Relevant bibliographies by topics / Spike

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Academic literature on the topic 'Spike'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Spike"

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Mesbah, Abdel O., and Stephen D. Miller. "Fertilizer Placement Affects Jointed Goatgrass (Aegilops cylindrica) Competition In Winter Wheat (Triticum aestivum)." Weed Technology 13, no. 2 (June 1999): 374–77. http://dx.doi.org/10.1017/s0890037x00041889.

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A 3-yr study was conducted in eastern Wyoming from 1995 to 1997 to evaluate the effect of fertilizer placement on jointed goatgrass competitiveness with winter wheat. Fertilizer placement methods consisted of applying 45 kg/ha of nitrogen (50% as urea and 50% as ammonium nitrate) in a deep band 5 cm below and 2.5 cm to the side of the wheat row, broadcasting on the soil surface, or injecting fertilizer by spoke wheel 10 cm deep and 5 cm to the side of the wheat row. Neither fertilizer placement nor jointed goatgrass presence affected winter wheat stand. Wheat yield reductions from jointed goatgrass competition were 7 and 10% higher with the broadcast than deep-band or spoke-wheel injection methods, respectively. Wheat spikes/plant, seeds/spike, 200-seed weight, and plant height were not influenced by fertilizer placement; however, the presence of 35 jointed goatgrass plants/m2reduced spikes/plant 21%, seeds/spike 12%, and 200-seed weight 6%. Jointed goatgrass populations were not influenced by fertilizer placement method; however, the number of spikes/plant was reduced 8 and 10%, joints/spike 3%, and biomass 15 and 21% by deep band or spoke wheel fertilizer placement.
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Kalimuthu, R., R. C. Mehta, and E. Rathakrishnan. "Investigation of aerodynamic coefficients at Mach 6 over conical, hemispherical and flat-face spiked body." Aeronautical Journal 121, no. 1245 (October 2, 2017): 1711–32. http://dx.doi.org/10.1017/aer.2017.100.

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ABSTRACTA forward spike attached to a blunt body significantly alters its flow field characteristics and influences aerodynamic characteristics at hypersonic flow due to formation of separated flow and re-circulation region around the spiked body. An experimental investigation was performed to measure aerodynamic forces for spikes blunt bodies with a conical, hemispherical and flat-face spike at Mach 6 and at an angle-of-attack range from 0° to 8° and length-to-diameterL/Dratio of spike varies from 0.5 to 2.0, whereLis the length of the spike andDis diameter of blunt body. The shape of the leading edge of the spiked blunt body reveals different types of flow field features in the formation of a shock wave, shear layer, flow separation, re-circulation region and re-attachment shock. They are analysed with the help of schlieren pictures. The shock distance ahead of the hemisphere and the flat-face spike is compared with the analytical solution and is showing satisfactory agreement with the schlieren pictures. The influence of geometrical parameters of the spike, the shape of the spike tip and angle-of-attack on the aerodynamic coefficients are investigated by measuring aerodynamic forces in a hypersonic wind tunnel. It is found that a maximum reduction of drag of about 77% was found for hemisphere spike ofL/D= 2.0 at zero angle-of-attack. Consideration for compensation of increased pitching moment is required to stabilise the aerodynamic forces.
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Scott, John W., and Lisa Sherrill. "Effects of Odor Stimulation on Antidromic Spikes in Olfactory Sensory Neurons." Journal of Neurophysiology 100, no. 6 (December 2008): 3074–85. http://dx.doi.org/10.1152/jn.90399.2008.

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Spikes were evoked in rat olfactory sensory neuron (OSN) populations by electrical stimulation of the olfactory bulb nerve layer in pentobarbital anesthetized rats. The latencies and recording positions for these compound spikes showed that they originated in olfactory epithelium. Dual simultaneous recordings indicated conduction velocities in the C-fiber range, around 0.5 m/s. These spikes are concluded to arise from antidromically activated olfactory sensory neurons. Electrical stimulation at 5 Hz was used to track changes in the size and latency of the antidromic compound population spike during the odor response. Strong odorant stimuli suppressed the spike size and prolonged its latency. The latency was prolonged throughout long odor stimuli, indicating continued activation of olfactory receptor neuron axons. The amounts of spike suppression and latency change were strongly correlated with the electroolfactogram (EOG) peak size evoked at the same site across odorants and across stimulus intensities. We conclude that the curve of antidromic spike suppression gives a reasonable representation of spiking activity in olfactory sensory neurons driven by odorants and that the correlation of peak spike suppression with the peak EOG shows the accuracy of the EOG as an estimate of intracellular potential in the population of olfactory sensory neurons. In addition, these results have important implications about traffic in olfactory nerve bundles. We did not observe multiple peaks corresponding to stimulated and unstimulated receptor neurons. This suggests synchronization of spikes in olfactory nerve, perhaps by ephaptic interactions. The long-lasting effect on spike latency shows that action potentials continue in the nerve throughout the duration of an odor stimulus in spite of many reports of depolarization block in olfactory receptor neuron cell bodies. Finally, strong odor stimulation caused almost complete block of antidromic spikes. This indicates that a very large proportion of olfactory axons was activated by single strong odor stimuli.
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Raastad, Morten, and Ole Kiehn. "Spike Coding During Locomotor Network Activity in Ventrally Located Neurons in the Isolated Spinal Cord From Neonatal Rat." Journal of Neurophysiology 83, no. 5 (May 1, 2000): 2825–34. http://dx.doi.org/10.1152/jn.2000.83.5.2825.

