WorldWideScience

Sample records for strongly shape spiking

  1. 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.

  2. The spatial structure of stimuli shapes the timescale of correlations in population spiking activity.

    Directory of Open Access Journals (Sweden)

    Ashok Litwin-Kumar

    Full Text Available Throughout the central nervous system, the timescale over which pairs of neural spike trains are correlated is shaped by stimulus structure and behavioral context. Such shaping is thought to underlie important changes in the neural code, but the neural circuitry responsible is largely unknown. In this study, we investigate a stimulus-induced shaping of pairwise spike train correlations in the electrosensory system of weakly electric fish. Simultaneous single unit recordings of principal electrosensory cells show that an increase in the spatial extent of stimuli increases correlations at short (≈ 10 ms timescales while simultaneously reducing correlations at long (≈ 100 ms timescales. A spiking network model of the first two stages of electrosensory processing replicates this correlation shaping, under the assumptions that spatially broad stimuli both saturate feedforward afferent input and recruit an open-loop inhibitory feedback pathway. Our model predictions are experimentally verified using both the natural heterogeneity of the electrosensory system and pharmacological blockade of descending feedback projections. For weak stimuli, linear response analysis of the spiking network shows that the reduction of long timescale correlation for spatially broad stimuli is similar to correlation cancellation mechanisms previously suggested to be operative in mammalian cortex. The mechanism for correlation shaping supports population-level filtering of irrelevant distractor stimuli, thereby enhancing the population response to relevant prey and conspecific communication inputs.

  3. The Shape of Strongly Disturbed Dayside Magnetopause

    Directory of Open Access Journals (Sweden)

    Alexei V. Dmitriev Alla V. Suvorova

    2013-01-01

    Full Text Available During strong geomagnetic disturbances, the Earth¡¦s magnetosphere exhibits unusual and nonlinear interaction with the incident flow of magnetized solar wind plasma. Global Magneto-hydro-dynamic (MHD modeling of the magnetosphere predicts that the storm-time effects at the magnetopause result from the abnormal plasma transport and/or extremely strong field aligned currents. In-situ observations of the magnetospheric boundary, magnetopause, by Geosynchronous Operational Environmental Satellite (GOES allowed us to find experimentally such effects as a saturation of the dayside reconnection, unusual bluntness and prominent duskward skewing of the nose magnetopause. The saturation and duskward skewing were attributed to the storm-time magnetopause formation under strong southward interplanetary magnetic field (IMF. The unusual bluntness was observed during both high solar wind pressure and strong southward IMF. We suggest that these phenomena are caused by a substantial contribution of the cross-tail current magnetic field and the hot magnetospheric plasma from the asymmetrical ring current into the pressure balance at the dayside magnetopause.

  4. The shaping of two distinct dendritic spikes by A-type voltage-gated K+ channels

    Directory of Open Access Journals (Sweden)

    Sungchil eYang

    2015-12-01

    Full Text Available Dendritic ion channels have been a subject of intense research in neuroscience because active ion channels in dendrites shape input signals. Ca2+-permeable channels including NMDA receptors (NMDARs have been implicated in supralinear dendritic integration, and the IA conductance in sublinear integration. Despite their essential roles in dendritic integration, it has remained uncertain whether these conductances coordinate with, or counteract, each other in the process of dendritic integration. To address this question, experiments were designed in hippocampal CA1 neurons with a recent 3D digital holography system that has shown excellent performance for spatial photoactivation. The results demonstrated a role of IA as a key contributor to two distinct dendritic spikes, low- and high-threshold Ca2+ spikes, through a preferential action of IA on Ca2+-permeable channel-mediated currents, over fast AMPAR-mediated currents. It is likely that the rapid kinetics of IA provides feed-forward inhibition to counteract the delayed Ca2+ channel-mediated dendritic excitability. This research reveals one dynamic ionic mechanism of dendritic integration, and may contribute to a new understanding of neuronal hyperexcitability embedded in several neural diseases such as epilepsy, fragile X syndrome and Alzheimer's disease.

