WorldWideScience

Sample records for spiked test spectra

  1. AMORE Mo-99 Spike Test Results

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-09-27

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

  2. Accelerated spike resampling for accurate multiple testing controls.

    Science.gov (United States)

    Harrison, Matthew T

    2013-02-01

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

  3. A two-dimensionally coincident second difference cosmic ray spike removal method for the fully automated processing of Raman spectra.

    Science.gov (United States)

    Schulze, H Georg; Turner, Robin F B

    2014-01-01

    Charge-coupled device detectors are vulnerable to cosmic rays that can contaminate Raman spectra with positive going spikes. Because spikes can adversely affect spectral processing and data analyses, they must be removed. Although both hardware-based and software-based spike removal methods exist, they typically require parameter and threshold specification dependent on well-considered user input. Here, we present a fully automated spike removal algorithm that proceeds without requiring user input. It is minimally dependent on sample attributes, and those that are required (e.g., standard deviation of spectral noise) can be determined with other fully automated procedures. At the core of the method is the identification and location of spikes with coincident second derivatives along both the spectral and spatiotemporal dimensions of two-dimensional datasets. The method can be applied to spectra that are relatively inhomogeneous because it provides fairly effective and selective targeting of spikes resulting in minimal distortion of spectra. Relatively effective spike removal obtained with full automation could provide substantial benefits to users where large numbers of spectra must be processed.

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

    DEFF Research Database (Denmark)

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

    2009-01-01

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

  5. Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

    Science.gov (United States)

    Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

    2018-01-01

    Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as

  6. Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo F. O. Pena

    2018-03-01

    Full Text Available Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i different neural subpopulations (e.g., excitatory and inhibitory neurons have different cellular or connectivity parameters; (ii the number and strength of the input connections are random (Erdős-Rényi topology and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of

  7. Statistical analysis of uncertainties of gamma-peak identification and area calculation in particulate air-filter environment radionuclide measurements using the results of a Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) organized intercomparison, Part I: Assessment of reliability and uncertainties of isotope detection and energy precision using artificial spiked test spectra, Part II: Assessment of the true type I error rate and the quality of peak area estimators in relation to type II errors using large numbers of natural spectra

    International Nuclear Information System (INIS)

    Zhang, W.; Zaehringer, M.; Ungar, K.; Hoffman, I.

    2008-01-01

    In this paper, the uncertainties of gamma-ray small peak analysis have been examined. As the intensity of a gamma-ray peak approaches its detection decision limit, derived parameters such as centroid channel energy, peak area, peak area uncertainty, baseline determination, and peak significance are statistically sensitive. The intercomparison exercise organized by the CTBTO provided an excellent opportunity for this to be studied. Near background levels, the false-positive and false-negative peak identification frequencies in artificial test spectra have been compared to statistically predictable limiting values. In addition, naturally occurring radon progeny were used to compare observed variance against nominal uncertainties. The results infer that the applied fit algorithms do not always represent the best estimator. Understanding the statistically predicted peak-finding limit is important for data evaluation and analysis assessment. Furthermore, these results are useful to optimize analytical procedures to achieve the best results

  8. Extracting ion emission lines from femtosecond-laser plasma x-ray spectra heavily contaminated by spikes

    International Nuclear Information System (INIS)

    Gasilov, S. V.; Faenov, A. Ya.; Pikuz, T. A.; Villoresi, P.; Poletto, L.; Stagira, S.; Calegari, F.; Vozzi, C.; Nisoli, M.

    2007-01-01

    Nowadays charged-coupled device (CCD) detectors are widely used for the registration of multicharged ions x-ray spectra. These spectra are generated in a plasma during interaction of ultrashort, ultraintense laser pulses with solid targets. Strong parasitic radiation from the plasma affects CCD detectors and contaminates resulting spectra, so that spectral features can be completely covered by noise even during measurements with a very short accumulation time. In this work we propose a ''mean to median'' (M2M) algorithm for noise suppression in femtosecond laser plasma x-ray spectra. Series of spectra is necessary for the identification of corrupted data points by the developed method. The algorithm was tested with model spectra which reflect main features of experimental data. In practice we used it for extracting information about spectral lines of Ne-like Fe ions and He-like Al ions which allowed us to calculate plasma parameters. It is demonstrated that M2M method is able to clean spectra with more than 10% of corrupted pixels. Fluctuations in intensity of spectral lines induced by laser instability do not affect validity of the proposed method

  9. Spiked environmental matrix for use as a reference material for gamma-ray spectrometry: Production and homogeneity test

    International Nuclear Information System (INIS)

    Sobiech-Matura, K.; Máté, B.; Altzitzoglou, T.

    2016-01-01

    The application of a spiking method for reference material production and its utilisation for a food matrix is presented. The raw rice powder was tested by means of γ-ray spectrometry and spiked with a "1"3"7Cs solution. The spiked material was mixed and tested for homogeneity. The future use of the rice powder reference material after the entire characterisation cycle will be for γ-ray spectrometry method validation. - Highlights: • Spiking blank substance with a traceable radioactive solution • Spiked reference material for γ-ray emitting radionuclides in food matrix • Results of the homogeneity tests are presented

  10. Green Pharmaceutical Analysis of Drugs Coformulated with Highly Different Concentrations Using Spiking and Manipulation of Their Ratio Spectra.

    Science.gov (United States)

    Ayoub, Bassam M

    2017-07-01

    Introducing green analysis to pharmaceutical products is considered a significant approach to preserving the environment. This method can be an environmentally friendly alternative to the existing methods, accompanied by a validated automated procedure for the analysis of a drug with the lowest possible number of samples. Different simple spectrophotometric methods were developed for the simultaneous determination of empagliflozin (EG) and metformin (MT) by manipulating their ratio spectra in their application on a recently approved pharmaceutical combination, Synjardy tablets. A spiking technique was used to increase the concentration of EG in samples prepared from the tablets to allow for the simultaneous determination of EG with MT without prior separation. Validation parameters according to International Conference on Harmonization guidelines were acceptable over a concentration range of 2-12 μg/mL for both drugs using derivative ratio and ratio subtraction coupled with extended ratio subtraction. The optimized methods were compared using one-way analysis of variance and proved to be suitable as ecofriendly approaches for industrial QC laboratories.

  11. Preparation and characterization of nickel-spiked freshwater sediments for toxicity tests: toward more environmentally realistic nickel partitioning

    Science.gov (United States)

    Brumbaugh, William G.; Besser, John M.; Ingersoll, Christopher G.; May, Thomas W.; Ivey, Chris D.; Schlekat, Christian E.; Garman, Emily R.

    2013-01-01

    Two spiking methods were compared and nickel (Ni) partitioning was evaluated during a series of toxicity tests with 8 different freshwater sediments having a range of physicochemical characteristics. A 2-step spiking approach with immediate pH adjustment by addition of NaOH at a 2:1 molar ratio to the spiked Ni was effective in producing consistent pH and other chemical characteristics across a range of Ni spiking levels. When Ni was spiked into sediment having a high acid-volatile sulfide and organic matter content, a total equilibration period of at least 10 wk was needed to stabilize Ni partitioning. However, highest spiking levels evidently exceeded sediment binding capacities; therefore, a 7-d equilibration in toxicity test chambers and 8 volume-additions/d of aerobic overlying water were used to avoid unrealistic Ni partitioning during toxicity testing. The 7-d pretest equilibration allowed excess spiked Ni and other ions from pH adjustment to diffuse from sediment porewater and promoted development of an environmentally relevant, 0.5- to 1-cm oxic/suboxic sediment layer in the test chambers. Among the 8 different spiked sediments, the logarithm of sediment/porewater distribution coefficient values (log Kd) for Ni during the toxicity tests ranged from 3.5 to 4.5. These Kd values closely match the range of values reported for various field Ni-contaminated sediments, indicating that testing conditions with our spiked sediments were environmentally realistic.

  12. Breaking HIV News to Clients: SPIKES Strategy in Post-Test Counseling Session

    Directory of Open Access Journals (Sweden)

    Hamid Emadi-Koochak

    2016-05-01

    Full Text Available Breaking bad news is one of the most burdensome tasks physicians face in their everyday practice. It becomes even more challenging in the context of HIV+ patients because of stigma and discrimination. The aim of the current study is to evaluate the quality of giving HIV seroconversion news according to SPIKES protocol. Numbers of 154 consecutive HIV+ patients from Imam Khomeini Hospital testing and counseling center were enrolled in this study. Patients were inquired about how they were given the HIV news and whether or not they received pre- and post-test counseling sessions. Around 51% of them were men, 80% had high school education, and 56% were employed. Regarding marital status, 32% were single, and 52% were married at the time of the interview. Among them, 31% had received the HIV news in a counseling center, and only 29% had pre-test counseling. SPIKES criteria were significantly met when the HIV news was given in an HIV counseling and testing center (P.value<0.05. Low coverage of HIV counseling services was observed in the study. SPIKES criteria were significantly met when the HIV seroconversion news was given in a counseling center. The need to further train staff to deliver HIV news seems a priority in the field of HIV care and treatment.

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Ecotoxicological assessment of the pharmaceutical compound Triclosan to freshwater invertebrates with emphasis to spiked sediment tests

    International Nuclear Information System (INIS)

    Pusceddu, Fabio Hermes

    2009-01-01

    The increasing of Pharmaceutical and Personal Care Products (PPCPs) occurrence in the aquatic environment cause adverse effects on the human health and aquatic communities. The environmental risk of the PPCPs associated with the possibility of synergic effects between PCPPs and the increase of the use of synthetic organic compounds, unchained a great concern on the toxic potential to biota aquatic. Triclosan (5-chloro-2-(2,4-dichlorophenoxy)-phenol) is a pharmaceutical compound widely used due your antibacterial mechanism effect, found in at least 932 products such as shampoos, toilet soaps, deodorants, lotions, toothpaste, detergents, socks and underwear, among others. Currently, studies about the Triclosan toxicity in the water and, mainly in the sediment, are poorly. We have the knowledge that the photodegradation of this product results into dichlorodibenzo-p-dioxin, and now it has great discussion on environmental agencies, like EPA, about the release or restriction of this product. The aim of this work is to assess the effects of Triclosan on mortality of insect larvae Chironomus xanthus and mortality and reproduction inhibition of microcrustacea Ceriodaphnia dubia exposed to Triclosan spiked sediments based on standard methods EPA and OECD. The EC50;96H obtained on acute toxicity tests with C. xanthus was 45,26 mg.Kg -1 . The chronic toxicity tests with C. dubia using spiked sediments were performed following the procedure in Burton and MacPherson (1995). A no-observed-effect concentrations and lowest-observed-effect concentration were 5,78 e 6,94 mg.Kg -1 , respectively. (author)

  15. Analysis of Isp-42, panda test with the spectra code

    International Nuclear Information System (INIS)

    Stempniewicz, M.M.

    2001-01-01

    International Standard Problems (ISP) are organized in order to assess the ability of computer codes to predict the outcome of accidents in Nuclear Power Plants. The ISP-42 test was performed at Paul Scherrer Institute in 1998, as a sequence of six phases, Phase A through F Blind and open calculations of ISP-42 were performed with the computer code SPECTRA for each of the six phases. SPECTRA is a general tool for thermal-hydraulic analyses. Results of blind calculations are in good agreement with experiment. For open calculations several modifications were made in the model. These were mainly corrections of some input errors made in the model used for blind analysis. Some small improvements to the nodalization were made. Results of open calculations are generally closer to the experiment than the blind results. For phase D the containment pressure prediction was somewhat worse in the open calculation. Based on comparisons of blind and open results with experiment several conclusions may be drawn: - use of long ID structures, in contact with pool and atmosphere should be avoided, - PCC units are better represented with larger amount of Control Volumes, - two parallel junctions should be used to represent large openings between vessels, like drywell air line, etc., - careful verification of input decks is needed, - stratification models in SPECTRA are useful for cases with light gas injection; for complex cases a complementary SPECTRA-CFD analysis may be performed. (author)

  16. Removal of bacteriophages with different surface charges by diverse ceramic membrane materials in pilot spiking tests.

    Science.gov (United States)

    Hambsch, B; Bösl, M; Eberhagen, I; Müller, U

    2012-01-01

    This study examines mechanisms for removal of bacteriophages (MS2 and phiX174) by ceramic membranes without application of flocculants. The ceramic membranes considered included ultra- and microfiltration membranes of different materials. Phages were spiked into the feed water in pilot scale tests in a waterworks. The membranes with pore sizes of 10 nm provided a 2.5-4.0 log removal of the phages. For pore sizes of 50 nm, the log removal dropped to 0.96-1.8. The membrane with a pore size of 200 nm did not remove phages. So, the removal of both MS2- and phiX174-phages depended on the pore size of the membranes. But apart from pore size also other factors influence the removal of phages. Removal was 0.5-0.9 log higher for MS2-phages compared with phiX174-phages. Size exclusion seems to be the major but not the only mechanism which influences the efficiency of phage removal by ceramic membranes.

  17. Gross α/β analysis of spiked qater for IAEA 2008 world-wide open proficiency test

    International Nuclear Information System (INIS)

    Zhang Yaoling; Zhao Feng; Wu Meigui; Du Jinzhou

    2010-01-01

    The gross α/β analysis of the IAEA 2008 world-wide open proficiency test on the determination of natural radionuclides in spiked water was conducted. The Sample 03, Sample 04 and Sample 05 were tested for the gross α/β radioactivity according to the requirement of IAEA. The feedback statistics showed that the values of gross α/β of all of the three samples fell in the acceptable range of IAEA. (authors)

  18. WSEAT Shock Testing Margin Assessment Using Energy Spectra Final Report.

    Energy Technology Data Exchange (ETDEWEB)

    Sisemore, Carl; Babuska, Vit; Booher, Jason

    2018-02-01

    Several programs at Sandia National Laboratories have adopted energy spectra as a metric to relate the severity of mechanical insults to structural capacity. The purpose being to gain insight into the system's capability, reliability, and to quantify the ultimate margin between the normal operating envelope and the likely system failure point -- a system margin assessment. The fundamental concern with the use of energy metrics was that the applicability domain and implementation details were not completely defined for many problems of interest. The goal of this WSEAT project was to examine that domain of applicability and work out the necessary implementation details. The goal of this project was to provide experimental validation for the energy spectra based methods in the context of margin assessment as they relate to shock environments. The extensive test results concluded that failure predictions using energy methods did not agree with failure predictions using S-N data. As a result, a modification to the energy methods was developed following the form of Basquin's equation to incorporate the power law exponent for fatigue damage. This update to the energy-based framework brings the energy based metrics into agreement with experimental data and historical S-N data.

  19. The use of synthetic spectra to test the preparedness to evaluate and analyze complex gamma spectra

    International Nuclear Information System (INIS)

    Nikkinen, M

    2001-10-01

    This is the report of two exercises that were run under the NKS BOK-1.1 sub-project. In these exercises synthetic gamma spectra were developed to exercise the analysis of difficult spectra typically seen after a severe nuclear accident. The spectra were analyzed twice; first, participants were given short time to give results to resemble an actual emergency preparedness situation, then a longer period of time was allowed to tune the laboratory analysis results for quality assurance purposes. The exercise did prove that it is possible to move measurement data from one laboratory to another if second opinion of the analysis is needed. It was also felt that this kind of exercise would enhance the experience the laboratories have in analyzing accident data. Participants expressed the need for additional exercises of this type, this is inexpensive and an easy way to exercise quick emergency response situations not normally seen in daily laboratory routines. (au)

  20. Effects of sediment-spiked lufenuron on benthic macroinvertebrates in outdoor microcosms and single-species toxicity tests

    Energy Technology Data Exchange (ETDEWEB)

    Brock, T.C.M., E-mail: theo.brock@wur.nl [Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands); Bas, D.A. [Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam (Netherlands); Belgers, J.D.M. [Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands); Bibbe, L. [Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam (Netherlands); Boerwinkel, M-C.; Crum, S.J.H. [Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands); Diepens, N.J. [Department of Aquatic Ecology and Water Quality Management, Wageningen University, P.O. Box 47, 6700 AA Wageningen (Netherlands); Kraak, M.H.S.; Vonk, J.A. [Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam (Netherlands); Roessink, I. [Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen (Netherlands)

    2016-08-15

    Highlights: • In outdoor microcosms constructed with lufenuron-spiked sediment we observed that this insecticide persistent in the sediment compartment. • Sediment exposure to lufenuron caused population-level declines (insects and crustaceans) and increases (mainly oligochaete worms) of benthic invertebrates. • The direct and indirect effects observed in the microcosms were supported by results of sediment-spiked single species tests with Chironomus riparius, Hyalella azteca and Lumbriculus variegatus. • The tier-1 effect assessment procedure for sediment organisms recommended by the European Food Safety Authority is protective for the treatment-related responses observed in the microcosm test. - Abstract: Sediment ecotoxicity studies were conducted with lufenuron to (i) complement the results of a water-spiked mesocosm experiment with this lipophilic benzoylurea insecticide, (ii) to explore the predictive value of laboratory single-species tests for population and community-level responses of benthic macroinvertebrates, and (iii) to calibrate the tier-1 effect assessment procedure for sediment organisms. For this purpose the concentration-response relationships for macroinvertebrates between sediment-spiked microcosms and those of 28-d sediment-spiked single-species toxicity tests with Chironomus riparius, Hyalella azteca and Lumbriculus variegatus were compared. Lufenuron persisted in the sediment of the microcosms. On average, 87.7% of the initial lufenuron concentration could still be detected in the sediment after 12 weeks. Overall, benthic insects and crustaceans showed treatment-related declines and oligochaetes treatment-related increases. The lowest population-level NOEC in the microcosms was 0.79 μg lufenuron/g organic carbon in dry sediment (μg a.s./g OC) for Tanytarsini, Chironomini and Dero sp. Multivariate analysis of the responses of benthic macroinvertebrates revealed a community-level NOEC of 0.79 μg a.s./g OC. The treatment

  1. Sequential motor task (Luria's Fist-Edge-Palm Test in children with benign focal epilepsy of childhood with centrotemporal spikes

    Directory of Open Access Journals (Sweden)

    Carmen Silvia Molleis Galego Miziara

    2013-06-01

    Full Text Available This study evaluated the sequential motor manual actions in children with benign focal epilepsy of childhood with centrotemporal spikes (BECTS and compares the results with matched control group, through the application of Luria's fist-edge-palm test. The children with BECTS underwent interictal single photon emission computed tomography (SPECT and School Performance Test (SPT. Significant difference occurred between the study and control groups for manual motor action through three equal and three different movements. Children with lower school performance had higher error rate in the imitation of hand gestures. Another factor significantly associated with the failure was the abnormality in SPECT. Children with BECTS showed abnormalities in the test that evaluated manual motor programming/planning. This study may suggest that the functional changes related to epileptiform activity in rolandic region interfere with the executive function in children with BECTS.

  2. Initial pressure spike and its propagation phenomena in sodium-water reaction tests for MONJU steam generators

    International Nuclear Information System (INIS)

    Sato, M.; Hiroi, H.; Tanaka, N.; Hori, M.

    1977-01-01

    With the objective of demonstrating the safe design of steam generators for prototype LMFBR MONJU against the postulated large-leak accident, a number of large-leak sodium-water reaction tests have been conducted using the SWAT-1 and SWAT-3 rigs. Investigation of the potential effects of pressure load on the system is one of the major concerns in these tests. This paper reports the behavior of initial pressure spike in the reaction vessel, its propagation phenomena to the simulated secondary cooling system, and the comparisons with the computer code for one-dimensional pressure wave propagation problems. Both rigs used are the scaled-down models of the helically coiled steam generators of MONJU. The SWAT-1 rig is a simplified model and consists of a reaction vessel (1/8 scale of MONJU evaporator with 0.4 m dia. and 2.5 m height) and a pressure relief system i.e., a pressure relief line and a reaction products tank. On the other hand, the SWAT-3 rig is a 1/2.5 scale of MONJU SG system and consists of an evaporator (reaction vessel with 1.3 m dia. and 6.35 m height), a superheater, an intermediate heat exchanger (IHX), a piping system simulating the secondary cooling circuit and a pressure relief system. The both water injection systems consist of a water injection line with a rupture disk installed in front of injection hole and an electrically heated water tank. Choice of water injection rates in the scaled-down models is made based on the method of iso-velocity modeling. Test results indicated that the characteristics of the initial pressure spike are dominated by those of initial water injection which are controlled by the conditions of water heater and the size of water injection hole, etc

  3. Comparative study of six sequential spectrophotometric methods for quantification and separation of ribavirin, sofosbuvir and daclatasvir: An application on Laboratory prepared mixture, pharmaceutical preparations, spiked human urine, spiked human plasma, and dissolution test.

    Science.gov (United States)

    Hassan, Wafaa S; Elmasry, Manal S; Elsayed, Heba M; Zidan, Dalia W

    2018-05-18

    In accordance with International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) guidelines, six novel, simple and precise sequential spectrophotometric methods were developed and validated for the simultaneous analysis of Ribavirin (RIB), Sofosbuvir (SOF), and Daclatasvir (DAC) in their mixture without prior separation steps. These drugs are described as co-administered for treatment of Hepatitis C virus (HCV). HCV is the cause of hepatitis C and some cancers such as liver cancer (hepatocellular carcinoma) and lymphomas in humans. These techniques consisted of several sequential steps using zero, ratio and/or derivative spectra. DAC was first determined through direct spectrophotometry at 313.7 nm without any interference of the other two drugs while RIB and SOF can be determined after ratio subtraction through five methods; Ratio difference spectrophotometric method, successive derivative ratio method, constant center, isoabsorptive method at 238.8 nm, and mean centering of the ratio spectra (MCR) at 224 nm and 258 nm for RIB and SOF, respectively. The calibration curve is linear over the concentration ranges of (6-42), (10-70) and (4-16) μg/mL for RIB, SOF, and DAC, respectively. This method was successfully applied to commercial pharmaceutical preparation of the drugs, spiked human urine, and spiked human plasma. The above methods are very simple methods that were developed for the simultaneous determination of binary and ternary mixtures and so enhance signal-to-noise ratio. The method has been successfully applied to the simultaneous analysis of RIB, SOF, and DAC in laboratory prepared mixtures. The obtained results are statistically compared with those obtained by the official or reported methods, showing no significant difference with respect to accuracy and precision at p = 0.05. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. Analysis of remotely accrued complex gamma ray spectra - proficiency test

    Energy Technology Data Exchange (ETDEWEB)

    Dowdall, M. (Norwegian Radiation Protection Authority (Norway))

    2009-03-15

    This report presents details pertaining to an exercise conducted as part of the NKS-B programme using synthetic gamma ray spectra to simulate the type of data that may be encountered in the early phase of a nuclear accident. The aim of the exercise was to provide participants with an opportunity to exercise in the type of situation and with the type of data that may result after a nuclear accident. Attempting to conduct such exercise internationally using actual samples presents practical and logistical difficulties and a synthetic spectrum was employed to negate some of these problems. A HPGe spectrum was synthesized containing a range of typical fallout isotopes and distributed, along with calibration information, to the participant laboratories. The participants were required to submit results within three hours of receipt and with the option of submitting further results within one week. The results provided by the laboratories indicate that all laboratories were able to identify and quantify some of the isotopes but only some labs were in a position to identify and quantify virtually all the constituents of the spectrum. Results indicate that there remain some problems with aspects such as true coincidence summation and using file formats with which labs may not be familiar with. The exercise provided a useful opportunity in exploring the possibilities of using synthetic spectra for exercise purposes and offered participants the chance to practice with the sort of scenario that may result after an accident. (au)

  5. Analysis of remotely accrued complex gamma ray spectra - proficiency test

    International Nuclear Information System (INIS)

    Dowdall, M.

    2009-03-01

    This report presents details pertaining to an exercise conducted as part of the NKS-B programme using synthetic gamma ray spectra to simulate the type of data that may be encountered in the early phase of a nuclear accident. The aim of the exercise was to provide participants with an opportunity to exercise in the type of situation and with the type of data that may result after a nuclear accident. Attempting to conduct such exercise internationally using actual samples presents practical and logistical difficulties and a synthetic spectrum was employed to negate some of these problems. A HPGe spectrum was synthesized containing a range of typical fallout isotopes and distributed, along with calibration information, to the participant laboratories. The participants were required to submit results within three hours of receipt and with the option of submitting further results within one week. The results provided by the laboratories indicate that all laboratories were able to identify and quantify some of the isotopes but only some labs were in a position to identify and quantify virtually all the constituents of the spectrum. Results indicate that there remain some problems with aspects such as true coincidence summation and using file formats with which labs may not be familiar with. The exercise provided a useful opportunity in exploring the possibilities of using synthetic spectra for exercise purposes and offered participants the chance to practice with the sort of scenario that may result after an accident. (au)

  6. Prediction of flyover jet noise spectra from static tests

    Science.gov (United States)

    Michel, U.; Michalke, A.

    A scaling law for predicting the overall flyover noise of a single stream shock-free circular jet from static experiments is outlined. It is valid for isothermal and hot jets. It assumes that the jet flow and turbulence field are axially stretched in flight. Effects of the boundary layer within the nozzle and along the engine nacelle are neglected. The scaling laws for the power spectral density and spectra with constant relative bandwidth can be derived. In order to compare static and inflight directivities, the far field point relative to the source position must be denoted by the emission angle and the wave normal distance. From the solution of the convective Lighthill equation in a coordinate system fixed to the jet nozzle (wind tunnel case), the power spectral density of sound pressure at a given frequency is found. Predictions for Aerotrain compare well with measured values.

  7. Components of Program for Analysis of Spectra and Their Testing

    Directory of Open Access Journals (Sweden)

    Ivan Taufer

    2013-11-01

    Full Text Available The spectral analysis of aqueous solutions of multi-component mixtures is used for identification and distinguishing of individual componentsin the mixture and subsequent determination of protonation constants and absorptivities of differently protonated particles in the solution in steadystate (Meloun and Havel 1985, (Leggett 1985. Apart from that also determined are the distribution diagrams, i.e. concentration proportions ofthe individual components at different pH values. The spectra are measured with various concentrations of the basic components (one or severalpolyvalent weak acids or bases and various pH values within the chosen range of wavelengths. The obtained absorbance response area has to beanalyzed by non-linear regression using specialized algorithms. These algorithms have to meet certain requirements concerning the possibility ofcalculations and the level of outputs. A typical example is the SQUAD(84 program, which was gradually modified and extended, see, e.g., (Melounet al. 1986, (Meloun et al. 2012.

