinant S450-650, but not S511-650, was able to block the infection of Vero E6 cells by the SARS-CoV-S pseudovirus. Co-precipitation experiments confirmed that S450-650 was able to specifically bind with ACE2 molecules in lysate of Vero E6 cells. However, the ability of S450-510, either alone or in fusion with GFP, to bind with ACE2 was significantly poorer compared with S450-650. Our data suggest a possibility that, although the β6 strand alone is able to bind with ACE2 with relatively high affinity, residues outside the S-RBM could also assist the receptor binding of SARS-CoV-S protein

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...

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/.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.

Science.gov (United States)

Hagen, Espen; Ness, Torbjørn V; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne; Hafting, Torkel; Franke, Felix; Einevoll, Gaute T

2015-04-30

New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

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...

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.

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.

Determination and application of immunodominant regions of SARS coronavirus spike and nucleocapsid proteins recognized by sera from different animal species.

Science.gov (United States)

Yu, Meng; Stevens, Vicky; Berry, Jody D; Crameri, Gary; McEachern, Jennifer; Tu, Changchun; Shi, Zhengli; Liang, Guodong; Weingartl, Hana; Cardosa, Jane; Eaton, Bryan T; Wang, Lin-Fa

2008-02-29

Knowledge of immunodominant regions in major viral antigens is important for rational design of effective vaccines and diagnostic tests. Although there have been many reports of such work done for SARS-CoV, these were mainly focused on the immune responses of humans and mice. In this study, we aim to search for and compare immunodominant regions of the spike (S) and nucleocapsid (N) proteins which are recognized by sera from different animal species, including mouse, rat, rabbit, civet, pig and horse. Twelve overlapping recombinant protein fragments were produced in Escherichia coli, six each for the S and N proteins, which covered the entire coding region of the two proteins. Using a membrane-strip based Western blot approach, the reactivity of each antigen fragment against a panel of animal sera was determined. Immunodominant regions containing linear epitopes, which reacted with sera from all the species tested, were identified for both proteins. The S3 fragment (aa 402-622) and the N4 fragment (aa 220-336) were the most immunodominant among the six S and N fragments, respectively. Antibodies raised against the S3 fragment were able to block the binding of a panel of S-specific monoclonal antibodies (mAb) to SARS-CoV in ELISA, further demonstrating the immunodominance of this region. Based on these findings, one-step competition ELISAs were established which were able to detect SARS-CoV antibodies from human and at least seven different animal species. Considering that a large number of animal species are known to be susceptible to SARS-CoV, these assays will be a useful tool to trace the origin and transmission of SARS-CoV and to minimise the risk of animal-to-human transmission.

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.

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...

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.

Host cell entry of Middle East respiratory syndrome coronavirus after two-step, furin-mediated activation of the spike protein

Science.gov (United States)

Millet, Jean Kaoru; Whittaker, Gary R.

2014-01-01

Middle East respiratory syndrome coronavirus (MERS-CoV) is a newly identified betacoronavirus causing high morbidity and mortality in humans. The coronavirus spike (S) protein is the main determinant of viral entry, and although it was previously shown that MERS-CoV S can be activated by various proteases, the details of the mechanisms of proteolytic activation of fusion are still incompletely characterized. Here, we have uncovered distinctive characteristics of MERS-CoV S. We identify, by bioinformatics and peptide cleavage assays, two cleavage sites for furin, a ubiquitously expressed protease, which are located at the S1/S2 interface and at the S2′ position of the S protein. We show that although the S1/S2 site is proteolytically processed by furin during protein biosynthesis, the S2′ site is cleaved upon viral entry. MERS-CoV pseudovirion infection was shown to be enhanced by elevated levels of furin expression, and entry could be decreased by furin siRNA silencing. Enhanced furin activity appeared to partially override the low pH-dependent nature of MERS-CoV entry. Inhibition of furin activity was shown to decrease MERS-CoV S-mediated entry, as well as infection by the virus. Overall, we show that MERS-CoV has evolved an unusual two-step furin activation for fusion, suggestive of a role during the process of emergence into the human population. The ability of MERS-CoV to use furin in this manner, along with other proteases, may explain the polytropic nature of the virus. PMID:25288733

Safe taste memory consolidation is disrupted by a protein synthesis inhibitor in the nucleus accumbens shell.

Science.gov (United States)

Pedroza-Llinás, R; Ramírez-Lugo, L; Guzmán-Ramos, K; Zavala-Vega, S; Bermúdez-Rattoni, F

2009-07-01

Consolidation is the process by which a new memory is stabilized over time, and is dependent on de novo protein synthesis. A useful model for studying memory formation is gustatory memory, a type of memory in which a novel taste may become either safe by not being followed by negative consequences (attenuation of neophobia, AN), or aversive by being followed by post-digestive malaise (conditioned taste aversion, CTA). Here we evaluated the effects of the administration of a protein synthesis inhibitor in the nucleus accumbens (NAc) shell for either safe or aversive taste memory trace consolidation. To test the effects on CTA and AN of protein synthesis inhibition, anisomycin (100microg/microl) was bilaterally infused into the NAc shell of Wistar rats' brains. We found that post-trial protein synthesis blockade impaired the long-term safe taste memory. However, protein synthesis inhibition failed to disrupt the long-term memory of CTA. In addition, we infused anisomycin in the NAc shell after the pre-exposure to saccharin in a latent inhibition of aversive taste. We found that the protein synthesis inhibition impaired the consolidation of safe taste memory, allowing the aversive taste memory to form and consolidate. Our results suggest that protein synthesis is required in the NAc shell for consolidation of safe but not aversive taste memories, supporting the notion that consolidation of taste memory is processed in several brain regions in parallel, and implying that inhibitory interactions between both taste memory traces do occur.

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.

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.

Prospective Identification of Oligoclonal/Abnormal Band of the Same Immunoglobulin Type as the Malignant Clone by Differential Location of M-Spike and Oligoclonal Band.

Science.gov (United States)

Vyas, Shikhar G; Singh, Gurmukh

2017-10-01

Serum and urine protein electrophoreses and immunofixation electrophoreses are the gold standards in diagnosing monoclonal gammopathy. Identification of oligoclonal bands in post-treatment patients has emerged as an important issue and recording the location of the malignant monoclonal peak may facilitate prospective identification of a new "monoclonal" spike as being distinct from the malignant peak. We recorded the locations of monoclonal spikes in descriptive terms, such as being in the cathodal region, mid-gamma region, anodal region, and beta region. The location of monoclonal or restricted heterogeneity bands in subsequent protein electrophoreses was compared to the location of the original malignant spike. In a patient with plasma cell myeloma, the original monoclonal IgG kappa band was located at the anodal end of gamma region. Post-treatment, an IgG kappa band was noted in mid-gamma region and the primary malignant clone was not detectable by serum protein immunofixation electrophoresis (SIFE) in post-treatment sample. Even though the κ/λ ratio remained abnormal, we were able to recognize stringent complete response by noting the different location of the new IgG kappa band as a benign regenerative process. Recording the location of the malignant monoclonal spike facilitates the identification of post-treatment oligoclonal bands, prospectively. Recognizing the regenerative, benign, bands in post-transplant patients facilitates the determination of stringent complete response despite an abnormal κ/λ ratio.

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.

Vernonia DGATs can complement the disrupted oil and protein metabolism in epoxygenase-expressing soybean seeds.

Science.gov (United States)

Li, Runzhi; Yu, Keshun; Wu, Yongmei; Tateno, Mizuki; Hatanaka, Tomoko; Hildebrand, David F

2012-01-01

Plant oils can be useful chemical feedstocks such as a source of epoxy fatty acids. High seed-specific expression of a Stokesia laevis epoxygenase (SlEPX) in soybeans only results in 3-7% epoxide levels. SlEPX-transgenic soybean seeds also exhibited other phenotypic alterations, such as altered seed fatty acid profiles, reduced oil accumulation, and variable protein levels. SlEPX-transgenic seeds showed a 2-5% reduction in total oil content and protein levels of 30.9-51.4%. To address these pleiotrophic effects of SlEPX expression on other traits, transgenic soybeans were developed to co-express SlEPX and DGAT (diacylglycerol acyltransferase) genes (VgDGAT1 & 2) isolated from Vernonia galamensis, a high accumulator of epoxy fatty acids. These side effects of SlEPX expression were largely overcome in the DGAT co-expressing soybeans. Total oil and protein contents were restored to the levels in non-transgenic soybeans, indicating that both VgDGAT1 and VgDGAT2 could complement the disrupted phenotypes caused by over-expression of an epoxygenase in soybean seeds. Copyright © 2011 Elsevier Inc. All rights reserved.

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,

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

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.

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.

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.

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.

Implementing Signature Neural Networks with Spiking Neurons.

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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

A single amino acid substitution in the S1 and S2 Spike protein domains determines the neutralization escape phenotype of SARS-CoV.

Science.gov (United States)

Mitsuki, Yu-ya; Ohnishi, Kazuo; Takagi, Hirotaka; Oshima, Masamichi; Yamamoto, Takuya; Mizukoshi, Fuminori; Terahara, Kazutaka; Kobayashi, Kazuo; Yamamoto, Naoki; Yamaoka, Shoji; Tsunetsugu-Yokota, Yasuko

2008-07-01

In response to SARS-CoV infection, neutralizing antibodies are generated against the Spike (S) protein. Determination of the active regions that allow viral escape from neutralization would enable the use of these antibodies for future passive immunotherapy. We immunized mice with UV-inactivated SARS-CoV to generate three anti-S monoclonal antibodies, and established several neutralization escape mutants with S protein. We identified several amino acid substitutions, including Y442F and V601G in the S1 domain and D757N and A834V in the S2 region. In the presence of each neutralizing antibody, double mutants with substitutions in both domains exhibited a greater growth advantage than those with only one substitution. Importantly, combining two monoclonal antibodies that target different epitopes effected almost complete suppression of wild type virus replication. Thus, for effective passive immunotherapy, it is important to use neutralizing antibodies that recognize both the S1 and S2 regions.

pH-induced conformational change of the rotavirus VP4 spike: implications for cell entry and antibody neutralization.

Science.gov (United States)

Pesavento, Joseph B; Crawford, Sue E; Roberts, Ed; Estes, Mary K; Prasad, B V Venkataram

2005-07-01

The rotavirus spike protein, VP4, is a major determinant of infectivity and neutralization. Previously, we have shown that trypsin-enhanced infectivity of rotavirus involves a transformation of the VP4 spike from a flexible to a rigid bilobed structure. Here we show that at elevated pH the spike undergoes a drastic, irreversible conformational change and becomes stunted, with a pronounced trilobed appearance. These particles with altered spikes, at a normal pH of 7.5, despite the loss of infectivity and the ability to hemagglutinate, surprisingly exhibit sialic acid (SA)-independent cell binding in contrast to the SA-dependent cell binding exhibited by native virions. Remarkably, a neutralizing monoclonal antibody that remains bound to spikes throughout the pH changes (pH 7 to 11 and back to pH 7) completely prevents this conformational change, preserving the SA-dependent cell binding and hemagglutinating functions of the virion. A hypothesis that emerges from the present study is that high-pH treatment triggers a conformational change that mimics a post-SA-attachment step to expose an epitope recognized by a downstream receptor in the rotavirus cell entry process. This process involves sequential interactions with multiple receptors, and the mechanism by which the antibody neutralizes is by preventing this conformational change.

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.

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...

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.

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.

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.

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.

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.

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.

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.

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 ...

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.

Propitious Therapeutic Modulators to Prevent Blood-Spinal Cord Barrier Disruption in Spinal Cord Injury.

Science.gov (United States)

Kumar, Hemant; Ropper, Alexander E; Lee, Soo-Hong; Han, Inbo

2017-07-01

The blood-spinal cord barrier (BSCB) is a specialized protective barrier that regulates the movement of molecules between blood vessels and the spinal cord parenchyma. Analogous to the blood-brain barrier (BBB), the BSCB plays a crucial role in maintaining the homeostasis and internal environmental stability of the central nervous system (CNS). After spinal cord injury (SCI), BSCB disruption leads to inflammatory cell invasion such as neutrophils and macrophages, contributing to permanent neurological disability. In this review, we focus on the major proteins mediating the BSCB disruption or BSCB repair after SCI. This review is composed of three parts. Section 1. SCI and the BSCB of the review describes critical events involved in the pathophysiology of SCI and their correlation with BSCB integrity/disruption. Section 2. Major proteins involved in BSCB disruption in SCI focuses on the actions of matrix metalloproteinases (MMPs), tumor necrosis factor alpha (TNF-α), heme oxygenase-1 (HO-1), angiopoietins (Angs), bradykinin, nitric oxide (NO), and endothelins (ETs) in BSCB disruption and repair. Section 3. Therapeutic approaches discusses the major therapeutic compounds utilized to date for the prevention of BSCB disruption in animal model of SCI through modulation of several proteins.

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.

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.)

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

Spike-threshold adaptation predicted by membrane potential dynamics in vivo.

Directory of Open Access Journals (Sweden)

Bertrand Fontaine

2014-04-01

Full Text Available Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

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.

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.

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.

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

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

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.

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.

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

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.

Voltage spikes in Nb3Sn and NbTi strands

International Nuclear Information System (INIS)

Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin, A.V.; Fermilab

2005-01-01

As part of the High Field Magnet program at Fermilab several NbTi and Nb 3 Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current

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.

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.

A Markovian event-based framework for stochastic spiking neural networks.

Science.gov (United States)

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

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

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.

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.

Hepatitis B virus core protein allosteric modulators can distort and disrupt intact capsids.

Science.gov (United States)

Schlicksup, Christopher John; Wang, Joseph Che-Yen; Francis, Samson; Venkatakrishnan, Balasubramanian; Turner, William W; VanNieuwenhze, Michael; Zlotnick, Adam

2018-01-29

Defining mechanisms of direct-acting antivirals facilitates drug development and our understanding of virus function. Heteroaryldihydropyrimidines (HAPs) inappropriately activate assembly of hepatitis B virus (HBV) core protein (Cp), suppressing formation of virions. We examined a fluorophore-labeled HAP, HAP-TAMRA. HAP-TAMRA induced Cp assembly and also bound pre-assembled capsids. Kinetic and spectroscopic studies imply that HAP-binding sites are usually not available but are bound cooperatively. Using cryo-EM, we observed that HAP-TAMRA asymmetrically deformed capsids, creating a heterogeneous array of sharp angles, flat regions, and outright breaks. To achieve high resolution reconstruction (HAP-TAMRA caused quasi-sixfold vertices to become flatter and fivefold more angular. This transition led to asymmetric faceting. That a disordered crosslink could rescue symmetry implies that capsids have tensegrity properties. Capsid distortion and disruption is a new mechanism by which molecules like the HAPs can block HBV infection. © 2017, Schlicksup et al.

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

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.

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.