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

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

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

  6. Reactivation of seizure-related changes to interictal spike shape and synchrony during postseizure sleep in patients.

    Science.gov (United States)

    Bower, Mark R; Kucewicz, Michal T; St Louis, Erik K; Meyer, Fredric B; Marsh, W Richard; Stead, Matt; Worrell, Gregory A

    2017-01-01

    Local field potentials (LFPs) arise from synchronous activation of millions of neurons, producing seemingly consistent waveform shapes and relative synchrony across electrodes. Interictal spikes (IISs) are LFPs associated with epilepsy that are commonly used to guide surgical resection. Recently, changes in neuronal firing patterns observed in the minutes preceding seizure onset were found to be reactivated during postseizure sleep, a process called seizure-related consolidation (SRC), due to similarities with learning-related consolidation. Because IISs arise from summed neural activity, we hypothesized that changes in IIS shape and relative synchrony would be observed in the minutes preceding seizure onset and would be reactivated preferentially during postseizure slow-wave sleep (SWS). Scalp and intracranial recordings were obtained continuously across multiple days from clinical macroelectrodes implanted in patients undergoing treatment for intractable epilepsy. Data from scalp electrodes were used to stage sleep. Data from intracranial electrodes were used to detect IISs using a previously established algorithm. Partial correlations were computed for sleep and wake periods before and after seizures as a function of correlations observed in the minutes preceding seizures. Magnetic resonance imaging (MRI) and computed tomography (CT) scans were co-registered with electroencephalography (EEG) to determine the location of the seizure-onset zone (SOZ). Changes in IIS shape and relative synchrony were observed on a subset of macroelectrodes minutes before seizure onset, and these changes were reactivated preferentially during postseizure SWS. Changes in synchrony were greatest for pairs of electrodes where at least one electrode was located in the SOZ. These data suggest preseizure changes in neural activity and their subsequent reactivation occur across a broad spatiotemporal scale: from single neurons to LFPs, both within and outside the SOZ. The preferential

  7. Strong electroactive biodegradable shape memory polymer networks based on star-shaped polylactide and aniline trimer for bone tissue engineering.

    Science.gov (United States)

    Xie, Meihua; Wang, Ling; Ge, Juan; Guo, Baolin; Ma, Peter X

    2015-04-01

    Preparation of functional shape memory polymer (SMP) for tissue engineering remains a challenge. Here the synthesis of strong electroactive shape memory polymer (ESMP) networks based on star-shaped polylactide (PLA) and aniline trimer (AT) is reported. Six-armed PLAs with various chain lengths were chemically cross-linked to synthesize SMP. After addition of an electroactive AT segment into the SMP, ESMP was obtained. The polymers were characterized by (1)H NMR, GPC, FT-IR, CV, DSC, DMA, tensile test, and degradation test. The SMP and ESMP exhibited strong mechanical properties (modulus higher than GPa) and excellent shape memory performance: short recovery time (several seconds), high recovery ratio (over 94%), and high fixity ratio (almost 100%). Moreover, cyclic voltammetry test confirmed the electroactivity of the ESMP. The ESMP significantly enhanced the proliferation of C2C12 cells compared to SMP and linear PLA (control). In addition, the ESMP greatly improved the osteogenic differentiation of C2C12 myoblast cells compared to PH10 and PLA in terms of ALP enzyme activity, immunofluorescence staining, and relative gene expression by quantitative real-time polymerase chain reaction (qRT-PCR). These intelligent SMPs and electroactive SMP with strong mechanical properties, tunable degradability, good electroactivity, biocompatibility, and enhanced osteogenic differentiation of C2C12 cells show great potential for bone regeneration.

  8. A Boundary Condition Relaxation Algorithm for Strongly Coupled, Ablating Flows Including Shape Change

    Science.gov (United States)

    Gnoffo, Peter A.; Johnston, Christopher O.