  8. Spectra of heliumlike krypton from tokamak fusion test reactor plasmas

    International Nuclear Information System (INIS)

    Bitter, M.; Hsuan, H.; Bush, C.; Cohen, S.; Cummings, C.J.; Grek, B.; Hill, K.W.; Schivell, J.; Zarnstorff, M.; Smith, A.; Fraenkel, B.

    1993-04-01

    Krypton has been injected into ohmically-heated TFTR plasmas with peak electron temperatures of 6 key to study the effects of krypton on the plasma performance and to investigate the emitted krypton line radiation, which is of interest for future-generation tokamaks such as ITER, both as a diagnostic of the central ion temperature and for the control of energy release from the plasma by radiative cooling. The emitted radiation was monitored with a bolometer array, an X-ray pulse height analysis system, and a high-resolution Johann-type crystal spectrometer; and it was found to depend very sensitively on the electron temperature profile. Satellite spectra of heliumlike krypton, KrXXXV, near 0.95 Angstrom including lithiumlike, berylliumlike and boronlike features were recorded in second order Bragg reflection. Radiative cooling and reduced particle recycling at the plasma edge region were observed as a result of the krypton injection for all investigated discharges. The observations are in reasonable agreement with modeling calculations of the krypton ion charge state distribution including radial transport

  9. Leach tests on grouts made with actual and trace metal-spiked synthetic phosphate/sulfate waste

    International Nuclear Information System (INIS)

    Serne, R.J.; Martin, W.J.; LeGore, V.L.; Lindenmeier, C.W.; McLaurine, S.B.; Martin, P.F.C.; Lokken, R.O.

    1989-10-01

    Pacific Northwest Laboratory conducted experiments to produce empirical leach rate data for phosphate-sulfate waste (PSW) grout. Effective diffusivities were measured for various radionuclides ( 90 Sr, 99 Tc, 14 C, 129 I, 137 Cs, 60 Co, 54 Mn, and U), stable major components (NO 3 - , SO 4 2- , H 3 BO 3 , K and Na) and the trace constituents Ag, As, Cd, Hg, Pb, and Se. Two types of leach tests were used on samples of actual PSW grout and synthetic PSW grout: the American Nuclear Society (ANS) 16.1 intermittent replacement leach test and a static leach test. Grout produced from both synthetic and real PSW showed low leach rates for the trace metal constituents and most of the waste radionuclides. Many of the spiked trace metals and radionuclides were not detected in any leachates. None of the effluents contained measurable quantities of 137 Cs, 60 Co, 54 Mn, 109 Cd, 51 Cr, 210 Pb, 203 Hg, or As. For those trace species with detectable leach rates, 125 I appeared to have the greatest leach rate, followed by 99 Tc, 75 Se, and finally U, 14 C, and 110m Ag. Leach rates for nitrate are between those for I and Tc, but there is much scatter in the nitrate data because of the very low nitrate inventory. 32 refs., 6 figs., 15 tabs

  10. 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.

  11. Multiple testing issues in discriminating compound-related peaks and chromatograms from high frequency noise, spikes and solvent-based nois in LC-MS data sets

    NARCIS (Netherlands)

    Nyangoma, S.O.; Van Kampen, A.A.; Reijmers, T.H.; Govorukhina, N.I; van der Zee, A.G.; Billingham, I.J; Bischoff, Rainer; Jansen, R.C.

    2007-01-01

    Multiple testing issues in discriminating compound-related peaks and chromatograms from high frequency noise, spikes and solvent-based noise in LC-MS data sets.Nyangoma SO, van Kampen AA, Reijmers TH, Govorukhina NI, van der Zee AG, Billingham LJ, Bischoff R, Jansen RC. University of Birmingham.

  12. A distribution-free test for anomalous gamma-ray spectra

    International Nuclear Information System (INIS)

    Chan, Kung-sik; Li, Jinzheng; Eichinger, William; Bai, Er-Wei

    2014-01-01

    Gamma-ray spectra are increasingly acquired in monitoring cross-border traffic, or in an area search for lost or orphan special nuclear material (SNM). The signal in such data is generally weak, resulting in poorly resolved spectra, thereby making it hard to detect the presence of SNM. We develop a new test for detecting anomalous spectra by characterizing the complete shape change in a spectrum from background radiation; the proposed method may serve as a tripwire for routine screening for SNM. We show that, with increasing detection time, the limiting distribution of the test is given by some functional of the Brownian bridge. The efficacy of the proposed method is illustrated by simulations. - Highlights: • We develop a new non-parametric test for detecting anomalous gamma-ray spectra. • The proposed test has good empirical power for detecting weak signals. • It can serve as an effective tripwire for invoking more thorough scrutiny of the source

  13. Analysis of design floor response spectra and testing of the electrical systems

    International Nuclear Information System (INIS)

    Ambriashvili, Y.

    1996-01-01

    This report covers the following activities as foreseen according to the working plan of 'Atmoenergoproject': analysis of calculated floor response spectra used during the design of Kozloduy NPP and comparison with other spectra recommended for this NPP; analysis of floor response spectrum for the most important systems (reactor, main coolant loop, electrical systems); tests of main electrical systems and analysis of the results on seismic stability of those systems. Results of the response spectra analysis are given, some of the electrical systems are identified by the Kozloduy authorities to be analyzed in future according to the results of the test on seismicity

  14. Quantitative Evaluation of gamma-Spectrum Analysis Methods using IAEA Test Spectra

    DEFF Research Database (Denmark)

    Nielsen, Sven Poul

    1982-01-01

    A description is given of a γ-spectrum analysis method based on nonlinear least-squares fitting. The quality of the method is investigated by using statistical tests on the results from analyses of IAEA test spectra. By applying an empirical correction factor of 0.75 to the calculated peak-area u...

  15. Ecotoxicological assessment of the pharmaceutical compound Triclosan to freshwater invertebrates with emphasis to spiked sediment tests; Avaliacao ecotoxicologica do farmaco Triclosan para invertebrados de agua doce com enfase em ensaios com sedimento marcado ('spiked sediment')

    Energy Technology Data Exchange (ETDEWEB)

    Pusceddu, Fabio Hermes

    2009-07-01

    The increasing of Pharmaceutical and Personal Care Products (PPCPs) occurrence in the aquatic environment cause adverse effects on the human health and aquatic communities. The environmental risk of the PPCPs associated with the possibility of synergic effects between PCPPs and the increase of the use of synthetic organic compounds, unchained a great concern on the toxic potential to biota aquatic. Triclosan (5-chloro-2-(2,4-dichlorophenoxy)-phenol) is a pharmaceutical compound widely used due your antibacterial mechanism effect, found in at least 932 products such as shampoos, toilet soaps, deodorants, lotions, toothpaste, detergents, socks and underwear, among others. Currently, studies about the Triclosan toxicity in the water and, mainly in the sediment, are poorly. We have the knowledge that the photodegradation of this product results into dichlorodibenzo-p-dioxin, and now it has great discussion on environmental agencies, like EPA, about the release or restriction of this product. The aim of this work is to assess the effects of Triclosan on mortality of insect larvae Chironomus xanthus and mortality and reproduction inhibition of microcrustacea Ceriodaphnia dubia exposed to Triclosan spiked sediments based on standard methods EPA and OECD. The EC50;96H obtained on acute toxicity tests with C. xanthus was 45,26 mg.Kg{sup -1}. The chronic toxicity tests with C. dubia using spiked sediments were performed following the procedure in Burton and MacPherson (1995). A no-observed-effect concentrations and lowest-observed-effect concentration were 5,78 e 6,94 mg.Kg{sup -1}, respectively. (author)

  16. Ecotoxicological assessment of the pharmaceutical compound Triclosan to freshwater invertebrates with emphasis to spiked sediment tests; Avaliacao ecotoxicologica do farmaco Triclosan para invertebrados de agua doce com enfase em ensaios com sedimento marcado ('spiked sediment')

    Energy Technology Data Exchange (ETDEWEB)

    Pusceddu, Fabio Hermes

    2009-07-01

    The increasing of Pharmaceutical and Personal Care Products (PPCPs) occurrence in the aquatic environment cause adverse effects on the human health and aquatic communities. The environmental risk of the PPCPs associated with the possibility of synergic effects between PCPPs and the increase of the use of synthetic organic compounds, unchained a great concern on the toxic potential to biota aquatic. Triclosan (5-chloro-2-(2,4-dichlorophenoxy)-phenol) is a pharmaceutical compound widely used due your antibacterial mechanism effect, found in at least 932 products such as shampoos, toilet soaps, deodorants, lotions, toothpaste, detergents, socks and underwear, among others. Currently, studies about the Triclosan toxicity in the water and, mainly in the sediment, are poorly. We have the knowledge that the photodegradation of this product results into dichlorodibenzo-p-dioxin, and now it has great discussion on environmental agencies, like EPA, about the release or restriction of this product. The aim of this work is to assess the effects of Triclosan on mortality of insect larvae Chironomus xanthus and mortality and reproduction inhibition of microcrustacea Ceriodaphnia dubia exposed to Triclosan spiked sediments based on standard methods EPA and OECD. The EC50;96H obtained on acute toxicity tests with C. xanthus was 45,26 mg.Kg{sup -1}. The chronic toxicity tests with C. dubia using spiked sediments were performed following the procedure in Burton and MacPherson (1995). A no-observed-effect concentrations and lowest-observed-effect concentration were 5,78 e 6,94 mg.Kg{sup -1}, respectively. (author)

  17. 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

  18. Dielectronic satellite spectra of hydrogenlike iron from TFTR [Tokamak Fusion Test Reactor

    International Nuclear Information System (INIS)

    Decaux, V.; Bitter, M.; Hsuan, H.; von Goeler, S.; Hill, K.W.; Hulse, R.A.; Taylor, G.; Park, H.; Bhalla, C.P.

    1990-08-01

    Spectra of hydrogenlike iron, Fe26, have been observed from Tokamak Fusion Test Reactor (TFTR) plasmas with a high-resolution crystal spectrometer. The experimental arrangement permits simultaneous observation of the Fe26 Ly-α 1 and Ly-α 2 lines and the associated dielectronic satellites, which are due to transitions 1snl-2pnl' with n ≥ 2, as well as the heliumlike 1s 2 ( 1 S 0 )-1s4p( 1 P 1 )and both hydrogenlike Ly-β 1 and Ly-β 2 lines from chromium. Relative wavelengths and line intensities can be determined very accurately. The spectral data are in very good agreement with theoretical calculations. The observed spectra have also been used to estimate the total dielectronic recombination rate coefficient of Fe26. 30 refs., 4 figs., 3 tabs

  19. Derivation of design response spectra for analysis and testing of components and systems

    International Nuclear Information System (INIS)

    Krutzik, N.

    1996-01-01

    Some institutions participating in the Benchmark Project performed parallel calculations for the WWER-1000 Kozloduy NPP. The investigations were based on various mathematical models and procedures for consideration of soil-structure interaction effects, simultaneously applying uniform soil dynamic and seismological input data. The methods, mathematical models and dynamic response results were evaluated and discussed in detail and finally compared by means of different structural models and soil representations with the aim of deriving final enveloped and smoothed dynamic response data (benchmark response spectra). This should be used for requalification by analysis testing of the mechanical and electrical components and systems located in this type of reactor building

  20. Testing of the IRDF-90 cross-section library in benchmark neutron spectra

    International Nuclear Information System (INIS)

    Nolthenius, H.J.; Zsolnay, E.M.; Szondi, E.J.

    1993-09-01

    The new version of the International Reactor Dosimetry File IRDF-90 (called ''Version April 1993'') has been tested by calculation of average cross-sections and their uncertainties in a coarse three energy group structure and by neutron spectrum adjustments in reference neutron spectra. This paper presents the results obtained and compares them with the corresponding ones of the old IRDF-85 and with the data of the Nuclear Data Guide for Reactor Neutron Metrology. The applicability of the new library in the field of neutron metrology is discussed. (orig.)

  1. Irradiation tests of ITER candidate Hall sensors using two types of neutron spectra

    International Nuclear Information System (INIS)

    Duran, I.; Bolshakova, I.; Holyaka, R.; Viererbl, L.; Lahodova, Z.; Sentkerestiova, J.; Bem, P.

    2010-01-01

    We report on irradiation tests of InSb based Hall sensors at two irradiation facilities with two distinct types of neutron spectra. One was a fission reactor neutron spectrum with a significant presence of thermal neutrons, while another one was purely fast neutron field. Total neutron fluence of the order of 10 16 cm -2 was accumulated in both cases, leading to significant drop of Hall sensor sensitivity in case of fission reactor spectrum, while stable performance was observed at purely fast neutron spectrum. This finding suggests that performance of this particular type of Hall sensors is governed dominantly by transmutation. Additionally, it further stresses the need to test ITER candidate Hall sensors under neutron flux with ITER relevant spectrum.

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

    Science.gov (United States)

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

    2016-01-01

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

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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.

  8. Calculation of neutron spectra produced in neutron generator target: Code testing.

    Science.gov (United States)

    Gaganov, V V

    2018-03-01

    DT-neutron spectra calculated using the SRIANG code was benchmarked against the results obtained by widely used Monte Carlo codes: PROFIL, SHORIN, TARGET, ENEA-JSI, MCUNED, DDT and NEUSDESC. The comparison of the spectra obtained by different codes confirmed the correctness of SRIANG calculations. The cross-checking of the compared spectra revealed some systematic features and possible errors of analysed codes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Performance of a nuclide identification of HYPERGAM on the IAEA 2002 test spectra

    International Nuclear Information System (INIS)

    Park, B. G.; Jung, N. S.; Kim, J. H.; Choi, H. D.; Park, C. S.

    2010-01-01

    An important part of the γ-ray spectrum analysis software is the ability to identify radionuclides on the spectrum, and to determine activity of each radionuclide. Rapid determination and a low number of missing and false hit are required to the γ-ray spectrum analysis software to be useful. HyperGam has been developed to analyze an HPGe γ-ray spectrum by Applied Nuclear Physics Group in Seoul National University. Through a series of subsequent studies, the on-line analysis as well as the off-line analysis was possible. In addition, the automatic algorithm of nuclide identification has been developed to identify the peaks on the spectrum considering with yield, efficiency, energy and peak area of the γ-ray line from radionuclide. In this study, the performance of the nuclide identification of HyperGam is tested by using the IAEA 2002 set of test spectra and is compared to the well-known γ-ray spectrum analysis software

  10. A field test of the effect of spiked ivermectin concentrations on the biodiversity of coprophagous dung insects in Switzerland.

    Science.gov (United States)

    Jochmann, Ralf; Lipkow, Erhard; Blanckenhorn, Wolf U

    2016-08-01

    Veterinary medical product residues can cause severe damage in the dung ecosystem. Depending on the manner of application and the time after treatment, the excreted concentration of a given pharmaceutical varies. The popular anthelmintic drug ivermectin can be applied to livestock in several different ways and is fecally excreted over a period of days to months after application. In a field experiment replicated in summer and autumn, the authors mixed 6 ivermectin concentrations plus a null control into fresh cow dung to assess the reaction of the dung insect community. Taxon richness of the insect dung fauna emerging from the dung, but not Hill diversity ((1) D) or the total number of individuals (abundance), decreased as ivermectin concentration increased. Corresponding declines in the number of emerging insects were found for most larger brachyceran flies and hymenopteran parasitoids, but not for most smaller nematoceran flies or beetles (except Hydrophilidae). Parallel pitfall traps recovered all major dung organism groups that emerged from the experimental dung, although at times in vastly different numbers. Ivermectin generally did not change the attractiveness of dung: differences in emergence therefore reflect differences in survival of coprophagous offspring of colonizing insects. Because sample size was limited to 6 replicates, the authors generally recommend more than 10 (seasonal) replicates and also testing higher concentrations than used in the present study as positive controls in future studies. Results accord with parallel experiments in which the substance was applied and passed through the cow's digestive system. In principle, therefore, the authors' experimental design is suitable for such higher-tier field tests of the response of the entire dung community to pharmaceutical residues, at least for ivermectin. Environ Toxicol Chem 2016;35:1947-1952. © 2015 SETAC. © 2015 SETAC.

  11. Characterization of 200-UP-1 Aquifer Sediments and Results of Sorption-Desorption Tests Using Spiked Uncontaminated Groundwater

    Energy Technology Data Exchange (ETDEWEB)

    Um, Wooyong; Serne, R JEFFREY.; Bjornstad, Bruce N.; Schaef, Herbert T.; Brown, Christopher F.; Legore, Virginia L.; Geiszler, Keith N.; Baum, Steven R.; Valenta, Michelle M.; Kutnyakov, Igor V.; Vickerman, Tanya S.; Lindberg, Michael J.

    2005-11-16

    increasing concentrations of carbonate up to a point. Then as carbonate and calcium concentrations in the groundwater reach values that exceed the solubility limit for the mineral calcite there is a slight increase in U(VI) Kd likely caused by uranium co-precipitation with the fresh calcite. If remediation of the UP-1 groundwater plume is required, such as pump and treat, it is recommended that the aquifer be treated with chemicals to increase pH and alkalinity and decrease dissolved calcium and magnesium [so that the precipitation of calcite is prevented]. Alternative methods to immobilize the uranium in place might be more effective than trying to remove the uranium by pump and treat. Unfortunately, no aquifer sediments were obtained that contained enough Hanford generated uranium to perform quantitative desorption tests germane to the UP-1 plume remediation issue. Recommended Kd values that should be used for risk predictions for the UP-1 groundwater plume traveling through the lithologies within the aquifer present at the UP-1 (and by proxy ZP-1) operable units were provided. The recommended values Kd values are chosen to include some conservatism (lower values are emphasized from the available range) as is standard risk assessment practice. In general, desorption Kd values for aged contaminated sediments can be larger than Kd values determined in short-term laboratory experiments. To accommodate the potential for desorption hysteresis and other complications, a second suite of uranium desorption Kd values were provided to be used to estimate removal of uranium by pump and treat techniques.

  12. Control method for multi-input multi-output non-Gaussian random vibration test with cross spectra consideration

    Directory of Open Access Journals (Sweden)

    Ronghui ZHENG

    2017-12-01

    Full Text Available A control method for Multi-Input Multi-Output (MIMO non-Gaussian random vibration test with cross spectra consideration is proposed in the paper. The aim of the proposed control method is to replicate the specified references composed of auto spectral densities, cross spectral densities and kurtoses on the test article in the laboratory. It is found that the cross spectral densities will bring intractable coupling problems and induce difficulty for the control of the multi-output kurtoses. Hence, a sequential phase modification method is put forward to solve the coupling problems in multi-input multi-output non-Gaussian random vibration test. To achieve the specified responses, an improved zero memory nonlinear transformation is utilized first to modify the Fourier phases of the signals with sequential phase modification method to obtain one frame reference response signals which satisfy the reference spectra and reference kurtoses. Then, an inverse system method is used in frequency domain to obtain the continuous stationary drive signals. At the same time, the matrix power control algorithm is utilized to control the spectra and kurtoses of the response signals further. At the end of the paper, a simulation example with a cantilever beam and a vibration shaker test are implemented and the results support the proposed method very well. Keywords: Cross spectra, Kurtosis control, Multi-input multi-output, Non-Gaussian, Random vibration test

  13. 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.

  14. Measurement and analysis of neutron flux spectra in a neutronics mock-up of the HCLL test blanket module

    International Nuclear Information System (INIS)

    Klix, A.; Batistoni, P.; Boettger, R.; Lebrun-Grandie, D.; Fischer, U.; Henniger, J.; Leichtle, D.; Villari, R.

    2010-01-01

    Fast neutron and gamma-ray flux spectra and time-of-arrival spectra of slow neutrons have been measured in a neutronics mock-up of the European Helium-Cooled Lithium-Lead Test Blanket Module with the aim to validate nuclear cross-section data. The mock-up was irradiated with fusion peak neutrons from the DT neutron generator of the Technical University of Dresden. A well characterized cylindrical NE-213 scintillator was inserted into two positions in the LiPb/EUROFER assembly. Pulse height spectra from neutrons and gamma-rays were recorded from the NE-213 output. The spectra were then unfolded with experimentally obtained response matrices of the NE-213 detector. Time-of-arrival spectra of slow neutrons were measured with a 3 He counter placed in the mock-up, and the neutron generator was operated in pulsed mode. Monte Carlo calculations using the MCNP code and nuclear cross-section data from the JEFF-3.1.1 and FENDL-2.1 libraries were performed and the results are compared with the experimental results. A good agreement of measurement and calculation was found with some deviations in certain energy intervals.

  15. 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)

  16. 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.

  17. Testing the Planet-Metallicity Correlation in M-dwarfs with Gemini GNIRS Spectra

    Science.gov (United States)

    Hobson, M. J.; Jofré, E.; García, L.; Petrucci, R.; Gómez, M.

    2018-04-01

    While the planet-metallicity correlation for FGK main-sequence stars hosting giant planets is well established, it is less clear for M-dwarf stars. We determine stellar parameters and metallicities for 16 M-dwarf stars, 11 of which host planets, with near-infrared spectra from the Gemini Near-Infrared Spectrograph (GNIRS). We find that M-dwarfs with planets are preferentially metal-rich compared to those without planets. This result is supported by the analysis of a larger catalogue of 18 M stars with planets and 213 M stars without known planets T15, and demonstrates the utility of GNIRS spectra to obtain reliable stellar parameters of M stars. We also find that M dwarfs with giant planets are preferentially more metallic than those with low-mass planets, in agreement with previous results for solar-type stars. These results favor the core accretion model of planetary formation.

  18. 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

  19. SPITZER IRS SPECTRA OF LUMINOUS 8 μm SOURCES IN THE LARGE MAGELLANIC CLOUD: TESTING COLOR-BASED CLASSIFICATIONS

    International Nuclear Information System (INIS)

    Buchanan, Catherine L.; Kastner, Joel H.; Hrivnak, Bruce J.; Sahai, Raghvendra

    2009-01-01

    We present archival Spitzer Infrared Spectrograph (IRS) spectra of 19 luminous 8 μm selected sources in the Large Magellanic Cloud (LMC). The object classes derived from these spectra and from an additional 24 spectra in the literature are compared with classifications based on Two Micron All Sky Survey (2MASS)/MSX (J, H, K, and 8 μm) colors in order to test the 'JHK8' (Kastner et al.) classification scheme. The IRS spectra confirm the classifications of 22 of the 31 sources that can be classified under the JHK8 system. The spectroscopic classification of 12 objects that were unclassifiable in the JHK8 scheme allow us to characterize regions of the color-color diagrams that previously lacked spectroscopic verification, enabling refinements to the JHK8 classification system. The results of these new classifications are consistent with previous results concerning the identification of the most infrared-luminous objects in the LMC. In particular, while the IRS spectra reveal several new examples of asymptotic giant branch (AGB) stars with O-rich envelopes, such objects are still far outnumbered by carbon stars (C-rich AGB stars). We show that Spitzer IRAC/MIPS color-color diagrams provide improved discrimination between red supergiants and oxygen-rich and carbon-rich AGB stars relative to those based on 2MASS/MSX colors. These diagrams will enable the most luminous IR sources in Local Group galaxies to be classified with high confidence based on their Spitzer colors. Such characterizations of stellar populations will continue to be possible during Spitzer's warm mission through the use of IRAC [3.6]-[4.5] and 2MASS colors.

  20. Consensus-Based Sorting of Neuronal Spike Waveforms.

    Science.gov (United States)

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

    2016-01-01

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

  1. 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.)

  2. 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.

  3. Zero crossing and ratio spectra derivative spectrophotometry for the dissolution tests of amlodipine and perindopril in their fixed dose formulations

    Directory of Open Access Journals (Sweden)

    Maczka Paulina

    2014-06-01

    Full Text Available Dissolution tests of amlodipine and perindopril from their fixed dose formulations were performed in 900 mL of phosphate buffer of pH 5.5 at 37°C using the paddle apparatus. Then, two simple and rapid derivative spectrophotometric methods were used for the quantitative measurements of amlodipine and perindopril. The first method was zero crossing first derivative spectrophotometry in which measuring of amplitudes at 253 nm for amlodipine and 229 nm for perindopril were used. The second method was ratio derivative spectrophotometry in which spectra of amlodipine over the linearity range were divided by one selected standard spectrum of perindopril and then amplitudes at 242 nm were measured. Similarly, spectra of perindopril were divided by one selected standard spectrum of amlodipine and then amplitudes at 298 nm were measured. Both of the methods were validated to meet official requirements and were demonstrated to be selective, precise and accurate. Since there is no official monograph for these drugs in binary formulations, the dissolution tests and quantification procedure presented here can be used as a quality control test for amlodipine and perindopril in respective dosage forms.

  4. 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.

  5. EGFR T790M mutation testing of non-small cell lung cancer tissue and blood samples artificially spiked with circulating cell-free tumor DNA: results of a round robin trial.

    Science.gov (United States)

    Fassunke, Jana; Ihle, Michaela Angelika; Lenze, Dido; Lehmann, Annika; Hummel, Michael; Vollbrecht, Claudia; Penzel, Roland; Volckmar, Anna-Lena; Stenzinger, Albrecht; Endris, Volker; Jung, Andreas; Lehmann, Ulrich; Zeugner, Silke; Baretton, Gustavo; Kreipe, Hans; Schirmacher, Peter; Kirchner, Thomas; Dietel, Manfred; Büttner, Reinhard; Merkelbach-Bruse, Sabine

    2017-10-01

    The European Commision (EC) recently approved osimertinib for the treatment of adult patients with locally advanced or metastatic non-small-cell lung cancer (NSCLC) harboring EGFR T790M mutations. Besides tissue-based testing, blood samples containing cell-free circulating tumor DNA (ctDNA) can be used to interrogate T790M status. Herein, we describe the conditions and results of a round robin trial (RRT) for T790M mutation testing in NSCLC tissue specimens and peripheral blood samples spiked with cell line DNA mimicking tumor-derived ctDNA. The underlying objectives of this two-staged external quality assessment (EQA) approach were (a) to evaluate the accuracy of T790M mutations testing across multiple centers and (b) to investigate if a liquid biopsy-based testing for T790M mutations in spiked blood samples is feasible in routine diagnostic. Based on a successfully completed internal phase I RRT, an open RRT for EGFR T790M mutation testing in tumor tissue and blood samples was initiated. In total, 48 pathology centers participated in the EQA. Of these, 47 (97.9%) centers submitted their analyses within the pre-defined time frame and 44 (tissue), respectively, 40 (plasma) successfully passed the test. The overall success rates in the RRT phase II were 91.7% (tissue) and 83.3% (blood), respectively. Thirty-eight out of 48 participants (79.2%) successfully passed both parts of the RRT. The RRT for blood-based EGFR testing initiated in Germany is, to the best of our knowledge, the first of his kind in Europe. In summary, our results demonstrate that blood-based genotyping for EGFR resistance mutations can be successfully integrated in routine molecular diagnostics complementing the array of molecular methods already available at pathology centers in Germany.