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)

Disruption of mouse Cenpj, a regulator of centriole biogenesis, phenocopies Seckel syndrome

OpenAIRE

McIntyre, Rebecca E; Lakshminarasimhan Chavali, Pavithra; Ismail, Ozama; Carragher, Damian M; Sanchez-Andrade, Gabriela; Forment, Josep V; Fu, Beiyuan; Del Castillo Velasco-Herrera, Martin; Edwards, Andrew; van der Weyden, Louise; Yang, Fengtang; Ramirez-Solis, Ramiro; Estabel, Jeanne; Gallagher, Ferdia A; Logan, Darren W

2012-01-01

Disruption of the centromere protein J gene, CENPJ (CPAP, MCPH6, SCKL4), which is a highly conserved and ubiquitiously expressed centrosomal protein, has been associated with primary microcephaly and the microcephalic primordial dwarfism disorder Seckel syndrome. The mechanism by which disruption of CENPJ causes the proportionate, primordial growth failure that is characteristic of Seckel syndrome is unknown. By generating a hypomorphic allele of Cenpj, we have developed a mouse (Cenpj(tm/tm)...

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.

Brain amyloid β protein and memory disruption in Alzheimer’s disease

Directory of Open Access Journals (Sweden)

Weiming Xia

2010-09-01

Full Text Available Weiming XiaCenter for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USAAbstract: The development of amyloid-containing neuritic plaques is an invariable characteristic of Alzheimer’s diseases (AD. The conversion from monomeric amyloid β protein (Aβ to oligomeric Aβ and finally neuritic plaques is highly dynamic. The specific Aß species that is correlated with disease severity remains to be discovered. Oligomeric Aβ has been detected in cultured cells, rodent and human brains, as well as human cerebrospinal fluid. Synthetic, cell, and brain derived Aβ oligomers have been found to inhibit hippocampal long-term potentiation (LTP and this effect can be suppressed by the blockage of Aβ oligomer formation. A large body of evidence suggests that Aβ oligomers inhibit N-methyl-D-aspartate receptor dependent LTP; additional receptors have also been found to elicit downstream pathways upon binding to Aβ oligomers. Amyloid antibodies and small molecular compounds that reduce brain Aβ levels and block Aβ oligomer formation are capable of reversing synaptic dysfunction and these approaches hold a promising therapeutic potential to rescue memory disruption.Keywords: Alzheimer, amyloid, oligomer, long-term potentiation, NMDA

Effect of Soil Filtration and Ozonation in the Change of Baseline Toxicity in Wastewater Spiked with Organic Micro-pollutants

KAUST Repository

Gan, Alexander

2012-07-01

Bioassays for baseline toxicity, which measure toxicants’ non-specific effects, have been shown in previous studies to effectively correlate with the increased presence of pharmaceuticals, personal care products, endocrine-disrupting compounds, and other synthetic organics in treated sewage effluent. This study investigated how the baseline toxicity of anthropogenic compounds-spiked wastewater changed during the treatment of biofiltration and ozone oxidation, as measured by the bioluminescence inhibition of the Vibrio fischeri bacterium. The water quality parameters of dissolved organic carbon, seven common anions, and fluorescence spectroscopy were used to corroborate and collate with the toxicity results. Water quality was evaluated on two bench-scale soil filtration columns, which were configured for pre-ozonation and post-ozonation. Both systems’ soil aerobically removed similar amounts of dissolved organic carbon, and the reduction ranged between 57.7% and 62.1% for the post-ozonation and pre-ozonation systems, respectively. Biological removal of DOC, protein-like, humic-like, and soluble microbial product-like material was highest in the first 28.5 cm of each 114 cm-long system. While bioluminescence inhibition showed that ozonation was effective at lowering baseline toxicity, this study’s bioassay procedure was a very poor indicator of soil filtration treatment; both system’s effluents were significantly more toxic than their non-ozonated influents.

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.

Voltage spikes in Nb3Sn and NbTi strands

Energy Technology Data Exchange (ETDEWEB)

Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin,; /Fermilab

2005-09-01

As part of the High Field Magnet program at Fermilab several NbTi and Nb{sub 3}Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current.

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

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

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

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...

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...

Mutation in Spike Protein Cleavage Site and Pathogenesis of Feline Coronavirus

Science.gov (United States)

Licitra, Beth N.; Millet, Jean K.; Regan, Andrew D.; Hamilton, Brian S.; Rinaldi, Vera D.; Duhamel, Gerald E.

2013-01-01

Feline coronaviruses (FCoV) exist as 2 biotypes: feline enteric coronavirus (FECV) and feline infectious peritonitis virus (FIPV). FECV causes subclinical infections; FIPV causes feline infectious peritonitis (FIP), a systemic and fatal disease. It is thought that mutations in FECV enable infection of macrophages, causing FIP. However, the molecular basis for this biotype switch is unknown. We examined a furin cleavage site in the region between receptor-binding (S1) and fusion (S2) domains of the spike of serotype 1 FCoV. FECV sequences were compared with FIPV sequences. All FECVs had a conserved furin cleavage motif. For FIPV, there was a correlation with the disease and >1 substitution in the S1/S2 motif. Fluorogenic peptide assays confirmed that the substitutions modulate furin cleavage. We document a functionally relevant S1/S2 mutation that arises when FIP develops in a cat. These insights into FIP pathogenesis may be useful in development of diagnostic, prevention, and treatment measures against coronaviruses. PMID:23763835

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.

Disruption of the Acyl-CoA binding protein gene delays hepatic adaptation to metabolic changes at weaning

DEFF Research Database (Denmark)

Neess, Ditte; Marcher, Ann-Britt; Bloksgaard, Maria

The acyl-CoA binding protein/diazepam binding inhibitor (ACBP/DBI) is an evolutionary conserved intracellular protein that binds C14-C22 acyl-CoA esters with very high affinity. ACBP is thought to act as an acyl-CoA transporter, and in vitro analyses have indicated that ACBP can transport acyl......-CoA esters between different enzymatic systems. However, little is known about the in vivo function in mammalian cells. We have generated mice with targeted disruption of ACBP (ACBP-/-). These mice are viable and fertile and develop normally. However, around weaning the ACBP-/- mice show decreased growth......) family, around the weaning period. As a result, the hepatic de novo cholesterogenesis is significantly decreased at weaning. The delayed induction of SREBP target genes around weaning is caused by a compromised processing and decreased expression of SREBP precursors leading to reduced binding of SREBP...

The Analysis and Suppression of the spike noise in vibrator record

Science.gov (United States)

Jia, H.; Jiang, T.; Xu, X.; Ge, L.; Lin, J.; Yang, Z.

2013-12-01

During the seismic exploration with vibrator, seismic recording systems have often been affected by random spike noise in the background, which leads to strong data distortions as a result of the cross-correlation processing of the vibrator method. Partial or total loss of the desired seismic information is possible if no automatic spike reduction is available in the field prior to correlation of the field record. Generally speaking, original record of vibrator is uncorrelated data, in which the signal is non-wavelet form. In order to obtain the seismic record similar to explosive source, the signal of uncorrelated data needs to use the correlation algorithm to compress into wavelet form. The correlation process results in that the interference of spike in correlated data is not only being suppressed, but also being expanded. So the spike noise suppression of vibrator is indispensable. According to numerical simulation results, the effect of spike in the vibrator record is mainly affected by the amplitude and proportional points in the uncorrelated record. When the spike noise ratio in uncorrelated record reaches 1.5% and the average amplitude exceeds 200, it will make the SNR(signal-to-noise ratio) of the correlated record lower than 0dB, so that it is difficult to separate the signal. While the amplitude and ratio is determined by the intensity of background noise. Therefore, when the noise level is strong, in order to improve SNR of the seismic data, the uncorrelated record of vibrator need to take necessary steps to suppress spike noise. For the sake of reducing the influence of the spike noise, we need to make the detection and suppression of spike noise process for the uncorrelated record. Because vibrator works by inputting sweep signal into the underground long time, ideally, the peak and valley values of each trace have little change. On the basis of the peak and valley values, we can get a reference amplitude value. Then the spike can be detected and

Study of current oscillations and hard x-ray emissions in pre-cursor phase of major disruptions in Damavand tokamak

International Nuclear Information System (INIS)

Amrollahi, R.

2002-01-01

We notice that the hard x-ray activity before disruption consists of a series of spikes, uniformly distributed in time domain forming an orderly periodic series of oscillations at a frequency of 6.0 kHz. Disruption starts with an initial fast rise followed by decay. Current decay occurs in two regimes: the first corresponds to slow decay, in which the current is oscillating and reducing down to ∼70% its max value, and the second corresponds to fast decay, in which it totally vanishes abruptly in about 0.2 ms. In the first regime, the loop voltage also oscillates with considerable amplitude. The frequency of oscillations in the first regime is measured to be also about 6.0 kHz. As well, they follow the oscillation phase of hard x-rays. Thus the micro-instabilities driven by runaway electrons, being responsible for the production of hard x-rays bursts and small current oscillations, play a significant role in the disruption. (author)

Hepatitis B virus core protein allosteric modulators can distort and disrupt intact capsids

Science.gov (United States)

Schlicksup, Christopher John; Wang, Joseph Che-Yen; Francis, Samson; Venkatakrishnan, Balasubramanian; Turner, William W; VanNieuwenhze, Michael

2018-01-01

Defining mechanisms of direct-acting antivirals facilitates drug development and our understanding of virus function. Heteroaryldihydropyrimidines (HAPs) inappropriately activate assembly of hepatitis B virus (HBV) core protein (Cp), suppressing formation of virions. We examined a fluorophore-labeled HAP, HAP-TAMRA. HAP-TAMRA induced Cp assembly and also bound pre-assembled capsids. Kinetic and spectroscopic studies imply that HAP-binding sites are usually not available but are bound cooperatively. Using cryo-EM, we observed that HAP-TAMRA asymmetrically deformed capsids, creating a heterogeneous array of sharp angles, flat regions, and outright breaks. To achieve high resolution reconstruction (particle symmetry. We deduced that HAP-TAMRA caused quasi-sixfold vertices to become flatter and fivefold more angular. This transition led to asymmetric faceting. That a disordered crosslink could rescue symmetry implies that capsids have tensegrity properties. Capsid distortion and disruption is a new mechanism by which molecules like the HAPs can block HBV infection. PMID:29377794

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.

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.

Femtosecond laser fabricated spike structures for selective control of cellular behavior.

Science.gov (United States)

Schlie, Sabrina; Fadeeva, Elena; Koch, Jürgen; Ngezahayo, Anaclet; Chichkov, Boris N

2010-09-01

In this study we investigate the potential of femtosecond laser generated micrometer sized spike structures as functional surfaces for selective cell controlling. The spike dimensions as well as the average spike to spike distance can be easily tuned by varying the process parameters. Moreover, negative replications in soft materials such as silicone elastomer can be produced. This allows tailoring of wetting properties of the spike structures and their negative replicas representing a reduced surface contact area. Furthermore, we investigated material effects on cellular behavior. By comparing human fibroblasts and SH-SY5Y neuroblastoma cells we found that the influence of the material was cell specific. The cells not only changed their morphology, but also the cell growth was affected. Whereas, neuroblastoma cells proliferated at the same rate on the spike structures as on the control surfaces, the proliferation of fibroblasts was reduced by the spike structures. These effects can result from the cell specific adhesion patterns as shown in this work. These findings show a possibility to design defined surface microstructures, which could control cellular behavior in a cell specific manner.

Enhanced interlaminar excitation or reduced superficial layer inhibition in neocortex generates different spike-and-wave-like electrographic events in vitro.

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Hall, Stephen P; Traub, Roger D; Adams, Natalie E; Cunningham, Mark O; Schofield, Ian; Jenkins, Alistair J; Whittington, Miles A

2018-01-01

Acute in vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike-and-wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalized and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden, and apparent anatomical origin (thalamic, neocortical, or both), but any relationship between this heterogeneity and underlying pathology remains elusive. In this study we demonstrate that physiological delta-frequency rhythms act as an effective substrate to permit modeling of SpW of cortical origin and may help to address this issue. For a starting point of delta activity, multiple subtypes of SpW could be modeled computationally and experimentally by either enhancing the magnitude of excitatory synaptic events ascending from neocortical layer 5 to layers 2/3 or selectively modifying superficial layer GABAergic inhibition. The former generated SpW containing multiple field spikes with long interspike intervals, whereas the latter generated SpW with short-interval multiple field spikes. Both types had different laminar origins and each disrupted interlaminar cortical dynamics in a different manner. A small number of examples of human recordings from patients with different diagnoses revealed SpW subtypes with the same temporal signatures, suggesting that detailed quantification of the pattern of spikes in SpW discharges may be a useful indicator of disparate underlying epileptogenic pathologies. NEW & NOTEWORTHY Spike-and-wave-type discharges (SpW) are a common feature in many epilepsies. Their electrographic manifestation is highly varied, as are available genetic clues to associated underlying pathology. Using computational and in vitro models, we demonstrate that distinct subtypes of SpW are generated by lamina-selective disinhibition or enhanced

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.

Spike-timing theory of working memory.

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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.

Dual roles for spike signaling in cortical neural populations

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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.

Characterization of components released by alkali disruption of simian virus 40

DEFF Research Database (Denmark)

Christiansen, Gunna; Landers, T; Griffith, J

1977-01-01

Treatment of simian virus 40 (SV40) particles at pH 9.8 in the presence of 1 mM dithiothreitol for 5 min at 37 degrees C disrupted the virions into a 60S DNA-protein complex and DNA-free 7S protein particles. The DNA-protein complex contained approximately equal amounts of DNA and protein, and ap...

Chimeric Feline Coronaviruses That Encode Type II Spike Protein on Type I Genetic Background Display Accelerated Viral Growth and Altered Receptor Usage▿

Science.gov (United States)

Tekes, Gergely; Hofmann-Lehmann, Regina; Bank-Wolf, Barbara; Maier, Reinhard; Thiel, Heinz-Jürgen; Thiel, Volker

2010-01-01

Persistent infection of domestic cats with feline coronaviruses (FCoVs) can lead to a highly lethal, immunopathological disease termed feline infectious peritonitis (FIP). Interestingly, there are two serotypes, type I and type II FCoVs, that can cause both persistent infection and FIP, even though their main determinant of host cell tropism, the spike (S) protein, is of different phylogeny and displays limited sequence identity. In cell culture, however, there are apparent differences. Type II FCoVs can be propagated to high titers by employing feline aminopeptidase N (fAPN) as a cellular receptor, whereas the propagation of type I FCoVs is usually difficult, and the involvement of fAPN as a receptor is controversial. In this study we have analyzed the phenotypes of recombinant FCoVs that are based on the genetic background of type I FCoV strain Black but encode the type II FCoV strain 79-1146 S protein. Our data demonstrate that recombinant FCoVs expressing a type II FCoV S protein acquire the ability to efficiently use fAPN for host cell entry and corroborate the notion that type I FCoVs use another main host cell receptor. We also observed that recombinant FCoVs display a large-plaque phenotype and, unexpectedly, accelerated growth kinetics indistinguishable from that of type II FCoV strain 79-1146. Thus, the main phenotypic differences for type I and type II FCoVs in cell culture, namely, the growth kinetics and the efficient usage of fAPN as a cellular receptor, can be attributed solely to the FCoV S protein. PMID:19906918

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

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.

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

The Mechanisms of Repetitive Spike Generation in an Axonless Retinal Interneuron

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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.