    2011-01-01

    Implementations of a model for equilibrium, steady-state ablation boundary conditions are tested for the purpose of providing strong coupling with a hypersonic flow solver. The objective is to remove correction factors or film cooling approximations that are usually applied in coupled implementations of the flow solver and the ablation response. Three test cases are considered - the IRV-2, the Galileo probe, and a notional slender, blunted cone launched at 10 km/s from the Earth's surface. A successive substitution is employed and the order of succession is varied as a function of surface temperature to obtain converged solutions. The implementation is tested on a specified trajectory for the IRV-2 to compute shape change under the approximation of steady-state ablation. Issues associated with stability of the shape change algorithm caused by explicit time step limits are also discussed.

  9. Off-specular polarized neutron reflectometry study of magnetic dots with a strong shape anisotropy

    CERN Document Server

    Temst, K; Moshchalkov, V V; Bruynseraede, Y; Fritzsche, H; Jonckheere, R

    2002-01-01

    We have measured the off-specular polarized neutron reflectivity of a regular array of rectangular magnetic polycrystalline Co dots, which were prepared by a combination of electron-beam lithography, molecular beam deposition, and lift-off processes. The dots have a length-to-width ratio of 4:1 imposing a strong shape anisotropy. The intensity of the off-specular satellite reflection was monitored as a function of the magnetic field applied parallel to the rows of dots and in the plane of the sample, allowing us to analyze the magnetization-reversal process using the four spin-polarized cross sections. (orig.)

  10. Strong Stellar-driven Outflows Shape the Evolution of Galaxies at Cosmic Dawn

    International Nuclear Information System (INIS)

    Fontanot, Fabio; De Lucia, Gabriella; Hirschmann, Michaela

    2017-01-01

    We study galaxy mass assembly and cosmic star formation rate (SFR) at high redshift (z ≳ 4), by comparing data from multiwavelength surveys with predictions from the GAlaxy Evolution and Assembly (gaea) model. gaea implements a stellar feedback scheme partially based on cosmological hydrodynamical simulations, which features strong stellar-driven outflows and mass-dependent timescales for the re-accretion of ejected gas. In previous work, we have shown that this scheme is able to correctly reproduce the evolution of the galaxy stellar mass function (GSMF) up to z ∼ 3. We contrast model predictions with both rest-frame ultraviolet (UV) and optical luminosity functions (LFs), which are mostly sensitive to the SFR and stellar mass, respectively. We show that gaea is able to reproduce the shape and redshift evolution of both sets of LFs. We study the impact of dust on the predicted LFs, and we find that the required level of dust attenuation is in qualitative agreement with recent estimates based on the UV continuum slope. The consistency between data and model predictions holds for the redshift evolution of the physical quantities well beyond the redshift range considered for the calibration of the original model. In particular, we show that gaea is able to recover the evolution of the GSMF up to z ∼ 7 and the cosmic SFR density up to z ∼ 10.

  11. Fabrication of a membrane filter with controlled pore shape and its application to cell separation and strong single cell trapping

    International Nuclear Information System (INIS)

    Choi, Dong-Hoon; Yoon, Gun-Wook; Yoon, Jun-Bo; Park, Jeong Won; Lee, Dae-Sik; Ihm, Chunhwa

    2015-01-01

    A porous membrane filter is one of the key components for sample preparation in lab-on-a-chip applications. However, most of the membranes reported to date have only been used for size-based separation since it is difficult to provide functionality to the membrane or improve the performance of the membrane. In this work, as a method to functionalize the membrane filter, controlling the shape of the membrane pores is suggested, and a convenient and mass-producible fabrication method is provided. With the proposed method, membrane filters with round, conical and funnel shape pores were successfully fabricated, and we demonstrated that the sidewall slope of the conical shape pores could be precisely controlled. To verify that the membrane filter can be functionalized by controlled pore shape, we investigated filtration and trapping performance of the membrane filter with conical shape pores. In a filtration test of 1000 cancer cells (MCF-7, a breast cancer cell line) spiked in phosphate buffered saline (PBS) solution, 77% of the total cancer cells were retained on the membrane, and each cell from among 99.3% of the retained cells was automatically isolated in a single conical pore during the filtration process. Thanks to its engineered pore shape, trapping ability of the membrane with conical pores is dramatically improved. Microparticles trapped in the conical pores maintain their locations without any losses even at a more than 30 times faster external flow rate com-pared with those mounted on conventional cylindrical pores. Also, 78% of the cells trapped in the conical pores withstand an external flow of over 300 μl min −1 whereas only 18% of the cells trapped in the cylindrical pores remain on the membrane after 120 μl min −1 of an external flow is applied. (paper)