  6. Testing of a Code for the Calculation of Spectra of Neutrons Produced in a Target of a Neutron Generator

    Science.gov (United States)

    Gaganov, V. V.

    2017-12-01

    The correctness of calculations performed with the SRIANG code for modeling the spectra of DT neutrons is estimated by comparing the obtained spectra to the results of calculations carried out with five different codes based on the Monte Carlo method.

  7. Prediction of Pseudo relative velocity response spectra at Yucca Mountain for underground nuclear explosions conducted in the Pahute Mesa testing area at the Nevada testing site

    International Nuclear Information System (INIS)

    Phillips, J.S.

    1991-12-01

    The Yucca Mountain Site Characterization Project (YMP), managed by the Office of Geologic Disposal of the Office of Civilian Radioactive Waste Management of the US Department of Energy, is examining the feasibility of siting a repository for commercial, high-level nuclear wastes at Yucca Mountain on and adjacent to the Nevada Test Site (NTS). This work, intended to extend our understanding of the ground motion at Yucca Mountain resulting from testing of nuclear weapons on the NTS, was funded by the Yucca Mountain project and the Military Applications Weapons Test Program. This report summarizes one aspect of the weapons test seismic investigations conducted in FY88. Pseudo relative velocity response spectra (PSRV) have been calculated for a large body of surface ground motions generated by underground nuclear explosions. These spectra have been analyzed and fit using multiple linear regression techniques to develop a credible prediction technique for surface PSRVs. In addition, a technique for estimating downhole PSRVs at specific stations is included. A data summary, data analysis, prediction development, prediction evaluation, software summary and FORTRAN listing of the prediction technique are included in this report

  8. Coronavirus spike-receptor interactions

    NARCIS (Netherlands)

    Mou, H.

    2015-01-01

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

  9. Method and device for materials testing making use of the Doppler effect in nuclear gamma spectra

    International Nuclear Information System (INIS)

    Hauser, U.; Pietsch, W.; Neuwirth, W.

    1977-01-01

    A sample to be tested, e.g. steel or petroleum, is irradiated with isotropically incident 14MeV neutrons or thermal neutrons from a neutron generator. If the sample contains C,N,O,F,Na, or Mg, natural or implanted, the neutrons produce recoil nuclei excited by inelastic scattering or the (n,α) process. Their excitation energy is emitted in the form of γ quanta. The Doppler spectrum obtained with the aid of a gamma spectrometer yields information on the microstructure of the sample via the differential energy losses. (ORU) [de

  10. 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.

  11. 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.

  12. 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.

  13. Detection and quantification of creep strain using process compensated resonance testing (PCRT) sorting modules trained with modeled resonance spectra

    Science.gov (United States)

    Heffernan, Julieanne; Biedermann, Eric; Mayes, Alexander; Livings, Richard; Jauriqui, Leanne; Goodlet, Brent; Aldrin, John C.; Mazdiyasni, Siamack

    2018-04-01

    Process Compensated Resonant Testing (PCRT) is a full-body nondestructive testing (NDT) method that measures the resonance frequencies of a part and correlates them to the part's material and/or damage state. PCRT testing is used in the automotive, aerospace, and power generation industries via automated PASS/FAIL inspections to distinguish parts with nominal process variation from those with the defect(s) of interest. Traditional PCRT tests are created through the statistical analysis of populations of "good" and "bad" parts. However, gathering a statistically significant number of parts can be costly and time-consuming, and the availability of defective parts may be limited. This work uses virtual databases of good and bad parts to create two targeted PCRT inspections for single crystal (SX) nickel-based superalloy turbine blades. Using finite element (FE) models, populations were modeled to include variations in geometric dimensions, material properties, crystallographic orientation, and creep damage. Model results were verified by comparing the frequency variation in the modeled populations with the measured frequency variations of several physical blade populations. Additionally, creep modeling results were verified through the experimental evaluation of coupon geometries. A virtual database of resonance spectra was created from the model data. The virtual database was used to create PCRT inspections to detect crystallographic defects and creep strain. Quantification of creep strain values using the PCRT inspection results was also demonstrated.

  14. Pre-HEAT: submillimeter site testing and astronomical spectra from Dome A, Antarctica

    Science.gov (United States)

    Kulesa, C. A.; Walker, C. K.; Schein, M.; Golish, D.; Tothill, N.; Siegel, P.; Weinreb, S.; Jones, G.; Bardin, J.; Jacobs, K.; Martin, C. L.; Storey, J.; Ashley, M.; Lawrence, J.; Luong-Van, D.; Everett, J.; Wang, L.; Feng, L.; Zhu, Z.; Yan, J.; Yang, J.; Zhang, X.-G.; Cui, X.; Yuan, X.; Hu, J.; Xu, Z.; Jiang, Z.; Yang, H.; Li, Y.; Sun, B.; Qin, W.; Shang, Z.

    2008-07-01

    Pre-HEAT is a 20 cm aperture submillimeter-wave telescope with a 660 GHz (450 micron) Schottky diode heterodyne receiver and digital FFT spectrometer for the Plateau Observatory (PLATO) developed by the University of New South Wales. In January 2008 it was deployed to Dome A, the summit of the Antarctic plateau, as part of a scientific traverse led by the Polar Research Institute of China and the Chinese Academy of Sciences. Dome A may be one of the best sites in the world for ground based Terahertz astronomy, based on the exceptionally cold, dry and stable conditions which prevail there. Pre-HEAT is measuring the 450 micron sky opacity at Dome A and mapping the Galactic Plane in the 13CO J=6-5 line, constituting the first submillimeter measurements from Dome A. It is field-testing many of the key technologies for its namesake -- a successor mission called HEAT: the High Elevation Antarctic Terahertz telescope. Exciting prospects for submillimeter astronomy from Dome A and the status of Pre-HEAT will be presented.

  15. Mapping spikes to sensations

    Directory of Open Access Journals (Sweden)

    Maik Christopher Stüttgen

    2011-11-01

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

  16. 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.

  17. Wavelet analysis of epileptic spikes

    Science.gov (United States)

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

    2003-05-01

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

  18. 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.

  19. 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

  20. 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.

  1. The reference peak areas of the 1995 IAEA test spectra for gamma-ray spectrum analysis programs are absolute and traceable

    CERN Document Server

    Blaauw, M

    1999-01-01

    A previously validated algorithm for absolute peak area determination was used to verify the reference peak areas supplied with the 1995 IAEA test spectra for gamma-ray spectrometry. These reference peak areas turn out to be absolute and traceable to a precision of 0.9%: The reference peak areas are possibly too low by a factor 0.992+-0.009. It is proposed to employ the test spectra and reference areas to validate the peak areas obtained with any algorithm in gamma-ray spectrometry. (author)

  2. Integral test of neutron cross section data for future reactor materials through measurement and analysis of neutron spectra

    International Nuclear Information System (INIS)

    Mori, Takamasa

    1985-05-01

    In order to assess the cross section data for future reactor materials, such as molybdenum, niobium, titanium, lithium and fluorine, the angular neutron spectra in test piles of these materials or their chemical compounds have been measured in the energy range from a few keV to a few MeV by the linac time-of-flight method. The results have been compared with those theoretically calculated from the evaluated cross section data in such as JENDL-2 (or JENDL-1, JENDL-3PR1) and ENDF/B-IV. For both of molybdenum and niobium, it has been found that the energy distribution of inelastically scattered neutrons plays an important role in the analysis, and the JENDL library gives better predictions of spectrum shapes than ENDF/B-IV for both cases. In the case of niobium, however, it appears that the values of inelastic scattering cross section in JENDL-2 are too small around 2 MeV. It has been also found for niobium that the cross section data below 100 keV in ENDF/B-IV are inadequate. In a titanium pile, a discrepancy between the measured spectrum and the calculated one from ENDF/B-IV has been found in the energy range from about 60 keV to a few 100 keV. In order to investigate the cause of this discrepancy, the total cross sections for titanium have been measured by the transmission method. In the case of lithium, the discrepancy between the measured and calculated spectra is considerably reduced by adopting the angular distribution for 7 Li from ENDF/B-IV above about 500 keV. In the case of fluorine, spatial distributions of neutrons and X-rays have been also measured in both piles by the activation method to estimate the influence of photoneutrons generated in the sample material on the neutron distribution, and it has been found that their influence below 1 MeV is not so large as is necessary to be taken into account for the present assessment. (J.P.N)

  3. Computing the Absorption and Emission Spectra of 5-Methylcytidine in Different Solvents: A Test-Case for Different Solvation Models.

    Science.gov (United States)

    Martínez-Fernández, L; Pepino, A J; Segarra-Martí, J; Banyasz, A; Garavelli, M; Improta, R

    2016-09-13

    The optical spectra of 5-methylcytidine in three different solvents (tetrahydrofuran, acetonitrile, and water) is measured, showing that both the absorption and the emission maximum in water are significantly blue-shifted (0.08 eV). The absorption spectra are simulated based on CAM-B3LYP/TD-DFT calculations but including solvent effects with three different approaches: (i) a hybrid implicit/explicit full quantum mechanical approach, (ii) a mixed QM/MM static approach, and (iii) a QM/MM method exploiting the structures issuing from molecular dynamics classical simulations. Ab-initio Molecular dynamics simulations based on CAM-B3LYP functionals have also been performed. The adopted approaches all reproduce the main features of the experimental spectra, giving insights on the chemical-physical effects responsible for the solvent shifts in the spectra of 5-methylcytidine and providing the basis for discussing advantages and limitations of the adopted solvation models.

  4. X-ray Emitting GHz-Peaked Spectrum Galaxies: Testing a Dynamical-Radiative Model with Broad-Band Spectra

    International Nuclear Information System (INIS)

    Ostorero, L.; Moderski, R.; Stawarz, L.; Diaferio, A.; Kowalska, I.; Cheung, C.C.; Kataoka, J.; Begelman, M.C.; Wagner, S.J.

    2010-01-01

    In a dynamical-radiative model we recently developed to describe the physics of compact, GHz-Peaked-Spectrum (GPS) sources, the relativistic jets propagate across the inner, kpc-sized region of the host galaxy, while the electron population of the expanding lobes evolves and emits synchrotron and inverse-Compton (IC) radiation. Interstellar-medium gas clouds engulfed by the expanding lobes, and photoionized by the active nucleus, are responsible for the radio spectral turnover through free-free absorption (FFA) of the synchrotron photons. The model provides a description of the evolution of the GPS spectral energy distribution (SED) with the source expansion, predicting significant and complex high-energy emission, from the X-ray to the γ-ray frequency domain. Here, we test this model with the broad-band SEDs of a sample of eleven X-ray emitting GPS galaxies with Compact-Symmetric-Object (CSO) morphology, and show that: (i) the shape of the radio continuum at frequencies lower than the spectral turnover is indeed well accounted for by the FFA mechanism; (ii) the observed X-ray spectra can be interpreted as non-thermal radiation produced via IC scattering of the local radiation fields off the lobe particles, providing a viable alternative to the thermal, accretion-disk dominated scenario. We also show that the relation between the hydrogen column densities derived from the X-ray (N H ) and radio (N HI ) data of the sources is suggestive of a positive correlation, which, if confirmed by future observations, would provide further support to our scenario of high-energy emitting lobes.

  5. Rotation-vibration interactions in the spectra of polycyclic aromatic hydrocarbons: Quinoline as a test-case species

    International Nuclear Information System (INIS)

    Pirali, O.; Gruet, S.; Kisiel, Z.; Goubet, M.; Martin-Drumel, M. A.; Cuisset, A.; Hindle, F.; Mouret, G.

    2015-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are highly relevant for astrophysics as possible, though controversial, carriers of the unidentified infrared emission bands that are observed in a number of different astronomical objects. In support of radio-astronomical observations, high resolution laboratory spectroscopy has already provided the rotational spectra in the vibrational ground state of several molecules of this type, although the rotational study of their dense infrared (IR) bands has only recently become possible using a limited number of experimental set-ups. To date, all of the rotationally resolved data have concerned unperturbed spectra. We presently report the results of a high resolution study of the three lowest vibrational states of quinoline C 9 H 7 N, an N-bearing naphthalene derivative. While the pure rotational ground state spectrum of quinoline is unperturbed, severe complications appear in the spectra of the ν 45 and ν 44 vibrational modes (located at about 168 cm −1 and 178 cm −1 , respectively). In order to study these effects in detail, we employed three different and complementary experimental techniques: Fourier-transform microwave spectroscopy, millimeter-wave spectroscopy, and Fourier-transform far-infrared spectroscopy with a synchrotron radiation source. Due to the high density of states in the IR spectra of molecules as large as PAHs, perturbations in the rotational spectra of excited states should be ubiquitous. Our study identifies for the first time this effect and provides some insights into an appropriate treatment of such perturbations

  6. Rotation-vibration interactions in the spectra of polycyclic aromatic hydrocarbons: Quinoline as a test-case species

    Science.gov (United States)

    Pirali, O.; Kisiel, Z.; Goubet, M.; Gruet, S.; Martin-Drumel, M. A.; Cuisset, A.; Hindle, F.; Mouret, G.

    2015-03-01

    Polycyclic aromatic hydrocarbons (PAHs) are highly relevant for astrophysics as possible, though controversial, carriers of the unidentified infrared emission bands that are observed in a number of different astronomical objects. In support of radio-astronomical observations, high resolution laboratory spectroscopy has already provided the rotational spectra in the vibrational ground state of several molecules of this type, although the rotational study of their dense infrared (IR) bands has only recently become possible using a limited number of experimental set-ups. To date, all of the rotationally resolved data have concerned unperturbed spectra. We presently report the results of a high resolution study of the three lowest vibrational states of quinoline C9H7N, an N-bearing naphthalene derivative. While the pure rotational ground state spectrum of quinoline is unperturbed, severe complications appear in the spectra of the ν45 and ν44 vibrational modes (located at about 168 cm-1 and 178 cm-1, respectively). In order to study these effects in detail, we employed three different and complementary experimental techniques: Fourier-transform microwave spectroscopy, millimeter-wave spectroscopy, and Fourier-transform far-infrared spectroscopy with a synchrotron radiation source. Due to the high density of states in the IR spectra of molecules as large as PAHs, perturbations in the rotational spectra of excited states should be ubiquitous. Our study identifies for the first time this effect and provides some insights into an appropriate treatment of such perturbations.

  7. 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...

  8. 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

  9. 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

  10. 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.

  11. BETA SPECTRA. I. Negatrons spectra

    International Nuclear Information System (INIS)

    Grau Malonda, A.; Garcia-Torano, E.

    1978-01-01

    Using the Fermi theory of beta decay, the beta spectra for 62 negatrons emitters have been computed introducing a correction factor for unique forbidden transitions. These spectra are plotted vs. energy, once normal i sed, and tabulated with the related Fermi functions. The average and median energies are calculated. (Author)

  12. 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.

  13. Rotation-vibration interactions in the spectra of polycyclic aromatic hydrocarbons: Quinoline as a test-case species

    Energy Technology Data Exchange (ETDEWEB)

    Pirali, O.; Gruet, S. [AILES Beamline, Synchrotron SOLEIL, l’Orme des Merisiers, Saint-Aubin, 91192 Gif-sur-Yvette cedex (France); Institut des Sciences Moléculaires d’Orsay, UMR8214 CNRS – Université Paris-Sud, Bât. 210, 91405 Orsay cedex (France); Kisiel, Z. [Institute of Physics, Polish Academy of Sciences, Al. Lotników 32/46, 02-668 Warsaw (Poland); Goubet, M. [Laboratoire de Physique des Lasers, Atomes et Molécules, UMR 8523 CNRS - Université Lille 1, Bâtiment P5, F-59655 Villeneuve d’Ascq Cedex (France); Martin-Drumel, M. A.; Cuisset, A.; Hindle, F.; Mouret, G. [Laboratoire de Physico-Chimie de l’Atmosphère, EA-4493, Université du Littoral – Côte d’Opale, 59140 Dunkerque (France)

    2015-03-14

    Polycyclic aromatic hydrocarbons (PAHs) are highly relevant for astrophysics as possible, though controversial, carriers of the unidentified infrared emission bands that are observed in a number of different astronomical objects. In support of radio-astronomical observations, high resolution laboratory spectroscopy has already provided the rotational spectra in the vibrational ground state of several molecules of this type, although the rotational study of their dense infrared (IR) bands has only recently become possible using a limited number of experimental set-ups. To date, all of the rotationally resolved data have concerned unperturbed spectra. We presently report the results of a high resolution study of the three lowest vibrational states of quinoline C{sub 9}H{sub 7}N, an N-bearing naphthalene derivative. While the pure rotational ground state spectrum of quinoline is unperturbed, severe complications appear in the spectra of the ν{sub 45} and ν{sub 44} vibrational modes (located at about 168 cm{sup −1} and 178 cm{sup −1}, respectively). In order to study these effects in detail, we employed three different and complementary experimental techniques: Fourier-transform microwave spectroscopy, millimeter-wave spectroscopy, and Fourier-transform far-infrared spectroscopy with a synchrotron radiation source. Due to the high density of states in the IR spectra of molecules as large as PAHs, perturbations in the rotational spectra of excited states should be ubiquitous. Our study identifies for the first time this effect and provides some insights into an appropriate treatment of such perturbations.

  14. 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...

  15. 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.

  16. Testing electric field models using ring current ion energy spectra from the Equator-S ion composition (ESIC instrument

    Directory of Open Access Journals (Sweden)

    L. M. Kistler

    Full Text Available During the main and early recovery phase of a geomagnetic storm on February 18, 1998, the Equator-S ion composition instrument (ESIC observed spectral features which typically represent the differences in loss along the drift path in the energy range (5–15 keV/e where the drift changes from being E × B dominated to being gradient and curvature drift dominated. We compare the expected energy spectra modeled using a Volland-Stern electric field and a Weimer electric field, assuming charge exchange along the drift path, with the observed energy spectra for H+ and O+. We find that using the Weimer electric field gives much better agreement with the spectral features, and with the observed losses. Neither model, however, accurately predicts the energies of the observed minima.

    Key words. Magnetospheric physics (energetic particles trapped; plasma convection; storms and substorms

  17. Tests of the GIC and Measurements of Angular Distributions and Energy Spectra for 58Ni(n,p)58Co Reaction at 4.1 MeV

    Institute of Scientific and Technical Information of China (English)

    Yu.M.Gledenov; M.Sedysheva; G.Khuukhenkhuu

    1997-01-01

    <正>On the basis of measurements of double differential cross sections for (n,α) reactions in 5-7 MeV neutron energy region using gridded ionization chamber (GIC), we constructed a new GIC which, compared with the old ones, can bear higher pressure and makes it possible to measure (n,p) reactions up to 6 MeV and (n,xα) reactions up to 20 MeV. To test the new chamber, the saturation property for argon and krypton mixed with a few percent CO2 was studied using 241Am and compound Pu α source and tritium from 6Li(nth,t)4He, and the two dimensional spectra for 241Am and Pu α source, 6Li(nth,t)4He and H(n,p) reactions were measured. The measured energy spectra and angular distributions for α and tritium are reasonable, and the derived data for α, proton and tritium in argon and krypton from the measured spectra data were compared with the calculated ones. They are in good agreement. The angular distributions and energy spectra for 58Ni(n,p)58Co reaction at 4.1 MeV neutron energy were m

  18. Neuronal coding and spiking randomness

    Czech Academy of Sciences Publication Activity Database

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

    2007-01-01

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

  19. 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)

  20. Eliminating thermal violin spikes from LIGO noise

    Energy Technology Data Exchange (ETDEWEB)

    Santamore, D. H.; Levin, Yuri

    2001-08-15

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

  1. Eliminating thermal violin spikes from LIGO noise

    International Nuclear Information System (INIS)

    Santamore, D. H.; Levin, Yuri

    2001-01-01

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

  2. Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

    International Nuclear Information System (INIS)

    Bobin, C.; Bichler, O.; Lourenço, V.; Thiam, C.; Thévenin, M.

    2016-01-01

    Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes’ rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ( 241 Am, 133 Ba, 207 Bi, 60 Co, 137 Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. - Highlights: • A fast radionuclide identification algorithm applicable in spectroscopic portal monitors is presented. • The proposed algorithm combines a Bayesian sequential approach and a spiking neural network. • The algorithm was validated using the mixture of γ-emitter spectra provided by a well-type NaI(Tl) detector. • The radionuclide identification process is implemented using the whole γ-spectrum without energy calibration.

  3. 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

  4. MEASURING DETAILED CHEMICAL ABUNDANCES FROM CO-ADDED MEDIUM-RESOLUTION SPECTRA. I. TESTS USING MILKY WAY DWARF SPHEROIDAL GALAXIES AND GLOBULAR CLUSTERS

    International Nuclear Information System (INIS)

    Yang Lei; Peng, Eric W.; Kirby, Evan N.; Guhathakurta, Puragra; Cheng, Lucy

    2013-01-01

    The ability to measure metallicities and α-element abundances in individual red giant branch (RGB) stars using medium-resolution spectra (R ≈ 6000) is a valuable tool for deciphering the nature of Milky Way dwarf satellites and the history of the Galactic halo. Extending such studies to more distant systems like Andromeda is beyond the ability of the current generation of telescopes, but by co-adding the spectra of similar stars, we can attain the necessary signal-to-noise ratio (S/N) to make detailed abundance measurements. In this paper, we present a method to determine metallicities and α-element abundances using the co-addition of medium-resolution spectra. We test the method of spectral co-addition using high-S/N spectra of more than 1300 RGB stars from Milky Way globular clusters and dwarf spheroidal galaxies obtained with the Keck II telescope/DEIMOS spectrograph. We group similar stars using photometric criteria and compare the weighted ensemble average abundances ([Fe/H], [Mg/Fe], [Si/Fe], [Ca/Fe], and [Ti/Fe]) of individual stars in each group with the measurements made on the corresponding co-added spectrum. We find a high level of agreement between the two methods, which permits us to apply this co-added spectra technique to more distant RGB stars, like stars in the M31 satellite galaxies. This paper outlines our spectral co-addition and abundance measurement methodology and describes the potential biases in making these measurements.

  5. Measuring Detailed Chemical Abundances from Co-added Medium-resolution Spectra. I. Tests Using Milky Way Dwarf Spheroidal Galaxies and Globular Clusters

    Science.gov (United States)

    Yang, Lei; Kirby, Evan N.; Guhathakurta, Puragra; Peng, Eric W.; Cheng, Lucy

    2013-05-01

    The ability to measure metallicities and α-element abundances in individual red giant branch (RGB) stars using medium-resolution spectra (R ≈ 6000) is a valuable tool for deciphering the nature of Milky Way dwarf satellites and the history of the Galactic halo. Extending such studies to more distant systems like Andromeda is beyond the ability of the current generation of telescopes, but by co-adding the spectra of similar stars, we can attain the necessary signal-to-noise ratio (S/N) to make detailed abundance measurements. In this paper, we present a method to determine metallicities and α-element abundances using the co-addition of medium-resolution spectra. We test the method of spectral co-addition using high-S/N spectra of more than 1300 RGB stars from Milky Way globular clusters and dwarf spheroidal galaxies obtained with the Keck II telescope/DEIMOS spectrograph. We group similar stars using photometric criteria and compare the weighted ensemble average abundances ([Fe/H], [Mg/Fe], [Si/Fe], [Ca/Fe], and [Ti/Fe]) of individual stars in each group with the measurements made on the corresponding co-added spectrum. We find a high level of agreement between the two methods, which permits us to apply this co-added spectra technique to more distant RGB stars, like stars in the M31 satellite galaxies. This paper outlines our spectral co-addition and abundance measurement methodology and describes the potential biases in making these measurements. Data herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and NASA. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation.

  6. Spike Pattern Recognition for Automatic Collimation Alignment

    CERN Document Server

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

    2017-01-01

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

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

    Science.gov (United States)

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

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

  8. Updated database plus software for line-mixing in CO2 infrared spectra and their test using laboratory spectra in the 1.5-2.3 μm region

    International Nuclear Information System (INIS)

    Lamouroux, J.; Tran, H.; Laraia, A.L.; Gamache, R.R.; Rothman, L.S.; Gordon, I.E.; Hartmann, J.-M.

    2010-01-01

    In a previous series of papers, a model for the calculation of CO 2 -air absorption coefficients taking line-mixing into account and the corresponding database/software package were described and widely tested. In this study, we present an update of this package, based on the 2008 version of HITRAN, the latest currently available. The spectroscopic data for the seven most-abundant isotopologues are taken from HITRAN. When the HITRAN data are not complete up to J''=70, the data files are augmented with spectroscopic parameters from the CDSD-296 database and the high-temperature CDSD-1000 if necessary. Previously missing spectroscopic parameters, the air-induced pressure shifts and CO 2 line broadening coefficients with H 2 O, have been added. The quality of this new database is demonstrated by comparisons of calculated absorptions and measurements using CO 2 high-pressure laboratory spectra in the 1.5-2.3 μm region. The influence of the imperfections and inaccuracies of the spectroscopic parameters from the 2000 version of HITRAN is clearly shown as a big improvement of the residuals is observed by using the new database. The very good agreements between calculated and measured absorption coefficients confirm the necessity of the update presented here and further demonstrate the importance of line-mixing effects, especially for the high pressures investigated here. The application of the updated database/software package to atmospheric spectra should result in an increased accuracy in the retrieval of CO 2 atmospheric amounts. This opens improved perspectives for the space-borne detection of carbon dioxide sources and sinks.

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

    Directory of Open Access Journals (Sweden)

    Carlo Lucheroni

    2012-01-01

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

  10. 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

  11. 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/.

  12. 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...

  13. 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.