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To characterize spike coding in spinal neurons during rhythmic locomotor activity, we recorded from individual cells in the lumbar spinal cord of neonatal rats by using the on-cell patch-clamp technique. Locomotor activity was induced by N-methyl-d aspartate (NMDA) and 5-hydroxytryptamine (5-HT) and monitored by ventral root recording. We made an estimator based on the assumption that the number of spikes arriving during two halves of the locomotor cycle could be a code used by the neuronal network to distinguish between the halves. This estimator, termed the spike contrast, was calculated as the difference between the number of spikes in the most and least active half of an average cycle. The root activity defined the individual cycles and the positions of the spikes were calculated relative to these cycles. By comparing the average spike contrast to the spike contrast in noncyclic, randomized spike trains we found that approximately one half the cells (19 of 42) contained a significant spike contrast, averaging 1.25 ± 0.23 (SE) spikes/cycle. The distribution of spike contrasts in the total population of cells was exponential, showing that weak modulation was more typical than strong modulation. To investigate if this low spike contrast was misleading because a higher spike contrast averaged out by occurring at different positions in the individual cycles we compared the spike contrast obtained from the average cycle to its maximal value in the individual cycles. The value was larger (3.13 ± 0.25 spikes) than the spike contrast in the average cycle but not larger than the spike contrast in the individual cycles of a random, noncyclic spike trains (3.21 ± 0.21 spikes). This result suggested that the important distinction between cyclic and noncyclic cells was only the repeated cycle position of the spike contrast and not its magnitude. Low spike frequencies (5.2 ± 0.82 spikes/cycle, that were on average 3.5 s long) and a minimal spike interval of 100–200 ms limited the spike contrast. The standard deviation (SD) of the spike contrast in the individual neurons was similar to the average spike contrasts and was probably stochastic because the SDs of the simulated, noncyclic spike trains were also similar. In conclusion we find a highly distributed and variable locomotor related cyclic signal that is represented in the individual neurons by very few spikes and that becomes significant only because the spike contrast is repeated at a preferred phase of the locomotor cycle.
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Lemon, N., and R. W. Turner. "Conditional Spike Backpropagation Generates Burst Discharge in a Sensory Neuron." Journal of Neurophysiology 84, no. 3 (September 1, 2000): 1519–30. http://dx.doi.org/10.1152/jn.2000.84.3.1519.

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Backpropagating dendritic Na+spikes generate a depolarizing afterpotential (DAP) at the soma of pyramidal cells in the electrosensory lateral line lobe (ELL) of weakly electric fish. Repetitive spike discharge is associated with a progressive depolarizing shift in somatic spike afterpotentials that eventually triggers a high-frequency spike doublet and subsequent burst afterhyperpolarization (bAHP). The rhythmic generation of a spike doublet and bAHP groups spike discharge into an oscillatory burst pattern. This study examined the soma-dendritic mechanisms controlling the depolarizing shift in somatic spike afterpotentials, and the mechanism by which spike doublets terminate spike discharge. Intracellular recordings were obtained from ELL pyramidal somata and apical dendrites in an in vitro slice preparation. The pattern of spike discharge was equivalent in somatic and dendritic regions, reflecting the backpropagation of spikes from soma to dendrites. There was a clear frequency-dependent threshold in the transition from tonic to burst discharge, with bursts initiated when interspike intervals fell between ∼3–7 ms. Removal of all backpropagating spikes by dendritic TTX ejection revealed that the isolated somatic AHPs were entirely stable at the interspike intervals that generated burst discharge. As such, the depolarizing membrane potential shift during repetitive discharge could be attributed to a potentiation of DAP amplitude. Potentiation of the DAP was due to a frequency-dependent broadening and temporal summation of backpropagating dendritic Na+ spikes. Spike doublets were generated with an interspike interval close to, but not within, the somatic spike refractory period. In contrast, the interspike interval of spike doublets always fell within the longer dendritic refractory period, preventing backpropagation of the second spike of the doublet. The dendritic depolarization was thus abruptly removed from one spike to the next, allowing the burst to terminate when the bAHP hyperpolarized the membrane. The transition from tonic to burst discharge was dependent on the number and frequency of spikes invoking dendritic spike summation, indicating that burst threshold depends on the immediate history of cell discharge. Spike frequency thus represents an important condition that determines the success of dendritic spike invasion, establishing an intrinsic mechanism by which backpropagating spikes can be used to generate a rhythmic burst output.
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Saito, Mitsuru, Yoshinaka Murai, Hajime Sato, Yong-Chul Bae, Tadashi Akaike, Masahiko Takada, and Youngnam Kang. "Two Opposing Roles of 4-AP–Sensitive K+ Current in Initiation and Invasion of Spikes in Rat Mesencephalic Trigeminal Neurons." Journal of Neurophysiology 96, no. 4 (October 2006): 1887–901. http://dx.doi.org/10.1152/jn.00176.2006.