  12. Strongly asymmetric hybridization barriers shape the origin of a new polyploid species and its hybrid ancestor.

    Science.gov (United States)

    Vallejo-Marín, Mario; Cooley, Arielle M; Lee, Michelle Yuequi; Folmer, Madison; McKain, Michael R; Puzey, Joshua R

    2016-07-01

    Hybridization between diploids and tetraploids can lead to new allopolyploid species, often via a triploid intermediate. Viable triploids are often produced asymmetrically, with greater success observed for "maternal-excess" crosses where the mother has a higher ploidy than the father. Here we investigated the evolutionary origins of Mimulus peregrinus, an allohexaploid recently derived from the triploid M. ×robertsii, to determine whether reproductive asymmetry has shaped the formation of this new species. We used reciprocal crosses between the diploid (M. guttatus) and tetraploid (M. luteus) progenitors to determine the viability of triploid M. ×robertsii hybrids resulting from paternal- vs. maternal-excess crosses. To investigate whether experimental results predict patterns seen in the field, we performed parentage analyses comparing natural populations of M. peregrinus to its diploid, tetraploid, and triploid progenitors. Organellar sequences obtained from pre-existing genomic data, supplemented with additional genotyping was used to establish the maternal ancestry of multiple M. peregrinus and M. ×robertsii populations. We found strong evidence for asymmetric origins of M. peregrinus, but opposite to the common pattern, with paternal-excess crosses significantly more successful than maternal-excess crosses. These results successfully predicted hybrid formation in nature: 111 of 114 M. ×robertsii individuals, and 27 of 27 M. peregrinus, had an M. guttatus maternal haplotype. This study, which includes the first Mimulus chloroplast genome assembly, demonstrates the utility of parentage analysis through genome skimming. We highlight the benefits of complementing genomic analyses with experimental approaches to understand asymmetry in allopolyploid speciation. © 2016 Botanical Society of America.

  13. Bin-Picking based on Harmonic Shape Contexts and Graph-Based Matching<strong />

    DEFF Research Database (Denmark)

    Moeslund, Thomas B.; Kirkegaard, Jakob

    2006-01-01

    In this work we address the general bin-picking problem where 3D data is available. We apply Harmonic Shape Contexts (HSC) features since these are invariant to translation, scale, and 3D rotation. Each object is divided into a number of sub-models each represented by a number of HSC features. Th...

  14. Super strong dopamine hydrogels with shape memory and bioinspired actuating behaviours modulated by solvent exchange.

    Science.gov (United States)

    Huang, Jiahe; Liao, Jiexin; Wang, Tao; Sun, Weixiang; Tong, Zhen

    2018-03-07

    Dopamine-containing hydrogels were synthesized by copolymerization of dopamine methacrylamide (DMA), N,N-dimethylacrylamide (DMAA), and an N,N'-methylenebisacrylamide (BIS) crosslinker in a mixed solvent of water and DMSO. The association of DMA was formed by simply immersing in water to facilely reinforce the hydrogel due to the introduction of the second physical crosslinking. The tensile strength of the hydrogels was increased greatly and regulated in a wide range from 200 kPa to over 2 MPa. The association of DMA was destroyed upon immersing in DMSO. This reversible formation and dissociation of the association structure endowed the hydrogel with shape memory and actuating capabilities. Rapid shape fixing in water and complete shape recovery in DMSO was realized within several minutes. Bioinspired functional soft actuators were designed based on the reversible association and metal ion coordination of DMA, including fast responsive hydrogel tentacles, programable multiple shape change, reversible and versatile painting and writing "hydrogel paper". The facile preparation and strength regulation provide a new way to design novel soft actuators through solvent exchange, and will inspire more complex applications upon combining the association with other properties of mussel inspired dopamine derivatives.