  14. Multineuronal Spike Sequences Repeat with Millisecond Precision

    Directory of Open Access Journals (Sweden)

    Koki eMatsumoto

    2013-06-01

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

  15. 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...

  16. Sialic Acid Binding Properties of Soluble Coronavirus Spike (S1 Proteins: Differences between Infectious Bronchitis Virus and Transmissible Gastroenteritis Virus

    Directory of Open Access Journals (Sweden)

    Christine Winter

    2013-07-01

    Full Text Available The spike proteins of a number of coronaviruses are able to bind to sialic acids present on the cell surface. The importance of this sialic acid binding ability during infection is, however, quite different. We compared the spike protein of transmissible gastroenteritis virus (TGEV and the spike protein of infectious bronchitis virus (IBV. Whereas sialic acid is the only receptor determinant known so far for IBV, TGEV requires interaction with its receptor aminopeptidase N to initiate infection of cells. Binding tests with soluble spike proteins carrying an IgG Fc-tag revealed pronounced differences between these two viral proteins. Binding of the IBV spike protein to host cells was in all experiments sialic acid dependent, whereas the soluble TGEV spike showed binding to APN but had no detectable sialic acid binding activity. Our results underline the different ways in which binding to sialoglycoconjugates is mediated by coronavirus spike proteins.

  17. Measurement and Analysis of the Neutron and Gamma-Ray Flux Spectra in a Neutronics Mock-Up of the HCPB Test Blanket Module

    International Nuclear Information System (INIS)

    Seidel, K.; Freiesleben, H.; Poenitz, E.; Klix, A.; Unholzer, S.; Batistoni, P.; Fischer, U.; Leichtle, D.

    2006-01-01

    The nuclear parameters of a breeding blanket, such as tritium production rate, nuclear heating, activation and dose rate, are calculated by integral folding of an energy dependent cross section (or coefficient) with the neutron (or gamma-ray) flux energy spectra. The uncertainties of the designed parameters are determined by the uncertainties of both the cross section data and the flux spectra obtained by transport calculations. Also the analysis of possible discrepancies between measured and calculated integral nuclear parameter represents a two-step procedure. First, the energy region and the amount of flux discrepancies has to be found out and second, the cross section data have to be checked. To this end, neutron and gamma-ray flux spectra in a mock-up of the EU Helium-Cooled Pebble Bed (HCPB) breeder Test Blanket Module (TBM), irradiated with 14 MeV neutrons, were measured and analysed by means of Monte Carlo transport calculations. The flux spectra were determined for the energy ranges that are relevant for the most important nuclear parameters of the TBM, which are the tritium production rate and the shielding capability. The fast neutron flux which determines the tritium production on 7 Li and dominates the shield design was measured by the pulse-height distribution obtained from an organic liquid scintillation detector. Simultaneously, the gamma-ray flux spectra were measured. The neutron flux at lower energies, down to thermal, which determines the tritium production on 6 Li, was measured with time-of-arrival spectroscopy. For this purpose, the TUD neutron generator was operated in pulsed mode (pulse width 10 μs, frequency 1 kHz) and the neutrons arriving at a 3 He proportional counter in the mock-up were recorded as a function of time after the source neutron pulse. The spectral distributions for the two positions in the mock-up, where measurements were carried out, were calculated with the Monte Carlo code MCNP, version 5, and nuclear data from the

  18. Effect of local heat flux spikes on DNB in non-uniformly heated rod bundles

    International Nuclear Information System (INIS)

    Cadek, F.F.; Hill, K.W.; Motley, F.E.

    1975-02-01

    High pressure water tests were carried out to measure the DNB heat flux using an electrically heated rod bundle in which three adjacent rods had 20 percent heat flux spikes at the axial location where DNB is most likely to occur. This test series was run at the same conditions as those of two earlier test series which had unspiked rods, so that spiked and unspiked runs could be paired and spike effects could thus be isolated. Results are described. 7 references. (U.S.)

  19. Spike voltage topography in temporal lobe epilepsy.

    Science.gov (United States)

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

    2016-07-15

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

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

    Science.gov (United States)

    Shay, Christopher F.; Ferrante, Michele; Chapman, G. William; Hasselmo, Michael E.

    2015-01-01

    Rebound spiking properties of medial entorhinal cortex (mEC) stellate cells induced by inhibition may underlie their functional properties in awake behaving rats, including the temporal phase separation of distinct grid cells and differences in grid cell firing properties. We investigated rebound spiking properties using whole cell patch recording in entorhinal slices, holding cells near spiking threshold and delivering sinusoidal inputs, superimposed with realistic inhibitory synaptic inputs to test the capacity of cells to selectively respond to specific phases of inhibitory input. Stellate cells showed a specific phase range of hyperpolarizing inputs that elicited spiking, but non-stellate cells did not show phase specificity. In both cell types, the phase range of spiking output occurred between the peak and subsequent descending zero crossing of the sinusoid. The phases of inhibitory inputs that induced spikes shifted earlier as the baseline sinusoid frequency increased, while spiking output shifted to later phases. Increases in magnitude of the inhibitory inputs shifted the spiking output to earlier phases. Pharmacological blockade of h-current abolished the phase selectivity of hyperpolarizing inputs eliciting spikes. A network computational model using cells possessing similar rebound properties as found in vitro produces spatially periodic firing properties resembling grid cell firing when a simulated animal moves along a linear track. These results suggest that the ability of mEC stellate cells to fire rebound spikes in response to a specific range of phases of inhibition could support complex attractor dynamics that provide completion and separation to maintain spiking activity of specific grid cell populations. PMID:26385258

  1. Probing HeII Reionization at z>3.5 with Resolved HeII Lyman Alpha Forest Spectra

    Science.gov (United States)

    Worseck, Gabor

    2017-08-01

    The advent of GALEX and COS have revolutionized our view of HeII reionization, the final major phase transition of the intergalactic medium. COS spectra of the HeII Lyman alpha forest have confirmed with high confidence the high HeII transmission that signifies the completion of HeII reionization at z 2.7. However, the handful of z>3.5 quasars observed to date show a set of HeII transmission 'spikes' and larger regions with non-zero transmission that suggest HeII reionization was well underway by z=4. This is in striking conflict with predictions from state-of-the-art radiative transfer simulations of a HeII reionization driven by bright quasars. Explaining these measurements may require either faint quasars or more exotic sources of hard photons at z>4, with concomitant implications for HI reionization. However, many of the observed spikes are unresolved in G140L spectra and are significantly impacted by Poisson noise. Current data cannot reliably probe the ionization state of helium at z>3.5.We request 41 orbits to obtain science-grade G130M spectra of the two UV-brightest HeII-transmitting QSOs at z>3.5 to confirm and resolve their HeII transmission spikes as an unequivocal test of early HeII reionization. These spectra are complemented by recently obtained data from 8m telescopes: (1) Echelle spectra of the coeval HI Lya forest to map the underlying density field that modulates the HeII absorption, and (2) Our dedicated survey for foreground QSOs that may source the HeII transmission. Our recent HST programs revealed the only two viable targets to resolve the z>3.5 HeII Lyman alpha forest, and to conclusively solve this riddle.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

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

    Science.gov (United States)

    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.

  11. Analysis and solution of current spike occurred in dynamic compensation of self-powered neutron detectors

    International Nuclear Information System (INIS)

    Peng, Xingjie; Li, Qing; Wang, Kan

    2017-01-01

    Highlights: • The current spike problem is observed in the dynamic compensation process of SPNDs. • The current spike is caused by unphysical current change due to range switching. • Modification on the compensation algorithm is introduced to deal with current spike. - Abstract: Dynamic compensation methods are required to improve the response speed of the Self-Powered Neutron Detectors (SPNDs) and make it possible to apply the SPNDs for core monitoring and surveillance. During the experimental test of the compensation method based on linear matrix inequality (LMI), spikes are observed in the compensated SPND current. After analyzing the measurement data, the cause is fixed on the unphysical change of the uncompensated SPND current due to range switching. Then some modifications on the dynamic compensation algorithms are proposed to solve the current spike problem.

  12. Experimental phase diagram for random laser spectra

    International Nuclear Information System (INIS)

    El-Dardiry, Ramy G S; Mooiweer, Ronald; Lagendijk, Ad

    2012-01-01

    We systematically study the presence of narrow spectral features in a wide variety of random laser samples. Less gain or stronger scattering are shown to lead to a crossover from spiky to smooth spectra. A decomposition of random laser spectra into a set of Lorentzians provides unprecedented detail in the analysis of random laser spectra. We suggest an interpretation in terms of mode competition that enables an understanding of the observed experimental trends. In this interpretation, smooth random laser spectra are a consequence of competing modes for which the loss and gain are proportional. Spectral spikes are associated with modes that are uncoupled from the mode competition in the bulk of the sample. (paper)

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

    Science.gov (United States)

    Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

    2017-02-10

    Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

  18. 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.

  19. 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...

  20. 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.

  1. 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.

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

    Science.gov (United States)

    Gardner, Brian; Grüning, André

    2016-01-01

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

  3. 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.

  4. 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

  5. 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.

  6. Sequential motor task (Luria's Fist-Edge-Palm Test in children with benign focal epilepsy of childhood with centrotemporal spikes Tarefa motora sequencial (Teste de Lúria punho-lado-palma em crianças com epilepsia focal benigna da infância com descarga centrotemporal

    Directory of Open Access Journals (Sweden)

    Carmen Silvia Molleis Galego Miziara

    2013-06-01

    Full Text Available This study evaluated the sequential motor manual actions in children with benign focal epilepsy of childhood with centrotemporal spikes (BECTS and compares the results with matched control group, through the application of Luria's fist-edge-palm test. The children with BECTS underwent interictal single photon emission computed tomography (SPECT and School Performance Test (SPT. Significant difference occurred between the study and control groups for manual motor action through three equal and three different movements. Children with lower school performance had higher error rate in the imitation of hand gestures. Another factor significantly associated with the failure was the abnormality in SPECT. Children with BECTS showed abnormalities in the test that evaluated manual motor programming/planning. This study may suggest that the functional changes related to epileptiform activity in rolandic region interfere with the executive function in children with BECTS.Esse estudo avaliou ações motoras manuais sequenciais em crianças com epilepsia focal benigna da infância com descarga centrotemporal (EBICT e comparou os resultados com o grupo controle pareado, através do teste de Lúria (punho-lado-palma. As crianças com EBICT realizaram single photon emission computed tomography (SPECT interictal e Teste de Desempenho Escolar (TDE. Foram encontradas diferenças significativas entre os dois grupos nas atividades motoras de três movimentos iguais e três movimentos diferentes. As crianças com piores resultados no TDE e com SPECT alterado apresentaram mais erros no teste de imitação manual. Crianças com epilepsia fracassaram nos testes de avaliação motora que envolvem programação/planejamento. Esse estudo sugere que mudanças funcionais relacionadas à atividade epileptiforme na região rolândica interfere com as funções executivas de crianças com EBICT.

  7. Prediction of Pseudo relative velocity response spectra at Yucca Mountain for underground nuclear explosions conducted in the Pahute Mesa testing area at the Nevada testing site; Yucca Mountain Site Characterization Project

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, J.S.

    1991-12-01

    The Yucca Mountain Site Characterization Project (YMP), managed by the Office of Geologic Disposal of the Office of Civilian Radioactive Waste Management of the US Department of Energy, is examining the feasibility of siting a repository for commercial, high-level nuclear wastes at Yucca Mountain on and adjacent to the Nevada Test Site (NTS). This work, intended to extend our understanding of the ground motion at Yucca Mountain resulting from testing of nuclear weapons on the NTS, was funded by the Yucca Mountain project and the Military Applications Weapons Test Program. This report summarizes one aspect of the weapons test seismic investigations conducted in FY88. Pseudo relative velocity response spectra (PSRV) have been calculated for a large body of surface ground motions generated by underground nuclear explosions. These spectra have been analyzed and fit using multiple linear regression techniques to develop a credible prediction technique for surface PSRVs. In addition, a technique for estimating downhole PSRVs at specific stations is included. A data summary, data analysis, prediction development, prediction evaluation, software summary and FORTRAN listing of the prediction technique are included in this report.

  8. 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

  9. 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.

  10. THE POLITICAL CRITIQUE OF SPIKE Lee's Bamboozled

    African Journals Online (AJOL)

    Admin

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

  11. 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.

  12. 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...

  13. 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.

  14. Voltage spike detection in high field superconducting accelerator magnets

    Energy Technology Data Exchange (ETDEWEB)

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

    2004-12-01

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

  15. Voltage spike detection in high field superconducting accelerator magnets

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  16. Analysis of voltage spikes in superconducting Nb3Sn magnets

    International Nuclear Information System (INIS)

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

    2008-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Andreas Stöckel

    2017-08-01

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

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

    Science.gov (United States)

    Wang, Weichen; Fan, Jianqing

    2017-06-01

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

  19. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    Science.gov (United States)

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

    2016-01-01

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

  20. 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)

  1. Deconvoluting double Doppler spectra

    International Nuclear Information System (INIS)

    Ho, K.F.; Beling, C.D.; Fung, S.; Chan, K.L.; Tang, H.W.

    2001-01-01

    The successful deconvolution of data from double Doppler broadening of annihilation radiation (D-DBAR) spectroscopy is a promising area of endeavour aimed at producing momentum distributions of a quality comparable to those of the angular correlation technique. The deconvolution procedure we test in the present study is the constrained generalized least square method. Trials with computer simulated DDBAR spectra are generated and deconvoluted in order to find the best form of regularizer and the regularization parameter. For these trials the Neumann (reflective) boundary condition is used to give a single matrix operation in Fourier space. Experimental D-DBAR spectra are also subject to the same type of deconvolution after having carried out a background subtraction and using a symmetrize resolution function obtained from an 85 Sr source with wide coincidence windows. (orig.)

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Vernon eLawhern

    2011-05-01

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

  4. 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

  5. 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.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Monika Korte

    2018-03-01

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

  9. Minimizing the effect of process mismatch in a neuromorphic system using spike-timing-dependent adaptation.

    Science.gov (United States)

    Cameron, Katherine; Murray, Alan

    2008-05-01

    This paper investigates whether spike-timing-dependent plasticity (STDP) can minimize the effect of mismatch within the context of a depth-from-motion algorithm. To improve noise rejection, this algorithm contains a spike prediction element, whose performance is degraded by analog very large scale integration (VLSI) mismatch. The error between the actual spike arrival time and the prediction is used as the input to an STDP circuit, to improve future predictions. Before STDP adaptation, the error reflects the degree of mismatch within the prediction circuitry. After STDP adaptation, the error indicates to what extent the adaptive circuitry can minimize the effect of transistor mismatch. The circuitry is tested with static and varying prediction times and chip results are presented. The effect of noisy spikes is also investigated. Under all conditions the STDP adaptation is shown to improve performance.

  10. 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...

  11. 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.

  12. 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.

  13. 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)

  14. 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.

  15. Spike timing precision of neuronal circuits.

    Science.gov (United States)

    Kilinc, Deniz; Demir, Alper

    2018-04-17

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

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

    Science.gov (United States)

    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.

  17. 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

  18. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

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

  19. Improved SpikeProp for Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Falah Y. H. Ahmed

    2013-01-01

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

  20. 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

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

    Directory of Open Access Journals (Sweden)

    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

  2. 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.

  3. Physics of volleyball: Spiking with a purpose

    Science.gov (United States)

    Behroozi, F.

    1998-05-01

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

  4. Spike-timing theory of working memory.

    Directory of Open Access Journals (Sweden)

    Botond Szatmáry

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

  5. Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

    Science.gov (United States)

    Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

    2018-06-01

    Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

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

    Science.gov (United States)

    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.

  7. An Investigation on the Role of Spike Latency in an Artificial Olfactory System

    Directory of Open Access Journals (Sweden)

    Corrado eDi Natale

    2011-12-01

    Full Text Available Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time.

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

    Science.gov (United States)

    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.

  9. 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.

  10. Techniques for Handling Channeling in High Resolution Fourier Transform Spectra Recorded with Synchrotron Sources

    International Nuclear Information System (INIS)

    Ibrahim, Amr; PredoiCross, Adriana; Teillet, P. M.

    2010-01-01

    Seven different techniques in dealing the problem of channel spectra in Fourier transform Spectroscopy utilizing synchrotron source were examined and compared. Five of these techniques deal with the artifacts (spikes) in the recorded interferogram which in turn result in channel spectra within the spectral domain. Such interferogram editing method include replacing these spikes with zeros, straight line, fitted polynomial curve, rescaled spike and spike reduced with Gauss Function. Another two techniques try to target this issue in the spectral domain instead by either generating a synthetic background simulating the channels or measuring the channels parameters (amplitude, spacing and phase) to use in the spectral fitting program. Results showed spectral domain techniques produces higher quality results in terms of signal to noise and fitting residual. The effect of each method on the line parameters such as position, intensity are air broadening are also measured and discussed.

  11. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

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

    2017-12-01

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

  12. Beta spectra. II-Positron spectra

    International Nuclear Information System (INIS)

    Grau, A.; Garcia-Torano, E.

    1981-01-01

    Using the Fermi theory of beta decay, the beta spectra for 30 positron emitters have been computed, introducing a correction factor for unique forbidden transitions. The spectra are ploted vs. energy, once normalised, and tabulated with the related Fermi functions. The average and median energies are calculated. (author)

  13. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  14. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

    Science.gov (United States)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

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

    Science.gov (United States)

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

    2013-05-01

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

  16. 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.

  17. Graphics based PC analysis of alpha spectra

    International Nuclear Information System (INIS)

    Chapman, T.C.

    1991-01-01

    New personal computer (PC) software performs interactive analysis of alpha spectra using EGA graphics. Spectra are collected with a commercial MCA board and analyzed using the software described here. The operator is required to approve each peak integration area before analysis proceeds. Sample analysis can use detector efficiencies or spike yields or both. Background corrections are made and upper limit values are calculated when specified. Nuclide identification uses a library of up to 64 nuclides with up to 8 alpha lines for each nuclide. Any one of 32 subset libraries can be used in an analysis. Analysis time is short and is limited by interaction with the operator, not by calculation time. Utilities include nuclide library editing, library subset editing, energy calibration, efficiency calibration, and background update

  18. Methodological issues about techniques for the spiking of standard OECD soil with nanoparticles: evidence of different behaviours

    International Nuclear Information System (INIS)

    Miglietta, Maria Lucia; Rametta, Gabriella; Manzo, Sonia; Salluzzo, Antonio; Rimauro, Juri; Francia, Girolamo Di

    2015-01-01

    The aim of this study is to investigate at what extent the results of standard nanoparticle (NP) toxicity testing methodologies are affected by the different exposure procedures on soil organisms. In this view, differences in physicochemical properties of ZnO NPs (<100 nm), ZnO bulk (<200 nm) and ionic Zinc (ZnCl 2 ) and their ecotoxicological potential toward Lepidium sativum were investigated with respect to three different spiking methods. Results show that the spiking procedures give homogeneous distribution of the testing nanomaterial in soil but the physicochemical and ecotoxicological properties of the testing species differ according to the spiking procedure. Dry spiking produced the highest ZnO solubility whereas spiking through dispersions of ZnO in water and in aqueous soil extracts produced the lowest. At the same time, the ecotoxic effects showed different trends with regard to the spiking route. The need for a definition of agreed methods concerning the NP spiking procedures is, therefore, urgent

  19. Methodological issues about techniques for the spiking of standard OECD soil with nanoparticles: evidence of different behaviours

    Energy Technology Data Exchange (ETDEWEB)

    Miglietta, Maria Lucia, E-mail: mara.miglietta@enea.it; Rametta, Gabriella; Manzo, Sonia; Salluzzo, Antonio; Rimauro, Juri; Francia, Girolamo Di [ENEA, Portici Technical Unit, C.R. Portici (Italy)

    2015-07-15

    The aim of this study is to investigate at what extent the results of standard nanoparticle (NP) toxicity testing methodologies are affected by the different exposure procedures on soil organisms. In this view, differences in physicochemical properties of ZnO NPs (<100 nm), ZnO bulk (<200 nm) and ionic Zinc (ZnCl{sub 2}) and their ecotoxicological potential toward Lepidium sativum were investigated with respect to three different spiking methods. Results show that the spiking procedures give homogeneous distribution of the testing nanomaterial in soil but the physicochemical and ecotoxicological properties of the testing species differ according to the spiking procedure. Dry spiking produced the highest ZnO solubility whereas spiking through dispersions of ZnO in water and in aqueous soil extracts produced the lowest. At the same time, the ecotoxic effects showed different trends with regard to the spiking route. The need for a definition of agreed methods concerning the NP spiking procedures is, therefore, urgent.

  20. Determination of the optical absorption spectra of thin layers from their photoacoustic spectra

    Science.gov (United States)

    Bychto, Leszek; Maliński, Mirosław; Patryn, Aleksy; Tivanov, Mikhail; Gremenok, Valery

    2018-05-01

    This paper presents a new method for computations of the optical absorption coefficient spectra from the normalized photoacoustic amplitude spectra of thin semiconductor samples deposited on the optically transparent and thermally thick substrates. This method was tested on CuIn(Te0.7Se0.3)2 thin films. From the normalized photoacoustic amplitude spectra, the optical absorption coefficient spectra were computed with the new formula as also with the numerical iterative method. From these spectra, the value of the energy gap of the thin film material and the type of the optical transitions were determined. From the experimental optical transmission spectra, the optical absorption coefficient spectra were computed too, and compared with the optical absorption coefficient spectra obtained from photoacoustic spectra.

  1. 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...

  2. 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.

  3. 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...

  4. 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,

  5. On the robustness of EC-PC spike detection method for online neural recording.

    Science.gov (United States)

    Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

    2014-09-30

    Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

  6. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

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

    2012-08-01

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

  7. Evolving spiking networks with variable resistive memories.

    Science.gov (United States)

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

    2014-01-01

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

  8. 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...

  9. Spiked instantons from intersecting D-branes

    Directory of Open Access Journals (Sweden)

    Nikita Nekrasov

    2017-01-01

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

  10. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

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

  11. Non-singular spiked harmonic oscillator

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  12. 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.

  13. 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.

  14. 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.

  15. A Test of Thick-Target Nonuniform Ionization as an Explanation for Breaks in Solar Flare Hard X-Ray Spectra

    Science.gov (United States)

    Holman, gordon; Dennis Brian R.; Tolbert, Anne K.; Schwartz, Richard

    2010-01-01

    Solar nonthermal hard X-ray (HXR) flare spectra often cannot be fitted by a single power law, but rather require a downward break in the photon spectrum. A possible explanation for this spectral break is nonuniform ionization in the emission region. We have developed a computer code to calculate the photon spectrum from electrons with a power-law distribution injected into a thick-target in which the ionization decreases linearly from 100% to zero. We use the bremsstrahlung cross-section from Haug (1997), which closely approximates the full relativistic Bethe-Heitler cross-section, and compare photon spectra computed from this model with those obtained by Kontar, Brown and McArthur (2002), who used a step-function ionization model and the Kramers approximation to the cross-section. We find that for HXR spectra from a target with nonuniform ionization, the difference (Delta-gamma) between the power-law indexes above and below the break has an upper limit between approx.0.2 and 0.7 that depends on the power-law index delta of the injected electron distribution. A broken power-law spectrum with a. higher value of Delta-gamma cannot result from nonuniform ionization alone. The model is applied to spectra obtained around the peak times of 20 flares observed by the Ramaty High Energy Solar Spectroscopic Imager (RHESSI from 2002 to 2004 to determine whether thick-target nonuniform ionization can explain the measured spectral breaks. A Monte Carlo method is used to determine the uncertainties of the best-fit parameters, especially on Delta-gamma. We find that 15 of the 20 flare spectra require a downward spectral break and that at least 6 of these could not be explained by nonuniform ionization alone because they had values of Delta-gamma with less than a 2.5% probability of being consistent with the computed upper limits from the model. The remaining 9 flare spectra, based on this criterion, are consistent with the nonuniform ionization model.

  16. Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains

    Science.gov (United States)

    Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G.

    2017-08-01

    The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L . Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.

  17. Software filtering method to suppress spike pulse interference in multi-channel scaler

    International Nuclear Information System (INIS)

    Huang Shun; Zhao Xiuliang; Li Zhiqiang; Zhao Yanhui

    2008-01-01

    In the test on anti-jamming function of a multi-channel scaler, we found that the spike pulse interference on the second level counter caused by the motor start-stop operations brings a major count error. There are resolvable characteristics between effective signal and spike pulse interference, and multi-channel hardware filtering circuit is too huge and can't filter thoroughly, therefore we designed a software filtering method. In this method based on C8051F020 MCU, we dynamically store sampling values of one channel in only a one-byte variable and distinguish the rise-trail edge of a signal and spike pulse interference because of value changes of the variable. Test showed that the filtering software method can solve the error counting problem of the multi-channel scaler caused by the motor start-stop operations. The flow chart and source codes of the method were detailed in this paper. (authors)

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

    Directory of Open Access Journals (Sweden)

    Qiang Yu

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

  1. Parametric models to relate spike train and LFP dynamics with neural information processing.

    Science.gov (United States)

    Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

    2012-01-01

    Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial

  2. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  3. Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2017-06-01

    Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

  4. 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.

  5. 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)

  6. 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.

  7. 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.

  8. Spike avalanches exhibit universal dynamics across the sleep-wake cycle.

    Directory of Open Access Journals (Sweden)

    Tiago L Ribeiro

    2010-11-01

    Full Text Available Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB animals is still missing, thus raising doubts about their relevance for brain function.To address this issue, we employed chronically implanted multielectrode arrays (MEA to record avalanches of action potentials (spikes from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN. We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus.Altogether, the data provide a comprehensive link between behavior

  9. 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...

  10. Automatic identification of mass spectra

    International Nuclear Information System (INIS)

    Drabloes, F.