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.

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.

The novel mouse mutant, chuzhoi, has disruption of Ptk7 protein and exhibits defects in neural tube, heart and lung development and abnormal planar cell polarity in the ear

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Paudyal Anju

2010-08-01

Full Text Available Abstract Background The planar cell polarity (PCP signalling pathway is fundamental to a number of key developmental events, including initiation of neural tube closure. Disruption of the PCP pathway causes the severe neural tube defect of craniorachischisis, in which almost the entire brain and spinal cord fails to close. Identification of mouse mutants with craniorachischisis has proven a powerful way of identifying molecules that are components or regulators of the PCP pathway. In addition, identification of an allelic series of mutants, including hypomorphs and neomorphs in addition to complete nulls, can provide novel genetic tools to help elucidate the function of the PCP proteins. Results We report the identification of a new N-ethyl-N-nitrosourea (ENU-induced mutant with craniorachischisis, which we have named chuzhoi (chz. We demonstrate that chuzhoi mutant embryos fail to undergo initiation of neural tube closure, and have characteristics consistent with defective convergent extension. These characteristics include a broadened midline and reduced rate of increase of their length-to-width ratio. In addition, we demonstrate disruption in the orientation of outer hair cells in the inner ear, and defects in heart and lung development in chuzhoi mutants. We demonstrate a genetic interaction between chuzhoi mutants and both Vangl2Lp and Celsr1Crsh mutants, strengthening the hypothesis that chuzhoi is involved in regulating the PCP pathway. We demonstrate that chuzhoi maps to Chromosome 17 and carries a splice site mutation in Ptk7. This mutation results in the insertion of three amino acids into the Ptk7 protein and causes disruption of Ptk7 protein expression in chuzhoi mutants. Conclusions The chuzhoi mutant provides an additional genetic resource to help investigate the developmental basis of several congenital abnormalities including neural tube, heart and lung defects and their relationship to disruption of PCP. The chuzhoi mutation

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

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)

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.

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.

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.

Mild myelin disruption elicits early alteration in behavior and proliferation in the subventricular zone.

Science.gov (United States)

Gould, Elizabeth A; Busquet, Nicolas; Shepherd, Douglas; Dietz, Robert M; Herson, Paco S; Simoes de Souza, Fabio M; Li, Anan; George, Nicholas M; Restrepo, Diego; Macklin, Wendy B

2018-02-13

Myelin, the insulating sheath around axons, supports axon function. An important question is the impact of mild myelin disruption. In the absence of the myelin protein proteolipid protein (PLP1), myelin is generated but with age, axonal function/maintenance is disrupted. Axon disruption occurs in Plp1 -null mice as early as 2 months in cortical projection neurons. High-volume cellular quantification techniques revealed a region-specific increase in oligodendrocyte density in the olfactory bulb and rostral corpus callosum that increased during adulthood. A distinct proliferative response of progenitor cells was observed in the subventricular zone (SVZ), while the number and proliferation of parenchymal oligodendrocyte progenitor cells was unchanged. This SVZ proliferative response occurred prior to evidence of axonal disruption. Thus, a novel SVZ response contributes to the region-specific increase in oligodendrocytes in Plp1 -null mice. Young adult Plp1- null mice exhibited subtle but substantial behavioral alterations, indicative of an early impact of mild myelin disruption. © 2018, Gould et al.

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.

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.

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.

Topical delivery of low-cost protein drug candidates made in chloroplasts for biofilm disruption and uptake by oral epithelial cells.

Science.gov (United States)

Liu, Yuan; Kamesh, Aditya C; Xiao, Yuhong; Sun, Victor; Hayes, Michael; Daniell, Henry; Koo, Hyun

2016-10-01

Protein drugs (PD) are minimally utilized in dental medicine due to high cost and invasive surgical delivery. There is limited clinical advancement in disrupting virulent oral biofilms, despite their high prevalence in causing dental caries. Poor efficacy of antimicrobials following topical treatments or to penetrate and disrupt formed biofilms is a major challenge. We report an exciting low-cost approach using plant-made antimicrobial peptides (PMAMPs) retrocyclin or protegrin with complex secondary structures (cyclic/hairpin) for topical use to control biofilms. The PMAMPs rapidly killed the pathogen Streptococcus mutans and impaired biofilm formation following a single topical application of tooth-mimetic surface. Furthermore, we developed a synergistic approach using PMAMPs combined with matrix-degrading enzymes to facilitate their access into biofilms and kill the embedded bacteria. In addition, we identified a novel role for PMAMPs in delivering drugs to periodontal and gingival cells, 13-48 folds more efficiently than any other tested cell penetrating peptides. Therefore, PDs fused with protegrin expressed in plant cells could potentially play a dual role in delivering therapeutic proteins to gum tissues while killing pathogenic bacteria when delivered as topical oral formulations or in chewing gums. Recent FDA approval of plant-produced PDs augurs well for clinical advancement of this novel concept. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

Amino acid substitutions within the heptad repeat domain 1 of murine coronavirus spike protein restrict viral antigen spread in the central nervous system

International Nuclear Information System (INIS)

Tsai, Jean C.; Groot, Linda de; Pinon, Josefina D.; Iacono, Kathryn T.; Phillips, Joanna J.; Seo, Suhun; Lavi, Ehud; Weiss, Susan R.

2003-01-01

Targeted recombination was carried out to select mouse hepatitis viruses (MHVs) in a defined genetic background, containing an MHV-JHM spike gene encoding either three heptad repeat 1 (HR1) substitutions (Q1067H, Q1094H, and L1114R) or L1114R alone. The recombinant virus, which expresses spike with the three substitutions, was nonfusogenic at neutral pH. Its replication was significantly inhibited by lysosomotropic agents, and it was highly neuroattenuated in vivo. In contrast, the recombinant expressing spike with L1114R alone mediated cell-to-cell fusion at neutral pH and replicated efficiently despite the presence of lysosomotropic agents; however, it still caused only subclinical morbidity and no mortality in animals. Thus, both recombinant viruses were highly attenuated and expressed viral antigen which was restricted to the olfactory bulbs and was markedly absent from other regions of the brains at 5 days postinfection. These data demonstrate that amino acid substitutions, in particular L1114R, within HR1 of the JHM spike reduced the ability of MHV to spread in the central nervous system. Furthermore, the requirements for low pH for fusion and viral entry are not prerequisites for the highly attenuated phenotype

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

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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.

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.

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.

SV40 late protein VP4 forms toroidal pores to disrupt membranes for viral release.

Science.gov (United States)

Raghava, Smita; Giorda, Kristina M; Romano, Fabian B; Heuck, Alejandro P; Hebert, Daniel N

2013-06-04

Nonenveloped viruses are generally released from the cell by the timely lysis of host cell membranes. SV40 has been used as a model virus for the study of the lytic nonenveloped virus life cycle. The expression of SV40 VP4 at later times during infection is concomitant with cell lysis. To investigate the role of VP4 in viral release and its mechanism of action, VP4 was expressed and purified from bacteria as a fusion protein for use in membrane disruption assays. Purified VP4 perforated membranes as demonstrated by the release of fluorescent markers encapsulated within large unilamellar vesicles or liposomes. Dynamic light scattering results revealed that VP4 treatment did not cause membrane lysis or change the size of the liposomes. Liposomes encapsulated with 4,4-difluoro-5,7-dimethyl-4-bora-3a,4a-diaza-3-indacene-labeled streptavidin were used to show that VP4 formed stable pores in membranes. These VP4 pores had an inner diameter of 1-5 nm. Asymmetrical liposomes containing pyrene-labeled lipids in the outer monolayer were employed to monitor transbilayer lipid diffusion. Consistent with VP4 forming toroidal pore structures in membranes, VP4 induced transbilayer lipid diffusion or lipid flip-flop. Altogether, these studies support a central role for VP4 acting as a viroporin in the disruption of cellular membranes to trigger SV40 viral release by forming toroidal pores that unite the outer and inner leaflets of membrane bilayers.

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.

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

Transcriptome Analysis for Abnormal Spike Development of the Wheat Mutant dms.

Science.gov (United States)

Zhu, Xin-Xin; Li, Qiao-Yun; Shen, Chun-Cai; Duan, Zong-Biao; Yu, Dong-Yan; Niu, Ji-Shan; Ni, Yong-Jing; Jiang, Yu-Mei

2016-01-01

Wheat (Triticum aestivum L.) spike development is the foundation for grain yield. We obtained a novel wheat mutant, dms, characterized as dwarf, multi-pistil and sterility. Although the genetic changes are not clear, the heredity of traits suggests that a recessive gene locus controls the two traits of multi-pistil and sterility in self-pollinating populations of the medium plants (M), such that the dwarf genotype (D) and tall genotype (T) in the progeny of the mutant are ideal lines for studies regarding wheat spike development. The objective of this study was to explore the molecular basis for spike abnormalities of dwarf genotype. Four unigene libraries were assembled by sequencing the mRNAs of the super-bulked differentiating spikes and stem tips of the D and T plants. Using integrative analysis, we identified 419 genes highly expressed in spikes, including nine typical homeotic genes of the MADS-box family and the genes TaAP2, TaFL and TaDL. We also identified 143 genes that were significantly different between young spikes of T and D, and 26 genes that were putatively involved in spike differentiation. The result showed that the expression levels of TaAP1-2, TaAP2, and other genes involved in the majority of biological processes such as transcription, translation, cell division, photosynthesis, carbohydrate transport and metabolism, and energy production and conversion were significantly lower in D than in T. We identified a set of genes related to wheat floral organ differentiation, including typical homeotic genes. Our results showed that the major causal factors resulting in the spike abnormalities of dms were the lower expression homeotic genes, hormonal imbalance, repressed biological processes, and deficiency of construction materials and energy. We performed a series of studies on the homeotic genes, however the other three causal factors for spike abnormal phenotype of dms need further study.

Cytoplasmic tail of Coronavirus spike protein has intracellular ...

Indian Academy of Sciences (India)

58

1Department of Biological Sciences, Indian Institute of Science Education and ... Introduction .... A major effort is underway to understand the interplay of .... For protein analysis, cells were plated on 100 mm tissue culture dishes and transfected.

Axonal propagation of simple and complex spikes in cerebellar Purkinje neurons.

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Khaliq, Zayd M; Raman, Indira M

2005-01-12

In cerebellar Purkinje neurons, the reliability of propagation of high-frequency simple spikes and spikelets of complex spikes is likely to regulate inhibition of Purkinje target neurons. To test the extent to which a one-to-one correspondence exists between somatic and axonal spikes, we made dual somatic and axonal recordings from Purkinje neurons in mouse cerebellar slices. Somatic action potentials were recorded with a whole-cell pipette, and the corresponding axonal signals were recorded extracellularly with a loose-patch pipette. Propagation of spontaneous and evoked simple spikes was highly reliable. At somatic firing rates of approximately 200 spikes/sec, 375 Hz during somatic hyperpolarizations that silenced spontaneous firing to approximately 150 Hz during spontaneous activity. The probability of propagation of individual spikelets could be described quantitatively as a saturating function of spikelet amplitude, rate of rise, or preceding interspike interval. The results suggest that ion channels of Purkinje axons are adapted to produce extremely short refractory periods and that brief bursts of forward-propagating action potentials generated by complex spikes may contribute transiently to inhibition of postsynaptic neurons.

Fluid-thermal analysis of aerodynamic heating over spiked blunt body configurations

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Qin, Qihao; Xu, Jinglei; Guo, Shuai

2017-03-01

When flying at hypersonic speeds, the spiked blunt body is constantly subjected to severe aerodynamic heating. To illustrate the thermal response of different configurations and the relevant flow field variation, a loosely-coupled fluid-thermal analysis is performed in this paper. The Mesh-based parallel Code Coupling Interface (MpCCI) is adopted to implement the data exchange between the fluid solver and the thermal solver. The results indicate that increases in spike diameter and length will result in a sharp decline of the wall temperature along the spike, and the overall heat flux is remarkably reduced to less than 300 W/cm2 with the aerodome mounted at the spike tip. Moreover, the presence and evolution of small vortices within the recirculation zone are observed and proved to be induced by the stagnation effect of reattachment points on the spike. In addition, the drag coefficient of the configuration with a doubled spike length presents a maximum drop of 4.59% due to the elevated wall temperature. And the growing difference of the drag coefficient is further increased during the accelerating process.

The chronotron: a neuron that learns to fire temporally precise spike patterns.

Directory of Open Access Journals (Sweden)

Răzvan V Florian

Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

Accelerated spike resampling for accurate multiple testing controls.

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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.

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

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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.

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.

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...

BH3-only proteins and BH3 mimetics induce autophagy by competitively disrupting the interaction between Beclin 1 and Bcl-2/Bcl-X(L).

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Maiuri, Maria Chiara; Criollo, Alfredo; Tasdemir, Ezgi; Vicencio, José Miguel; Tajeddine, Nicolas; Hickman, John A; Geneste, Olivier; Kroemer, Guido

2007-01-01

Beclin 1 has recently been identified as novel BH3-only protein, meaning that it carries one Bcl-2-homology-3 (BH3) domain. As other BH3-only proteins, Beclin 1 interacts with anti-apoptotic multidomain proteins of the Bcl-2 family (in particular Bcl-2 and its homologue Bcl-X(L)) by virtue of its BH3 domain, an amphipathic alpha-helix that binds to the hydrophobic cleft of Bcl-2/Bcl-X(L). The BH3 domains of other BH3-only proteins such as Bad, as well as BH3-mimetic compounds such as ABT737, competitively disrupt the inhibitory interaction between Beclin 1 and Bcl-2/Bcl-X(L). This causes autophagy of mitochondria (mitophagy) but not of the endoplasmic reticulum (reticulophagy). Only ER-targeted (not mitochondrion-targeted) Bcl-2/Bcl-X(L) can inhibit autophagy induced by Beclin 1, and only Beclin 1-Bcl-2/Bcl-X(L) complexes present in the ER (but not those present on heavy membrane fractions enriched in mitochondria) are disrupted by ABT737. These findings suggest that the Beclin 1-Bcl-2/Bcl-X(L) complexes that normally inhibit autophagy are specifically located in the ER and point to an organelle-specific regulation of autophagy. Furthermore, these data suggest a spatial organization of autophagy and apoptosis control in which BH3-only proteins exert two independent functions. On the one hand, they can induce apoptosis, by (directly or indirectly) activating the mitochondrion-permeabilizing function of pro-apoptotic multidomain proteins from the Bcl-2 family. On the other hand, they can activate autophagy by liberating Beclin 1 from its inhibition by Bcl-2/Bcl-X(L) at the level of the endoplasmic reticulum.

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.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

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Ghahari, Alireza; Badea, Tudor C

2016-08-01

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

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.

Cochlear spike synchronization and neuron coincidence detection model

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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.

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)

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)

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.

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.

A case-control study of wicket spikes using video-EEG monitoring.