  15. Strong field line shapes and photon statistics from a single molecule under anomalous noise.

    Science.gov (United States)

    Sanda, Frantisek

    2009-10-01

    We revisit the line-shape theory of a single molecule with anomalous stochastic spectral diffusion. Waiting time profiles for bath induced spectral jumps in the ground and excited states become different when a molecule, probed by continuous-wave laser field, reaches the steady state. This effect is studied for the stationary dichotomic continuous-time-random-walk spectral diffusion of a single two-level chromophore with power-law distributions of waiting times. Correlated waiting time distributions, line shapes, two-point fluorescence correlation function, and Mandel Q parameter are calculated for arbitrary magnitude of laser field. We extended previous weak field results and examined the breakdown of the central limit theorem in photon statistics, indicated by asymptotic power-law growth of Mandel Q parameter. Frequency profile of the Mandel Q parameter identifies the peaks of spectrum, which are related to anomalous spectral diffusion dynamics.

  16. Space-Time Dynamics of Membrane Currents Evolve to Shape Excitation, Spiking, and Inhibition in the Cortex at Small and Large Scales

    DEFF Research Database (Denmark)

    Roland, Per E.

    2017-01-01

    In the cerebral cortex, membrane currents, i.e., action potentials and other membrane currents, express many forms of space-time dynamics. In the spontaneous asynchronous irregular state, their space-time dynamics are local non-propagating fluctuations and sparse spiking appearing at unpredictable...... positions. After transition to active spiking states, larger structured zones with active spiking neurons appear, propagating through the cortical network, driving it into various forms of widespread excitation, and engaging the network from microscopic scales to whole cortical areas. At each engaged...... cortical site, the amount of excitation in the network, after a delay, becomes matched by an equal amount of space-time fine-tuned inhibition that might be instrumental in driving the dynamics toward perception and action....

  17. Winemaking and bioprocesses strongly shaped the genetic diversity of the ubiquitous yeast Torulaspora delbrueckii.

    Directory of Open Access Journals (Sweden)

    Warren Albertin

    Full Text Available The yeast Torulaspora delbrueckii is associated with several human activities including oenology, bakery, distillery, dairy industry, etc. In addition to its biotechnological applications, T. delbrueckii is frequently isolated in natural environments (plant, soil, insect. T. delbrueckii is thus a remarkable ubiquitous yeast species with both wild and anthropic habitats, and appears to be a perfect yeast model to search for evidence of human domestication. For that purpose, we developed eight microsatellite markers that were used for the genotyping of 110 strains from various substrates and geographical origins. Microsatellite analysis showed four genetic clusters: two groups contained most nature strains from Old World and Americas respectively, and two clusters were associated with winemaking and other bioprocesses. Analysis of molecular variance (AMOVA confirmed that human activities significantly shaped the genetic variability of T. delbrueckii species. Natural isolates are differentiated on the basis of geographical localisation, as expected for wild population. The domestication of T. delbrueckii probably dates back to the Roman Empire for winemaking (∼ 1900 years ago, and to the Neolithic era for bioprocesses (∼ 4000 years ago. Microsatellite analysis also provided valuable data regarding the life-cycle of the species, suggesting a mostly diploid homothallic life. In addition to population genetics and ecological studies, the microsatellite tool will be particularly useful for further biotechnological development of T. delbrueckii strains for winemaking and other bioprocesses.