    1992-01-01

    Several approaches to preprocessing and comparison of low resolution mass spectra have been evaluated by various test methods related to library search. It is shown that there is a clear correlation between the nature of any contamination of a spectrum, the basic principle of the transformation or distance measure, and the performance of the identification system. The identification of functionality from low resolution spectra has also been evaluated using several classification methods. It is shown that there is an upper limit to the success of this approach, but also that this can be improved significantly by using a very limited amount of additional information. 10 refs

  11. Training Spiking Neural Models Using Artificial Bee Colony

    Science.gov (United States)

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  12. Characterization of reliability of spike timing in spinal interneurons during oscillating inputs

    DEFF Research Database (Denmark)

    Beierholm, Ulrik; Nielsen, Carsten D.; Ryge, Jesper

    2001-01-01

    that interneurons can respond with a high reliability of spike timing, but only by combining fast and slow oscillations is it possible to obtain a high reliability of firing during rhythmic locomotor movements. Theoretical analysis of the rotation number provided new insights into the mechanism for obtaining......The spike timing in rhythmically active interneurons in the mammalian spinal locomotor network varies from cycle to cycle. We tested the contribution from passive membrane properties to this variable firing pattern, by measuring the reliability of spike timing, P, in interneurons in the isolated...... the analysis we used a leaky integrate and fire (LIF) model with a noise term added. The LIF model was able to reproduce the experimentally observed properties of P as well as the low-pass properties of the membrane. The LIF model enabled us to use the mathematical theory of nonlinear oscillators to analyze...

  13. 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.

  14. Spike-based population coding and working memory.

    Directory of Open Access Journals (Sweden)

    Martin Boerlin

    2011-02-01

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

  15. Is the thermal-spike model consistent with experimentally determined electron temperature?

    International Nuclear Information System (INIS)

    Ajryan, Eh.A.; Fedorov, A.V.; Kostenko, B.F.

    2000-01-01

    Carbon K-Auger electron spectra from amorphous carbon foils induced by fast heavy ions are theoretically investigated. The high-energy tail of the Auger structure showing a clear projectile charge dependence is analyzed within the thermal-spike model framework as well as in the frame of another model taking into account some kinetic features of the process. A poor comparison results between theoretically and experimentally determined temperatures are suggested to be due to an improper account of double electron excitations or due to shake-up processes which leave the system in a more energetic initial state than a statically screened core hole

  16. Changes in the frequency of benign focal spikes accompany changes in central information processing speed : a prospective 2-year follow-up study

    NARCIS (Netherlands)

    Ebus, S.C.M.; IJff, D.M.; den Boer, J.T.; Debeij-van Hall, M.H.J.A.; Klinkenberg, S.; van der Does, A.; Boon, P.J.; Arends, J.B.A.M.; Aldenkamp, A.P.

    We prospectively examined whether changes in the frequency of benign focal spikes accompany changes in cognition. Twenty-six children with benign focal spikes (19 with Rolandic epilepsy) and learning difficulties were examined with repeated 24-hour EEG recordings, three cognitive tests on central

  17. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

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

  18. Communication through resonance in spiking neuronal networks.

    Science.gov (United States)

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

    2014-08-01

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

  19. 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)

  20. 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...

  1. 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.

  2. 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.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

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

  5. 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.

  6. The past and the future of Alzheimer’s disease CSF biomarkers – a journey towards validated biochemical tests covering the whole spectra of molecular events

    Directory of Open Access Journals (Sweden)

    Kaj eBlennow

    2015-09-01

    Full Text Available This paper gives a short review on cerebrospinal fluid (CSF biomarkers for Alzheimer’s disease (AD, from early developments to high-precision validated assays on fully automated lab analyzers. We also discuss developments on novel biomarkers, such as synaptic proteins and Aβ oligomers. Our vision for the future is that assaying a set of biomarkers in a single CSF tube can monitor the whole spectra of AD molecular pathogenic events. CSF biomarkers will have a central position not only for clinical diagnosis, but also for the understanding of the sequence of molecular events in the pathogenic process underlying AD and as tools to monitor the effects of novel drug candidates targeting these different mechanisms.

  7. METALLICITY AND TEMPERATURE INDICATORS IN M DWARF K-BAND SPECTRA: TESTING NEW AND UPDATED CALIBRATIONS WITH OBSERVATIONS OF 133 SOLAR NEIGHBORHOOD M DWARFS

    Energy Technology Data Exchange (ETDEWEB)

    Rojas-Ayala, Barbara [Department of Astrophysics, American Museum of Natural History, Central Park West at 79th Street, New York, NY 10024 (United States); Covey, Kevin R.; Lloyd, James P. [Department of Astronomy, Cornell University, 122 Sciences Drive, Ithaca, NY 14853 (United States); Muirhead, Philip S., E-mail: babs@amnh.org [Department of Astronomy, California Institute of Technology, 1200 East California Boulevard, MC 249-17, Pasadena, CA 91125 (United States)

    2012-04-01

    We present K-band spectra for 133 nearby (d < 33 ps) M dwarfs, including 18 M dwarfs with reliable metallicity estimates (as inferred from an FGK type companion), 11 M dwarf planet hosts, more than 2/3 of the M dwarfs in the northern 8 pc sample, and several M dwarfs from the LSPM catalog. From these spectra, we measure equivalent widths of the Ca and Na lines, and a spectral index quantifying the absorption due to H{sub 2}O opacity (the H{sub 2}O-K2 index). Using empirical spectral type standards and synthetic models, we calibrate the H{sub 2}O-K2 index as an indicator of an M dwarf's spectral type and effective temperature. We also present a revised relationship that estimates the [Fe/H] and [M/H] metallicities of M dwarfs from their Na I, Ca I, and H{sub 2}O-K2 measurements. Comparisons to model atmosphere provide a qualitative validation of our approach, but also reveal an overall offset between the atomic line strengths predicted by models as compared to actual observations. Our metallicity estimates also reproduce expected correlations with Galactic space motions and H{alpha} emission line strengths, and return statistically identical metallicities for M dwarfs within a common multiple system. Finally, we find systematic residuals between our H{sub 2}O-based spectral types and those derived from optical spectral features with previously known sensitivity to stellar metallicity, such as TiO, and identify the CaH1 index as a promising optical index for diagnosing the metallicities of near-solar M dwarfs.

  8. Spectra of Graphs

    NARCIS (Netherlands)

    Brouwer, A.E.; Haemers, W.H.

    2012-01-01

    This book gives an elementary treatment of the basic material about graph spectra, both for ordinary, and Laplace and Seidel spectra. The text progresses systematically, by covering standard topics before presenting some new material on trees, strongly regular graphs, two-graphs, association

  9. Spectra of alkali atoms

    International Nuclear Information System (INIS)

    Santoso, Budi; Arumbinang, Haryono.

    1981-01-01

    Emission spectra of alkali atoms has been determined by using spectrometer at the ultraviolet to infra red waves range. The spectra emission can be obtained by absorption spectrophotometric analysis. Comparative evaluations between experimental data and data handbook obtained by spark method were also presented. (author tr.)

  10. Generalized activity equations for spiking neural network dynamics

    Directory of Open Access Journals (Sweden)

    Michael A Buice

    2013-11-01

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

  11. 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.

  12. Surfing a spike wave down the ventral stream.

    Science.gov (United States)

    VanRullen, Rufin; Thorpe, Simon J

    2002-10-01

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

  13. Comparison of neuronal spike exchange methods on a Blue Gene/P supercomputer

    Directory of Open Access Journals (Sweden)

    Michael eHines

    2011-11-01

    Full Text Available The performance of several spike exchange methods using a Blue Gene/P supercomputerhas been tested with 8K to 128K cores using randomly connected networks of up to 32M cells with 1k connections per cell and 4M cells with 10k connections per cell. The spike exchange methods used are the standard Message Passing Interface collective, MPI_Allgather, and several variants of the non-blocking multisend method either implemented via non-blocking MPI_Isend, or exploiting the possibility of very low overhead direct memory access communication available on the Blue Gene/P. In all cases the worst performing method was that using MPI_Isend due to the high overhead of initiating a spike communication. The two best performing methods --- the persistent multisend method using the Record-Replay feature of the Deep Computing Messaging Framework DCMF_Multicast;and a two phase multisend in which a DCMF_Multicast is used to first send to a subset of phase 1 destination cores which then pass it on to their subset of phase 2 destination cores --- had similar performance with very low overhead for the initiation of spike communication. Departure from ideal scaling for the multisend methods is almost completely due to load imbalance caused by the largevariation in number of cells that fire on each processor in the interval between synchronization. Spike exchange time itself is negligible since transmission overlaps with computation and is handled by a direct memory access controller. We conclude that ideal performance scaling will be ultimately limited by imbalance between incoming processor spikes between synchronization intervals. Thus, counterintuitively, maximization of load balance requires that the distribution of cells on processors should not reflect neural net architecture but be randomly distributed so that sets of cells which are burst firing together should be on different processors with their targets on as large a set of processors as possible.

  14. Attention deficit associated with early life interictal spikes in a rat model is improved with ACTH.

    Directory of Open Access Journals (Sweden)

    Amanda E Hernan

    Full Text Available Children with epilepsy often present with pervasive cognitive and behavioral comorbidities including working memory impairments, attention deficit hyperactivity disorder (ADHD and autism spectrum disorder. These non-seizure characteristics are severely detrimental to overall quality of life. Some of these children, particularly those with epilepsies classified as Landau-Kleffner Syndrome or continuous spike and wave during sleep, have infrequent seizure activity but frequent focal epileptiform activity. This frequent epileptiform activity is thought to be detrimental to cognitive development; however, it is also possible that these IIS events initiate pathophysiological pathways in the developing brain that may be independently associated with cognitive deficits. These hypotheses are difficult to address due to the previous lack of an appropriate animal model. To this end, we have recently developed a rat model to test the role of frequent focal epileptiform activity in the prefrontal cortex. Using microinjections of a GABA(A antagonist (bicuculline methiodine delivered multiple times per day from postnatal day (p 21 to p25, we showed that rat pups experiencing frequent, focal, recurrent epileptiform activity in the form of interictal spikes during neurodevelopment have significant long-term deficits in attention and sociability that persist into adulthood. To determine if treatment with ACTH, a drug widely used to treat early-life seizures, altered outcome we administered ACTH once per day subcutaneously during the time of the induced interictal spike activity. We show a modest amelioration of the attention deficit seen in animals with a history of early life interictal spikes with ACTH, in the absence of alteration of interictal spike activity. These results suggest that pharmacological intervention that is not targeted to the interictal spike activity is worthy of future study as it may be beneficial for preventing or ameliorating adverse

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

    Science.gov (United States)

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

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

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

    Science.gov (United States)

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

    2014-04-01

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

  17. A propane price spike nails users

    International Nuclear Information System (INIS)

    Milke, M.

    1997-01-01

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

  18. 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.

  19. 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.

  20. Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

    Directory of Open Access Journals (Sweden)

    Benjamin eDummer

    2014-09-01

    Full Text Available A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, J. Comp. Neurosci. 2000 and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide excellent approximations to the autocorrelation of spike trains in the recurrent network.

  1. 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...

  2. 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

  3. 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

  4. 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.

  5. 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...

  6. 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

  7. Cytoplasmic tail of coronavirus spike protein has intracellular

    Indian Academy of Sciences (India)

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

  8. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Directory of Open Access Journals (Sweden)

    Claudia Casellato

    Full Text Available The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning, a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  9. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Science.gov (United States)

    Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio

    2014-01-01

    The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  10. Stimulus-dependent spiking relationships with the EEG

    Science.gov (United States)

    Snyder, Adam C.

    2015-01-01

    The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

  11. 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

  12. 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.

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

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

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

  14. Computer programs for locating and fitting full energie peak in γ-ray spectra. Test and rules for an estimation of the main results

    International Nuclear Information System (INIS)

    1980-12-01

    After the different interlaboratory tests on gamma spectrum analysis organised by the 'Laboratoire de Metrologie des Rayonnements Ionisants' and by the International Atomic Energy Agency, it looked useful to manage a same type of intercomparison with the different supplies of Data acquisition and Analysis systems including mini-ordinator or microprocessor. Four spectrum have been chosen between those of the interlaboratory tests. The test dealt with the investigation of total absorption peaks of different levels in a complex spectrum and the calculation of their main parameters. Four supplies participed in the intercomparison with their own logicial. The result allow to suggest a few tests in order to try a new logicial, or to compare results with standards [fr

  15. Evolving spiking neural networks: a novel growth algorithm exhibits unintelligent design

    Science.gov (United States)

    Schaffer, J. David

    2015-06-01

    Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of "unintelligent design"; extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.

  16. Application of Savitzky-Golay differentiation filters and Fourier functions to simultaneous determination of cefepime and the co-administered drug, levofloxacin, in spiked human plasma

    Science.gov (United States)

    Abdel-Aziz, Omar; Abdel-Ghany, Maha F.; Nagi, Reham; Abdel-Fattah, Laila

    2015-03-01

    The present work is concerned with simultaneous determination of cefepime (CEF) and the co-administered drug, levofloxacin (LEV), in spiked human plasma by applying a new approach, Savitzky-Golay differentiation filters, and combined trigonometric Fourier functions to their ratio spectra. The different parameters associated with the calculation of Savitzky-Golay and Fourier coefficients were optimized. The proposed methods were validated and applied for determination of the two drugs in laboratory prepared mixtures and spiked human plasma. The results were statistically compared with reported HPLC methods and were found accurate and precise.

  17. Application of Savitzky-Golay differentiation filters and Fourier functions to simultaneous determination of cefepime and the co-administered drug, levofloxacin, in spiked human plasma.

    Science.gov (United States)

    Abdel-Aziz, Omar; Abdel-Ghany, Maha F; Nagi, Reham; Abdel-Fattah, Laila

    2015-03-15

    The present work is concerned with simultaneous determination of cefepime (CEF) and the co-administered drug, levofloxacin (LEV), in spiked human plasma by applying a new approach, Savitzky-Golay differentiation filters, and combined trigonometric Fourier functions to their ratio spectra. The different parameters associated with the calculation of Savitzky-Golay and Fourier coefficients were optimized. The proposed methods were validated and applied for determination of the two drugs in laboratory prepared mixtures and spiked human plasma. The results were statistically compared with reported HPLC methods and were found accurate and precise. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. 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.

  19. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    Directory of Open Access Journals (Sweden)

    Omri Harish

    2015-07-01

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

  20. Memory recall and spike-frequency adaptation

    Science.gov (United States)

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

    2016-05-01

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

  1. Phase diagram of spiking neural networks.

    Science.gov (United States)

    Seyed-Allaei, Hamed

    2015-01-01

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

  2. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

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

  3. Solar Energetic Particle Spectra

    Science.gov (United States)

    Ryan, J. M.; Boezio, M.; Bravar, U.; Bruno, A.; Christian, E. R.; de Nolfo, G. A.; Martucci, M.; Mergè, M.; Munini, R.; Ricci, M.; Sparvoli, R.; Stochaj, S.

    2017-12-01

    We report updated event-integrated spectra from several SEP events measured with PAMELA. The measurements were made from 2006 to 2014 in the energy range starting at 80 MeV and extending well above the neutron monitor threshold. The PAMELA instrument is in a high inclination, low Earth orbit and has access to SEPs when at high latitudes. Spectra have been assembled from these high-latitude measurements. The field of view of PAMELA is small and during the high-latitude passes it scans a wide range of asymptotic directions as the spacecraft orbits. Correcting for data gaps, solid angle effects and improved background corrections, we have compiled event-integrated intensity spectra for twenty-eight SEP events. Where statistics permit, the spectra exhibit power law shapes in energy with a high-energy exponential roll over. The events analyzed include two genuine ground level enhancements (GLE). In those cases the roll-over energy lies above the neutron monitor threshold ( 1 GV) while the others are lower. We see no qualitative difference between the spectra of GLE vs. non-GLE events, i.e., all roll over in an exponential fashion with rapidly decreasing intensity at high energies.

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. Observational and theoretical spectra of supernovae

    Science.gov (United States)

    Wheeler, J. Craig; Swartz, Douglas A.; Harkness, Robert P.

    1993-05-01

    Progress in nuclear astrophysics by means of quantitative supernova spectroscopy is discussed with special concentration on type Ia, Ib and Ic and on SN 1987A. Spectral calculations continue to support an exploding C/O white dwarf as the best model of a SN Ia. Deflagration model W7 produces good maximum light spectra of SN Ia and seems to have a better composition distribution compared to delayed detonation models, but proper treatment of opacity remains a problem and the physical basis of SN Ia explosions is still not completely understood. All models for SN Ia predict large quantities of 56Co in the ejecta, but it is not clear that observations confirm this. Although the evolutionary origin of SN Ia remains uncertain, there is recent evidence that transfer of hydrogen in a binary system may be involved, as long suspected. There has been progress in comparing dynamical models with the optical/IR spectra of SN 1987A. The evolution of the [OI] λλ6300, 6364 feature and the presence of strong persistent HeI λ10 830 indicate that both the envelope and core material contribute substantially to the formation of emission lines in the nebular phase and that neither the core nor the envelope can be neglected. Blending with nearby hydrogen lines may affect both of these spectral features, thereby complicating the analysis of the lines. The effects of continuum transfer and photoionization have been included and are under study. The discrepancies between theoretical and observed spectra are due primarily to the one-dimensional hydrodynamic models. The spectral data are not consistent with the high density ``spike'' (in radial coordinate) of the core material that is predicted by all such models. Analysis of the light curves of SN Ib and SN Ic supernovae implies that there are significant differences in their physical properties. Some SN Ib have considerably more ejecta mass than SN Ic events. SN Ib require He-rich atmospheres to produce the observed strong optical lines of

  10. Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2016-05-01

    Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the

  11. Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling

    International Nuclear Information System (INIS)

    Janczura, Joanna; Trück, Stefan; Weron, Rafał; Wolff, Rodney C.

    2013-01-01

    An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification. - Highlights: • First comprehensive study on the impact of spikes on seasonal pattern estimation • The effects of different treatments of spikes on model estimation are examined. • Cleaning spot prices for outliers yields superior estimates of the seasonal pattern. • Removing outliers provides better parameter estimates for the stochastic process. • Rankings of filtering techniques suggested in the literature are provided

  12. ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

    Science.gov (United States)

    Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

    2016-01-01

    With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

  13. Transgenerational isotopic marking of carp Cyprinus carpio, L. using a 86Sr /84Sr double spike

    Science.gov (United States)

    Zitek, Andreas; Cervicek, Magdalena; Irrgeher, Johanna; Horsky, Monika; Kletzl, Manfred; Weismann, Thomas; Prohaska, Thomas

    2013-04-01

    Transgenerational isotopic marking has been recognized recently as an effective tool for mass marking and tracking of individual fish to their original source. Compared to other conventional marking techniques, transgenerational marking offers several advantages. Most importantly, it is possible to mark all offspring of one individual female without the necessity of handling eggs or larval fish. Furthermore it is possible to vary the concentrations of individual isotopes to obtain specific marks for individual female fish. An enriched isotopic spike solution is usually applied to gravid female spawners by injection into the body cavity for transgenerational marking. The isotope is then incorporated into the central otolith region of the offspring which is known to be built up by maternally derived material. Within this study transgenerational marking of a typical cyprinid fish species, Cyprinus carpio, L., was tested using a 86Sr /84Sr double spike. Buffered solutions with different isotopic composition and concentrations were administered to 4 female individuals by intraperitoneal injection 5 days before spawning, while one female was injected a blank solution. After spawning, otoliths (Lapilli) from juvenile fish were sampled at the age of about 5 months at fish sizes between 3 and 4 cm and analyzed for their isotopic composition by LA-ICPMS applying cross sectional line scans. Central otolith regions of the progeny showed a shift in the natural isotope ratios for the administered isotopes. Deconvolution of the blank corrected measurement data of the Sr isotopes was done to trace back the original spike ratio. The different spike ratios could be well distinguished reflecting the original composition of the spike solution. This study proved that it is possible to create batch-specific unique transgenerational marks in otolith cores by varying the concentrations of two naturally occurring Sr isotopes. This method has high potential to reduce the marking effort for

  14. 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 ...

  15. ACCELERATED FITTING OF STELLAR SPECTRA

    Energy Technology Data Exchange (ETDEWEB)

    Ting, Yuan-Sen; Conroy, Charlie [Harvard–Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Rix, Hans-Walter [Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg (Germany)

    2016-07-20

    Stellar spectra are often modeled and fitted by interpolating within a rectilinear grid of synthetic spectra to derive the stars’ labels: stellar parameters and elemental abundances. However, the number of synthetic spectra needed for a rectilinear grid grows exponentially with the label space dimensions, precluding the simultaneous and self-consistent fitting of more than a few elemental abundances. Shortcuts such as fitting subsets of labels separately can introduce unknown systematics and do not produce correct error covariances in the derived labels. In this paper we present a new approach—Convex Hull Adaptive Tessellation (chat)—which includes several new ideas for inexpensively generating a sufficient stellar synthetic library, using linear algebra and the concept of an adaptive, data-driven grid. A convex hull approximates the region where the data lie in the label space. A variety of tests with mock data sets demonstrate that chat can reduce the number of required synthetic model calculations by three orders of magnitude in an eight-dimensional label space. The reduction will be even larger for higher dimensional label spaces. In chat the computational effort increases only linearly with the number of labels that are fit simultaneously. Around each of these grid points in the label space an approximate synthetic spectrum can be generated through linear expansion using a set of “gradient spectra” that represent flux derivatives at every wavelength point with respect to all labels. These techniques provide new opportunities to fit the full stellar spectra from large surveys with 15–30 labels simultaneously.

  16. Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.

    Science.gov (United States)

    Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa; Grün, Sonja

    2016-08-10

    The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons ("cell assemblies"). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published

  17. Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.)

    International Nuclear Information System (INIS)

    Dunagan, Sarah C.; Gilmore, Martha S.; Varekamp, Johan C.

    2007-01-01

    Mustard spinach plants were grown in mercury-spiked and contaminated soils collected in the field under controlled laboratory conditions over a full growth cycle to test if vegetation grown in these soils has discernible characteristics in visible/near-infrared (VNIR) spectra. Foliar Hg concentrations (0.174-3.993 ppm) of the Mustard spinach plants were positively correlated with Hg concentration of soils and varied throughout the growing season. Equations relating foliar Hg concentration to spectral reflectance, its first derivative, and selected vegetation indices were generated using stepwise multiple linear regression. Significant correlations are found for limited wavelengths for specific treatments and dates. Ratio Vegetation Index (RVI) and Red Edge Position (REP) values of plants in Hg-spiked and field-contaminated soils are significantly lower relative to control plants during the early and middle portions of the growth cycle which may be related to lower chlorophyll abundance or functioning in Hg-contaminated plants. - Some spectral characteristics of leaves of Brassica rapa P. may be associated with foliar mercury content

  18. Analysis and solution of spike current of intermediate range for nuclear instrumentation system

    International Nuclear Information System (INIS)

    Li Xingqiang; Xiao Yu; Xue Bin; Wang Yinli

    2015-01-01

    During the initial start-up of HYH NPP unit 1, spike currents occurred in intermediate range channel of RPN system and reactor trip was triggered. After analyzing the operation principle of intermediate range channel and site inspection, and doing simulating test, the root cause was fixed on the bug of range switching of intermediate range channel. Then a solution based on parameters optimizing was made and executed on site. (authors)

  19. Integrated workflows for spiking neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Ján eAntolík

    2013-12-01

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

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

    Science.gov (United States)

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

    2017-08-01

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

  1. Parameterization of rotational spectra

    International Nuclear Information System (INIS)

    Zhou Chunmei; Liu Tong

    1992-01-01

    The rotational spectra of the strongly deformed nuclei with low rotational frequencies and weak band mixture are analyzed. The strongly deformed nuclei are commonly encountered in the rare-earth region (e. g., 150 220). A lot of rotational band knowledge are presented

  2. Atomic Spectra Database (ASD)

    Science.gov (United States)

    SRD 78 NIST Atomic Spectra Database (ASD) (Web, free access)   This database provides access and search capability for NIST critically evaluated data on atomic energy levels, wavelengths, and transition probabilities that are reasonably up-to-date. The NIST Atomic Spectroscopy Data Center has carried out these critical compilations.

  3. Uranium spectra in the ICP

    Energy Technology Data Exchange (ETDEWEB)

    Ghazi, A.A.; Qamar, S.; Atta, M.A. (Khan (A.Q.) Research Labs., Rawalpindi (Pakistan))

    1994-05-01

    Uranium spectra have been studied by inductively coupled plasma atomic emission spectroscopy (ICP-AES). In total, 8361 uranium lines were observed in the wavelength range of 235-500 nm. This article is an electronic publication in Spectrochimica Acta Electronica (SAE), the electronic section of Spectrochimica Acta Part B (SAB). The hard copy text is accompanied by a disk with data files and test files for an IBM-compatible computer. The main article discusses the scientific aspects of the subject and explains the purpose of the data files. (Author).

  4. Uranium spectra in the ICP

    International Nuclear Information System (INIS)

    Ghazi, A.A.; Qamar, S.; Atta, M.A.

    1994-01-01

    Uranium spectra have been studied by inductively coupled plasma atomic emission spectroscopy (ICP-AES). In total, 8361 uranium lines were observed in the wavelength range of 235-500 nm. This article is an electronic publication in Spectrochimica Acta Electronica (SAE), the electronic section of Spectrochimica Acta Part B (SAB). The hard copy text is accompanied by a disk with data files and test files for an IBM-compatible computer. The main article discusses the scientific aspects of the subject and explains the purpose of the data files. (Author)

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

    Science.gov (United States)

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

    2010-03-09

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

  6. 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.

  7. 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...

  8. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

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

    2015-01-01

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

  9. 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.

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Gordon ePipa

    2011-06-01

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

  12. 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

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

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

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-06

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

  15. 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.

  16. 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)

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. X-ray absorption spectra and emission spectra of plasmas

    International Nuclear Information System (INIS)

    Peng Yonglun; Yang Li; Wang Minsheng; Li Jiaming

    2002-01-01

    The author reports a theoretical method to calculate the resolved absorption spectra and emission spectra (optically thin) of hot dense plasmas. Due to its fully relativistic treatment incorporated with the quantum defect theory, it calculates the absorption spectra and emission spectra for single element or multi-element plasmas with little computational efforts. The calculated absorption spectra of LTE gold plasmas agree well with the experimental ones. It also calculates the optical thin emission spectra of LTE gold plasmas, which is helpful to diagnose the plasmas of relevant ICF plasmas. It can also provide the relevant parameters such as population density of various ionic stages, precise radiative properties for ICF studies

  3. NE-213-scintillator-based neutron detection system for diagnostic measurements of energy spectra for neutrons having energies greater than or equal to 0.8 MeV created during plasma operations at the Princeton Tokamak Fusion Test Reactor

    International Nuclear Information System (INIS)

    Dickens, J.K.; Hill, N.W.; Hou, F.S.; McConnell, J.W.; Spencer, R.R.; Tsang, F.Y.