Science.gov (United States)

Vallabhaneni, Maya; Baldassari, Laura E; Scribner, James T; Cho, Yong Won; Motamedi, Gholam K

2013-01-01

To investigate clinical characteristics associated with wicket spikes in patients undergoing long-term video-EEG monitoring. A case-control study was performed in 479 patients undergoing video-EEG monitoring, with 3 age- (±3 years) and gender-matched controls per patient with wicket spikes. Logistic regression was utilized to investigate the association between wicket spikes and other factors, including conditions that have been previously associated with wicket spikes. Wicket spikes were recorded in 48 patients. There was a significantly higher prevalence of dizziness/vertigo (p=0.002), headaches (p=0.005), migraine (p=0.015), and seizures (p=0.016) in patients with wickets. The majority of patients with wicket spikes did not exhibit epileptiform activity on EEG; however, patients with history of seizures were more likely to have wickets (p=0.017). There was no significant difference in the prevalence of psychogenic non-epileptic seizures between the groups. Wickets were more common on the left, during sleep, and more likely to be first recorded on day 1-2 of monitoring. Patients with wicket spikes are more likely to have dizziness/vertigo, headaches, migraine, and seizures. Patients with history of seizures are more likely to have wickets. The prevalence of psychogenic non-epileptic seizures is not significantly higher in patients with wickets. Copyright © 2012 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

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

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.

Peptides corresponding to the predicted heptad repeat 2 domain of the feline coronavirus spike protein are potent inhibitors of viral infection.

Directory of Open Access Journals (Sweden)

I-Jung Liu

Full Text Available BACKGROUND: Feline infectious peritonitis (FIP is a lethal immune-mediated disease caused by feline coronavirus (FCoV. Currently, no therapy with proven efficacy is available. In searching for agents that may prove clinically effective against FCoV infection, five analogous overlapping peptides were designed and synthesized based on the putative heptad repeat 2 (HR2 sequence of the spike protein of FCoV, and the antiviral efficacy was evaluated. METHODS: Plaque reduction assay and MTT (3-(4,5-dimethylthiazol-2-yl-2,5-diphenyltetrazolium bromide cytotoxicity assay were performed in this study. Peptides were selected using a plaque reduction assay to inhibit Feline coronavirus infection. RESULTS: The results demonstrated that peptide (FP5 at concentrations below 20 μM inhibited viral replication by up to 97%. The peptide (FP5 exhibiting the most effective antiviral effect was further combined with a known anti-viral agent, human interferon-α (IFN-α, and a significant synergistic antiviral effect was observed. CONCLUSION: Our data suggest that the synthetic peptide FP5 could serve as a valuable addition to the current FIP prevention methods.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

Science.gov (United States)

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

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.

The Non-structural Protein of Crimean-Congo Hemorrhagic Fever Virus Disrupts the Mitochondrial Membrane Potential and Induces Apoptosis*

Science.gov (United States)

Barnwal, Bhaskar; Karlberg, Helen; Mirazimi, Ali; Tan, Yee-Joo

2016-01-01

Viruses have developed distinct strategies to overcome the host defense system. Regulation of apoptosis in response to viral infection is important for virus survival and dissemination. Like other viruses, Crimean-Congo hemorrhagic fever virus (CCHFV) is known to regulate apoptosis. This study, for the first time, suggests that the non-structural protein NSs of CCHFV, a member of the genus Nairovirus, induces apoptosis. In this report, we demonstrated the expression of CCHFV NSs, which contains 150 amino acid residues, in CCHFV-infected cells. CCHFV NSs undergoes active degradation during infection. We further demonstrated that ectopic expression of CCHFV NSs induces apoptosis, as reflected by caspase-3/7 activity and cleaved poly(ADP-ribose) polymerase, in different cell lines that support CCHFV replication. Using specific inhibitors, we showed that CCHFV NSs induces apoptosis via both intrinsic and extrinsic pathways. The minimal active region of the CCHFV NSs protein was determined to be 93–140 amino acid residues. Using alanine scanning, we demonstrated that Leu-127 and Leu-135 are the key residues for NSs-induced apoptosis. Interestingly, CCHFV NSs co-localizes in mitochondria and also disrupts the mitochondrial membrane potential. We also demonstrated that Leu-127 and Leu-135 are important residues for disruption of the mitochondrial membrane potential by NSs. Therefore, these results indicate that the C terminus of CCHFV NSs triggers mitochondrial membrane permeabilization, leading to activation of caspases, which, ultimately, leads to apoptosis. Given that multiple factors contribute to apoptosis during CCHFV infection, further studies are needed to define the involvement of CCHFV NSs in regulating apoptosis in infected cells. PMID:26574543

Isotope and Patient Age Predict for PSA Spikes After Permanent Prostate Brachytherapy

International Nuclear Information System (INIS)

Bostancic, Chelsea; Merrick, Gregory S.; Butler, Wayne M.; Wallner, Kent E.; Allen, Zachariah; Galbreath, Robert; Lief, Jonathan; Gutman, Sarah E.

2007-01-01

Purpose: To evaluate prostate-specific antigen (PSA) spikes after permanent prostate brachytherapy in low-risk patients. Methods and Materials: The study population consisted of 164 prostate cancer patients who were part of a prospective randomized trial comparing 103 Pd and 125 I for low-risk disease. Of the 164 patients, 61 (37.2%) received short-course androgen deprivation therapy. The median follow-up was 5.4 years. On average, 11.1 post-treatment PSA measurements were obtained per patient. Biochemical disease-free survival was defined as a PSA level of ≤0.40 ng/mL after nadir. A PSA spike was defined as an increase of ≥0.2 ng/mL, followed by a durable decline to prespike levels. Multiple parameters were evaluated as predictors for a PSA spike. Results: Of the 164 patients, 44 (26.9%) developed a PSA spike. Of the 46 hormone-naive 125 I patients and 57 hormone-naive 103 Pd patients, 21 (45.7%) and 8 (14.0%) developed a PSA spike. In the hormone-naive patients, the mean time between implantation and the spike was 22.6 months and 18.7 months for 125 I and 103 Pd, respectively. In patients receiving neoadjuvant androgen deprivation therapy, the incidence of spikes was comparable between isotopes ( 125 I 28.1% and 103 Pd 20.7%). The incidence of spikes was substantially different in patients 125 I and/or <65 years of age. Differences in isotope-related spikes are most pronounced in hormone-naive patients

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 ...

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.

Science.gov (United States)

Kuroda, S; Yamamoto, K; Miyamoto, H; Doya, K; Kawat, M

2001-03-01

Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike

The BH3 α-Helical Mimic BH3-M6 Disrupts Bcl-XL, Bcl-2, and MCL-1 Protein-Protein Interactions with Bax, Bak, Bad, or Bim and Induces Apoptosis in a Bax- and Bim-dependent Manner*

Science.gov (United States)

Kazi, Aslamuzzaman; Sun, Jiazhi; Doi, Kenichiro; Sung, Shen-Shu; Takahashi, Yoshinori; Yin, Hang; Rodriguez, Johanna M.; Becerril, Jorge; Berndt, Norbert; Hamilton, Andrew D.; Wang, Hong-Gang; Sebti, Saïd M.

2011-01-01

A critical hallmark of cancer cell survival is evasion of apoptosis. This is commonly due to overexpression of anti-apoptotic proteins such as Bcl-2, Bcl-XL, and Mcl-1, which bind to the BH3 α-helical domain of pro-apoptotic proteins such as Bax, Bak, Bad, and Bim, and inhibit their function. We designed a BH3 α-helical mimetic BH3-M6 that binds to Bcl-XL and Mcl-1 and prevents their binding to fluorescently labeled Bak- or Bim-BH3 peptides in vitro. Using several approaches, we demonstrate that BH3-M6 is a pan-Bcl-2 antagonist that inhibits the binding of Bcl-XL, Bcl-2, and Mcl-1 to multi-domain Bax or Bak, or BH3-only Bim or Bad in cell-free systems and in intact human cancer cells, freeing up pro-apoptotic proteins to induce apoptosis. BH3-M6 disruption of these protein-protein interactions is associated with cytochrome c release from mitochondria, caspase-3 activation and PARP cleavage. Using caspase inhibitors and Bax and Bak siRNAs, we demonstrate that BH3-M6-induced apoptosis is caspase- and Bax-, but not Bak-dependent. Furthermore, BH3-M6 disrupts Bcl-XL/Bim, Bcl-2/Bim, and Mcl-1/Bim protein-protein interactions and frees up Bim to induce apoptosis in human cancer cells that depend for tumor survival on the neutralization of Bim with Bcl-XL, Bcl-2, or Mcl-1. Finally, BH3-M6 sensitizes cells to apoptosis induced by the proteasome inhibitor CEP-1612. PMID:21148306

Computational investigations of blunt body drag-reduction spikes in hypersonic flows

International Nuclear Information System (INIS)

Kamran, N.; Zahir, S.; Khan, M.A.

2003-01-01

Drag is an important parameter in the designing of high-speed vehicles. Such vehicles include hypervelocity projectiles, reentry modules, and hypersonic aircrafts. Therefore, there exists an active or passive technique to reduce drag due to the high pressures at nosetip region of the vehicle. Drag can be reduced by attaching a forward facing spike on the nose of the vehicle. The present study reviews and deals with the CFD analysis made on a standard blunt body to reduce aerodynamic drag due to the attachment of forward facing spikes for High-Speed vehicles. Different spike lengths have been examined to study the forebody flowfield. The investigation concludes that spikes are an effective way to reduce the aerodynamic drag due to reduced dynamic pressure on the nose caused by the separated flow on the spikes. With the accomplishment of confidence on computational data, study was extended in hypersonic Mach range with a drag prediction accuracy of ± 10%. In the present work, viscous fluid dynamics studies were performed for a complete freestream Mach number range of 5.0, 6.0, 7.0 and 8.0 for different spike lengths and zero degree angle of attack. (author)

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.

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...

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

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.

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.

A mammalian model for Laron syndrome produced by targeted disruption of the mouse growth hormone receptor/binding protein gene (the Laron mouse)

OpenAIRE

Zhou, Yihua; Xu, Bixiong C.; Maheshwari, Hiralal G.; He, Li; Reed, Michael; Lozykowski, Maria; Okada, Shigeru; Cataldo, Lori; Coschigamo, Karen; Wagner, Thomas E.; Baumann, Gerhard; Kopchick, John J.

1997-01-01

Laron syndrome [growth hormone (GH) insensitivity syndrome] is a hereditary dwarfism resulting from defects in the GH receptor (GHR) gene. GHR deficiency has not been reported in mammals other than humans. Many aspects of GHR dysfunction remain unknown because of ethical and practical limitations in studying humans. To create a mammalian model for this disease, we generated mice bearing a disrupted GHR/binding protein (GHR/BP) gene through a homologous gene targeting approach. Homozygous GHR/...

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...

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

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.

Transformation-invariant visual representations in self-organizing spiking neural networks.

Science.gov (United States)

Evans, Benjamin D; Stringer, Simon M

2012-01-01

The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transformation-invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the presynaptic and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model parameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Transform-invariant visual representations in self-organizing spiking neural networks

Directory of Open Access Journals (Sweden)

Benjamin eEvans

2012-07-01

Full Text Available The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

Science.gov (United States)

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

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.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

Science.gov (United States)

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-08-01

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.

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.

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.

Interspike Interval Based Filtering of Directional Selective Retinal Ganglion Cells Spike Trains

Directory of Open Access Journals (Sweden)

Aurel Vasile Martiniuc

2012-01-01

Full Text Available The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs. Among these, the directional selective cells (DSRGC are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system.

Structured chaos shapes spike-response noise entropy in balanced neural networks

Directory of Open Access Journals (Sweden)

Guillaume eLajoie

2014-10-01

Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

The BH3 alpha-helical mimic BH3-M6 disrupts Bcl-X(L), Bcl-2, and MCL-1 protein-protein interactions with Bax, Bak, Bad, or Bim and induces apoptosis in a Bax- and Bim-dependent manner.

Science.gov (United States)

Kazi, Aslamuzzaman; Sun, Jiazhi; Doi, Kenichiro; Sung, Shen-Shu; Takahashi, Yoshinori; Yin, Hang; Rodriguez, Johanna M; Becerril, Jorge; Berndt, Norbert; Hamilton, Andrew D; Wang, Hong-Gang; Sebti, Saïd M

2011-03-18

A critical hallmark of cancer cell survival is evasion of apoptosis. This is commonly due to overexpression of anti-apoptotic proteins such as Bcl-2, Bcl-X(L), and Mcl-1, which bind to the BH3 α-helical domain of pro-apoptotic proteins such as Bax, Bak, Bad, and Bim, and inhibit their function. We designed a BH3 α-helical mimetic BH3-M6 that binds to Bcl-X(L) and Mcl-1 and prevents their binding to fluorescently labeled Bak- or Bim-BH3 peptides in vitro. Using several approaches, we demonstrate that BH3-M6 is a pan-Bcl-2 antagonist that inhibits the binding of Bcl-X(L), Bcl-2, and Mcl-1 to multi-domain Bax or Bak, or BH3-only Bim or Bad in cell-free systems and in intact human cancer cells, freeing up pro-apoptotic proteins to induce apoptosis. BH3-M6 disruption of these protein-protein interactions is associated with cytochrome c release from mitochondria, caspase-3 activation and PARP cleavage. Using caspase inhibitors and Bax and Bak siRNAs, we demonstrate that BH3-M6-induced apoptosis is caspase- and Bax-, but not Bak-dependent. Furthermore, BH3-M6 disrupts Bcl-X(L)/Bim, Bcl-2/Bim, and Mcl-1/Bim protein-protein interactions and frees up Bim to induce apoptosis in human cancer cells that depend for tumor survival on the neutralization of Bim with Bcl-X(L), Bcl-2, or Mcl-1. Finally, BH3-M6 sensitizes cells to apoptosis induced by the proteasome inhibitor CEP-1612.

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.

Science.gov (United States)

Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum

2017-12-01

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.

Cytoskeletal Components Define Protein Location to Membrane Microdomains*

Science.gov (United States)

Szymanski, Witold G.; Zauber, Henrik; Erban, Alexander; Gorka, Michal; Wu, Xu Na; Schulze, Waltraud X.

2015-01-01

The plasma membrane is an important compartment that undergoes dynamic changes in composition upon external or internal stimuli. The dynamic subcompartmentation of proteins in ordered low-density (DRM) and disordered high-density (DSM) membrane phases is hypothesized to require interactions with cytoskeletal components. Here, we systematically analyzed the effects of actin or tubulin disruption on the distribution of proteins between membrane density phases. We used a proteomic screen to identify candidate proteins with altered submembrane location, followed by biochemical or cell biological characterization in Arabidopsis thaliana. We found that several proteins, such as plasma membrane ATPases, receptor kinases, or remorins resulted in a differential distribution between membrane density phases upon cytoskeletal disruption. Moreover, in most cases, contrasting effects were observed: Disruption of actin filaments largely led to a redistribution of proteins from DRM to DSM membrane fractions while disruption of tubulins resulted in general depletion of proteins from the membranes. We conclude that actin filaments are necessary for dynamic movement of proteins between different membrane phases and that microtubules are not necessarily important for formation of microdomains as such, but rather they may control the protein amount present in the membrane phases. PMID:26091700

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.

Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.