  18. Fortune favours the brave: Movement responses shape demographic dynamics in strongly competing populations.

    Science.gov (United States)

    Potts, Jonathan R; Petrovskii, Sergei V

    2017-05-07

    Animal movement is a key mechanism for shaping population dynamics. The effect of interactions between competing animals on a population's survival has been studied for many decades. However, interactions also affect an animal's subsequent movement decisions. Despite this, the indirect effect of these decisions on animal survival is much less well-understood. Here, we incorporate movement responses to foreign animals into a model of two competing populations, where inter-specific competition is greater than intra-specific competition. When movement is diffusive, the travelling wave moves from the stronger population to the weaker. However, by incorporating behaviourally induced directed movement towards the stronger population, the weaker one can slow the travelling wave down, even reversing its direction. Hence movement responses can switch the predictions of traditional mechanistic models. Furthermore, when environmental heterogeneity is combined with aggressive movement strategies, it is possible for spatially segregated co-existence to emerge. In this situation, the spatial patterns of the competing populations have the unusual feature that they are slightly out-of-phase with the environmental patterns. Finally, incorporating dynamic movement responses can also enable stable co-existence in a homogeneous environment, giving a new mechanism for spatially segregated co-existence. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Winemaking and bioprocesses strongly shaped the genetic diversity of the ubiquitous yeast Torulaspora delbrueckii.

    Science.gov (United States)

    Albertin, Warren; Chasseriaud, Laura; Comte, Guillaume; Panfili, Aurélie; Delcamp, Adline; Salin, Franck; Marullo, Philippe; Bely, Marina

    2014-01-01

    The yeast Torulaspora delbrueckii is associated with several human activities including oenology, bakery, distillery, dairy industry, etc. In addition to its biotechnological applications, T. delbrueckii is frequently isolated in natural environments (plant, soil, insect). T. delbrueckii is thus a remarkable ubiquitous yeast species with both wild and anthropic habitats, and appears to be a perfect yeast model to search for evidence of human domestication. For that purpose, we developed eight microsatellite markers that were used for the genotyping of 110 strains from various substrates and geographical origins. Microsatellite analysis showed four genetic clusters: two groups contained most nature strains from Old World and Americas respectively, and two clusters were associated with winemaking and other bioprocesses. Analysis of molecular variance (AMOVA) confirmed that human activities significantly shaped the genetic variability of T. delbrueckii species. Natural isolates are differentiated on the basis of geographical localisation, as expected for wild population. The domestication of T. delbrueckii probably dates back to the Roman Empire for winemaking (∼ 1900 years ago), and to the Neolithic era for bioprocesses (∼ 4000 years ago). Microsatellite analysis also provided valuable data regarding the life-cycle of the species, suggesting a mostly diploid homothallic life. In addition to population genetics and ecological studies, the microsatellite tool will be particularly useful for further biotechnological development of T. delbrueckii strains for winemaking and other bioprocesses.

  20. Evolution of the mean jet shape and dijet asymmetry distribution of an ensemble of holographic jets in strongly coupled plasma

    Science.gov (United States)

    Brewer, Jasmine; Rajagopal, Krishna; Sadofyev, Andrey; van der Schee, Wilke

    2018-02-01

    Some of the most important experimentally accessible probes of the quark- gluon plasma (QGP) produced in heavy ion collisions come from the analysis of how the shape and energy of sprays of energetic particles produced within a cone with a specified opening angle (jets) in a hard scattering are modified by their passage through the strongly coupled, liquid, QGP. We model an ensemble of back-to-back dijets for the purpose of gaining a qualitative understanding of how the shapes of the individual jets and the asymmetry in the energy of the pairs of jets in the ensemble are modified by their passage through an expanding cooling droplet of strongly coupled plasma, in the model in a holographic gauge theory that is dual to a 4+1-dimensional black-hole spacetime that is asymptotically anti-de Sitter (AdS). We build our model by constructing an ensemble of strings in the dual gravitational description of the gauge theory. We model QCD jets in vacuum using strings whose endpoints are moving "downward" into the gravitational bulk spacetime with some fixed small angle, an angle that represents the opening angle (ratio of jet mass to jet energy) that the QCD jet would have in vacuum. Such strings must be moving through the gravitational bulk at (close to) the speed of light; they must be (close to) null. This condition does not specify the energy distribution along the string, meaning that it does not specify the shape of the jet being modeled. We study the dynamics of strings that are initially not null and show that strings with a wide range of initial conditions rapidly accelerate and become null and, as they do, develop a similar distribution of their energy density. We use this distribution of the energy density along the string, choose an ensemble of strings whose opening angles and energies are distributed as in perturbative QCD, and show that we can then fix one of the two model parameters such that the mean jet shape for the jets in the ensemble that we have built