    1985-08-01

    A system for making diagnostic measurements of the energy spectra of greater than or equal to 0.8-MeV neutrons produced during plasma operations of the Princeton Tokamak Fusion Test Reactor (TFTR) has been fabricated and tested and is presently in operation in the TFTR Test Cell Basement. The system consists of two separate detectors, each made up of cells containing liquid NE-213 scintillator attached permanently to RCA-8850 photomultiplier tubes. Pulses obtained from each photomultiplier system are amplified and electronically analyzed to identify and separate those pulses due to neutron-induced events in the detector from those due to photon-induced events in the detector. Signals from each detector are routed to two separate Analog-to-Digital Converters, and the resulting digitized information, representing: (1) the raw neutron-spectrum data; and (2) the raw photon-spectrum data, are transmited to the CICADA data-acquisition computer system of the TFTR. Software programs have been installed on the CICADA system to analyze the raw data to provide moderate-resolution recreations of the energy spectrum of the neutron and photon fluences incident on the detector during the operation of the TFTR. A complete description of, as well as the operation of, the hardware and software is given in this report

  4. Spike-Timing of Orbitofrontal Neurons Is Synchronized With Breathing.

    Science.gov (United States)

    Kőszeghy, Áron; Lasztóczi, Bálint; Forro, Thomas; Klausberger, Thomas

    2018-01-01

    The orbitofrontal cortex (OFC) has been implicated in a multiplicity of complex brain functions, including representations of expected outcome properties, post-decision confidence, momentary food-reward values, complex flavors and odors. As breathing rhythm has an influence on odor processing at primary olfactory areas, we tested the hypothesis that it may also influence neuronal activity in the OFC, a prefrontal area involved also in higher order processing of odors. We recorded spike timing of orbitofrontal neurons as well as local field potentials (LFPs) in awake, head-fixed mice, together with the breathing rhythm. We observed that a large majority of orbitofrontal neurons showed robust phase-coupling to breathing during immobility and running. The phase coupling of action potentials to breathing was significantly stronger in orbitofrontal neurons compared to cells in the medial prefrontal cortex. The characteristic synchronization of orbitofrontal neurons with breathing might provide a temporal framework for multi-variable processing of olfactory, gustatory and reward-value relationships.

  5. Spike-Timing of Orbitofrontal Neurons Is Synchronized With Breathing

    Directory of Open Access Journals (Sweden)

    Áron Kőszeghy

    2018-04-01

    Full Text Available The orbitofrontal cortex (OFC has been implicated in a multiplicity of complex brain functions, including representations of expected outcome properties, post-decision confidence, momentary food-reward values, complex flavors and odors. As breathing rhythm has an influence on odor processing at primary olfactory areas, we tested the hypothesis that it may also influence neuronal activity in the OFC, a prefrontal area involved also in higher order processing of odors. We recorded spike timing of orbitofrontal neurons as well as local field potentials (LFPs in awake, head-fixed mice, together with the breathing rhythm. We observed that a large majority of orbitofrontal neurons showed robust phase-coupling to breathing during immobility and running. The phase coupling of action potentials to breathing was significantly stronger in orbitofrontal neurons compared to cells in the medial prefrontal cortex. The characteristic synchronization of orbitofrontal neurons with breathing might provide a temporal framework for multi-variable processing of olfactory, gustatory and reward-value relationships.

  6. 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

  7. 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.

  8. Temporally coordinated spiking activity of human induced pluripotent stem cell-derived neurons co-cultured with astrocytes.

    Science.gov (United States)

    Kayama, Tasuku; Suzuki, Ikuro; Odawara, Aoi; Sasaki, Takuya; Ikegaya, Yuji

    2018-01-01

    In culture conditions, human induced-pluripotent stem cells (hiPSC)-derived neurons form synaptic connections with other cells and establish neuronal networks, which are expected to be an in vitro model system for drug discovery screening and toxicity testing. While early studies demonstrated effects of co-culture of hiPSC-derived neurons with astroglial cells on survival and maturation of hiPSC-derived neurons, the population spiking patterns of such hiPSC-derived neurons have not been fully characterized. In this study, we analyzed temporal spiking patterns of hiPSC-derived neurons recorded by a multi-electrode array system. We discovered that specific sets of hiPSC-derived neurons co-cultured with astrocytes showed more frequent and highly coherent non-random synchronized spike trains and more dynamic changes in overall spike patterns over time. These temporally coordinated spiking patterns are physiological signs of organized circuits of hiPSC-derived neurons and suggest benefits of co-culture of hiPSC-derived neurons with astrocytes. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Lattice vibration spectra. 16

    International Nuclear Information System (INIS)

    Lutz, H.D.; Willich, P.

    1977-01-01

    The FIR absorption spectra of pyrite type compounds RuS 2 , RuSsub(2-x)Sesub(x), RuSe 2 , RuTe 2 , OsS 2 , OsSe 2 , and PtP 2 as well as loellingite type phosphides FeP 2 , RuP 2 , and OsP 2 are reported. For RuS 2 , RuSe 2 , RuTe 2 , OsS 2 , and PtP 2 all of the five infrared allowed modes (k = 0) are observed. As a first result of a numerical normal coordinate treatment vibration forms of pyrite structure are communicated. The spectra show that lattice forces of corresponding sulfides, tellurides, and phosphides are about the same strength, but increase strongly by substitution of iron by ruthenium and especially of ruthenium by osmium. The lattice constants of the RuSsub(2-x)Sesub(x) solid solution obey Vegard's rule. (author)

  10. Spectra, Winter 2014

    Science.gov (United States)

    2014-01-01

    additional copies or more information, please email spectra@nrl.navy.mil. LEADINGEDGE 1 Contents 30 Navy Launches UAV from Submerged Submarine 31... multitasking have become mainstream concerns. For example, the New York Times in 2005 and Time magazine in 2006 both reported stories about...interruptions and multitasking , and how they affect performance by increasing human er- ror. In 2005, the information technol- ogy research firm Basex

  11. Thermoluminescence spectra of amethyst

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Q. [Suzhou Railway Teachers College (China). Dept. of Physics; Yang, B. [Beijing Normal University (China). Dept. of Physics; Wood, R.A.; White, D.R.R.; Townsend, P.D.; Luff, B.J. [Sussex Univ., Brighton (United Kingdom). School of Mathematical and Physical Sciences

    1994-04-01

    Thermoluminescence and cathodoluminescence data from natural and synthetic amethyst and synthetic quartz samples are compared. The spectra include features from the quartz host lattice and from impurity-generated recombination sites. Emission features exist throughout the wavelength range studied, 250-800 nm. The near infrared emission at 740-750 nm appears to be characteristic of the amethyst and is proposed to be due to Fe ion impurity. (Author).

  12. Auger spectra of alkanes

    International Nuclear Information System (INIS)

    Rye, R.R.; Jennison, D.R.; Houston, J.E.

    1980-01-01

    The gas-phase Auger line shapes of the linear alkanes C 1 through C 6 and of neopentane are presented and analyzed. The general shape of the spectra are characteristic of carbon in a tetrahedral environment with the major feature in all cases occurring at approx.249 eV. The relatively large spectral changes found between methane and ethane results from the direct interaction of the terminal methyl groups in ethane, and the spectra of the higher alkanes are shown to be a composite of contributions from terminal methyl and interior methylene group carbon atoms. Theoretical analysis based on a one-electron approximation is shown to be capable of making a molecular orbital assignment by comparing calculated vertical transitions to features in the Auger spectra of ethane and propane, and, in the case of ethane, of differentiating between the 2 E/sub g/ and 2 A/sub 1g/ assignment of the ground state of (C 2 H 6 ) + . A one-electron based molecular orbital treatment, however, is shown to partially break down in propane and neopentane. Analysis of neopentane and the observed absence of any noticeable major peak energy shift with increasing molecular size (as predicted by the one-electron treatment) suggests that some Auger final states occur in which both valence holes are localized on the same subunit of the molecule

  13. Pattern recognition in spectra

    International Nuclear Information System (INIS)

    Gebran, M; Paletou, F

    2017-01-01

    We present a new automated procedure that simultaneously derives the effective temperature T eff , surface gravity log g , metallicity [ Fe/H ], and equatorial projected rotational velocity v e sin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones. (paper)

  14. Removal of polycyclic aromatic hydrocarbons in soil spiked with model mixtures of petroleum hydrocarbons and heterocycles using biosurfactants from Rhodococcus ruber IEGM 231.

    Science.gov (United States)

    Ivshina, Irina; Kostina, Ludmila; Krivoruchko, Anastasiya; Kuyukina, Maria; Peshkur, Tatyana; Anderson, Peter; Cunningham, Colin

    2016-07-15

    Removal of polycyclic aromatic hydrocarbons (PAHs) in soil using biosurfactants (BS) produced by Rhodococcus ruber IEGM 231 was studied in soil columns spiked with model mixtures of major petroleum constituents. A crystalline mixture of single PAHs (0.63g/kg), a crystalline mixture of PAHs (0.63g/kg) and polycyclic aromatic sulfur heterocycles (PASHs), and an artificially synthesized non-aqueous phase liquid (NAPL) containing PAHs (3.00g/kg) dissolved in alkanes C10-C19 were used for spiking. Percentage of PAH removal with BS varied from 16 to 69%. Washing activities of BS were 2.5 times greater than those of synthetic surfactant Tween 60 in NAPL-spiked soil and similar to Tween 60 in crystalline-spiked soil. At the same time, amounts of removed PAHs were equal and consisted of 0.3-0.5g/kg dry soil regardless the chemical pattern of a model mixture of petroleum hydrocarbons and heterocycles used for spiking. UV spectra for soil before and after BS treatment were obtained and their applicability for differentiated analysis of PAH and PASH concentration changes in remediated soil was shown. The ratios A254nm/A288nm revealed that BS increased biotreatability of PAH-contaminated soils. Copyright © 2016 Elsevier B.V. All rights reserved.

  15. 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

  16. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

    Science.gov (United States)

    Lefebvre, Baptiste; Deny, Stéphane; Gardella, Christophe; Stimberg, Marcel; Jetter, Florian; Zeck, Guenther; Picaud, Serge; Duebel, Jens

    2018-01-01

    In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain ‘ground truth’ data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes. PMID:29557782

  17. Epileptic Negative Myoclonus as the First and Only Symptom in a Challenging Diagnosis of Benign Epilepsy With Centrotemporal Spikes

    Directory of Open Access Journals (Sweden)

    Jing Chen MD

    2017-07-01

    Full Text Available Objective: To investigate the clinical and neurophysiological characteristics of epileptic negative myoclonus as the first and only ictal symptom of benign epilepsy with centrotemporal spikes. Methods: Electrophysiological evaluations included polygraphic recordings with simultaneous video electroencephalogram monitoring and tests performed with patient’s upper limb outstretched in standing posture. Epileptic negative myoclonus manifestations, electrophysiological features, and responses to antiepileptic drugs were analyzed. Results: The authors report 2 patients with benign epilepsy with centrotemporal spikes, who had epileptic negative myoclonus as the first and only seizure type. Video electroencephalogram monitoring results showed that their negative myoclonus seizures were emanating from the contralateral central and the parietal regions. Epileptic negative myoclonus was controlled by administration of valproate and levetiracetam. Conclusion: Epileptic negative myoclonus can be the first and only seizure type of benign epilepsy with centrotemporal spikes, and long-term follow-up monitoring should be the care for the recurrence and/or presence of other types of seizures.

  18. 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.

  19. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

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

  20. 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 ...

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. Deconvolution of Positrons' Lifetime spectra

    International Nuclear Information System (INIS)

    Calderin Hidalgo, L.; Ortega Villafuerte, Y.

    1996-01-01

    In this paper, we explain the iterative method previously develop for the deconvolution of Doppler broadening spectra using the mathematical optimization theory. Also, we start the adaptation and application of this method to the deconvolution of positrons' lifetime annihilation spectra

  9. Vibrational spectra of aminoacetonitrile

    International Nuclear Information System (INIS)

    Bak, B.; Hansen, E.L.; Nicolaisen, F.M.; Nielsen, O.F.

    1975-01-01

    The preparation of pure, stable aminoacetonitrile(1-amino, 1'-cyanomethane)CH 2 NH 2 CN (1) is described. The Raman spectrum, now complete, and a novel infrared spectrum extending over the 50-3600 cm -1 region are reported. A tentative normal vibration analysis is presented and supported by Raman and infrared data from the spectra of CH 2 NHDCN (2) and CH 2 ND 2 CN (3). The predominance of the trans rotamer may be attributed to intramolecular hydrogen bonding but this is too unimportant to influence the vibrational frequencies of gaseous 1, 2, and 3. However, large gas/liquid frequency shifts occur. (author)

  10. Signal post-processing for acoustic velocimeters: detecting and replacing spikes

    International Nuclear Information System (INIS)

    Razaz, Mahdi; Kawanisi, Kiyosi

    2011-01-01

    Time series recorded by acoustic velocimeters are often affected by a combination of factors, including turbulent velocity fluctuations, Doppler noise and signal aliasing. Although it is not possible to find a comprehensive threshold for identifying spurious data, the present work attempts to describe an effective technique for detecting spikes. This technique is based on transforming data into wavelet space and thresholding the wavelet basis by a consistent threshold. The universal threshold modified by a robust scale estimator such as Q n is proven to work extremely well. The suggested methods for replacing identified spikes combine times series analyses (linear time series modelling or a Kalman predictor) with a straightforward method, polynomial interpolation, to generate substitutions retaining both the trends and the fluctuations in the surrounding clean data. Then, tests were performed to reveal the influence of replacing methods on the total number of detected spikes, required iterations and physical properties of the restored signal. From the overall results, it is inferred that using the wavelet-Q n as the detecting module and integrating it with linear time series modelling/Kalman filtering as the replacement module constitutes an effective despiking algorithm. This methodology is capable of restoring the contaminated signal in such a way that its statistical and physical properties correlate well with those of the original record

  11. The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

    Science.gov (United States)

    Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

    2011-06-01

    The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

  12. Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

    Directory of Open Access Journals (Sweden)

    Javier A Caballero

    Full Text Available Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices. Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity. These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.

  13. Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

    Science.gov (United States)

    Caballero, Javier A; Lepora, Nathan F; Gurney, Kevin N

    2015-01-01

    Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.

  14. 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.

  15. 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...

  16. 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.

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

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  18. 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.

  19. Energetic Proton Spectra Measured by the Van Allen Probes

    Science.gov (United States)

    Summers, Danny; Shi, Run; Engebretson, Mark J.; Oksavik, Kjellmar; Manweiler, Jerry W.; Mitchell, Donald G.

    2017-10-01

    We test the hypothesis that pitch angle scattering by electromagnetic ion cyclotron (EMIC) waves can limit ring current proton fluxes. For two chosen magnetic storms, during 17-20 March 2013 and 17-20 March 2015, we measure proton energy spectra in the region 3 ≤ L ≤ 6 using the RBSPICE-B instrument on the Van Allen Probes. The most intense proton spectra are observed to occur during the recovery periods of the respective storms. Using proton precipitation data from the POES (NOAA and MetOp) spacecraft, we deduce that EMIC wave action was prevalent at the times and L-shell locations of the most intense proton spectra. We calculate limiting ring current proton energy spectra from recently developed theory. Comparisons between the observed proton energy spectra and the theoretical limiting spectra show reasonable agreement. We conclude that the measurements of the most intense proton spectra are consistent with self-limiting by EMIC wave scattering.

  20. 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.

  1. Artificial intelligence analysis of paraspinal power spectra.

    Science.gov (United States)

    Oliver, C W; Atsma, W J

    1996-10-01

    OBJECTIVE: As an aid to discrimination of sufferers with back pain an artificial intelligence neural network was constructed to differentiate paraspinal power spectra. DESIGN: Clinical investigation using surface electromyography. METHOD: The surface electromyogram power spectra from 60 subjects, 33 non-back-pain sufferers and 27 chronic back pain sufferers were used to construct a back propagation neural network that was then tested. Subjects were placed on a test frame in 30 degrees of lumbar forward flexion. An isometric load of two-thirds maximum voluntary contraction was held constant for 30 s whilst surface electromyograms were recorded at the level of the L(4-5). Paraspinal power spectra were calculated and loaded into the input layer of a three-layer back propagation network. The neural network classified the spectra into normal or back pain type. RESULTS: The back propagation neural was shown to have satisfactory convergence with a specificity of 79% and a sensitivity of 80%. CONCLUSIONS: Artificial intelligence neural networks appear to be a useful method of differentiating paraspinal power spectra in back-pain sufferers.

  2. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

    Directory of Open Access Journals (Sweden)

    Evangelos Stromatias

    2017-06-01

    Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  3. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

    Science.gov (United States)

    Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2017-01-01

    This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  4. 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

  5. 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...

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

    International Nuclear Information System (INIS)

    Chang Wonil; Jin, Dezhe Z.

    2009-01-01

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

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

    International Nuclear Information System (INIS)

    Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

    2006-01-01

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

  8. Fractal analysis of power spectra

    International Nuclear Information System (INIS)

    Johnston, S.

    1982-01-01

    A general argument is presented concerning the Hausdorff dimension D of the power spectrum curve for a system of N weakly-coupled oscillators. Explicit upper and lower bounds for D are derived in terms of the number N of interacting modes. The mathematical reasoning relies upon the celebrated KAM theorem concerning the perturbation of Hamiltonian systems and the finite measure of the set of destroyed tori in phase space; this set can be related to Hausdorff dimension by certain mathematical theorems. An important consequence of these results is a simple empirical test for the applicability of Hamiltonian perturbation theory in the analysis of an experimentally observed spectrum. As an illustration, the theory is applied to the interpretation of a recent numerical analysis of both the power spectrum of the Sun and certain laboratory spectra of hydrodynamic turbulence. (Auth.)

  9. Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4

    Science.gov (United States)

    El Yazidi, Abdelhadi; Ramonet, Michel; Ciais, Philippe; Broquet, Gregoire; Pison, Isabelle; Abbaris, Amara; Brunner, Dominik; Conil, Sebastien; Delmotte, Marc; Gheusi, Francois; Guerin, Frederic; Hazan, Lynn; Kachroudi, Nesrine; Kouvarakis, Giorgos; Mihalopoulos, Nikolaos; Rivier, Leonard; Serça, Dominique

    2018-03-01

    This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS) and standard deviation of the background (SD) to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System) Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE), a high-mountain observatory in the south-west of France (PDM), a regional marine background site in Crete (FKL) and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the

  10. Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4

    Directory of Open Access Journals (Sweden)

    A. El Yazidi

    2018-03-01

    Full Text Available This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV, robust extraction of baseline signal (REBS and standard deviation of the background (SD to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the European ICOS (Integrated Carbon Observation System Research Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in eastern France (OPE, a high-mountain observatory in the south-west of France (PDM, a regional marine background site in Crete (FKL and a marine clean-air background site in the Southern Hemisphere on Amsterdam Island (AMS. This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes that are well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in

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

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

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

  12. 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.

  13. Catalogue of neutron spectra

    International Nuclear Information System (INIS)

    Buxerolle, M.; Massoutie, M.; Kurdjian, J.

    1987-09-01

    Neutron dosimetry problems have arisen as a result of developments in the applications of nuclear energy. The largest number of possible irradiation situations has been collected: they are presented in the form of a compilation of 44 neutron spectra. Diagrams show the variations of energy fluence and energy fluence weighted by the dose equivalent/fluence conversion factor, with the logarithm of the corresponding energy. The equivalent dose distributions are presented as percentages for the following energy bins: 0.01 eV/0.5 eV/50 keV/1 MeV/5 MeV/15 MeV. The dose equivalent, the mean energy and the effective energy for the dose equivalent for 1 neutron cm -2 are also given [fr

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

    Directory of Open Access Journals (Sweden)

    Pietro Quaglio

    2017-05-01

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

  15. 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.

  16. Cochlear spike synchronization and neuron coincidence detection model

    Science.gov (United States)

    Bader, Rolf

    2018-02-01

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

  17. 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.

  18. 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.

  19. 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.

  20. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

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

    2014-01-01

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

  1. Effect of Rolandic Spikes on ADHD Impulsive Behavior

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2007-01-01

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

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

    NARCIS (Netherlands)

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

    1995-01-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  4. 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.

  5. 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

  6. 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.

  7. Dynamics of directional coupling underlying spike-wave discharges

    NARCIS (Netherlands)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

    Lugiato, L A; Narducci, L M

    1983-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Tim P Vogels

    2013-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Saket Kumar Choudhary

    2016-06-01

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

  11. Seismic spectra of events at regional distances

    International Nuclear Information System (INIS)

    Springer, D.L.; Denny, M.D.

    1976-01-01

    About 40 underground nuclear explosions detonated at the Nevada Test Site (NTS) were chosen for analysis of their spectra and any relationships they might have to source parameters such as yield, depth of burial, etc. The sample covered a large yield range (less than 20 kt to greater than 1 Mt). Broadband (0.05 to 20 Hz) data recorded by the four-station seismic network operated by Lawrence Livermore Laboratory were analyzed in a search for unusual explosion signatures in their spectra. Long time windows (total wave train) as well as shorter windows (for instance, P/sub n/) were used as input to calculate the spectra. Much variation in the spectra of the long windows is typical although some gross features are similar, such as a dominant peak in the microseismic window. The variation is such that selection of corner frequencies is impractical and yield scaling could not be determined. Spectra for one NTS earthquake showed more energy in the short periods (less than 1 sec) as well as in the long periods (greater than 8 sec) compared to those for NTS explosions

  12. The Severe Acute Respiratory Syndrome (SARS-coronavirus 3a protein may function as a modulator of the trafficking properties of the spike protein

    Directory of Open Access Journals (Sweden)

    Tan Yee-Joo

    2005-02-01

    Full Text Available Abstract Background A recent publication reported that a tyrosine-dependent sorting signal, present in cytoplasmic tail of the spike protein of most coronaviruses, mediates the intracellular retention of the spike protein. This motif is missing from the spike protein of the severe acute respiratory syndrome-coronavirus (SARS-CoV, resulting in high level of surface expression of the spike protein when it is expressed on its own in vitro. Presentation of the hypothesis It has been shown that the severe acute respiratory syndrome-coronavirus genome contains open reading frames that encode for proteins with no homologue in other coronaviruses. One of them is the 3a protein, which is expressed during infection in vitro and in vivo. The 3a protein, which contains a tyrosine-dependent sorting signal in its cytoplasmic domain, is expressed on the cell surface and can undergo internalization. In addition, 3a can bind to the spike protein and through this interaction, it may be able to cause the spike protein to become internalized, resulting in a decrease in its surface expression. Testing the hypothesis The effects of 3a on the internalization of cell surface spike protein can be examined biochemically and the significance of the interplay between these two viral proteins during viral infection can be studied using reverse genetics methodology. Implication of the hypothesis If this hypothesis is proven, it will indicate that the severe acute respiratory syndrome-coronavirus modulates the surface expression of the spike protein via a different mechanism from other coronaviruses. The interaction between 3a and S, which are expressed from separate subgenomic RNA, would be important for controlling the trafficking properties of S. The cell surface expression of S in infected cells significantly impacts viral assembly, viral spread and viral pathogenesis. Modulation by this unique pathway could confer certain advantages during the replication of the severe

  13. Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

    Science.gov (United States)

    Goense, J B M; Ratnam, R

    2003-10-01

    An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

  14. A new method of accurate determination of isotopic composition and concentration of strontium in a spike solution used for geochronological works

    International Nuclear Information System (INIS)

    Yanagi, Takeru

    1990-01-01

    A new method of accurate determination of isotopic composition and concentration of a strontium-84 spike solution was devised for simultaneous determination of strontium contents and isotopic compositions in rocks and minerals by measuring strontium isotopic ratios in spiked samples. In this method, the isotopic composition of strontium in the spike were determined so as to minimize the sum of squares of deviations of spike strontium-84 concentrations which were calculated from measured isotopic ratios of strontium in five different mixtures of the spike and the standard solution. The method can eliminate all mass discriminations occurred during the measurements on a surface ionization mass spectrometer. The results were tested by measuring 87 Sr/ 86 Sr ratios of Eimer and Amend SrCO 3 and JB-1 geochemical reference material, and by determining the strontium content in JB-1. The measurements of strontium isotope ratios in spiked samples give average values of 0.708007±0.000052 and 0.70417±0.00004 for 87 Sr/ 86 Sr ratios of E and A SrCO 3 and JB-1, respectively. The strontium content in JB-1 was estimated at 457.1±1.3 ppm. These values are very close to reported respective values. (author)

  15. Design energy spectra for Turkey

    OpenAIRE

    López Almansa, Francisco; Yazgan, Ahmet Utku; Benavent Climent, Amadeo

    2012-01-01

    This work proposes design energy spectra in terms of velocity, derived through linear dynamic analyses on Turkish registers and intended for regions with design peak acceleration 0.3 g or higher. In the long and mid period ranges the analyses are linear, taking profit of the rather insensitivity of the spectra to the structural parameters other than the fundamental period; in the short period range, the spectra are more sensitive to the structural parameters and nonlinear analyses would be re...

  16. Spectra of chemical trees

    International Nuclear Information System (INIS)

    Balasubramanian, K.