Science.gov (United States)

Owen, Scott F; Berke, Joshua D; Kreitzer, Anatol C

2018-02-08

Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning. Copyright © 2018 Elsevier Inc. All rights reserved.

Should spikes on post-resection ECoG guide pediatric epilepsy surgery?

Science.gov (United States)

Greiner, Hansel M; Horn, Paul S; Tenney, Jeffrey R; Arya, Ravindra; Jain, Sejal V; Holland, Katherine D; Leach, James L; Miles, Lili; Rose, Douglas F; Fujiwara, Hisako; Mangano, Francesco T

2016-05-01

There is wide variation in clinical practice regarding the role of electrocorticography immediately after resection (post-resection ECoG) for pediatric epilepsy surgery. Results can guide further resection of potentially epileptogenic tissue. We hypothesized that post-resection ECoG spiking represents a biomarker of the epileptogenic zone and predicts seizure outcome in children undergoing epilepsy surgery. We retrospectively identified 124 children with post-resection ECoG performed on the margins of resection. ECoG records were scored in a blinded fashion based on presence of frequent spiking. For patients identified as having additional resection based on clinical post-resection ECoG interpretation, these "second-look" ECoG results were re-reviewed for ongoing discharges or completeness of resection. Frequent spike populations were grouped using a standard scoring system into three ranges: 0.1-0.5Hz, 0.5-1Hz, >1Hz. Seizure outcomes were determined at minimum 12-month followup. Of 124 patients who met inclusion criteria, 60 (48%) had an identified spike population on post-resection ECoG. Thirty (50%) of these had further resection based on clinical interpretation. Overall, good outcome (ILAE 1) was seen in 56/124 (45%). Completeness of resection of spiking (absence of spiking on initial post-resection ECoG or resolution of spiking after further resection) showed a trend toward good outcome (OR 2.03, p=0.099). Patients with completeness of resection had good outcome in 41/80 (51%) of cases; patients with continued spikes had good outcome in 15/44 (35%) of cases. Post-resection ECoG identifies residual epileptogenic tissue in a significant number of children. Lower frequency or absence of discharges on initial recording showed a trend toward good outcome. Completeness of resection demonstrated on final ECoG recording did not show a significant difference in outcome. This suggests that post-resection discharges represent a prognostic marker rather than a remediable

Simulations of Low-q Disruptions in the Compact Toroidal Hybrid Experiment

Science.gov (United States)

Howell, E. C.; Hanson, J. D.; Ennis, D. A.; Hartwell, G. J.; Maurer, D. A.

2017-10-01

Resistive MHD simulations of low-q disruptions in the Compact Toroidal Hybrid Device (CTH) are performed using the NIMROD code. CTH is a current-carrying stellarator used to study the effects of 3D shaping on MHD stability. Experimentally, it is observed that the application of 3D vacuum fields allows CTH to operate with edge safety factor less than 2.0. However, these low-q discharges often disrupt after peak current if the applied 3D fields are too weak. Nonlinear simulations are initialized using model VMEC equilibria representative of low-q discharges with weak vacuum transform. Initially a series of symmetry preserving island chains are excited at the q=6/5, 7/5, 8/5, and 9/5 rational surfaces. These island chains act as transport barriers preventing stochastic magnetic fields in the edge from penetrating into the core. As the simulation progresses, predominately m/n=3/2 and 4/3 instabilities are destabilized. As these instabilities grow to large amplitude they destroy the symmetry preserving islands leading to large regions of stochastic fields. A current spike and loss of core thermal confinement occurs when the innermost island chain (6/5) is destroyed. Work Supported by US-DOE Grant #DE-FG02-03ER54692.

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.

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

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Oliynyk Andriy

2012-08-01

Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Science.gov (United States)

Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

2012-08-08

Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike

Residue analysis of a CTL epitope of SARS-CoV spike protein by IFN-gamma production and bioinformatics prediction

Directory of Open Access Journals (Sweden)

Huang Jun

2012-09-01

Full Text Available Abstract Background Severe acute respiratory syndrome (SARS is an emerging infectious disease caused by the novel coronavirus SARS-CoV. The T cell epitopes of the SARS CoV spike protein are well known, but no systematic evaluation of the functional and structural roles of each residue has been reported for these antigenic epitopes. Analysis of the functional importance of side-chains by mutational study may exaggerate the effect by imposing a structural disturbance or an unusual steric, electrostatic or hydrophobic interaction. Results We demonstrated that N50 could induce significant IFN-gamma response from SARS-CoV S DNA immunized mice splenocytes by the means of ELISA, ELISPOT and FACS. Moreover, S366-374 was predicted to be an optimal epitope by bioinformatics tools: ANN, SMM, ARB and BIMAS, and confirmed by IFN-gamma response induced by a series of S358-374-derived peptides. Furthermore, each of S366-374 was replaced by alanine (A, lysine (K or aspartic acid (D, respectively. ANN was used to estimate the binding affinity of single S366-374 mutants to H-2 Kd. Y367 and L374 were predicated to possess the most important role in peptide binding. Additionally, these one residue mutated peptides were synthesized, and IFN-gamma production induced by G368, V369, A371, T372 and K373 mutated S366-374 were decreased obviously. Conclusions We demonstrated that S366-374 is an optimal H-2 Kd CTL epitope in the SARS CoV S protein. Moreover, Y367, S370, and L374 are anchors in the epitope, while C366, G368, V369, A371, T372, and K373 may directly interact with TCR on the surface of CD8-T cells.

Investigating Disruption

DEFF Research Database (Denmark)

Lundgaard, Stine Schmieg; Rosenstand, Claus Andreas Foss

This book shares knowledge collected from 2015 and onward within the Consortium for Digital Disruption anchored at Aalborg University (www.dd.aau.dk). Evidenced by this publication, the field of disruptive innovation research has gone through several stages of operationalizing the theory. In recent...... years, researchers are increasingly looking back towards the origins of the theory in attempts to cure it from its most obvious flaws. This is especially true for the use of the theory in making predictions about future disruptions. In order to continue to develop a valuable theory of disruption, we...... find it useful to first review what the theory of disruptive innovation initially was, how it has developed, and where we are now. A cross section of disruptive innovation literature has been reviewed in order to form a general foundation from which we might better understand the changing world...

The influence of single bursts vs. single spikes at excitatory dendrodendritic synapses

Science.gov (United States)

Masurkar, Arjun V.; Chen, Wei R.

2015-01-01

The synchronization of neuronal activity is thought to enhance information processing. There is much evidence supporting rhythmically bursting external tufted cells (ETCs) of the rodent olfactory bulb glomeruli coordinating the activation of glomerular interneurons and mitral cells via dendrodendritic excitation. However, as bursting has variable significance at axodendritic cortical synapses, it is not clear if ETC bursting imparts a specific functional advantage over the preliminary spike in dendrodendritic synaptic networks. To answer this question, we investigated the influence of single ETC bursts and spikes with the in-vitro rat olfactory bulb preparation at different levels of processing, via calcium imaging of presynaptic ETC dendrites, dual electrical recording of ETC–interneuron synaptic pairs, and multicellular calcium imaging of ETC-induced population activity. Our findings supported single ETC bursts, vs. single spikes, driving robust presynaptic calcium signaling, which in turn was associated with profound extension of the initial monosynaptic spike-driven dendrodendritic excitatory postsynaptic potential. This extension could be driven by either the spike-dependent or spike-independent components of the burst. At the population level, burst-induced excitation was more widespread and reliable compared with single spikes. This further supports the ETC network, in part due to a functional advantage of bursting at excitatory dendrodendritic synapses, coordinating synchronous activity at behaviorally relevant frequencies related to odor processing in vivo. PMID:22277089

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)

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.

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Science.gov (United States)

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

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

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

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...

Neural spike sorting using iterative ICA and a deflation-based approach.

Science.gov (United States)

Tiganj, Z; Mboup, M

2012-12-01

We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

Directory of Open Access Journals (Sweden)

Jan Hahne

2017-05-01

Full Text Available Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

Science.gov (United States)

Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

2017-01-01

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

Superficial dorsal horn neurons with double spike activity in the rat.

Science.gov (United States)

Rojas-Piloni, Gerardo; Dickenson, Anthony H; Condés-Lara, Miguel

2007-05-29

Superficial dorsal horn neurons promote the transfer of nociceptive information from the periphery to supraspinal structures. The membrane and discharge properties of spinal cord neurons can alter the reliability of peripheral signals. In this paper, we analyze the location and response properties of a particular class of dorsal horn neurons that exhibits double spike discharge with a very short interspike interval (2.01+/-0.11 ms). These neurons receive nociceptive C-fiber input and are located in laminae I-II. Double spikes are generated spontaneously or by depolarizing current injection (interval of 2.37+/-0.22). Cells presenting double spike (interval 2.28+/-0.11) increased the firing rate by electrical noxious stimulation, as well as, in the first minutes after carrageenan injection into their receptive field. Carrageenan is a polysaccharide soluble in water and it is used for producing an experimental model of semi-chronic pain. In the present study carrageenan also produces an increase in the interval between double spikes and then, reduced their occurrence after 5-10 min. The results suggest that double spikes are due to intrinsic membrane properties and that their frequency is related to C-fiber nociceptive activity. The present work shows evidence that double spikes in superficial spinal cord neurones are related to the nociceptive stimulation, and they are possibly part of an acute pain-control mechanism.

Activity-Dependent Plasticity of Spike Pauses in Cerebellar Purkinje Cells

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Giorgio Grasselli

2016-03-01

Full Text Available The plasticity of intrinsic excitability has been described in several types of neurons, but the significance of non-synaptic mechanisms in brain plasticity and learning remains elusive. Cerebellar Purkinje cells are inhibitory neurons that spontaneously fire action potentials at high frequencies and regulate activity in their target cells in the cerebellar nuclei by generating a characteristic spike burst-pause sequence upon synaptic activation. Using patch-clamp recordings from mouse Purkinje cells, we find that depolarization-triggered intrinsic plasticity enhances spike firing and shortens the duration of spike pauses. Pause plasticity is absent from mice lacking SK2-type potassium channels (SK2−/− mice and in occlusion experiments using the SK channel blocker apamin, while apamin wash-in mimics pause reduction. Our findings demonstrate that spike pauses can be regulated through an activity-dependent, exclusively non-synaptic, SK2 channel-dependent mechanism and suggest that pause plasticity—by altering the Purkinje cell output—may be crucial to cerebellar information storage and learning.

Higher Order Spike Synchrony in Prefrontal Cortex during visual memory

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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.

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.

Disruption of a Guard Cell–Expressed Protein Phosphatase 2A Regulatory Subunit, RCN1, Confers Abscisic Acid Insensitivity in Arabidopsis

Science.gov (United States)

Kwak, June M.; Moon, Ji-Hye; Murata, Yoshiyuki; Kuchitsu, Kazuyuki; Leonhardt, Nathalie; DeLong, Alison; Schroeder, Julian I.

2002-01-01

Pharmacological studies have led to a model in which the phytohormone abscisic acid (ABA) may be positively transduced via protein phosphatases of the type 1 (PP1) or type 2A (PP2A) families. However, pharmacological evidence also exists that PP1s or PP2As may function as negative regulators of ABA signaling. Furthermore, recessive disruption mutants in protein phosphatases that function in ABA signal transduction have not yet been identified. A guard cell–expressed PP2A gene, RCN1, which had been characterized previously as a molecular component affecting auxin transport and gravity response, was isolated. A T-DNA disruption mutation in RCN1 confers recessive ABA insensitivity to Arabidopsis. The rcn1 mutation impairs ABA-induced stomatal closing and ABA activation of slow anion channels. Calcium imaging analyses show a reduced sensitivity of ABA-induced cytosolic calcium increases in rcn1, whereas mechanisms downstream of cytosolic calcium increases show wild-type responses, suggesting that RCN1 functions in ABA signal transduction upstream of cytosolic Ca2+ increases. Furthermore, rcn1 shows ABA insensitivity in ABA inhibition of seed germination and ABA-induced gene expression. The PP1 and PP2A inhibitor okadaic acid phenocopies the rcn1 phenotype in wild-type plants both in ABA-induced cytosolic calcium increases and in seed germination, and the wild-type RCN1 genomic DNA complements rcn1 phenotypes. These data show that RCN1 functions as a general positive transducer of early ABA signaling. PMID:12417706

Spike-adding in parabolic bursters: The role of folded-saddle canards

Science.gov (United States)

Desroches, Mathieu; Krupa, Martin; Rodrigues, Serafim

2016-09-01

The present work develops a new approach to studying parabolic bursting, and also proposes a novel four-dimensional canonical and polynomial-based parabolic burster. In addition to this new polynomial system, we also consider the conductance-based model of the Aplysia R15 neuron known as the Plant model, and a reduction of this prototypical biophysical parabolic burster to three variables, including one phase variable, namely the Baer-Rinzel-Carillo (BRC) phase model. Revisiting these models from the perspective of slow-fast dynamics reveals that the number of spikes per burst may vary upon parameter changes, however the spike-adding process occurs in an explosive fashion that involves special solutions called canards. This spike-adding canard explosion phenomenon is analysed by using tools from geometric singular perturbation theory in tandem with numerical bifurcation techniques. We find that the bifurcation structure persists across all considered systems, that is, spikes within the burst are incremented via the crossing of an excitability threshold given by a particular type of canard orbit, namely the true canard of a folded-saddle singularity. However there can be a difference in the spike-adding transitions in parameter space from one case to another, according to whether the process is continuous or discontinuous, which depends upon the geometry of the folded-saddle canard. Using these findings, we construct a new polynomial approximation of the Plant model, which retains all the key elements for parabolic bursting, including the spike-adding transitions mediated by folded-saddle canards. Finally, we briefly investigate the presence of spike-adding via canards in planar phase models of parabolic bursting, namely the theta model by Ermentrout and Kopell.

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

Cell membrane disruption stimulates cAMP and Ca2+ signaling to potentiate cell membrane resealing in neighboring cells

Directory of Open Access Journals (Sweden)

Tatsuru Togo

2017-12-01

Full Text Available Disruption of cellular plasma membranes is a common event in many animal tissues, and the membranes are usually rapidly resealed. Moreover, repeated membrane disruptions within a single cell reseal faster than the initial wound in a protein kinase A (PKA- and protein kinase C (PKC-dependent manner. In addition to wounded cells, recent studies have demonstrated that wounding of Madin-Darby canine kidney (MDCK cells potentiates membrane resealing in neighboring cells in the short-term by purinergic signaling, and in the long-term by nitric oxide/protein kinase G signaling. In the present study, real-time imaging showed that cell membrane disruption stimulated cAMP synthesis and Ca2+ mobilization from intracellular stores by purinergic signaling in neighboring MDCK cells. Furthermore, inhibition of PKA and PKC suppressed the ATP-mediated short-term potentiation of membrane resealing in neighboring cells. These results suggest that cell membrane disruption stimulates PKA and PKC via purinergic signaling to potentiate cell membrane resealing in neighboring MDCK cells.