  1. Porous shaped photonic crystal fiber with strong confinement field in sensing applications: Design and analysis

    Directory of Open Access Journals (Sweden)

    Sawrab Chowdhury

    2017-04-01

    Full Text Available In this article, porous core porous cladding photonic crystal fiber (P-PCF has been proposed for aqueous analytes sensing applications. Guiding properties of the proposed P-PCF has been numerically investigated by utilizing the full vectorial finite element method (FEM. The relative sensitivity and confinement loss are obtained by varying distinct geometrical parameters like the diameter of air holes, a pitch of the core and cladding region over a wider range of wavelength. The proposed P-PCF is organized with five rings air hole in the cladding and two rings air hole in a core territory which maximizes the relative sensitivity expressively and minimizes confinement loss depressively compare with the prior-PCF structures. After completing all investigations, it is also visualized that the relative sensitivity is increasing with the increment of the wavelength of communication band (O + E + S + C + L + U. Higher sensitivity is gained by using higher band for all applied liquids. Finally the investigating effects of different structural parameters of the proposed P-PCF are optimized which shows the sensitivity of 60.57%, 61.45% and 61.82%; the confinement loss of 8.71 × 10−08 dB/m, 1.41 × 10−10 dB/m and 6.51 × 10−10 dB/m for Water (n = 1.33, Ethanol (n = 1.354 and Benzene (n = 1.366 respectively at 1.33 μm wavelength. The optimized P-PCF with higher sensitivity and lower confinement loss has high impact in the area of the chemical as well as gas sensing purposes. Keywords: Porous shaped PCF, Sensitivity, Optical sensing, Liquid sensor, Confinement loss

  2. The strong effect of gaps on the required shaping of the ITER first wall

    International Nuclear Information System (INIS)

    Stangeby, Peter

    2011-01-01

    Divertor tokamaks such as ITER also need limiters, namely for startup, rampdown, as well as protection of the main wall from normal and off-normal loads during the diverted phase. In future fusion devices the volume within the magnetic coils will be at a premium and it will be important to make the limiters as thin as possible. A continuous, or almost continuous, wall-limiter can be made thinner than a set of well spaced discrete limiters. The need to be able to remove and replace the components of a wall-limiter requires that its individual panels in fact be discrete but the gaps between the panels should be made as small as possible relative to the panel width to maximize the wall coverage and to minimize the extent of exposed panel edges. The modularity of a wall-limiter leads inevitably to misalignments. The gaps and misalignments reduce the power-handling capability of a modular wall-limiter relative to an ideal wall-limiter, i.e. one without any gaps or misalignments. It is shown that even small gaps and radial misalignments between the individual panels of a modular wall-limiter can require so much shaping, i.e. chamfering, of the panels in order to protect the panel edges that the peak deposited power flux density on the panel face considerably exceeds that for an ideal wall-limiter, typically by an order of magnitude. Nevertheless, compared with a set of discrete limiters which are separated by gaps larger than the limiter toroidal size, a modular, small-gap wall-limiter can still be thinner and can have lower peak deposited power flux densities (MW m -2 ), for a given total power load (MW).

  3. Low genetic diversity and strong population structure shaped by anthropogenic habitat fragmentation in a critically endangered primate, Trachypithecus leucocephalus.