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The axon initial segment plays important roles in spike initiation and invasion of axonal spikes into the soma. Among primary sensory neurons, those in the mesencephalic trigeminal nucleus (MTN) are exceptional in their ability to initiate soma spikes (S-spikes) in response to synaptic inputs, consequently displaying two kinds of S-spikes, one caused by invasion of an axonal spike arising from the sensory receptor and the other initiated by somatic inputs. We investigated where spikes are initiated in such MTN neurons and whether there are any differences between the two kinds of S-spikes. Simultaneous patch-clamp recordings from the soma and axon hillock revealed a spike-backpropagation from the spike-initiation site in the stem axon to the soma in response to 1-ms somatic current pulse, which disclosed the delayed emergence of S-spikes after the current-pulse offset. These initiated S-spikes were smaller in amplitude than S-spikes generated by stimulation of the stem axon; however, 4-AP (≤0.5 mM) eliminated the amplitude difference. Furthermore, 4-AP dramatically shortened the delay in spike initiation without affecting the spike-backpropagation time in the stem axon, whereas it substantially prolonged the refractory period of S-spikes arising from axonal-spike invasion without significantly affecting that of presumed axonal spikes. These observations suggest that 4-AP–sensitive K+ currents exert two opposing effects on S-spikes depending on their origins: suppression of spike initiation and facilitation of axonal-spike invasion at higher frequencies. Consistent with these findings, strong immunoreactivities for Kv1.1 and Kv1.6, among 4-AP–sensitive and low-voltage–activated Kv1 family examined, were detected in the soma but not in the stem axon of MTN neurons.
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Snyder, Melissa, Angela Dispenzieri, S. Vincent Rajkumar, Robert Kyle, Joanne Benson, and Jerry A. Katzmann. "The Biologic and Analytic Variability of Serum Protein Electrophoresis M-Spike, Nephelometric Ig Quantitation, Serum FLC Quantitation, and Urine M-Spike in Monoclonal Gammopathies." Blood 114, no. 22 (November 20, 2009): 1803. http://dx.doi.org/10.1182/blood.v114.22.1803.1803.

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Abstract Abstract 1803 Poster Board I-829 Background Plasma cell proliferative disorders are monitored by a variety of methods. Serum protein electrophoresis (SPEP) and/or urine PEP M-spike quantitation are commonly assessed in patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM) to determine disease progression, response, or relapse. Serum immunoglobin (Ig) concentrations can be quantitated when the M-spike is large or if the migration is obscured within the SPEP beta fraction. Serum FLC quantitation provides a rapid indicator of response, will detect the rare occurrence of FLC escape, and will allow disease monitoring in the absence of a measurable serum or urine M-spike. The International Myeloma Working Group (IMWG) has recommended that the serum and urine M-spike should be used to monitor disease, and that FLC quantitation should be used only if there is no measurable disease by electrophoresis and if the monoclonal FLC concentration is greater than 10 mg/dL in the context of an abnormal FLC K/L ratio. We have studied serial samples in clinically stable patients in order to assess the total variability (analytic and biologic) of these assays and to confirm the IMWG recent recommendations. Methods Serial data from stable MGUS patients (n=35) were identified by the availability of all 3 serum test results (M-spike, Ig, FLC) in at least 4 serial samples that were obtained 9 months to 5 years apart and whose serum M-spikes varied by less than 25%. For MM (n=60) and SMM (n=48) the samples were within 9-15 months and serum M-spikes varied by less than 0.5 g/dL. Among the 60 MM, 48 SMM, and 35 MGUS patients, there were 23, 41, and 18 patients with measurable disease by serum M-spike (i.e. M-spike >1 g/dL); 19, 10, and 10 patients with an evaluable FLC (i.e. monoclonal FLC > 10 mg/dL and an abnormal FLC ratio); and 5, 5, and 1 patient with an evaluable urine (i.e. M-spike > 200mg/24 hr). The FLC data was analyzed as the involved FLC concentration (iFLC), the difference between the involved and uninvolved FLC concentration (dFLC), and the FLC K/L ratio (rFLC). The coefficients of variability (CV) were determined for each methodology in each patient sample set, and the average CVs were determined. Igs were quantitated by immunonephelometry using a Siemens BNII and Siemens reagent sets; kappa and lambda FLC were quantitated using a Siemens BNII and Freelite reagent sets from The Binding Site; M-spikes were quantitated using Helena SPIFE SPE and reagent sets. Results The CVs for the Ig quantitation, SPEP M-spike, FLC quantitation, and urine M-spike in each of the patient groups are listed in the table: Our laboratory's interassay analytic CV for replicate samples are 4-5% for Ig quantitation, 6-8% for SPEP M-spikes, 6-7% for FLC quantitation, and 5-7% for urine M-spikes. The analytic CVs of the methods are similar, but the total (analytic + biologic) CVs are very different. The samples have been selected by restricting the variability of serum M-spike values; when we apply the same criteria to the IgG quantitation, the IgG total CV comes closer to the serum M-spike CVs. The remaining differences, however, may be due to biologic variability contributed by polyclonal Ig. The total CV for iFLC is similar to the urine M-spike CV and suggests a previously unrecognized large biologic CV for serum FLC. The iFLC and dFLC CVs were comparable but were smaller than the rFLC CV. Conclusion The variability of the serum and urine M-spike, Ig, and FLC measurements confirm the IMWG recommendations for patient monitoring. If a patient has a measurable M-spike >1 g/dL, then the serum M-spike should be followed. If there is no measurable disease, then the iFLC can be monitored, provided that the rFLC is abnormal and the iFLC concentration is >10 mg/dL. Although the number of patients with evaluable urine M-spikes in this study is small, larger studies may confirm the utility of serum FLC compared to urine M-spike for monitoring patients with monoclonal gammopathies. Disclosures No relevant conflicts of interest to declare.
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Ciba, Manuel, Robert Bestel, Christoph Nick, Guilherme Ferraz de Arruda, Thomas Peron, Comin César Henrique, Luciano da Fontoura Costa, Francisco Aparecido Rodrigues, and Christiane Thielemann. "Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity." Neural Computation 32, no. 5 (May 2020): 887–911. http://dx.doi.org/10.1162/neco_a_01277.