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

Enhanced cell disruption strategy in the release of recombinant hepatitis B surface antigen from Pichia pastoris using response surface methodology

Science.gov (United States)

2012-01-01

Background Cell disruption strategies by high pressure homogenizer for the release of recombinant Hepatitis B surface antigen (HBsAg) from Pichia pastoris expression cells were optimized using response surface methodology (RSM) based on the central composite design (CCD). The factors studied include number of passes, biomass concentration and pulse pressure. Polynomial models were used to correlate the above mentioned factors to project the cell disruption capability and specific protein release of HBsAg from P. pastoris cells. Results The proposed cell disruption strategy consisted of a number of passes set at 20 times, biomass concentration of 7.70 g/L of dry cell weight (DCW) and pulse pressure at 1,029 bar. The optimized cell disruption strategy was shown to increase cell disruption efficiency by 2-fold and 4-fold for specific protein release of HBsAg when compared to glass bead method yielding 75.68% cell disruption rate (CDR) and HBsAg concentration of 29.20 mg/L respectively. Conclusions The model equation generated from RSM on cell disruption of P. pastoris was found adequate to determine the significant factors and its interactions among the process variables and the optimum conditions in releasing HBsAg when validated against a glass bead cell disruption method. The findings from the study can open up a promising strategy for better recovery of HBsAg recombinant protein during downstream processing. PMID:23039947

Code-specific learning rules improve action selection by populations of spiking neurons.

Science.gov (United States)

Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

2014-08-01

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Loss of Subcellular Lipid Transport Due to ARV1 Deficiency Disrupts Organelle Homeostasis and Activates the Unfolded Protein Response*

Science.gov (United States)

Shechtman, Caryn F.; Henneberry, Annette L.; Seimon, Tracie A.; Tinkelenberg, Arthur H.; Wilcox, Lisa J.; Lee, Eunjee; Fazlollahi, Mina; Munkacsi, Andrew B.; Bussemaker, Harmen J.; Tabas, Ira; Sturley, Stephen L.

2011-01-01

The ARV1-encoded protein mediates sterol transport from the endoplasmic reticulum (ER) to the plasma membrane. Yeast ARV1 mutants accumulate multiple lipids in the ER and are sensitive to pharmacological modulators of both sterol and sphingolipid metabolism. Using fluorescent and electron microscopy, we demonstrate sterol accumulation, subcellular membrane expansion, elevated lipid droplet formation, and vacuolar fragmentation in ARV1 mutants. Motif-based regression analysis of ARV1 deletion transcription profiles indicates activation of Hac1p, an integral component of the unfolded protein response (UPR). Accordingly, we show constitutive splicing of HAC1 transcripts, induction of a UPR reporter, and elevated expression of UPR targets in ARV1 mutants. IRE1, encoding the unfolded protein sensor in the ER lumen, exhibits a lethal genetic interaction with ARV1, indicating a viability requirement for the UPR in cells lacking ARV1. Surprisingly, ARV1 mutants expressing a variant of Ire1p defective in sensing unfolded proteins are viable. Moreover, these strains also exhibit constitutive HAC1 splicing that interacts with DTT-mediated perturbation of protein folding. These data suggest that a component of UPR induction in arv1Δ strains is distinct from protein misfolding. Decreased ARV1 expression in murine macrophages also results in UPR induction, particularly up-regulation of activating transcription factor-4, CHOP (C/EBP homologous protein), and apoptosis. Cholesterol loading or inhibition of cholesterol esterification further elevated CHOP expression in ARV1 knockdown cells. Thus, loss or down-regulation of ARV1 disturbs membrane and lipid homeostasis, resulting in a disruption of ER integrity, one consequence of which is induction of the UPR. PMID:21266578

Knowledge extraction from evolving spiking neural networks with rank order population coding.

Science.gov (United States)

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons.

Science.gov (United States)

Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew Cn; Swindale, Nicholas V; Murphy, Timothy H

2017-02-04

Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps.

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.

Relationship between focal penicillin spikes and cortical spindles in the cerveau isolé cat.

Science.gov (United States)

McLachlan, R S; Kaibara, M; Girvin, J P

1983-01-01

Using the unanesthetized, cerveau isolé preparation in the cat, the association between artificially induced penicillin (PCN) spikes and spontaneously occurring electrocorticographic (ECoG) spindles was investigated. Spikes were elicited by surface application of small pledgets of PCN. After the application of PCN, there was a decrease in spindle amplitude but no change in frequency, duration, or spindle wave frequency in the area of the focus. Examination of the times of occurrence of the spikes and spindles disclosed that in the majority of cases, within a few minutes of the initiation of the foci, there was very high simultaneity, usually 100% between the occurrences of these two events. Examination of the times of occurrence of the spikes within the ECoG spindles failed to disclose any compelling evidence which would favor either the hypothesis that spikes "trigger" spindles or the hypothesis that spindles predispose to focal spikes. Thus, whether spikes trigger spindles, or spikes simply occur in a nonspecific manner during the occurrence of the spindle, or whether it is a combination of both these explanations, must remain an open question on the basis of the data available.

Deletion of a 197-Amino-Acid Region in the N-Terminal Domain of Spike Protein Attenuates Porcine Epidemic Diarrhea Virus in Piglets.

Science.gov (United States)

Hou, Yixuan; Lin, Chun-Ming; Yokoyama, Masaru; Yount, Boyd L; Marthaler, Douglas; Douglas, Arianna L; Ghimire, Shristi; Qin, Yibin; Baric, Ralph S; Saif, Linda J; Wang, Qiuhong

2017-07-15

We previously isolated a porcine epidemic diarrhea virus (PEDV) strain, PC177, by blind serial passaging of the intestinal contents of a diarrheic piglet in Vero cell culture. Compared with the highly virulent U.S. PEDV strain PC21A, the tissue culture-adapted PC177 (TC-PC177) contains a 197-amino-acid (aa) deletion in the N-terminal domain of the spike (S) protein. We orally inoculated neonatal, conventional suckling piglets with TC-PC177 or PC21A to compare their pathogenicities. Within 7 days postinoculation, TC-PC177 caused mild diarrhea and lower fecal viral RNA shedding, with no mortality, whereas PC21A caused severe clinical signs and 55% mortality. To investigate whether infection with TC-PC177 can induce cross-protection against challenge with a highly virulent PEDV strain, all the surviving piglets were challenged with PC21A at 3 weeks postinoculation. Compared with 100% protection in piglets initially inoculated with PC21A, 88% and 100% TC-PC177- and mock-inoculated piglets had diarrhea following challenge, respectively, indicating incomplete cross-protection. To investigate whether this 197-aa deletion was the determinant for the attenuation of TC-PC177, we generated a mutant (icPC22A-S1Δ197) bearing the 197-aa deletion from an infectious cDNA clone of the highly virulent PEDV PC22A strain (infectious clone PC22A, icPC22A). In neonatal gnotobiotic pigs, the icPC22A-S1Δ197 virus caused mild to moderate diarrhea, lower titers of viral shedding, and no mortality, whereas the icPC22A virus caused severe diarrhea and 100% mortality. Our data indicate that deletion of this 197-aa fragment in the spike protein can attenuate a highly virulent PEDV, but the virus may lose important epitopes for inducing robust protective immunity. IMPORTANCE The emerging, highly virulent PEDV strains have caused substantial economic losses worldwide. However, the virulence determinants are not established. In this study, we found that a 197-aa deletion in the N-terminal region

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

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.

Which spike train distance is most suitable for distinguishing rate and temporal coding?

Science.gov (United States)

Satuvuori, Eero; Kreuz, Thomas

2018-04-01

It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates. The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

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.

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.

Digital Disruption

DEFF Research Database (Denmark)

Rosenstand, Claus Andreas Foss

det digitale domæne ud over det niveau, der kendetegner den nuværende debat, så præsenteres der ny viden om digital disruption. Som noget nyt udlægges Clayton Christens teori om disruptiv innovation med et særligt fokus på små organisationers mulighed for eksponentiel vækst. Specielt udfoldes...... forholdet mellem disruption og den stadig accelererende digitale udvikling i konturerne til ny teoridannelse om digital disruption. Bogens undertitel ”faretruende og fascinerende forandringer” peger på, at der er behov for en nuanceret debat om digital disruption i modsætning til den tone, der er slået an i...... videre kalder et ”disruption-råd”. Faktisk er rådet skrevet ind i 2016 regeringsgrundlaget for VLK-regeringen. Disruption af organisationer er ikke et nyt fænomen; men hastigheden, hvormed det sker, er stadig accelererende. Årsagen er den globale mega-trend: Digitalisering. Og derfor er specielt digital...

The influence of single bursts versus single spikes at excitatory dendrodendritic synapses.

Science.gov (United States)

Masurkar, Arjun V; Chen, Wei R

2012-02-01

The synchronization of neuronal activity is thought to enhance information processing. There is much evidence supporting rhythmically bursting external tufted cells (ETCs) of the rodent olfactory bulb glomeruli coordinating the activation of glomerular interneurons and mitral cells via dendrodendritic excitation. However, as bursting has variable significance at axodendritic cortical synapses, it is not clear if ETC bursting imparts a specific functional advantage over the preliminary spike in dendrodendritic synaptic networks. To answer this question, we investigated the influence of single ETC bursts and spikes with the in vitro rat olfactory bulb preparation at different levels of processing, via calcium imaging of presynaptic ETC dendrites, dual electrical recording of ETC -interneuron synaptic pairs, and multicellular calcium imaging of ETC-induced population activity. Our findings supported single ETC bursts, versus single spikes, driving robust presynaptic calcium signaling, which in turn was associated with profound extension of the initial monosynaptic spike-driven dendrodendritic excitatory postsynaptic potential. This extension could be driven by either the spike-dependent or spike-independent components of the burst. At the population level, burst-induced excitation was more widespread and reliable compared with single spikes. This further supports the ETC network, in part due to a functional advantage of bursting at excitatory dendrodendritic synapses, coordinating synchronous activity at behaviorally relevant frequencies related to odor processing in vivo. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons.

Science.gov (United States)

Hoseini, Mahmood S; Wessel, Ralf

2016-01-01

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits. Copyright © 2016 the American Physiological Society.

Delphine Letort, The Spike Lee Brand: A Study of Documentary Filmmaking

OpenAIRE

Lipson, David

2017-01-01

Spike Lee is known the world over for films like She’s Gotta Have It (1986), School Daze (1988), Do the Right Thing (1989), etc. This association with fiction films is so strong that one could mistakenly think that Delphine Letort’s book The Spike Lee Brand: A Study of Documentary Filmmaking would explore the connection between these fiction films and the documentary genre. However, the first pages of the book clearly indicate that it will focus on Spike Lee the documentary filmmaker. Making ...

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.

Space-filling, multifractal, localized thermal spikes in Si, Ge and ZnO

Science.gov (United States)

Ahmad, Shoaib; Abbas, Muhammad Sabtain; Yousuf, Muhammad; Javeed, Sumera; Zeeshan, Sumaira; Yaqub, Kashif

2018-04-01

The mechanism responsible for the emission of clusters from heavy ion irradiated solids is proposed to be thermal spikes. Collision cascade-based theories describe atomic sputtering but cannot explain the consistently observed experimental evidence for significant cluster emission. Statistical thermodynamic arguments for thermal spikes are employed here for qualitative and quantitative estimation of the thermal spike-induced cluster emission from Si, Ge and ZnO. The evolving cascades and spikes in elemental and molecular semiconducting solids are shown to have fractal characteristics. Power law potential is used to calculate the fractal dimension. With the loss of recoiling particles' energy the successive branching ratios get smaller. The fractal dimension is shown to be dependent upon the exponent of the power law interatomic potential D = 1/2m. Each irradiating ion has the probability of initiating a space-filling, multifractal thermal spike that may sublime a localized region near the surface by emitting clusters in relative ratios that depend upon the energies of formation of respective surface vacancies.

Forskolin suppresses delayed-rectifier K+ currents and enhances spike frequency-dependent adaptation of sympathetic neurons.

Science.gov (United States)

Angel-Chavez, Luis I; Acosta-Gómez, Eduardo I; Morales-Avalos, Mario; Castro, Elena; Cruzblanca, Humberto

2015-01-01

In signal transduction research natural or synthetic molecules are commonly used to target a great variety of signaling proteins. For instance, forskolin, a diterpene activator of adenylate cyclase, has been widely used in cellular preparations to increase the intracellular cAMP level. However, it has been shown that forskolin directly inhibits some cloned K+ channels, which in excitable cells set up the resting membrane potential, the shape of action potential and regulate repetitive firing. Despite the growing evidence indicating that K+ channels are blocked by forskolin, there are no studies yet assessing the impact of this mechanism of action on neuron excitability and firing patterns. In sympathetic neurons, we find that forskolin and its derivative 1,9-Dideoxyforskolin, reversibly suppress the delayed rectifier K+ current (IKV). Besides, forskolin reduced the spike afterhyperpolarization and enhanced the spike frequency-dependent adaptation. Given that IKV is mostly generated by Kv2.1 channels, HEK-293 cells were transfected with cDNA encoding for the Kv2.1 α subunit, to characterize the mechanism of forskolin action. Both drugs reversible suppressed the Kv2.1-mediated K+ currents. Forskolin inhibited Kv2.1 currents and IKV with an IC50 of ~32 μM and ~24 µM, respectively. Besides, the drug induced an apparent current inactivation and slowed-down current deactivation. We suggest that forskolin reduces the excitability of sympathetic neurons by enhancing the spike frequency-dependent adaptation, partially through a direct block of their native Kv2.1 channels.

Mixed signal learning by spike correlation propagation in feedback inhibitory circuits.

Directory of Open Access Journals (Sweden)

Naoki Hiratani

2015-04-01

Full Text Available The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.

Platinum stable isotope ratio measurements by double-spike multiple collector ICPMS

DEFF Research Database (Denmark)

Creech, John; Baker, Joel; Handler, Monica

2013-01-01

We present a new technique for the precise determination of platinum (Pt) stable isotope ratios by multiple-collector inductively coupled plasma mass spectrometry (MC-ICPMS) using two different Pt double-spikes ( Pt-Pt and Pt-Pt). Results are expressed relative to the IRMM-010 Pt isotope standard......) can be obtained on Pt stable isotope ratios with either double-spike. Elemental doping tests reveal that double-spike corrected Pt stable isotope ratios are insensitive to the presence of relatively high (up to 10%) levels of matrix elements, although the Pt-Pt double-spike is affected by an isobaric...... = 7.308%) results in a redefined Pt atomic weight of 195.08395 ± 0.00068. Using our technique we have measured small, reproducible and statistically significant offsets in Pt stable isotope ratios between different Pt element standards and the IRMM-010 standard, which potentially indicates...

Input-output relation and energy efficiency in the neuron with different spike threshold dynamics.

Science.gov (United States)

Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

2015-01-01

Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na(+) and K(+) currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism.

Disruption of Spectrin-Like Cytoskeleton in Differentiating Keratinocytes by PKCδ Activation Is Associated with Phosphorylated Adducin

Science.gov (United States)

Zhao, Kong-Nan; Masci, Paul P.; Lavin, Martin F.