    1982-01-01

    A method is developed for obtaining the spectra of trees of NMR and chemical interests. The characteristic polynomials of branched trees can be obtained in terms of the characteristic polynomials of unbranched trees and branches by pruning the tree at the joints. The unbranched trees can also be broken down further until a tree containing just two vertices is obtained. The effectively reduces the order of the secular determinant of the tree used at the beginning to determinants of orders atmost equal to the number of vertices in the branch containing the largest number of vertices. An illustrative example of a NMR graph is given for which the 22 x 22 secular determinant is reduced to determinants of orders atmost 4 x 4 in just the second step of the algorithm. The tree pruning algorithm can be applied even to trees with no symmetry elements and such a factoring can be achieved. Methods developed here can be elegantly used to find if two trees are cospectral and to construct cospectral trees

  17. Sequencing BPS spectra

    Energy Technology Data Exchange (ETDEWEB)

    Gukov, Sergei [Walter Burke Institute for Theoretical Physics, California Institute of Technology,1200 E California Blvd, Pasadena, CA 91125 (United States); Max-Planck-Institut für Mathematik,Vivatsgasse 7, D-53111 Bonn (Germany); Nawata, Satoshi [Walter Burke Institute for Theoretical Physics, California Institute of Technology,1200 E California Blvd, Pasadena, CA 91125 (United States); Centre for Quantum Geometry of Moduli Spaces, University of Aarhus,Nordre Ringgade 1, DK-8000 (Denmark); Saberi, Ingmar [Walter Burke Institute for Theoretical Physics, California Institute of Technology,1200 E California Blvd, Pasadena, CA 91125 (United States); Stošić, Marko [CAMGSD, Departamento de Matemática, Instituto Superior Técnico,Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Mathematical Institute SANU,Knez Mihajlova 36, 11000 Belgrade (Serbia); Sułkowski, Piotr [Walter Burke Institute for Theoretical Physics, California Institute of Technology,1200 E California Blvd, Pasadena, CA 91125 (United States); Faculty of Physics, University of Warsaw,ul. Pasteura 5, 02-093 Warsaw (Poland)

    2016-03-02

    This paper provides both a detailed study of color-dependence of link homologies, as realized in physics as certain spaces of BPS states, and a broad study of the behavior of BPS states in general. We consider how the spectrum of BPS states varies as continuous parameters of a theory are perturbed. This question can be posed in a wide variety of physical contexts, and we answer it by proposing that the relationship between unperturbed and perturbed BPS spectra is described by a spectral sequence. These general considerations unify previous applications of spectral sequence techniques to physics, and explain from a physical standpoint the appearance of many spectral sequences relating various link homology theories to one another. We also study structural properties of colored HOMFLY homology for links and evaluate Poincaré polynomials in numerous examples. Among these structural properties is a novel “sliding” property, which can be explained by using (refined) modular S-matrix. This leads to the identification of modular transformations in Chern-Simons theory and 3d N=2 theory via the 3d/3d correspondence. Lastly, we introduce the notion of associated varieties as classical limits of recursion relations of colored superpolynomials of links, and study their properties.

  18. Sequencing BPS spectra

    International Nuclear Information System (INIS)

    Gukov, Sergei; Nawata, Satoshi; Saberi, Ingmar; Stošić, Marko; Sułkowski, Piotr

    2016-01-01

    This paper provides both a detailed study of color-dependence of link homologies, as realized in physics as certain spaces of BPS states, and a broad study of the behavior of BPS states in general. We consider how the spectrum of BPS states varies as continuous parameters of a theory are perturbed. This question can be posed in a wide variety of physical contexts, and we answer it by proposing that the relationship between unperturbed and perturbed BPS spectra is described by a spectral sequence. These general considerations unify previous applications of spectral sequence techniques to physics, and explain from a physical standpoint the appearance of many spectral sequences relating various link homology theories to one another. We also study structural properties of colored HOMFLY homology for links and evaluate Poincaré polynomials in numerous examples. Among these structural properties is a novel “sliding” property, which can be explained by using (refined) modular S-matrix. This leads to the identification of modular transformations in Chern-Simons theory and 3d N=2 theory via the 3d/3d correspondence. Lastly, we introduce the notion of associated varieties as classical limits of recursion relations of colored superpolynomials of links, and study their properties.

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

    NARCIS (Netherlands)

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

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

  20. Chronic toxicity of nickel-spiked freshwater sediments: variation in toxicity among eight invertebrate taxa and eight sediments

    Science.gov (United States)

    Besser, John M.; Brumbaugh, William G.; Ingersoll, Christopher G.; Ivey, Chris D.; Kunz, James L.; Kemble, Nile E.; Schlekat, Christian E.; Garman, Emily R.

    2013-01-01

    This study evaluated the chronic toxicity of Ni-spiked freshwater sediments to benthic invertebrates. A 2-step spiking procedure (spiking and sediment dilution) and a 2-stage equilibration period (10 wk anaerobic and 1 wk aerobic) were used to spike 8 freshwater sediments with wide ranges of acid-volatile sulfide (AVS; 0.94–38 µmol/g) and total organic carbon (TOC; 0.42–10%). Chronic sediment toxicity tests were conducted with 8 invertebrates (Hyalella azteca, Gammarus pseudolimnaeus, Chironomus riparius, Chironomus dilutus, Hexagenia sp., Lumbriculus variegatus, Tubifex tubifex, and Lampsilis siliquoidea) in 2 spiked sediments. Nickel toxicity thresholds estimated from species-sensitivity distributions were 97 µg/g and 752 µg/g (total recoverable Ni; dry wt basis) for sediments with low and high concentrations of AVS and TOC, respectively. Sensitive species were tested with 6 additional sediments. The 20% effect concentrations (EC20s) for Hyalella and Gammarus, but not Hexagenia, were consistent with US Environmental Protection Agency benchmarks based on Ni in porewater and in simultaneously extracted metals (SEM) normalized to AVS and TOC. For Hexagenia, sediment EC20s increased at less than an equimolar basis with increased AVS, and toxicity occurred in several sediments with Ni concentrations in SEM less than AVS. The authors hypothesize that circulation of oxygenated water by Hexagenia led to oxidation of AVS in burrows, creating microenvironments with high Ni exposure. Despite these unexpected results, a strong relationship between Hexagenia EC20s and AVS could provide a basis for conservative site-specific sediment quality guidelines for Ni.

  1. Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

    Science.gov (United States)

    Zamani, Majid; Demosthenous, Andreas

    2014-07-01

    Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

  2. Dissipation and phytoremediation of polycyclic aromatic hydrocarbons in freshly spiked and long-term field-contaminated soils.

    Science.gov (United States)

    Wei, Ran; Ni, Jinzhi; Li, Xiaoyan; Chen, Weifeng; Yang, Yusheng

    2017-03-01

    Pot experiments were used to compare the dissipation and phytoremediation effect of alfalfa (Medicago sativa L.) for polycyclic aromatic hydrocarbons (PAHs) in a freshly spiked soil and two field-contaminated soils with different soil organic carbon (SOC) contents (Anthrosols, 1.41% SOC; Phaeozems, 8.51% SOC). In spiked soils, the dissipation rates of phenanthrene and pyrene were greater than 99.5 and 94.3%, respectively, in planted treatments and 95.0 and 84.5%, respectively, in unplanted treatments. In field-contaminated Anthrosols, there were limited but significant reductions of 10.2 and 15.4% of total PAHs in unplanted and planted treatments, respectively. In field-contaminated Phaeozems, there were no significant reductions of total PAHs in either unplanted or planted treatments. A phytoremediation effect was observed for the spiked soils and the Anthrosols, but not for the Phaeozems. The results indicated that laboratory tests with spiked soils cannot reflect the real state of field-contaminated soils. Phytoremediation efficiency of PAHs in field-contaminated soils was mainly determined by the content of SOC. Phytoremediation alone has no effect on the removal of PAHs in field-contaminated soils with high SOC content.

  3. 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.

  4. 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.

  5. Methodology for analyzing weak spectra

    International Nuclear Information System (INIS)

    Yankovich, T.L.; Swainson, I.P.

    2000-02-01

    There is considerable interest in quantifying radionuclide transfer between environmental compartments. However, in many cases, it can be a challenge to detect concentrations of gamma-emitting radionuclides due to their low levels in environmental samples. As a result, it is valuable to develop analytical protocols to ensure consistent analysis of the areas under weak peaks. The current study has focused on testing how reproducibly peak areas and baselines can be determined using two analytical approaches. The first approach, which can be carried out using Maestro software, involves extracting net counts under a curve without fitting a functional form to the peak, whereas the second approach, which is used by most other peak fitting programs, determines net counts from spectra by fitting a Gaussian form to the data. It was found that the second approach produces more consistent peak area and baseline measurements, with the ability to de-convolute multiple, overlapping peaks. In addition, programs, such as Peak Fit, which can be used to fit a form to spectral data, often provide goodness of fit analyses, since the Gaussian form can be described using a characteristic equation against which peak data can be tested for their statistical significance. (author)

  6. Different propagation speeds of recalled sequences in plastic spiking neural networks

    Science.gov (United States)

    Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

    2015-03-01

    Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

  7. 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

  8. 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

  9. Monte Carlo simulation of x-ray spectra in mammography

    Energy Technology Data Exchange (ETDEWEB)

    Ng, K.P. [Department of Optometry and Radiography, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China). E-mail: benngkp at netvigator.com; Kwok, C.S.; Ng, K.P.; Tang, F.H. [Department of Optometry and Radiography, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong (China)

    2000-05-01

    A model for generating x-ray spectra in mammography is presented. This model used the ITS version 3 Monte Carlo code for simulating the radiation transport. Various target/filter combinations such as tungsten/aluminium, molybdenum/molybdenum, molybdenum/rhodium and rhodium/rhodium were used in the simulation. Both bremsstrahlung and characteristic x-ray production were included in the model. The simulated x-ray emission spectra were compared with two sets of spectra, those of Boone et al (1997 Med. Phys. 24 1863-74) and IPEM report 78. The {chi}{sup 2} test was used for the overall goodness of fit of the spectral data. There is good agreement between the simulated x-ray spectra and the comparison spectra as the test yielded a probability value of nearly 1. When the transmitted x-ray spectra for specific target/filter combinations were generated and compared with a measured molybdenum/rhodium spectrum and spectra generated in IPEM report 78, close agreement is also observed. This was demonstrated by the probability value for the {chi}{sup 2} test being almost 1 for all the cases. However, minor differences between the simulated spectra and the 'standard' ones are observed. (author)

  10. Monte Carlo simulation of x-ray spectra in mammography

    International Nuclear Information System (INIS)

    Ng, K.P.

    2000-01-01

    A model for generating x-ray spectra in mammography is presented. This model used the ITS version 3 Monte Carlo code for simulating the radiation transport. Various target/filter combinations such as tungsten/aluminium, molybdenum/molybdenum, molybdenum/rhodium and rhodium/rhodium were used in the simulation. Both bremsstrahlung and characteristic x-ray production were included in the model. The simulated x-ray emission spectra were compared with two sets of spectra, those of Boone et al (1997 Med. Phys. 24 1863-74) and IPEM report 78. The χ 2 test was used for the overall goodness of fit of the spectral data. There is good agreement between the simulated x-ray spectra and the comparison spectra as the test yielded a probability value of nearly 1. When the transmitted x-ray spectra for specific target/filter combinations were generated and compared with a measured molybdenum/rhodium spectrum and spectra generated in IPEM report 78, close agreement is also observed. This was demonstrated by the probability value for the χ 2 test being almost 1 for all the cases. However, minor differences between the simulated spectra and the 'standard' ones are observed. (author)

  11. On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

    Science.gov (United States)

    Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

    2017-02-01

    Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a

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

    Science.gov (United States)

    Jalil, Sajiya; Belykh, Igor; Shilnikov, Andrey

    2012-03-01

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

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

    Science.gov (United States)

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

    2011-04-01

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

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

    Science.gov (United States)

    Ročková, Veronika; George, Edward I

    2014-08-01

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

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

    Science.gov (United States)

    Thomas, B L; Papini, M R

    2001-03-01

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

  16. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

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

    2002-01-01

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

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

    Science.gov (United States)

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

    2015-10-01

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

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

    Science.gov (United States)

    Lengler, Johannes; Steger, Angelika

    2017-08-01

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

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

    Directory of Open Access Journals (Sweden)

    E. D. Belousova

    2012-01-01

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

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

    Science.gov (United States)

    Nicola, Wilten; Clopath, Claudia

    2017-12-20

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

  1. The Ripple Pond: Enabling Spiking Networks to See

    Directory of Open Access Journals (Sweden)

    Saeed eAfshar

    2013-11-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  3. A Theory of Material Spike Formation in Flow Separation

    Science.gov (United States)

    Serra, Mattia; Haller, George

    2017-11-01

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

  4. Planning Annuaulised hours when spike in demand exists

    Directory of Open Access Journals (Sweden)

    MR Sureshkumar

    2012-04-01

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

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

    Science.gov (United States)

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

    2014-10-01

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

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

    Science.gov (United States)

    2008-03-01

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

  7. Simulating large-scale spiking neuronal networks with NEST

    OpenAIRE

    Schücker, Jannis; Eppler, Jochen Martin

    2014-01-01

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

  8. 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

  9. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    Stefano eCardanobile

    2011-12-01

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

  10. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

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

  11. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Sheng-Ying Lai

    2013-11-01

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

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

    International Nuclear Information System (INIS)

    Natschlaeger, T.

    1999-01-01

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

  13. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Science.gov (United States)

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

    2013-01-01

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

  14. Spike: Artificial intelligence scheduling for Hubble space telescope

    Science.gov (United States)

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

    1990-01-01

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

  15. 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.

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

    Directory of Open Access Journals (Sweden)

    Moritz eAugustin

    2013-02-01

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

  17. Enhanced polychronisation in a spiking network with metaplasticity

    Directory of Open Access Journals (Sweden)

    Mira eGuise

    2015-02-01

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

  18. Linking structure and activity in nonlinear spiking networks.

    Science.gov (United States)

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

    2017-06-01

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

  19. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Fernando Henrique de Sousa

    2017-03-01

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

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

    International Nuclear Information System (INIS)

    Mory, J.F.

    1992-01-01

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

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

    Science.gov (United States)

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

    2016-10-01

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

  3. VLSI implementation of a bio-inspired olfactory spiking neural network.

    Science.gov (United States)

    Hsieh, Hung-Yi; Tang, Kea-Tiong

    2012-07-01

    This paper presents a low-power, neuromorphic spiking neural network (SNN) chip that can be integrated in an electronic nose system to classify odor. The proposed SNN takes advantage of sub-threshold oscillation and onset-latency representation to reduce power consumption and chip area, providing a more distinct output for each odor input. The synaptic weights between the mitral and cortical cells are modified according to an spike-timing-dependent plasticity learning rule. During the experiment, the odor data are sampled by a commercial electronic nose (Cyranose 320) and are normalized before training and testing to ensure that the classification result is only caused by learning. Measurement results show that the circuit only consumed an average power of approximately 3.6 μW with a 1-V power supply to discriminate odor data. The SNN has either a high or low output response for a given input odor, making it easy to determine whether the circuit has made the correct decision. The measurement result of the SNN chip and some well-known algorithms (support vector machine and the K-nearest neighbor program) is compared to demonstrate the classification performance of the proposed SNN chip.The mean testing accuracy is 87.59% for the data used in this paper.

  4. Fractal characterization of acupuncture-induced spike trains of rat WDR neurons

    International Nuclear Information System (INIS)

    Chen, Yingyuan; Guo, Yi; Wang, Jiang; Hong, Shouhai; Wei, Xile; Yu, Haitao; Deng, Bin

    2015-01-01

    Highlights: •Fractal analysis is a valuable tool for measuring MA-induced neural activities. •In course of the experiments, the spike trains display different fractal properties. •The fractal properties reflect the long-term modulation of MA on WDR neurons. •The results may explain the long-lasting effects induced by acupuncture. -- Abstract: The experimental and the clinical studies have showed manual acupuncture (MA) could evoke multiple responses in various neural regions. Characterising the neuronal activities in these regions may provide more deep insights into acupuncture mechanisms. This paper used fractal analysis to investigate MA-induced spike trains of Wide Dynamic Range (WDR) neurons in rat spinal dorsal horn, an important relay station and integral component in processing acupuncture information. Allan factor and Fano factor were utilized to test whether the spike trains were fractal, and Allan factor were used to evaluate the scaling exponents and Hurst exponents. It was found that these two fractal exponents before and during MA were different significantly. During MA, the scaling exponents of WDR neurons were regulated in a small range, indicating a special fractal pattern. The neuronal activities were long-range correlated over multiple time scales. The scaling exponents during and after MA were similar, suggesting that the long-range correlations not only displayed during MA, but also extended to after withdrawing the needle. Our results showed that fractal analysis is a useful tool for measuring acupuncture effects. MA could modulate neuronal activities of which the fractal properties change as time proceeding. This evolution of fractal dynamics in course of MA experiments may explain at the level of neuron why the effect of MA observed in experiment and in clinic are complex, time-evolutionary, long-range even lasting for some time after stimulation

  5. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

    Science.gov (United States)

    Martens, Marijn B; Houweling, Arthur R; E Tiesinga, Paul H

    2017-02-01

    Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.

  6. Aspects of Quantitation in Mass Spectrometry Imaging Investigated on Cryo-Sections of Spiked Tissue Homogenates.

    Science.gov (United States)

    Hansen, Heidi Toft; Janfelt, Christian

    2016-12-06

    Internal standards have been introduced in quantitative mass spectrometry imaging in order to compensate for differences in intensities throughout an image caused by, for example, difference in ion suppression or analyte extraction efficiency. To test how well the internal standards compensate for differences in tissue types in, for example, whole-body imaging, a set of tissue homogenates of different tissue types (lung, liver, kidney, heart, and brain) from rabbit was spiked to the same concentration with the drug amitriptyline and imaged in the same experiment using isotope labeled amitriptyline as internal standard. The results showed, even after correction with internal standard, significantly lower intensities from brain and to some extent also lung tissue, differences which may be ascribed to binding of the drug to proteins or lipids as known from traditional bioanalysis. The differences, which for these results range approximately within a factor of 3 (but for other compounds in other tissues could be higher), underscore the importance of preparing the standard curve in the same matrix as the unknown sample whenever possible. In, for example, whole-body imaging where a diversity of tissue types are present, this variation across tissue types will therefore add to the overall uncertainty in quantitation. The tissue homogenates were also used in a characterization of various phenomena in quantitative MSI, such as to study how the signal depends of the thickness of the cryo-section, and to assess the accuracy of calibration by droplet deposition. For experiments on liver tissue, calibration by spiked tissue homogenates and droplet deposition was found to provide highly similar results and in both cases linearity with R 2 values of 0.99. In the process, a new method was developed for preparation of standard curves of spiked tissue homogenates, based on the drilling of holes in a block of frozen liver homogenate, providing easy cryo-slicing and good quantitative

  7. Development of the SPIKE code for analysis of the sodium-water reaction

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Sung Tai; Park, Jin Ho; Choi, Jong Hyeun; Kim, Tae Joon [Korea Atomic Energy Research Institute, Taejon (Korea)

    1998-08-01

    In the secondary loop of liquid metal reactors, including SG, water leak into sodium causes the sudden increase of pressure by the H{sub 2} and heat generated from reaction. At few miliseconds after leak, a sharp and short-lived increase of pressure is generated and its propagation depends on the acoustic constraint characteristics of secondary loop. As increasing leak amount of water, another pressure increase is caused by H{sub 2} and its transients depends on the resistance of pressure opening system, such as rupture disc. For prediction of the transients of initial spike pressure, a code of SPIKE was developed. The code was based on the following simplifications and assumptions: combination of total and half release of H{sub 2} rate, spherical shape of H{sub 2} bubble, compressible and Newtonian fluid for sodium. The program was built in FOTRAN language and consisted of 5 modules. Several sample calculations were performed to test the code and to determine the scale down factor of experimental facilities for experimental verification of the code: parameter study of the variables in chemical reaction model, comparison study with results calculated by superposition methods for simple piping structures, comparison study with results calculated by previous researchers, and calculation for KALIMER models of various size. With these calculation results, the generally predicted phenomena of sodium water reaction can be explained and the calculated ones by SPIKE code were well agreed with the previous study. And the scale down factor can be determined. (author). 88 refs., 99 figs., 39 tabs.

  8. Analysis of low-intensity scintillation spectra

    International Nuclear Information System (INIS)

    Muravsky, V.; Tolstov, S.A.

    2002-01-01

    The maximum likelihood algorithms for nuclides activities estimation from low intensity scintillation γ-ray spectra have been created. The algorithms treat full energy peaks and Compton parts of spectra, and they are more effective than least squares estimators. The factors that could lead to the bias of activity estimates are taken into account. Theoretical analysis of the problem of choosing the optimal set of initial spectra for the spectrum model to minimize errors of the activities estimation has been carried out for the general case of the N-components with Gaussian or Poisson statistics. The obtained criterion allows to exclude superfluous initial spectra of nuclides from the model. A special calibration procedure for scintillation γ-spectrometers has been developed. This procedure is required for application of the maximum likelihood activity estimators processing all the channels of the scintillation γ-spectrum, including the Compton part. It allows one to take into account the influence of the sample mass density variation. The algorithm for testing the spectrum model adequacy to the processed scintillation spectrum has been developed. The algorithms are realized in Borland Pascal 7 as a library of procedures and functions. The developed library is compatible with Delphi 1.0 and higher versions. It can be used as the algorithmic basis for analysis of highly sensitive scintillation γ- and β-spectrometric devices. (author)

  9. 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.

  10. 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

  11. Raman spectra of lithium compounds

    Science.gov (United States)

    Gorelik, V. S.; Bi, Dongxue; Voinov, Y. P.; Vodchits, A. I.; Gorshunov, B. P.; Yurasov, N. I.; Yurasova, I. I.

    2017-11-01

    The paper is devoted to the results of investigating the spontaneous Raman scattering spectra in the lithium compounds crystals in a wide spectral range by the fibre-optic spectroscopy method. We also present the stimulated Raman scattering spectra in the lithium hydroxide and lithium deuteride crystals obtained with the use of powerful laser source. The symmetry properties of the lithium hydroxide, lithium hydroxide monohydrate and lithium deuteride crystals optical modes were analyzed by means of the irreducible representations of the point symmetry groups. We have established the selection rules in the Raman and infrared absorption spectra of LiOH, LiOH·H2O and LiD crystals.

  12. FSFE: Fake Spectra Flux Extractor

    Science.gov (United States)

    Bird, Simeon

    2017-10-01

    The fake spectra flux extractor generates simulated quasar absorption spectra from a particle or adaptive mesh-based hydrodynamic simulation. It is implemented as a python module. It can produce both hydrogen and metal line spectra, if the simulation includes metals. The cloudy table for metal ionization fractions is included. Unlike earlier spectral generation codes, it produces absorption from each particle close to the sight-line individually, rather than first producing an average density in each spectral pixel, thus substantially preserving more of the small-scale velocity structure of the gas. The code supports both Gadget (ascl:0003.001) and AREPO.

  13. 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.

  14. 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.

  15. 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

  16. Exposure dose response relationships of the freshwater bivalve Hyridella australis to cadmium spiked sediments

    International Nuclear Information System (INIS)

    Marasinghe Wadige, Chamani P.M.; Maher, William A.; Taylor, Anne M.; Krikowa, Frank

    2014-01-01

    Highlights: • The exposure–dose–response approach was used to assess cadmium exposure and toxicity. • Accumulated cadmium in H. australis reflected the sediment cadmium exposure. • Spill over of cadmium into the biologically active pool was observed. • Increased cadmium resulted in measurable biological effects. • H. australis has the potential to be a cadmium biomonitor in freshwater environments. - Abstract: To understand how benthic biota may respond to the additive or antagonistic effects of metal mixtures in the environment it is first necessary to examine their responses to the individual metals. In this context, laboratory controlled single metal-spiked sediment toxicity tests are useful to assess this. The exposure–dose–response relationships of Hyridella australis to cadmium-spiked sediments were, therefore, investigated in laboratory microcosms. H. australis was exposed to individual cadmium spiked sediments (<0.05 (control), 4 ± 0.3 (low) and 15 ± 1 (high) μg/g dry mass) for 28 days. Dose was measured as cadmium accumulation in whole soft body and individual tissues at weekly intervals over the exposure period. Dose was further examined as sub-cellular localisation of cadmium in hepatopancreas tissues. The biological responses in terms of enzymatic and cellular biomarkers were measured in hepatopancreas tissues at day 28. H. australis accumulated cadmium from spiked sediments with an 8-fold (low exposure organisms) and 16-fold (high exposure organisms) increase at day 28 compared to control organisms. The accumulated tissue cadmium concentrations reflected the sediment cadmium exposure at day 28. Cadmium accumulation in high exposure organisms was inversely related to the tissue calcium concentrations. Gills of H. australis showed significantly higher cadmium accumulation than the other tissues. Accumulated cadmium in biologically active and biologically detoxified metal pools was not significantly different in cadmium exposed

  17. Phytotoxicity of trace metals in spiked and field-contaminated soils: Linking soil-extractable metals with toxicity.

    Science.gov (United States)

    Hamels, Fanny; Malevé, Jasmina; Sonnet, Philippe; Kleja, Dan Berggren; Smolders, Erik

    2014-11-01

    Soil tests have been widely developed to predict trace metal uptake by plants. The prediction of metal toxicity, however, has rarely been tested. The present study was set up to compare 8 established soil tests for diagnosing phytotoxicity in contaminated soils. Nine soils contaminated with Zn or Cu by metal mining, smelting, or processing were collected. Uncontaminated reference soils with similar soil properties were sampled, and series of increasing contamination were created by mixing each with the corresponding soil. In addition, each reference soil was spiked with either ZnCl2 or CuCl2 at several concentrations. Total metal toxicity to barley seedling growth in the field-contaminated soils was up to 30 times lower than that in corresponding spiked soils. Total metal (aqua regia-soluble) toxicity thresholds of 50% effective concentrations (EC50) varied by factors up to 260 (Zn) or 6 (Cu) among soils. For Zn, variations in EC50 thresholds decreased as aqua regia > 0.43 M HNO3  > 0.05 M ethylenediamine tetraacetic acid (EDTA) > 1 M NH4 NO3  > cobaltihexamine > diffusive gradients in thin films (DGT) > 0.001 M CaCl2 , suggesting that the last extraction is the most robust phytotoxicity index for Zn. The EDTA extraction was the most robust for Cu-contaminated soils. The isotopically exchangeable fraction of the total soil metal in the field-contaminated soils markedly explained the lower toxicity compared with spiked soils. The isotope exchange method can be used to translate soil metal limits derived from soils spiked with metal salts to site-specific soil metal limits. © 2014 SETAC.

  18. Infrared spectra of mineral species

    CERN Document Server

    Chukanov, Nikita V

    2014-01-01

    This book details more than 3,000 IR spectra of more than 2,000 mineral species collected during last 30 years. It features full descriptions and analytical data of each sample for which IR spectrum was obtained.