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As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erroneous spike detection, such as missed spikes (false negative) or noise (false positive). Therefore, different timescale-dependent (cross-correlation, mutual information, spike time tiling coefficient) and timescale-independent (Spike-contrast, phase synchronization (PS), A-SPIKE-synchronization, A-ISI-distance, ARI-SPIKE-distance) synchrony measures were compared in terms of their robustness to erroneous spike trains. For this purpose, erroneous spike trains were generated by randomly adding (false positive) or deleting (false negative) spikes (in silico manipulated data) from experimental data. In addition, experimental data were analyzed using different spike detection threshold factors in order to confirm the robustness of the synchrony measures. All experimental data were recorded from cortical neuronal networks on microelectrode array chips, which show epileptiform activity induced by the substance BIC. As a result of the in silico manipulated data, Spike-contrast was the only measure that was robust to false-negative as well as false-positive spikes. Analyzing the experimental data set revealed that all measures were able to capture the effect of BIC in a statistically significant way, with Spike-contrast showing the highest statistical significance even at low spike detection thresholds. In summary, we suggest using Spike-contrast to complement established synchrony measures because it is timescale independent and robust to erroneous spike trains.
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Salvagno, Gian, Brandon Henry, Piazza di, Laura Pighi, Nitto de, Damiano Bragantini, Gian Gianfilippi, and Giuseppe Lippi. "Anti-spike S1 IgA, anti-spike trimeric IgG, and anti-spike RBD IgG response after BNT162b2 COVID-19 mRNA vaccination in healthcare workers." Journal of Medical Biochemistry 40, no. 4 (2021): 327–34. http://dx.doi.org/10.5937/jomb0-32373.

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Background: Most studies on immune response after coronavirus disease 2019 (COVID-19) vaccination focused on serum IgG antibodies and cell-mediated immunity, discounting the role of anti-SARS-CoV-2 neutralizing IgA antibodies in preventing viral infection. This study was aimed to quantify serum IgG and IgA neutralizing antibodies after mRNA COVID-19 vaccination in baseline SARS-CoV-2 seronegative healthcare workers. Methods: The study population consisted of 181 SARSCoV-2 seronegative healthcare workers (median age 42 years, 59.7% women), receiving two doses of Pfizer COVID-19 vaccine BNT162b2 (Comirnaty). Serum samples were collected before receiving the first vaccine dose, 21 days (before the second vaccine dose) and 50 days afterwards. We then measured anti-spike trimeric IgG (Liaison XL, DiaSorin), anti-spike receptor binding domain (RBD) IgG (Access 2, Beckman Coulter) and anti-spike S1 subunit IgA (ELISA, Euroimmun). Results were presented as median and interquartile range (IQR). Results: Vaccine administration elicited all anti-SARS-CoV2 antibodies measured. Thirty days after the second vaccine dose, 100% positivization occurred for anti-spike trimeric IgG and anti-spike RBD IgG, whilst 1.7% subjects remained anti-spike S1 IgA negative. The overall increase of antibodies level ratio over baseline after the second vaccine dose was 576.1 (IQR, 360.7-867.8) for anti-spike trimeric IgG, 1426.0 (IQR, 742.0-2698.6) for anti-spike RBD IgG, and 20.2 (IQR, 12.5-32.1) for anti-spike S1 IgA. Significant inverse association was found between age and overall increase of anti-spike trimeric IgG (r=-0.24; p=0.001) and anti-spike S1 IgA (r=-0.16; p=0.028), but not with anti-spike RBD IgG (r=-0.05; p=0.497). Conclusions: mRNA COVID-19 vaccination elicits sustained serum levels of anti-spike trimeric IgG and anti-spike RBD IgG, while also modestly but significantly increasing those of anti-spike S1 IgA.
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Kang, Youngnam, Mitsuru Saito, Hajime Sato, Hiroki Toyoda, Yoshinobu Maeda, Toshihiro Hirai, and Yong-Chul Bae. "Involvement of Persistent Na+ Current in Spike Initiation in Primary Sensory Neurons of the Rat Mesencephalic Trigeminal Nucleus." Journal of Neurophysiology 97, no. 3 (March 2007): 2385–93. http://dx.doi.org/10.1152/jn.01191.2006.

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It was recently shown that the persistent Na+ current ( INaP) is generated in the proximal axon in response to somatic depolarization in neocortical pyramidal neurons, although the involvement of INaP in spike initiation is still unclear. Here we show a potential role of INaP in spike initiation of primary sensory neurons in the mesencephalic trigeminal nucleus (MTN) that display a backpropagation of the spike initiated in the stem axon toward the soma in response to soma depolarization. Riluzole (10 μM) and tetrodotoxin (TTX, 10 nM) caused an activation delay or a stepwise increase in the threshold for evoking soma spikes (S-spikes) without affecting the spike itself. Simultaneous patch-clamp recordings from the soma and axon hillock (AH) revealed that bath application of 50 nM TTX increased the delay in spike activation in response to soma depolarization, leaving the spike-backpropagation time from the AH to soma unchanged. This indicates that the increase in activation delay occurred in the stem axon. Furthermore, under a decreasing intracellular concentration gradient of QX-314 from the soma to AH created by QX-314–containing and QX-314–free patch pipettes, the amplitude and maximum rate of rise (MRR) of AH-spikes decreased with an increase in the activation delay following repetition of current-pulse injections, whereas S-spikes displayed decreases of considerably lesser degree in amplitude and MRR. This suggests that compared to S-spikes, AH-spikes more accurately reflect the attenuation of axonal spike by QX-314, consistent with the nature of spike backpropagation. These observations strongly suggest that low-voltage–activated INaP is involved in spike initiation in the stem axon of MTN neurons.
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Dissertations / Theses on the topic "Spike"

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Ervin, Brian. "Neural Spike Detection and Classification Using Massively Parallel Graphics Processing." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868773.