2011-01-01

Spectrin is a central component of the cytoskeletal protein network in a variety of erythroid and non-erythroid cells. In keratinocytes, this protein has been shown to be pericytoplasmic and plasma membrane associated, but its characteristics and function have not been established in these cells. Here we demonstrate that spectrin increases dramatically in amount and is assembled into the cytoskeleton during differentiation in mouse and human keratinocytes. The spectrin-like cytoskeleton was predominantly organized in the granular and cornified layers of the epidermis and disrupted by actin filament inhibitors, but not by anti-mitotic drugs. When the cytoskeleton was disrupted PKCδ was activated by phosphorylation on Thr505. Specific inhibition of PKCδ(Thr505) activation with rottlerin prevented disruption of the spectrin-like cytoskeleton and the associated morphological changes that accompany differentiation. Rottlerin also inhibited specific phosphorylation of the PKCδ substrate adducin, a cytoskeletal protein. Furthermore, knock-down of endogenous adducin affected not only expression of adducin, but also spectrin and PKCδ, and severely disrupted organization of the spectrin-like cytoskeleton and cytoskeletal distribution of both adducin and PKCδ. These results demonstrate that organization of a spectrin-like cytoskeleton is associated with keratinocytes differentiation, and disruption of this cytoskeleton is mediated by either PKCδ(Thr505) phosphorylation associated with phosphorylated adducin or due to reduction of endogenous adducin, which normally connects and stabilizes the spectrin-actin complex. PMID:22163289

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.

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.

Impairment of decision making and disruption of synchrony between basolateral amygdala and anterior cingulate cortex in the maternally separated rat.

Science.gov (United States)

Cao, Bing; Wang, Jun; Zhang, Xu; Yang, Xiangwei; Poon, David Chun-Hei; Jelfs, Beth; Chan, Rosa H M; Wu, Justin Che-Yuen; Li, Ying

2016-12-01

There is considerable evidence to suggest early life experiences, such as maternal separation (MS), play a role in the prevalence of emotional dysregulation and cognitive impairment. At the same time, optimal decision making requires functional integrity between the amygdala and anterior cingulate cortex (ACC), and any dysfunction of this system is believed to induce decision-making deficits. However, the impact of MS on decision-making behavior and the underlying neurophysiological mechanisms have not been thoroughly studied. As such, we consider the impact of MS on the emotional and cognitive functions of rats by employing the open-field test, elevated plus-maze test, and rat gambling task (RGT). Using multi-channel recordings from freely behaving rats, we assessed the effects of MS on the large scale synchrony between the basolateral amygdala (BLA) and the ACC; while also characterizing the relationship between neural spiking activity and the ongoing oscillations in theta frequency band across the BLA and ACC. The results indicated that the MS rats demonstrated anxiety-like behavior. While the RGT showed a decrease in the percentage of good decision-makers, and an increase in the percentage of poor decision-makers. Electrophysiological data revealed an increase in the total power in the theta band of the LFP in the BLA and a decrease in theta power in the ACC in MS rats. MS was also found to disrupt the spike-field coherence of the ACC single unit spiking activity to the ongoing theta oscillations in the BLA and interrupt the synchrony in the BLA-ACC pathway. We provide specific evidence that MS leads to decision-making deficits that are accompanied by alteration of the theta band LFP in the BLA-ACC circuitries and disruption of the neural network integrity. These observations may help revise fundamental notions regarding neurophysiological biomarkers to treat cognitive impairment induced by early life stress. Copyright © 2016 Elsevier Inc. All rights

Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

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Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition.

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Wu, Tong; Xu, Jian; Lian, Yong; Khalili, Azam; Rastegarnia, Amir; Guan, Cuntai; Yang, Zhi

2016-02-01

In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm (2) for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Science.gov (United States)

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Inference of neuronal network spike dynamics and topology from calcium imaging data

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Henry eLütcke

2013-12-01

Full Text Available Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP occurrence ('spike trains' from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

Disruption?

DEFF Research Database (Denmark)

2016-01-01

This is a short video on the theme disruption and entrepreneurship. It takes the form of an interview with John Murray......This is a short video on the theme disruption and entrepreneurship. It takes the form of an interview with John Murray...

A truncated receptor-binding domain of MERS-CoV spike protein potently inhibits MERS-CoV infection and induces strong neutralizing antibody responses: implication for developing therapeutics and vaccines.

Directory of Open Access Journals (Sweden)

Lanying Du

Full Text Available An emerging respiratory infectious disease with high mortality, Middle East respiratory syndrome (MERS, is caused by a novel coronavirus (MERS-CoV. It was first reported in 2012 in Saudi Arabia and has now spread to eight countries. Development of effective therapeutics and vaccines is crucial to save lives and halt the spread of MERS-CoV. Here, we show that a recombinant protein containing a 212-amino acid fragment (residues 377-588 in the truncated receptor-binding domain (RBD: residues 367-606 of MERS-CoV spike (S protein fused with human IgG Fc fragment (S377-588-Fc is highly expressed in the culture supernatant of transfected 293T cells. The purified S377-588-Fc protein efficiently binds to dipeptidyl peptidase 4 (DPP4, the receptor of MERS-CoV, and potently inhibited MERS-CoV infection, suggesting its potential to be further developed as a therapeutic modality for treating MERS-CoV infection and saving the patients' lives. The recombinant S377-588-Fc is able to induce in the vaccinated mice strong MERS-CoV S-specific antibodies, which blocks the binding of RBD to DPP4 receptor and effectively neutralizes MERS-CoV infection. These findings indicate that this truncated RBD protein shows promise for further development as an effective and safe vaccine for the prevention of MERS-CoV infection.

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.

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.

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.

Targeted disruption of the mouse Csrp2 gene encoding the cysteine- and glycine-rich LIM domain protein CRP2 result in subtle alteration of cardiac ultrastructure

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Stoll Doris

2008-08-01

Full Text Available Abstract Background The cysteine and glycine rich protein 2 (CRP2 encoded by the Csrp2 gene is a LIM domain protein expressed in the vascular system, particularly in smooth muscle cells. It exhibits a bimodal subcellular distribution, accumulating at actin-based filaments in the cytosol and in the nucleus. In order to analyze the function of CRP2 in vivo, we disrupted the Csrp2 gene in mice and analysed the resulting phenotype. Results A ~17.3 kbp fragment of the murine Csrp2 gene containing exon 3 through 6 was isolated. Using this construct we confirmed the recently determined chromosomal localization (Chromosome 10, best fit location between markers D10Mit203 proximal and D10Mit150 central. A gene disruption cassette was cloned into exon 4 and a mouse strain lacking functional Csrp2 was generated. Mice lacking CRP2 are viable and fertile and have no obvious deficits in reproduction and survival. However, detailed histological and electron microscopic studies reveal that CRP2-deficient mice have subtle alterations in their cardiac ultrastructure. In these mice, the cardiomyocytes display a slight increase in their thickness, indicating moderate hypertrophy at the cellular level. Although the expression of several intercalated disc-associated proteins such as β-catenin, N-RAP and connexin-43 were not affected in these mice, the distribution of respective proteins was changed within heart tissue. Conclusion We conclude that the lack of CRP2 is associated with alterations in cardiomyocyte thickness and hypertrophy.

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

Remifentanil-induced spike activity as a diagnostic tool in epilepsy surgery

DEFF Research Database (Denmark)

Gronlykke, L.; Knudsen, M.L.; Hogenhaven, H.

2008-01-01

. Electrocorticography (ECoG) recordings were performed on the intraventricular hippocampus and from the anterior inferior temporal and lateral neocortex before and after a 300 microg intravenous bolus of remifentanil. Spike activity was quantified as spike-count per minute. RESULTS: A significant increase (P

OPTIMIZATION OF CELL DISRUPTION IN RAPHIDOCELIS SUBCAPITATA AND CHLORELLA VULGARIS FOR BIOMARKER EVALUATION

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Adeolu Aderemi

2015-06-01

Full Text Available Raphidocelis subcapitata and Chlorella vulgaris are bioassay microalgae with rigid cellulosic cell wall which can hinder the release of intracellular proteins often studied as toxicity biomarkers. Since cell disruption is necessary for recovering intracellular biomolecules in these organisms, this study investigated the efficiency of ultrasonication bath; ultrasonication probe; vortexer; and bead mill in disintegrating the microalgae for anti-oxidative enzyme extraction. The extent of cell disruption was evaluated and quantified using bright field microscopy. Disrupted algae appeared as ghosts. The greatest disintegration of the microalgae (83-99.6 % was achieved using bead mill with 0.42-0.6 mm glass beads while the other methods induced little or no disruption. The degree of cell disruption using bead mill increased with exposure time, beads-solution ratio and agitation speed while larger beads caused less disruption. Findings revealed that bead milling, with specific parameters optimized, is one of the most effective methods of disintegrating the robust algal cells.

Endocrine disruption: In silico interactions between phthalate plasticizers and corticosteroid binding globulin.

Science.gov (United States)

Sheikh, Ishfaq A; Beg, Mohd A

2017-12-01

Endocrine disruption is a phenomenon when a man-made or natural compound interferes with normal hormone function in human or animal body systems. Endocrine-disrupting compounds (EDCs) have assumed considerable importance as a result of industrial activity, mass production of synthetic chemicals and environmental pollution. Phthalate plasticizers are a group of chemicals used widely and diversely in industry especially in the plastic industry, and many of the phthalate compounds have endocrine-disrupting properties. Increasing evidence indicates that steroid nuclear receptors and steroid binding proteins are the main targets of endocrine disruption. Corticosteroid-binding globulin (CBG) is a steroid binding protein that binds and transports cortisol in the blood circulation and is a potential target for endocrine disruption. An imbalance of cortisol in the body leads to many health problems. Induced fit docking of nine important and environmentally relevant phthalate plasticizers (DMP, BBP, DBP, DIBP, DnHP, DEHP, DINP, DnOP, DIDP) showed interactions with 10-19 amino acid residues of CBG. Comparison of the interacting residues of CBG with phthalate ligands and cortisol showed an overlapping of the majority (53-82%) of residues for each phthalate. Five of nine phthalate compounds and cortisol shared a hydrogen bonding interaction with the Arg-252 residue of CBG. Long-chain phthalates, such as DEHP, DINP, DnOP and DIDP displayed a higher binding affinity and formed a number of interactions with CBG in comparison to short-chain phthalates. The similarity in structural binding characteristics of phthalate compounds and native ligand cortisol suggested potential competitive conflicts in CBG-cortisol binding function and possible disruption of cortisol and progesterone homeostasis. Copyright © 2017 John Wiley & Sons, Ltd.

Tritium uptake in rainbow trout (Oncorhynchus mykiss): HTO and OBT-spiked feed exposures simultaneously

International Nuclear Information System (INIS)

Kim, S.B.; Shultz, C.; Stuart, M.; Festarini, A.

2015-01-01

There is currently considerable interest in organically bound tritium (OBT) formation in edible fish. The major questions revolve around whether or not tritium can accumulate in fish after being released into aquatic environments. Since OBT formation rates in large, edible fish are poorly understood, rainbow trout (Oncorhynchus mykiss) studies, where fish were simultaneously exposed to tritiated water (HTO) and OBT-spiked feed over 130 days, were conducted to evaluate tritium uptake. The measured HTO activity concentrations in fish tissue confirmed that HTO in fish tissue equilibrates quickly with HTO in tank water. The data obtained also confirmed that OBT uptake is faster when fish are ingesting OBT-spiked feed compared to when fish are living in tritiated water (and consuming non-OBT-spiked feed). The difference between the two exposure types is such that the groups exposed to tritiated water and OBT-spiked feed simultaneously were showing the same uptake rates as OBT-spiked feed only exposures. Contrary to what was expected, the rate of OBT uptake (from OBT-spiked feed) seemed to be higher in slow growing fish compared to fast growing fish. Another observation from these studies was that OBT activity concentrations in all organs (viscera) had a tendency to be higher than OBT activity concentrations measured in fish flesh. - Highlights: • Edible size of rainbow trout (Oncorhynchus mykiss) were simultaneously exposed to tritiated water (HTO) and OBT-spiked feed over 130 days. • OBT uptake is faster when fish are ingesting OBT-spiked feed compared to when fish are living in tritiated water (and consuming non-OBT-spiked feed). • The rate of OBT uptake (from OBT-spiked feed) seemed to be higher in slow growing fish compared to fast growing fish

EFFECTS OF DIFFERENT GROWING CONDITIONS ON THE MORPHOLOGICAL FEATURES OF THE SPIKE OF HEXAPLOID TRITICALE

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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.

A neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs.

Science.gov (United States)

Jimenez-Fernandez, Angel; Jimenez-Moreno, Gabriel; Linares-Barranco, Alejandro; Dominguez-Morales, Manuel J; Paz-Vicente, Rafael; Civit-Balcells, Anton

2012-01-01

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

DEFF Research Database (Denmark)

Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.

2018-01-01

current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

Science.gov (United States)

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2014-12-30

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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.

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)....

Reconstructing stimuli from the spike-times of leaky integrate and fire neurons

Directory of Open Access Journals (Sweden)

Sebastian eGerwinn

2011-02-01

Full Text Available Reconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case.

Expression of ICAM-1 in blood-spinal cord barrier disruption and CNS radiation injury

International Nuclear Information System (INIS)

Nordal, R.A.; Li, Y.-Q.; Wong, C.S.