  19. Correlation Functions and Power Spectra

    DEFF Research Database (Denmark)

    Larsen, Jan

    2006-01-01

    The present lecture note is a supplement to the textbook Digital Signal Processing by J. Proakis and D.G. Manolakis used in the IMM/DTU course 02451 Digital Signal Processing and provides an extended discussion of correlation functions and power spectra. The definitions of correlation functions...... and spectra for discrete-time and continuous-time (analog) signals are pretty similar. Consequently, we confine the discussion mainly to real discrete-time signals. The Appendix contains detailed definitions and properties of correlation functions and spectra for analog as well as discrete-time signals....... It is possible to define correlation functions and associated spectra for aperiodic, periodic and random signals although the interpretation is different. Moreover, we will discuss correlation functions when mixing these basic signal types. In addition, the note include several examples for the purpose...

  20. Multifractal spectra in shear flows

    Science.gov (United States)

    Keefe, L. R.; Deane, Anil E.

    1989-01-01

    Numerical simulations of three-dimensional homogeneous shear flow and fully developed channel flow, are used to calculate the associated multifractal spectra of the energy dissipation field. Only weak parameterization of the results with the nondimensional shear is found, and this only if the flow has reached its asymptotic development state. Multifractal spectra of these flows coincide with those from experiments only at the range alpha less than 1.

  1. Sequential Analysis of Gamma Spectra

    International Nuclear Information System (INIS)

    Fayez-Hassan, M.; Hella, Kh.M.

    2009-01-01

    This work shows how easy one can deal with a huge number of gamma spectra. The method can be used for radiation monitoring. It is based on the macro feature of the windows XP connected to QBASIC software. The routine was used usefully in generating accurate results free from human errors. One hundred measured gamma spectra were fully analyzed in 10 minutes using our fast and automated method controlling the Genie 2000 gamma acquisition analysis software.

  2. Response spectra in alluvial soils

    International Nuclear Information System (INIS)

    Chandrasekharan, A.R.; Paul, D.K.

    1975-01-01

    For aseismic design of structures, the ground motion data is assumed either in the form of ground acceleration as a function of time or indirectly in the form of response spectra. Though the response spectra approach has limitations like not being applicable for nonlinear problems, it is usually used for structures like nuclear power plants. Fifty accelerograms recorded at alluvial sites have been processed. Since different empirical formulas relating acceleration with magnitude and distance give a wide scatter of values, peak ground acceleration alone cannot be the parameter as is assumed by a number of authors. The spectra corresponding to 5% damping have been normalised with respect to three parameters, namely, peak ground acceleration, peak ground velocity and a nondimensional quantity ad/v 2 . Envelopee of maxima and minima as well as average response spectra has been obtained. A comparison with the USAEC spectra has been made. A relation between ground acceleration, ground velocity and ad/v 2 has been obtained which would nearly give the same magnification of the response. A design response spectra for alluvial soils has been recommended. (author)

  3. Does the thermal spike affect low energy ion-induced interfacial mixing?

    International Nuclear Information System (INIS)

    Suele, P.; Menyhard, M.; Nordlund, K.

    2003-01-01

    Molecular dynamics simulations have been used to obtain the three-dimensional distribution of interfacial mixing and cascade defects in Ti/Pt multilayer system due to single 1 keV Ar + impact at grazing angle of incidence. The Ti/Pt system was chosen because of its relatively high heat of mixing in the binary alloy and therefore a suitable candidate for testing the effect of heat of mixing on ion-beam mixing. However, the calculated mixing profile is not sensitive to the heat of mixing. Therefore the thermal spike model of mixing is not fully supported under these irradiation conditions. Instead we found that the majority of mixing occurs after the thermal spike during the relaxation process. These conclusions are supported by liquid, vacancy as well as adatom analysis. The interfacial mixing is in various aspects anomalous in this system: the time evolution of mixing is leading to a phase delay for Ti mixing, and Pt exhibits an unexpected double peaked mixing evolution. The reasons to these effects are discussed

  4. Inverse spiking filter based acquisition enhancement in software based global positioning system receiver

    Directory of Open Access Journals (Sweden)

    G. Arul Elango

    2015-01-01

    Full Text Available The lower visibility of the satellite in the acquisition stage of a GPS receiver under worst noisy situation leads to reacquisition of the data and thereby takes a longer time to obtain the first position fix. If the impulse noise affects the GPS signal, the conventional ways of acquiring the satellites do not guarantee to meet the minimum requirement of four satellites to find the user position. The performance of GPS receiver acquisition can be improved in the low SNR level using inverse spiking filtering technique. In the proposed method, the estimate of the desired GPS L1 signal corrupted by impulse noise (gn is obtained by the prediction error filter (hopt, which is the optimum inverse filter that reshapes the noisy signal (yn into a desired GPS signal (xn. In the proposed method, to detect the visible satellites under weak signal conditions the traditional differential coherent approach is combined with the inverse spiking filter method to increase the number of visible satellites and to avoid the reacquisition process. Montecarlo simulation is carried out to assess the performance of the proposed method for C/N0 of 20 dB-Hz and results indicate that the modified differential coherent method effectively excises the noise with 90% probability of detection. Subsequently tracking operation is also tested to confirm the acquisition performance by demodulating the navigation data successfully.

  5. Antiproton and positron signal enhancement in dark matter mini-spikes scenarios

    International Nuclear Information System (INIS)

    Brun, Pierre; Bertone, Gianfranco; Lavalle, Julien; Salati, Pierre; Taillet, Richard

    2007-04-01

    The annihilation of dark matter (DM) in the Galaxy could produce specific imprints on the spectra of antimatter species in Galactic cosmic rays, which could be detected by upcoming experiments such as PAMELA and AMS02. Recent studies show that the presence of substructures can enhance the annihilation signal by a 'boost factor' that not only depends on energy, but that is intrinsically a statistical property of the distribution of DM substructures inside the Milky Way. We investigate a scenario in which substructures consist of ∼100 'mini-spikes' around intermediate-mass black holes. Focusing on primary positrons and antiprotons, we find large boost factors, up to a few thousand, that exhibit a large variance at high energy in the case of positrons and at low energy in the case of antiprotons. As a consequence, an estimate of the DM particle mass based on the observed cut-off in the positron spectrum could lead to a substantial underestimate of its actual value. (authors)

  6. Systematic Regional Variations in Purkinje Cell Spiking Patterns

    Science.gov (United States)

    Xiao, Jianqiang; Cerminara, Nadia L.; Kotsurovskyy, Yuriy; Aoki, Hanako; Burroughs, Amelia; Wise, Andrew K.; Luo, Yuanjun; Marshall, Sarah P.; Sugihara, Izumi; Apps, Richard; Lang, Eric J.

    2014-01-01

    In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs) in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+) and negative (Z−) bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z− PCs, respectively. In both datasets significant differences in simple spike (SS) activity were observed between cortical regions. Specifically, Z− and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution) was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS) activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions. PMID:25144311

  7. From spiking neuron models to linear-nonlinear models.

    Science.gov (United States)

    Ostojic, Srdjan; Brunel, Nicolas

    2011-01-20

    Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  8. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Spike-timing dependent plasticity in the striatum

    Directory of Open Access Journals (Sweden)

    Elodie Fino

    2010-06-01

    Full Text Available The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs, are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, the NO synthase and cholinergic interneurons also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.

  10. Systematic regional variations in Purkinje cell spiking patterns.

    Directory of Open Access Journals (Sweden)

    Jianqiang Xiao

    Full Text Available In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+ and negative (Z- bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z- PCs, respectively. In both datasets significant differences in simple spike (SS activity were observed between cortical regions. Specifically, Z- and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Stimulus Sensitivity of a Spiking Neural Network Model

    Science.gov (United States)

    Chevallier, Julien

    2018-02-01

    Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.

  16. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

    DEFF Research Database (Denmark)

    Tully, Philip J; Lindén, Henrik; Hennig, Matthias H

    2016-01-01

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...

  17. 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.

  18. Quantifying bamboo coral growth rate nonlinearity with the radiocarbon bomb spike: A new model for paleoceanographic chronology development

    Science.gov (United States)

    Frenkel, M. M.; LaVigne, M.; Miller, H. R.; Hill, T. M.; McNichol, A.; Gaylord, M. Lardie

    2017-07-01

    Bamboo corals, long-lived cold water gorgonin octocorals, offer unique paleoceanographic archives of the intermediate ocean. These Isididae corals are characterized by alternating gorgonin nodes and high Mg-calcite internodes, which synchronously extend radially. Bamboo coral calcite internodes have been utilized to obtain geochemical proxy data, however, growth rate uncertainty has made it difficult to construct precise chronologies for these corals. Previous studies have relied upon a single tie point from records of the anthropogenic Δ14C bomb spike preserved in the gorgonin nodes of live-collected corals to calculate a mean radial extension rate for the outer 50 years of skeletal growth. Bamboo coral chronologies are typically constructed by applying this mean extension rate to the entire coral record, assuming constant radial extension with coral age. In this study, we aim to test this underlying assumption by analyzing the organic nodes of six California margin bamboo corals at high enough resolution (bomb spike, including two tie points at 1957 and 1970, plus the coral collection date (2007.5) for four samples. Radial extension rates between tie points ranged from 10 to 204 μm/year, with a decrease in growth rate evident between the 1957-1970 and 1970-2007.5 periods for all four corals. A negative correlation between growth rate and coral radius (r =-0.7; p=0.04) was determined for multiple bamboo coral taxa and individuals from the California margin, demonstrating a decline in radial extension rate with specimen age and size. To provide a mechanistic basis for these observations, a simple mathematical model was developed based on the assumption of a constant increase in circular cross sectional area with time to quantify this decline in radial extension rate with coral size between chronological tie points. Applying the area-based model to our Δ14C bomb spike time series from individual corals improves chronology accuracy for all live-collected corals

  19. 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.

  20. Analysis of the experimental positron lifetime spectra by neural networks

    International Nuclear Information System (INIS)

    Avdic, S.; Chakarova, R.; Pazsit, I.

    2003-01-01

    This paper deals with the analysis of experimental positron lifetime spectra in polymer materials by using various algorithms of neural networks. A method based on the use of artificial neural networks for unfolding the mean lifetime and intensity of the spectral components of simulated positron lifetime spectra was previously suggested and tested on simulated data [Pazsit et al., Applied Surface Science, 149 (1998), 97]. In this work, the applicability of the method to the analysis of experimental positron spectra has been verified in the case of spectra from polymer materials with three components. It has been demonstrated that the backpropagation neural network can determine the spectral parameters with a high accuracy and perform the decomposition of lifetimes which differ by 10% or more. The backpropagation network has not been suitable for the identification of both the parameters and the number of spectral components. Therefore, a separate artificial neural network module has been designed to solve the classification problem. Module types based on self-organizing map and learning vector quantization algorithms have been tested. The learning vector quantization algorithm was found to have better performance and reliability. A complete artificial neural network analysis tool of positron lifetime spectra has been constructed to include a spectra classification module and parameter evaluation modules for spectra with a different number of components. In this way, both flexibility and high resolution can be achieved. (author)

  1. A structural model for electricity prices with spikes: measurement of spike risk and optimal policies for hydropower plant operation

    International Nuclear Information System (INIS)

    Kanamura, Takashi

    2007-01-01

    This paper proposes a new model for electricity prices based on demand and supply, which we call a structural model. We show that the structural model can generate price spikes that fits the observed data better than those generated by other preceding models such as the jump diffusion model and the Box-Cox transformation model. We apply the structural model to obtain the optimal operation policy for a pumped-storage hydropower generator, and show that the structural model can provide more realistic optimal policies than the jump diffusion model. (author)

  2. A structural model for electricity prices with spikes: measurement of spike risk and optimal policies for hydropower plant operation

    Energy Technology Data Exchange (ETDEWEB)

    Kanamura, Takashi [Hitotsubashi University, Tokyo (Japan). Graduate School of International Corporate Strategy; Ohashi, Azuhiko [J-Power, Tokyo (Japan)

    2007-09-15

    This paper proposes a new model for electricity prices based on demand and supply, which we call a structural model. We show that the structural model can generate price spikes that fits the observed data better than those generated by other preceding models such as the jump diffusion model and the Box-Cox transformation model. We apply the structural model to obtain the optimal operation policy for a pumped-storage hydropower generator, and show that the structural model can provide more realistic optimal policies than the jump diffusion model. (author)

  3. Spiked natural matrix materials as quality assessment samples

    International Nuclear Information System (INIS)

    Feiner, M.S.; Sanderson, C.G.

    1988-01-01

    The Environmental Measurements Laboratory has conducted the Quality Assessment Program since 1976 to evaluate the quality of the environmental radioactivity data, which is reported to the Department of Energy by as many as 42 commercial contractors involved in nuclear work. In this program, matrix materials of known radionuclide concentrations are distributed routinely to the contractors and the reported results are compared. The five matrices used are: soil, vegetation, animal tissue, water and filter paper. Environmental soil, vegetation and animal tissue are used, but the water and filter paper samples are prepared by spiking with known amounts of standard solutions traceable to the National Bureau of Standards. A summary of results is given to illustrate the successful operation of the program. Because of the difficulty and high cost of collecting large samples of natural matrix material and to increase the versatility of the program, an attempt was recently made to prepare the soil, vegetation and animal tissue samples with spiked solutions. A description of the preparation of these reference samples and the results of analyses are presented along with a discussion of the pitfalls and advantages of this approach. 19 refs.; 6 tabs

  4. Motif statistics and spike correlations in neuronal networks

    International Nuclear Information System (INIS)

    Hu, Yu; Shea-Brown, Eric; Trousdale, James; Josić, Krešimir

    2013-01-01

    Motifs are patterns of subgraphs of complex networks. We studied the impact of such patterns of connectivity on the level of correlated, or synchronized, spiking activity among pairs of cells in a recurrent network of integrate and fire neurons. For a range of network architectures, we find that the pairwise correlation coefficients, averaged across the network, can be closely approximated using only three statistics of network connectivity. These are the overall network connection probability and the frequencies of two second order motifs: diverging motifs, in which one cell provides input to two others, and chain motifs, in which two cells are connected via a third intermediary cell. Specifically, the prevalence of diverging and chain motifs tends to increase correlation. Our method is based on linear response theory, which enables us to express spiking statistics using linear algebra, and a resumming technique, which extrapolates from second order motifs to predict the overall effect of coupling on network correlation. Our motif-based results seek to isolate the effect of network architecture perturbatively from a known network state. (paper)

  5. Dynamics of Monoterpene Formation in Spike Lavender Plants

    Directory of Open Access Journals (Sweden)

    Isabel Mendoza-Poudereux

    2017-12-01

    Full Text Available The metabolic cross-talk between the mevalonate (MVA and the methylerythritol phosphate (MEP pathways was analyzed in spike lavender (Lavandula latifolia Med on the basis of 13CO2-labelling experiments using wildtype and transgenic plants overexpressing the 3-hydroxy-3-methylglutaryl CoA reductase (HMGR, the first and key enzyme of the MVA pathway. The plants were labelled in the presence of 13CO2 in a gas chamber for controlled pulse and chase periods of time. GC/MS and NMR analysis of 1,8-cineole and camphor, the major monoterpenes present in their essential oil, indicated that the C5-precursors, isopentenyl diphosphate (IPP and dimethylallyl diphosphate (DMAPP of both monoterpenes are predominantly biosynthesized via the MEP pathway. Surprisingly, overexpression of HMGR did not have significant impact upon the crosstalk between the MVA and MEP pathways indicating that the MEP route is the preferred pathway for the synthesis of C5 monoterpene precursors in spike lavender.

  6. Performance evaluation of PCA-based spike sorting algorithms.

    Science.gov (United States)

    Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

    2008-09-01

    Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

  7. Physical implementation of pair-based spike timing dependent plasticity

    International Nuclear Information System (INIS)

    Azghadi, M.R.; Al-Sarawi, S.; Iannella, N.; Abbott, D.

    2011-01-01

    Full text: Objective Spike-timing-dependent plasticity (STOP) is one of several plasticity rules which leads to learning and memory in the brain. STOP induces synaptic weight changes based on the timing of the pre- and post-synaptic neurons. A neural network which can mimic the adaptive capability of biological brains in the temporal domain, requires the weight of single connections to be altered by spike timing. To physically realise this network into silicon, a large number of interconnected STOP circuits on the same substrate is required. This imposes two significant limitations in terms of power and area. To cover these limitations, very large scale integrated circuit (VLSI) technology provides attractive features in terms of low power and small area requirements. An example is demonstrated by (lndiveli et al. 2006). The objective of this paper is to present a new implementation of the STOP circuit which demonstrates better power and area in comparison to previous implementations. Methods The proposed circuit uses complementary metal oxide semiconductor (CMOS) technology as depicted in Fig. I. The synaptic weight can be stored on a capacitor and charging/discharging current can lead to potentiation and depression. HSpice simulation results demonstrate that the average power, peak power, and area of the proposed circuit have been reduced by 6, 8 and 15%, respectively, in comparison with Indiveri's implementation. These improvements naturally lead to packing more STOP circuits onto the same substrate, when compared to previous proposals. Hence, this new implementation is quite interesting for real-world large neural networks.

  8. Spike timing analysis in neural networks with unsupervised synaptic plasticity

    Science.gov (United States)

    Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

    2013-01-01

    The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

  9. Macroscopic phase-resetting curves for spiking neural networks

    Science.gov (United States)

    Dumont, Grégory; Ermentrout, G. Bard; Gutkin, Boris

    2017-10-01

    The study of brain rhythms is an open-ended, and challenging, subject of interest in neuroscience. One of the best tools for the understanding of oscillations at the single neuron level is the phase-resetting curve (PRC). Synchronization in networks of neurons, effects of noise on the rhythms, effects of transient stimuli on the ongoing rhythmic activity, and many other features can be understood by the PRC. However, most macroscopic brain rhythms are generated by large populations of neurons, and so far it has been unclear how the PRC formulation can be extended to these more common rhythms. In this paper, we describe a framework to determine a macroscopic PRC (mPRC) for a network of spiking excitatory and inhibitory neurons that generate a macroscopic rhythm. We take advantage of a thermodynamic approach combined with a reduction method to simplify the network description to a small number of ordinary differential equations. From this simplified but exact reduction, we can compute the mPRC via the standard adjoint method. Our theoretical findings are illustrated with and supported by numerical simulations of the full spiking network. Notably our mPRC framework allows us to predict the difference between effects of transient inputs to the excitatory versus the inhibitory neurons in the network.

  10. Inverse stochastic resonance in networks of spiking neurons.

    Science.gov (United States)

    Uzuntarla, Muhammet; Barreto, Ernest; Torres, Joaquin J

    2017-07-01

    Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average spiking rate is calculated over the neuronal population. We use Hodgkin-Huxley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network connectivity is implemented via electrical or chemical connections (i.e., gap junctions or excitatory/inhibitory synapses). We find that the emergence of ISR depends on the interplay between each neuron's intrinsic dynamical structure, channel noise, and network inputs, where the latter in turn depend on network structure parameters. We observe that with weak gap junction or excitatory synaptic coupling, network heterogeneity and sparseness tend to favor the emergence of ISR. With inhibitory coupling, ISR is quite robust. We also identify dynamical mechanisms that underlie various features of this ISR behavior. Our results suggest possible ways of experimentally observing ISR in actual neuronal systems.

  11. Evolving Spiking Neural Networks for Recognition of Aged Voices.

    Science.gov (United States)

    Silva, Marco; Vellasco, Marley M B R; Cataldo, Edson

    2017-01-01

    The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help determine a suitable treatment to avoid its progress or even to eliminate the problem. This work focuses on the development of a new model for the identification of aging voices (independently of their chronological age), using as input attributes parameters extracted from the voice and glottal signals. The proposed model, named Quantum binary-real evolving Spiking Neural Network (QbrSNN), is based on spiking neural networks (SNNs), with an unsupervised training algorithm, and a Quantum-Inspired Evolutionary Algorithm that automatically determines the most relevant attributes and the optimal parameters that configure the SNN. The QbrSNN model was evaluated in a database composed of 120 records, containing samples from three groups of speakers. The results obtained indicate that the proposed model provides better accuracy than other approaches, with fewer input attributes. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

  12. Macroscopic phase-resetting curves for spiking neural networks.

    Science.gov (United States)

    Dumont, Grégory; Ermentrout, G Bard; Gutkin, Boris

    2017-10-01

    The study of brain rhythms is an open-ended, and challenging, subject of interest in neuroscience. One of the best tools for the understanding of oscillations at the single neuron level is the phase-resetting curve (PRC). Synchronization in networks of neurons, effects of noise on the rhythms, effects of transient stimuli on the ongoing rhythmic activity, and many other features can be understood by the PRC. However, most macroscopic brain rhythms are generated by large populations of neurons, and so far it has been unclear how the PRC formulation can be extended to these more common rhythms. In this paper, we describe a framework to determine a macroscopic PRC (mPRC) for a network of spiking excitatory and inhibitory neurons that generate a macroscopic rhythm. We take advantage of a thermodynamic approach combined with a reduction method to simplify the network description to a small number of ordinary differential equations. From this simplified but exact reduction, we can compute the mPRC via the standard adjoint method. Our theoretical findings are illustrated with and supported by numerical simulations of the full spiking network. Notably our mPRC framework allows us to predict the difference between effects of transient inputs to the excitatory versus the inhibitory neurons in the network.

  13. Spike neural models (part I: The Hodgkin-Huxley model

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2017-05-01

    Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

  14. Chaos and reliability in balanced spiking networks with temporal drive.

    Science.gov (United States)

    Lajoie, Guillaume; Lin, Kevin K; Shea-Brown, Eric

    2013-05-01

    Biological information processing is often carried out by complex networks of interconnected dynamical units. A basic question about such networks is that of reliability: If the same signal is presented many times with the network in different initial states, will the system entrain to the signal in a repeatable way? Reliability is of particular interest in neuroscience, where large, complex networks of excitatory and inhibitory cells are ubiquitous. These networks are known to autonomously produce strongly chaotic dynamics-an obvious threat to reliability. Here, we show that such chaos persists in the presence of weak and strong stimuli, but that even in the presence of chaos, intermittent periods of highly reliable spiking often coexist with unreliable activity. We elucidate the local dynamical mechanisms involved in this intermittent reliability, and investigate the relationship between this phenomenon and certain time-dependent attractors arising from the dynamics. A conclusion is that chaotic dynamics do not have to be an obstacle to precise spike responses, a fact with implications for signal coding in large networks.

  15. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.

    Directory of Open Access Journals (Sweden)

    Philip J Tully

    2016-05-01

    Full Text Available Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx. We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  16. Fuel switching? Demand destruction? Gas market responses to price spikes

    International Nuclear Information System (INIS)

    Lippe, D.

    2004-01-01

    This presentation defined fuel switching and addressed the issue regarding which consumers have the capability to switch fuels. In response to short term price aberrations, consumers with fuel switching capabilities reduce their use of one fuel and increase consumption of an alternative fuel. For example, natural gas consumption by some consumers declines in response to price spikes relative to prices of alternative fuels. This presentation also addressed the issue of differentiating between fuel switching and demand destruction. It also demonstrated how to compare gas prices versus alternative fuel prices and how to determine when consumers will likely switch fuels. Price spikes have implications for long term trends in natural gas demand, supply/demand balances and prices. The power generating sector represents a particular class of gas consumers that reduce operating rates of gas fired plants and increase operating rates of other plants. Some gas consumers even shut down plants until gas prices declines and relative economies improve. Some practical considerations for fuel switching include storage tank capacity, domestic refinery production, winter heating season, and decline in working gas storage. tabs., figs

  17. Hg stable isotope analysis by the double-spike method.

    Science.gov (United States)

    Mead, Chris; Johnson, Thomas M

    2010-06-01

    Recent publications suggest great potential for analysis of Hg stable isotope abundances to elucidate sources and/or chemical processes that control the environmental impact of mercury. We have developed a new MC-ICP-MS method for analysis of mercury isotope ratios using the double-spike approach, in which a solution containing enriched (196)Hg and (204)Hg is mixed with samples and provides a means to correct for instrumental mass bias and most isotopic fractionation that may occur during sample preparation and introduction into the instrument. Large amounts of isotopic fractionation induced by sample preparation and introduction into the instrument (e.g., by batch reactors) are corrected for. This may greatly enhance various Hg pre-concentration methods by correcting for minor fractionation that may occur during preparation and removing the need to demonstrate 100% recovery. Current precision, when ratios are normalized to the daily average, is 0.06 per thousand, 0.06 per thousand, 0.05 per thousand, and 0.05 per thousand (2sigma) for (202)Hg/(198)Hg, (201)Hg/(198)Hg, (200)Hg/(198)Hg, and (199)Hg/(198)Hg, respectively. This is slightly better than previously published methods. Additionally, this precision was attained despite the presence of large amounts of other Hg isotopes (e.g., 5.0% atom percent (198)Hg) in the spike solution; substantially better precision could be achieved if purer (196)Hg were used.

  18. Biological Action Spectra (invited paper)

    International Nuclear Information System (INIS)

    Gruijl, F.R. de

    2000-01-01

    Ultraviolet (UV) radiation induces a wide variety of biological responses: ranging in humans from well-known short-term effects like sunburn to long-term effects like skin cancer. The wavelength dependencies ('action spectra') of the responses can differ significantly, depending on the UV-targeted molecules (their absorption spectra), their localisation (transmission to the target depth) and the photochemical reactions involved (e.g. quantum yields, competing reaction). An action spectrum (e.g. of sunburn) is usually determined in a wavelength by wavelength analysis of the response. This is not always possible (e.g. in case of skin cancer), and an action spectrum may then be extracted mathematically from differences in responses to broadband UV sources of various spectral compositions (yielding 'biological spectral weights'). However, relative spectral weights may shift with exposure levels and contributions from different wavelengths may not always add up. Under these circumstances conventional analyses will yield different action spectra for different experimental conditions. (author)

  19. Double photoionisation spectra of molecules

    CERN Document Server

    Eland, John

    2017-01-01

    This book contains spectra of the doubly charged positive ions (dications) of some 75 molecules, including the major constituents of terrestrial and planetary atmospheres and prototypes of major chemical groups. It is intended to be a new resource for research in all areas of molecular spectroscopy involving high energy environments, both terrestrial and extra-terrestrial. All the spectra have been produced by photoionisation using laboratory lamps or synchrotron radiation and have been measured using the magnetic bottle time-of-flight technique by coincidence detection of correlated electron pairs. Full references to published work on the same species are given, though for several molecules these are the first published spectra. Double ionisation energies are listed and discussed in relation to the molecular electronic structure of the molecules. A full introduction to the field of molecular double ionisation is included and the mechanisms by which double photoionisation can occur are examined in detail. A p...

  20. 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.