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Fisch, Karin. "The contribution of spike-frequency adaptation to the variability of spike responses in a sensory neuron." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-135111.

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Bergheim, Thomas Stian, and Arve Aleksander Nymo Skogvold. "Parallel Algorithms for Neuronal Spike Sorting." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14199.

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Neurons communicate through electrophysiological signals, which may be recorded using electrodes inserted into living tissue.When a neuron emits a signal, it is referred to as a spike, and an electrode can detect these from multiple neurons.Neuronal spike sorting is the process of classifying the spike activity based on which neuron each spike signal is emitted from.Advances in technology have introduced better recording equipment, which allows the recording of many neurons at the same time.However, clustering software is lagging behind.Currently, spike sorting is often performed semi-manually by experts, with computer assistance, in a drastically reduced feature space.This makes the clustering prone to subjectivity.Automating the process will make classification much more efficient, and may produce better results.Implementing accurate and efficient spike sorting algorithms is therefore increasingly important.We have developed parallel implementations of superparamagnetic clustering, a novel clustering algorithm, as well as k-means clustering, serving as a useful comparison.Several feature extraction methods have been implemented to test various input distributions with the clustering algorithms. To assess the quality of the results from the algorithms, we have also implemented different cluster quality algorithms.Our implementations have been benchmarked, and found to scale well both with increased problem sizes and when run on multi-core processors.The results from our cluster quality measurements are inconclusive, and we identify this as a problem related to the subjectivity in the manually classified datasets.To better assess the utility of the algorithms, comparisons with intracellular recordings should be performed.
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Barsakcioglu, Deren. "Resource efficient on-node spike sorting." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/34385.

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Current implantable brain-machine interfaces are recording multi-neuron activity by utilising multi-channel, multi-electrode micro-electrodes. With the rapid increase in recording capability has come more stringent constraints on implantable system power consumption and size. This is even more so with the increasing demand for wireless systems to increase the number of channels being monitored whilst overcoming the communication bottleneck (in transmitting raw data) via transcutaneous bio-telemetries. For systems observing unit activity, real-time spike sorting within an implantable device offers a unique solution to this problem. However, achieving such data compression prior to transmission via an on-node spike sorting system has several challenges. The inherent complexity of the spike sorting problem arising from various factors (such as signal variability, local field potentials, background and multi-unit activity) have required computationally intensive algorithms (e.g. PCA, wavelet transform, superparamagnetic clustering). Hence spike sorting systems have traditionally been implemented off-line, usually run on work-stations. Owing to their complexity and not-so-well scalability, these algorithms cannot be simply transformed into a resource efficient hardware. On the contrary, although there have been several attempts in implantable hardware, an implementation to match comparable accuracy to off-line within the required power and area requirements for future BMIs have yet to be proposed. Within this context, this research aims to fill in the gaps in the design towards a resource efficient implantable real-time spike sorter which achieves performance comparable to off-line methods. The research covered in this thesis target: 1) Identifying and quantifying the trade-offs on subsequent signal processing performance and hardware resource utilisation of the parameters associated with analogue-front-end. Following the development of a behavioural model of the analogue-front-end and an optimisation tool, the sensitivity of the spike sorting accuracy to different front-end parameters are quantified. 2) Identifying and quantifying the trade-offs associated with a two-stage hybrid solution to realising real-time on-node spike sorting. Initial part of the work focuses from the perspective of template matching only, while the second part of the work considers these parameters from the point of whole system including detection, sorting, and off-line training (template building). A set of minimum requirements are established which ensure robust, accurate and resource efficient operation. 3) Developing new feature extraction and spike sorting algorithms towards highly scalable systems. Based on waveform dynamics of the observed action potentials, a derivative based feature extraction and a spike sorting algorithm are proposed. These are compared with most commonly used methods of spike sorting under varying noise levels using realistic datasets to confirm their merits. The latter is implemented and demonstrated in real-time through an MCU based platform.
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Emhemmed, Yousef Mohammed. "Maximum likelihood analysis of neuronal spike trains." Thesis, University of Glasgow, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326019.

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Zhao, Chenyuan. "Spike Processing Circuit Design for Neuromorphic Computing." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/93591.