2003-01-01

Full text: Intercellular adhesion molecule-1 (ICAM-1) expression is increased following a number of CNS insults in association with blood-brain barrier (BBB) disruption. While disruption of ICAM-1 expression reduces injury in diverse pathologies ranging from trauma to ischemia, its role in CNS radiation injury is not understood. Adult rats received 0 to 22 Gy to a 1.6 cm length of the cervical spinal cord. Expression of ICAM-1 was studied using immunohistochemistry (IHC). Blood-spinal cord barrier (BSCB) disruption was detected by IHC for endogenous albumin and the BBB protein endothelial barrier antigen (EBA). To assess the role of ICAM-1 in the mechanisms of BSCB disruption, animals received IV injections of an ICAM-1-specific blocking antibody (IA-29) or vehicle control, and BSCB disruption was examined by albumin IHC. ICAM-1, albumin, and EBA staining areas were quantified by digital image analysis. ICAM-1 expression localized predominantly to endothelium in non-irradiated spinal cord sections. Some expression was also identified in astrocytes. ICAM-1 expression was increased in white matter, but not in grey matter following radiation. After 22 Gy, ICAM-1 protein increased at 24 hours, and increased again from baseline at 17-20 weeks. Induction was seen in both the total immunostained area, and in the number of ICAM-1 positive glia. A dose response was observed in ICAM-1 expression 20 weeks after 16-20 Gy. BSCB disruption also increased with doses 16-20 Gy at 20 weeks. Blocking ICAM-1 with IA-29 significantly decreased BSCB leakage of albumin at 24 hours (p=0.03). Regions with both increased ICAM-1 expression and BSCB disruption were identified in white matter. Thus the dose response and spatial distribution of increased ICAM-1 expression parallels that for BSCB disruption. These results are consistent with a role for increased ICAM-1 expression in radiation-induced BSCB disruption. The effect of blocking ICAM-1 with a neutralizing antibody suggests its

Phasic spike patterning in rat supraoptic neurones in vivo and in vitro

Science.gov (United States)

Sabatier, Nancy; Brown, Colin H; Ludwig, Mike; Leng, Gareth

2004-01-01

In vivo, most vasopressin cells of the hypothalamic supraoptic nucleus fire action potentials in a ‘phasic’ pattern when the systemic osmotic pressure is elevated, while most oxytocin cells fire continuously. The phasic firing pattern is believed to arise as a consequence of intrinsic activity-dependent changes in membrane potential, and these have been extensively studied in vitro. Here we analysed the discharge patterning of supraoptic nucleus neurones in vivo, to infer the characteristics of the post-spike sequence of hyperpolarization and depolarization from the observed spike patterning. We then compared patterning in phasic cells in vivo and in vitro, and we found systematic differences in the interspike interval distributions, and in other statistical parameters that characterized activity patterns within bursts. Analysis of hazard functions (probability of spike initiation as a function of time since the preceding spike) revealed that phasic firing in vitro appears consistent with a regenerative process arising from a relatively slow, late depolarizing afterpotential that approaches or exceeds spike threshold. By contrast, in vivo activity appears to be dominated by stochastic rather than deterministic mechanisms, and appears consistent with a relatively early and fast depolarizing afterpotential that modulates the probability that random synaptic input exceeds spike threshold. Despite superficial similarities in the phasic firing patterns observed in vivo and in vitro, there are thus fundamental differences in the underlying mechanisms. PMID:15146047

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

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Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Spike timing rigidity is maintained in bursting neurons under pentobarbital-induced anesthetic conditions

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Risako Kato

2016-11-01

Full Text Available Pentobarbital potentiates γ-aminobutyric acid (GABA-mediated inhibitory synaptic transmission by prolonging the open time of GABAA receptors. However, it is unknown how pentobarbital regulates cortical neuronal activities via local circuits in vivo. To examine this question, we performed extracellular unit recording in rat insular cortex under awake and anesthetic conditions. Not a few studies apply time-rescaling theorem to detect the features of repetitive spike firing. Similar to these methods, we define an average spike interval locally in time using random matrix theory (RMT, which enables us to compare different activity states on a universal scale. Neurons with high spontaneous firing frequency (> 5 Hz and bursting were classified as HFB neurons (n = 10, and those with low spontaneous firing frequency (< 10 Hz and without bursting were classified as non-HFB neurons (n = 48. Pentobarbital injection (30 mg/kg reduced firing frequency in all HFB neurons and in 78% of non-HFB neurons. RMT analysis demonstrated that pentobarbital increased in the number of neurons with repulsion in both HFB and non-HFB neurons, suggesting that there is a correlation between spikes within a short interspike interval. Under awake conditions, in 50% of HFB and 40% of non-HFB neurons, the decay phase of normalized histograms of spontaneous firing were fitted to an exponential function, which indicated that the first spike had no correlation with subsequent spikes. In contrast, under pentobarbital-induced anesthesia conditions, the number of non-HFB neurons that were fitted to an exponential function increased to 80%, but almost no change in HFB neurons was observed. These results suggest that under both awake and pentobarbital-induced anesthetized conditions, spike firing in HFB neurons is more robustly regulated by preceding spikes than by non-HFB neurons, which may reflect the GABAA receptor-mediated regulation of cortical activities. Whole-cell patch

Absolute spike frequency as a predictor of surgical outcome in temporal lobe epilepsy.

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Ngo, Ly; Sperling, Michael R; Skidmore, Christopher; Mintzer, Scott; Nei, Maromi

2017-04-01

Frequent interictal epileptiform abnormalities may correlate with poor prognosis after temporal lobe resection for refractory epilepsy. To date, studies have focused on limited resections such as selective amygdalohippocampectomy and apical temporal lobectomy without hippocampectomy. However, it is unclear whether the frequency of spikes predicts outcome after standard anterior temporal lobectomy. Preoperative scalp video-EEG monitoring data from patients who subsequently underwent anterior temporal lobectomy over a three year period and were followed for at least one year were reviewed for the frequency of interictal epileptiform abnormalities. Surgical outcome for those patients with frequent spikes (>60/h) was compared with those with less frequent spikes. Additionally, spike frequency was evaluated as a continuous variable and correlated with outcome to determine if increased spike frequency correlated with worse outcome, as assessed by modified Engel Class outcome. Forty-seven patients (18 men, 29 women; mean age 40 years at surgery) were included. Forty-six patients had standard anterior temporal lobectomy (24 right, 22 left) and one had a modified left temporal lobectomy. There was no significant difference in seizure outcome between those with frequent (57% Class I) vs. those with less frequent (58% Class I) spikes. Increased spike frequency did not correlate with worse outcome. Greater than 20 complex partial seizures/month and generalized tonic-clonic seizures within one year of surgery correlated with worse outcome. This study suggests that absolute spike frequency does not predict seizure outcome after anterior temporal lobectomy unlike in selective procedures, and should not be used as a prognostic factor in this population. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

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...

A Hybrid Setarx Model for Spikes in Tight Electricity Markets

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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

Disruption of the Cdc42/Par6/aPKC or Dlg/Scrib/Lgl Polarity Complex Promotes Epithelial Proliferation via Overlapping Mechanisms.

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Schimizzi, Gregory V; Maher, Meghan T; Loza, Andrew J; Longmore, Gregory D

2016-01-01

The establishment and maintenance of apical-basal polarity is a defining characteristic and essential feature of functioning epithelia. Apical-basal polarity (ABP) proteins are also tumor suppressors that are targeted for disruption by oncogenic viruses and are commonly mutated in human carcinomas. Disruption of these ABP proteins is an early event in cancer development that results in increased proliferation and epithelial disorganization through means not fully characterized. Using the proliferating Drosophila melanogaster wing disc epithelium, we demonstrate that disruption of the junctional vs. basal polarity complexes results in increased epithelial proliferation via distinct downstream signaling pathways. Disruption of the basal polarity complex results in JNK-dependent proliferation, while disruption of the junctional complex primarily results in p38-dependent proliferation. Surprisingly, the Rho-Rok-Myosin contractility apparatus appears to play opposite roles in the regulation of the proliferative phenotype based on which polarity complex is disrupted. In contrast, non-autonomous Tumor Necrosis Factor (TNF) signaling appears to suppress the proliferation that results from apical-basal polarity disruption, regardless of which complex is disrupted. Finally we demonstrate that disruption of the junctional polarity complex activates JNK via the Rho-Rok-Myosin contractility apparatus independent of the cortical actin regulator, Moesin.

Molecular targets in radiation-induced blood-brain barrier disruption

International Nuclear Information System (INIS)

Nordal, Robert A.; Wong, C. Shun

2005-01-01

Disruption of the blood-brain barrier (BBB) is a key feature of radiation injury to the central nervous system. Studies suggest that endothelial cell apoptosis, gene expression changes, and alteration of the microenvironment are important in initiation and progression of injury. Although substantial effort has been directed at understanding the impact of radiation on endothelial cells and oligodendrocytes, growing evidence suggests that other cell types, including astrocytes, are important in responses that include induced gene expression and microenvironmental changes. Endothelial apoptosis is important in early BBB disruption. Hypoxia and oxidative stress in the later period that precedes tissue damage might lead to astrocytic responses that impact cell survival and cell interactions. Cell death, gene expression changes, and a toxic microenvironment can be viewed as interacting elements in a model of radiation-induced disruption of the BBB. These processes implicate particular genes and proteins as targets in potential strategies for neuroprotection

A Neuro-Inspired Spike-Based PID Motor Controller for Multi-Motor Robots with Low Cost FPGAs

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Anton Civit-Balcells

2012-03-01

Full Text Available In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN, which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

Computational design of chimeric protein libraries for directed evolution.

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Silberg, Jonathan J; Nguyen, Peter Q; Stevenson, Taylor

2010-01-01

The best approach for creating libraries of functional proteins with large numbers of nondisruptive amino acid substitutions is protein recombination, in which structurally related polypeptides are swapped among homologous proteins. Unfortunately, as more distantly related proteins are recombined, the fraction of variants having a disrupted structure increases. One way to enrich the fraction of folded and potentially interesting chimeras in these libraries is to use computational algorithms to anticipate which structural elements can be swapped without disturbing the integrity of a protein's structure. Herein, we describe how the algorithm Schema uses the sequences and structures of the parent proteins recombined to predict the structural disruption of chimeras, and we outline how dynamic programming can be used to find libraries with a range of amino acid substitution levels that are enriched in variants with low Schema disruption.

A Three-Dimensional Movement Analysis of the Spike in Fistball

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Andreas Bund

2016-12-01

Full Text Available Due to its relevancy to point scoring, the spike is considered as one of the most important skills in fistball. Biomechanical analyses of this sport are very rare. In the present study, we performed a three-dimensional kinematic analysis of the fistball spike, which helps to specify performance parameters on a descriptive level. Recorded by four synchronized cameras (120 Hz and linked to the motion capture software Simi Motion® 5.0, three female fistball players of the second German league (24–26 years, 1.63–1.69 m performed several spikes under standardized conditions. Results show that the segment velocities of the arm reached their maximum successively from proximal to distal, following the principle of temporal coordination of single impulses. The wrist shows maximum speed when the fist hits the ball. The elbow joint angle performs a rapid transition from a strong flexion to a (almost full extension; however, the extension is completed after the moment of ball impact. In contrast, the shoulder joint angle increases almost linearly until the fistball contact and decreases afterward. The findings can be used to optimize the training of the spike.

Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices.

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Zarudnyi, Konstantin; Mehonic, Adnan; Montesi, Luca; Buckwell, Mark; Hudziak, Stephen; Kenyon, Anthony J

2018-01-01

Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiO x ) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.

Loss of Oca2 disrupts the unfolded protein response and increases resistance to endoplasmic reticulum stress in melanocytes.

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Cheng, Tsing; Orlow, Seth J; Manga, Prashiela

2013-11-01

Accumulation of proteins in the endoplasmic reticulum (ER) typically induces stress and initiates the unfolded protein response (UPR) to facilitate recovery. If homeostasis is not restored, apoptosis is induced. However, adaptation to chronic UPR activation can increase resistance to subsequent acute ER stress. We therefore investigated adaptive mechanisms in Oculocutaneous albinism type 2 (Oca2)-null melanocytes where UPR signaling is arrested despite continued tyrosinase accumulation leading to resistance to the chemical ER stressor thapsigargin. Although thapsigargin triggers UPR activation, instead of Perk-mediated phosphorylation of eIF2α, in Oca2-null melanocytes, eIF2α was rapidly dephosphorylated upon treatment. Dephosphorylation was mediated by the Gadd34-PP1α phosphatase complex. Gadd34-complex inhibition blocked eIF2α dephosphorylation and significantly increased Oca2-null melanocyte sensitivity to thapsigargin. Thus, Oca2-null melanocytes adapt to acute ER stress by disruption of pro-apoptotic Perk signaling, which promotes cell survival. This is the first study to demonstrate rapid eIF2α dephosphorylation as an adaptive mechanism to ER stress. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Plasmalemmal Vesicle Associated Protein-1 (PV-1 is a marker of blood-brain barrier disruption in rodent models

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Ali Zarina S

2008-02-01

Full Text Available Abstract Background Plasmalemmal vesicle associated protein-1 (PV-1 is selectively expressed in human brain microvascular endothelial cells derived from clinical specimens of primary and secondary malignant brain tumors, cerebral ischemia, and other central nervous system (CNS diseases associated with blood-brain barrier breakdown. In this study, we characterize the murine CNS expression pattern of PV-1 to determine whether localized PV-1 induction is conserved across species and disease state. Results We demonstrate that PV-1 is selectively upregulated in mouse blood vessels recruited by brain tumor xenografts at the RNA and protein levels, but is not detected in non-neoplastic brain. Additionally, PV-1 is induced in a mouse model of acute ischemia. Expression is confined to the cerebovasculature within the region of infarct and is temporally regulated. Conclusion Our results confirm that PV-1 is preferentially induced in the endothelium of mouse brain tumors and acute ischemic brain tissue and corresponds to blood-brain barrier disruption in a fashion analogous to human patients. Characterization of PV-1 expression in mouse brain is the first step towards development of rodent models for testing anti-edema and anti-angiogenesis therapeutic strategies based on this molecule.

Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization

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Gregor Strobbe

2016-01-01

Full Text Available Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time

Hardware implementation of stochastic spiking neural networks.

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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.

Forskolin suppresses delayed-rectifier K+ currents and enhances spike frequency-dependent adaptation of sympathetic neurons.

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Luis I Angel-Chavez

Full Text Available In signal transduction research natural or synthetic molecules are commonly used to target a great variety of signaling proteins. For instance, forskolin, a diterpene activator of adenylate cyclase, has been widely used in cellular preparations to increase the intracellular cAMP level. However, it has been shown that forskolin directly inhibits some cloned K+ channels, which in excitable cells set up the resting membrane potential, the shape of action potential and regulate repetitive firing. Despite the growing evidence indicating that K+ channels are blocked by forskolin, there are no studies yet assessing the impact of this mechanism of action on neuron excitability and firing patterns. In sympathetic neurons, we find that forskolin and its derivative 1,9-Dideoxyforskolin, reversibly suppress the delayed rectifier K+ current (IKV. Besides, forskolin reduced the spike afterhyperpolarization and enhanced the spike frequency-dependent adaptation. Given that IKV is mostly generated by Kv2.1 channels, HEK-293 cells were transfected with cDNA encoding for the Kv2.1 α subunit, to characterize the mechanism of forskolin action. Both drugs reversible suppressed the Kv2.1-mediated K+ currents. Forskolin inhibited Kv2.1 currents and IKV with an IC50 of ~32 μM and ~24 µM, respectively. Besides, the drug induced an apparent current inactivation and slowed-down current deactivation. We suggest that forskolin reduces the excitability of sympathetic neurons by enhancing the spike frequency-dependent adaptation, partially through a direct block of their native Kv2.1 channels.

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...

Rebound spiking in layer II medial entorhinal cortex stellate cells: Possible mechanism of grid cell function

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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

Validation of neural spike sorting algorithms without ground-truth information.

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Barnett, Alex H; Magland, Jeremy F; Greengard, Leslie F

2016-05-01

The throughput of electrophysiological recording is growing rapidly, allowing thousands of simultaneous channels, and there is a growing variety of spike sorting algorithms designed to extract neural firing events from such data. This creates an urgent need for standardized, automatic evaluation of the quality of neural units output by such algorithms. We introduce a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given dataset. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the internal workings of the algorithm, and minimal assumptions about the noise. We illustrate the new metrics on standard sorting algorithms applied to both in vivo and ex vivo recordings, including a time series with overlapping spikes. We compare the metrics to existing quality measures, and to ground-truth accuracy in simulated time series. We provide a software implementation. Metrics have until now relied on ground-truth, simulated data, internal algorithm variables (e.g. cluster separation), or refractory violations. By contrast, by standardizing the interface, our metrics assess the reliability of any automatic algorithm without reference to internal variables (e.g. feature space) or physiological criteria. Stability is a prerequisite for reproducibility of results. Such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.