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Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain increasing attention as compact and energy-efficient computing platforms. Spike based-neuromorphic computing systems require high performance and low power neural encoder and decoder to emulate the spiking behavior of neurons. These two spike-analog signals converting interface determine the whole spiking neuromorphic computing system's performance, especially the highest performance. Many state-of-the-art neuromorphic systems typically operate in the frequency range between 〖10〗^0KHz and 〖10〗^2KHz due to the limitation of encoding/decoding speed. In this dissertation, all these popular encoding and decoding schemes, i.e. rate encoding, latency encoding, ISI encoding, together with related hardware implementations have been discussed and analyzed. The contributions included in this dissertation can be classified into three main parts: neuron improvement, three kinds of ISI encoder design, two types of ISI decoder design. Two-path leakage LIF neuron has been fabricated and modular design methodology is invented. Three kinds of ISI encoding schemes including parallel signal encoding, full signal iteration encoding, and partial signal encoding are discussed. The first two types ISI encoders have been fabricated successfully and the last ISI encoder will be taped out by the end of 2019. Two types of ISI decoders adopted different techniques which are sample-and-hold based mixed-signal design and spike-timing-dependent-plasticity (STDP) based analog design respectively. Both these two ISI encoders have been evaluated through post-layout simulations successfully. The STDP based ISI encoder will be taped out by the end of 2019. A test bench based on correlation inspection has been built to evaluate the information recovery capability of the proposed spiking processing link.
Doctor of Philosophy
Neuromorphic computing is a kind of specific electronic system that could mimic biological bodies’ behavior. In most cases, neuromorphic computing system is built with analog circuits which have benefits in power efficient and low thermal radiation. Among neuromorphic computing system, one of the most important components is the signal processing interface, i.e. encoder/decoder. To increase the whole system’s performance, novel encoders and decoders have been proposed in this dissertation. In this dissertation, three kinds of temporal encoders, one rate encoder, one latency encoder, one temporal decoder, and one general spike decoder have been proposed. These designs could be combined together to build high efficient spike-based data link which guarantee the processing performance of whole neuromorphic computing system.
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Guido, Rodrigo Capobianco. "Spikelet: uma nova transformada wavelet aplicada ao reconhecimento digital de padrões, em tempo real, de spikes e overlaps em sinais neurofisiológicos do campo visual da mosca." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092008-172109/.

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A presente tese descreve a construção de uma nova transformada wavelet, aqui chamada de SPIKELET, que, combinada com um algoritmo proposto, é aplicada no reconhecimento computacional de padrões em spikes (picos) e spikes sobrepostos (overlaps) encontrados no sinal digitalizado correspondente às reações do neurônio H1 do cérebro de uma mosca de ordem Diptera, que é sensível aos estímulos visuais do meio externo. O algoritmo fornece, além do formato do sinal encontrado, o \'\'instante\'\' em que ele ocorreu, sendo que a implementação é feita, inclusive, em tempo-real, com o uso de um DSP.
This thesis describes the construction of a new wavelet transform, that is called SPIKELET, which is used together with a proposed algorithm, for spikes and overlaps pattern recognition, in a digitalized signal corresponding to the H1 visual neuron action potential from a Diptera\'s fly brain. The algorithm provides both the shape of the identified signal and the \'\'instant\'\' of time it happened. The implementation is also done in real time, using a DSP.
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Kwag, Jeehyun. "Synaptic control of spike timing and spike timing-dependent plasticity during theta frequency oscillation in hippocampal CA1 pyramidal neurons." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487275.

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Spike timing during oscillation has been suggested to play an important role in hippocampal processing. However, how the hippocampal network and the individual neurons interact to precisely control spike timing when they receive synaptic inputs from two major excitatory input pathways - Schaffer collateral and perforant path - during natural network oscillation is yet unknown. Investigation of spike timing control mechanism would shed light on how the local STDP learning rule could be influenced by different cortical inputs during theta oscillation. Here I used whole-cell path-clamp recording of CAl pyramidal neurons in vitro and dynamic clamp to simulate in vivo-like theta frequency oscillation at the soma to characterise the spike timing responses of CAl pyramidal neurons to Schaffer collateral and perforant path inputs during theta oscillation and present them as phase response curves (PRCs), Analysis of PRCs revealed that postsynaptic spike times could not only be advanced but also be delayed depending on the timing of excitatory inputs relative to the oscillation. Such control of spike timing during theta oscillation was dependent on the synaptic weight of the input and the frequency of the oscillation. Ih and GABAB receptor-mediated inhibition were identified as an intrinsic and synaptic mechanism, respectively, underlying spike time delay during oscillation. Activation of both Ih and GABAi3 receptor-mediated inhibition by perforant path stimulation contributed to greater spike time delay compared to that with Schaf.:fer collateral input stimulation which was only mediated by Ih. Such different spike timing characteristics were important in STDP induction at the Schaffer collateral-CAl pyramidal cell synapse, Depending on the timing of the perforant path activation during theta oscillation, perforant path input could control the timing of the postsynaptic spike during STDP induction which could reverse the sign of the synaptic modification, Thus, during natural network oscillation with multiple synaptic inputs active, timing of the heterosynaptic inputs from entorhinal cortex to the hippocampus could control the outcome of the homosynaptic plasticity in the CAL These results may have implications for how the external information could be encoded and stored in the hippocampal network.
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Ozturk, Ibrahim. "Learning spatio-temporal spike train encodings with ReSuMe, DelReSuMe, and Reward-modulated Spike-timing Dependent Plasticity in Spiking Neural Networks." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/21978/.

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SNNs are referred to as the third generation of ANNs. Inspired from biological observations and recent advances in neuroscience, proposed methods increase the power of SNNs. Today, the main challenge is to discover efficient plasticity rules for SNNs. Our research aims are to explore/extend computational models of plasticity. We make various achievements using ReSuMe, DelReSuMe, and R-STDP based on the fundamental plasticity of STDP. The information in SNNs is encoded in the patterns of firing activities. For biological plausibility, it is necessary to use multi-spike learning instead of single-spike. Therefore, we focus on encoding inputs/outputs using multiple spikes. ReSuMe is capable of generating desired patterns with multiple spikes. The trained neuron in ReSuMe can fire at desired times in response to spatio-temporal inputs. We propose alternative architecture for ReSuMe dealing with heterogeneous synapses. It is demonstrated that the proposed topology exactly mimic the ReSuMe. A novel extension of ReSuMe, called DelReSuMe, has better accuracy using less iteration by using multi-delay plasticity in addition to weight learning under noiseless and noisy conditions. The proposed heterogeneous topology is also used for DelReSuMe. Another plasticity extension based on STDP takes into account reward to modulate synaptic strength named R-STDP. We use dopamine-inspired STDP in SNNs to demonstrate improvements in mapping spatio-temporal patterns of spike trains with the multi-delay mechanism versus single connection. From the viewpoint of Machine Learning, Reinforcement Learning is outlined through a maze task in order to investigate the mechanisms of reward and eligibility trace which are the fundamental in R-STDP. To develop the approach we implement Temporal-Difference learning and novel knowledge-based RL techniques on the maze task. We develop rule extractions which are combined with RL and wall follower algorithms. We demonstrate the improvements on the exploration efficiency of TD learning for maze navigation tasks.
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Echtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.

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Books on the topic "Spike"

1

ill, Sweet Melissa 1956, ed. Spike. New York: Simon & Schuster Books for Young Readers, 2012.

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Marc, Katz, ed. Spike! San Diego, CA: Avant Books, 1985.

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ill, Ong Cristina, ed. Meet Spike. New York: Grosset & Dunlap, 2001.

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Rebeck, Theresa. Spike heels. New York: S. French, 1992.

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Abrams, Dennis. Spike Lee. New York: Chelsea House, 2008.

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Niles, Chris. Spike it. New York: Berkley Prime Crime, 1997.

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Christopher, Matt. Spike it! Boston: Little, Brown, 1999.

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Alex, Patterson. Spike Lee. London: Abacus, 1993.

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Peter, David. Spike omnibus. San Diego, Calif: IDW Pub., 2009.

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Harris, Laurie Lanzen. Spike Lee. Detroit, Mich: Lucent Books, 2011.

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Book chapters on the topic "Spike"

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Dalton, Jeff. "Spike (Design Spike)." In Great Big Agile, 233–34. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4206-3_56.

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Weik, Martin H. "spike." In Computer Science and Communications Dictionary, 1638. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17945.

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Dongarra, Jack, Piotr Luszczek, Felix Wolf, Jesper Larsson Träff, Patrice Quinton, Hermann Hellwagner, Martin Fränzle, et al. "SPIKE." In Encyclopedia of Parallel Computing, 1912–20. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_88.

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Gooch, Jan W. "Spike." In Encyclopedic Dictionary of Polymers, 925. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_14838.

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Kass, Robert E. "Spike Train." In Encyclopedia of Computational Neuroscience, 2807–8. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_408.

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Chernov, G. P. "Spike Bursts." In Astrophysics and Space Science Library, 19–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20015-1_3.

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Hardman, Casey. "Spike Traps." In Game Programming with Unity and C#, 285–97. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5656-5_24.

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Victor, Jonathan D., and Keith P. Purpura. "Spike Metrics." In Analysis of Parallel Spike Trains, 129–56. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5675-0_7.

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Weik, Martin H. "spike file." In Computer Science and Communications Dictionary, 1638. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_17946.

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Kass, Robert E. "Spike Train." In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_408-1.

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Conference papers on the topic "Spike"

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Madhukar, E. L. N. Rohit, and Harish Panjagala. "Recent Advancements in Spikes Used in Hypersonic Re-Entry Vehicles by Using CFD." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86550.

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The recent introduction of spike in the frontal region of high speed reentry vehicles has brought a tremendous improvement in space activities in the world. The major issue that the spikes resolves is aero heating of re-entry vehicles. Moreover, it preserves structural integrity and avoids damage. Usage of spike is economical and effective over different kinds of thermal protection system. Previous investigation on spiked re-entry vehicles leads to a conclusion that the Blunt and Snap spikes resulted in better reduction of temperature at nose of re-entry vehicle. This paper deals with geometry optimization of blunt and snap spike specifically the length, which is varied as L/8, L/4 and 3L/8 respectively where L is the length of the vehicle. ANSYS 17.2 FLUENT solver is incorporated for analysis purpose and the results are compared among the three different length spike re-entry vehicles. Modal analysis has also been carried out and natural frequency of spikes are obtained. This would provide a way to accept the safe and economical design with better thermal protection of the high-speed space vehicle.
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Tang, Jianxiong, Jian-Huang Lai, Xiaohua Xie, and Lingxiao Yang. "Spike Count Maximization for Neuromorphic Vision Recognition." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/473.

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Spiking Neural Networks (SNNs) are the promising models of neuromorphic vision recognition. The mean square error (MSE) and cross-entropy (CE) losses are widely applied to supervise the training of SNNs on neuromorphic datasets. However, the relevance between the output spike counts and predictions is not well modeled by the existing loss functions. This paper proposes a Spike Count Maximization (SCM) training approach for the SNN-based neuromorphic vision recognition model based on optimizing the output spike counts. The SCM is achieved by structural risk minimization (SRM) and a specially designed spike counting loss. The spike counting loss counts the output spikes of the SNN by using the L0-norm, and the SRM maximizes the distance between the margin boundaries of the classifier to ensure the generalization of the model. The SCM is non-smooth and non-differentiable, and we design a two-stage algorithm with fast convergence to solve the problem. Experiment results demonstrate that the SCM performs satisfactorily in most cases. Using the output spikes for prediction, the accuracies of SCM are 2.12%~16.50% higher than the popular training losses on the CIFAR10-DVS dataset. The code is available at https://github.com/TJXTT/SCM-SNN.
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"Golden Spike." In Planet Austria. Wien: Verlag der Österreichischen Akademie der Wissenschaften, 2009. http://dx.doi.org/10.1553/planetaustrias100.

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Ogawa, Takashi, and Toshimichi Saito. "Digital spike maps and learning of spike signals." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596555.

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Zhang, Yiyang, Ruiqin Xiong, and Tiejun Huang. "Spike Signal Reconstruction Based on Inter-Spike Similarity." In 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2022. http://dx.doi.org/10.1109/vcip56404.2022.10008868.

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Dick, Matthew G., David S. McConnell, and Hans C. Iwand. "Experimental Measurement and Finite Element Analysis of Screw Spike Fatigue Loads." In ASME/IEEE 2007 Joint Rail Conference and Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/jrc/ice2007-40090.

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Screw spikes, also known as coach screws, are an advanced alternative to common cut spikes for track fastening. Despite their ability to secure tie plates with a clamp load and utilization of high strength steels, they are still susceptible to bending fatigue failure from lateral wheel loads. A novel method of measuring these bending loads on screw spikes was developed and implemented to characterize the load environment of the screw spikes. Results indicated that measured peak bending loads under lateral wheel loads reached as high as 10,000 lbs for individual spikes, while others carried no load whatsoever. A finite element model was developed to determine the tensile stress fields created by the measured bending loads. A good correlation was found between the FEA model predicted point of highest stress and the location of fracture. Through the testing and analysis it was determined that lateral wheel loads are not distributed evenly among the four screw spikes of a single tie plate. Instead, it was found that one spike carried nearly no load while the spike opposite of it carried more load. Using the finite element analysis it was determined that the spike exposed to the higher loading was subjected to tensile stresses above its endurance limit, which would eventually lead to a bending fatigue failure.
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Dold, Dominik, and Josep Soler Garrido. "SpikE: spike-based embeddings for multi-relational graph data." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533548.

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Harrison, Alan K. "The Spike Dynamics Source Model for Ejecta in the FLAG Code." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-5455.

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Abstract The Lagrangian hydrocode FLAG employs a subgrid model to represent the ejection of particulate mass (“ejecta”) from a shocked metal surface. With a conforming mesh used in typical simulations, the calculations of ejecta production, properties and launch are carried out independently on each mesh face lying on the surface of the metal. Based on experimental evidence [1] that ejecta production is greatest when the shock releases to the liquid state, the ejection process is modeled as a Richtmyer-Meshkov instability (RMI) of the liquid metal surface, in which the metal spikes that form break up to become ejecta. The model applies to the case in which surface perturbations such as machining grooves can be well approximated as a single-mode sinusoidal perturbation; in this case the RMI spikes are actually sheets. The FLAG model includes (1) a description of RMI spike and bubble growth rates [2] and (2) the Self-Similar Velocity Distribution (SSVD) model of the velocity field within a spike as varying linearly from zero (in the fluid frame) at the base to a maximum value at the tip [3]. We report here on the improvement of this model by incorporating (3) a spike breakup treatment based on the Taylor Analogy Breakup (TAB) model [5], as extended to apply to sheet breakup [6,7], and (4) a new model for the inflow of metal into the base of the spikes. Combining all these elements allows us to self-consistently reconcile the evolving shape of the spikes (elongation and thinning) with the inflow, and with the corresponding properties of the bubbles, under the assumption of incompressibility. Since the model describes the motion of each fluid element into and along the spike, and subsequent fragmentation of the spike into ejecta, it predicts not only mass ejection rate but also the sizes and velocities of the particles launched in this process. We describe the new self-consistent model and its implementation in FLAG.
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Xiang, Xuyan, Yingchun Deng, and Xiangqun Yang. "Spike-Rate Perceptrons." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.556.

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Dibazar, Alireza A., Ali Yousefi, and Theodore W. Berger. "Multi-layer spike-in spike-out representation of Hippocampus circuitry." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696009.

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Reports on the topic "Spike"

1

Hall, Katelyn. Shoulder Spike. Ames: Iowa State University, Digital Repository, 2014. http://dx.doi.org/10.31274/itaa_proceedings-180814-1035.

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Sarpeshkar, Rahul. Spike-Based Hybrid Computers. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada417467.

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Russ, G. P. Inter-Laboratory Uranium Double-Spike Experiment. Office of Scientific and Technical Information (OSTI), November 1999. http://dx.doi.org/10.2172/792251.

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Youker, Amanda J., John F. Krebs, Kevin J. Quigley, James P. Byrnes, David A. Rotsch, Thomas Brossard, Kenneth Wesolowski, Kurt Alford, Sergey Chemerisov, and George F. Vandegrift. AMORE Mo-99 Spike Test Results. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1409216.

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Youker, Amanda J., John F. Krebs, Kevin J. Quigley, James P. Byrnes, David A. Rotsch, Thomas Brossard, Kenneth Wesolowski, et al. AMORE Mo-99 Spike Test Results. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1433488.

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Marzen, Sarah, Michael R. DeWeese, and Jim Crutchfield. Statistical Complexity of Neural Spike Trains. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada618984.

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Russell, H. A. J., J. Peets, G. Gorrell, D. R. Sharpe, and J. A M Hunter. Pontypool 'Golden Spike' borehole data compilation. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214401.

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Johnson, Caley. U.S. Virgin Islands Petroleum Price-Spike Preparation. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1046276.

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Logan, C. E., R. D. Knight, H. L. Crow, H. A. J. Russell, D R Sharpe, S. E. Pullan, and M J Hinton. Southern Ontario "Golden Spike" data release: Nobleton borehole. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2008. http://dx.doi.org/10.4095/225026.

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Parker, B., and E. Arnaud. Integration of 'golden spike' geologic and hydrogeological data sets. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297735.

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