    Science.gov (United States)

    Wang, W; Qiao, Y; Li, S; Pan, W; Yao, M

    2017-06-01

    Habitat fragmentation may strongly impact population genetic structure and reduce the genetic diversity and viability of small and isolated populations. The white-headed langur (Trachypithecus leucocephalus) is a critically endangered primate species living in a highly fragmented and human-modified habitat in southern China. We examined the population genetic structure and genetic diversity of the species and investigated the environmental and anthropogenic factors that may have shaped its population structure. We used 214 unique multi-locus genotypes from 41 social groups across the main distribution area of T. leucocephalus, and found strong genetic structure and significant genetic differentiation among local populations. Our landscape genetic analyses using a causal modelling framework suggest that a large habitat gap and geographical distance represent the primary landscape elements shaping genetic structure, yet high levels of genetic differentiation also exist between patches separated by a small habitat gap or road. This is the first comprehensive study that has evaluated the population genetic structure and diversity of T. leucocephalus using nuclear markers. Our results indicate strong negative impacts of anthropogenic land modifications and habitat fragmentation on primate genetic connectivity between forest patches. Our analyses suggest that two management units of the species could be defined, and indicate that habitat continuity should be enforced and restored to reduce genetic isolation and enhance population viability.

  4. The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is shaped by strong epistatic interactions

    DEFF Research Database (Denmark)

    Jochumsen, Nicholas; Marvig, Rasmus Lykke; Pedersen, Søren Damkiær

    2016-01-01

    Colistin is an antimicrobial peptide that has become the only remaining alternative for the treatment of multidrug-resistant Gram-negative bacterial infections, but little is known of how clinical levels of colistin resistance evolve. We use in vitro experimental evolution and whole-genome sequen......Colistin is an antimicrobial peptide that has become the only remaining alternative for the treatment of multidrug-resistant Gram-negative bacterial infections, but little is known of how clinical levels of colistin resistance evolve. We use in vitro experimental evolution and whole......-genome sequencing of colistin-resistant Pseudomonas aeruginosa isolates from cystic fibrosis patients to reconstruct the molecular evolutionary pathways open for high-level colistin resistance. We show that the evolution of resistance is a complex, multistep process that requires mutation in at least five...... independent loci that synergistically create the phenotype. Strong intergenic epistasis limits the number of possible evolutionary pathways to resistance. Mutations in transcriptional regulators are essential for resistance evolution and function as nodes that potentiate further evolution towards higher...

  5. 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

  6. 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.

  7. 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.

  8. High-throughput SHAPE analysis reveals structures in HIV-1 genomic RNA strongly conserved across distinct biological states.

    Directory of Open Access Journals (Sweden)

    Kevin A Wilkinson

    2008-04-01

    Full Text Available Replication and pathogenesis of the human immunodeficiency virus (HIV is tightly linked to the structure of its RNA genome, but genome structure in infectious virions is poorly understood. We invent high-throughput SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension technology, which uses many of the same tools as DNA sequencing, to quantify RNA backbone flexibility at single-nucleotide resolution and from which robust structural information can be immediately derived. We analyze the structure of HIV-1 genomic RNA in four biologically instructive states, including the authentic viral genome inside native particles. Remarkably, given the large number of plausible local structures, the first 10% of the HIV-1 genome exists in a single, predominant conformation in all four states. We also discover that noncoding regions functioning in a regulatory role have significantly lower (p-value < 0.0001 SHAPE reactivities, and hence more structure, than do viral coding regions that function as the template for protein synthesis. By directly monitoring protein binding inside virions, we identify the RNA recognition motif for the viral nucleocapsid protein. Seven structurally homologous binding sites occur in a well-defined domain in the genome, consistent with a role in directing specific packaging of genomic RNA into nascent virions. In addition, we identify two distinct motifs that are targets for the duplex destabilizing activity of this same protein. The nucleocapsid protein destabilizes local HIV-1 RNA structure in ways likely to facilitate initial movement both of the retroviral reverse transcriptase from its tRNA primer and of the ribosome in coding regions. Each of the three nucleocapsid interaction motifs falls in a specific genome domain, indicating that local protein interactions can be organized by the long-range architecture of an RNA. High-throughput SHAPE reveals a comprehensive view of HIV-1 RNA genome structure, and further

  9. 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

  10. 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

  11. TRH regulates action potential shape in cerebral cortex pyramidal neurons.

    Science.gov (United States)

    Rodríguez-Molina, Víctor; Patiño, Javier; Vargas, Yamili; Sánchez-Jaramillo, Edith; Joseph-Bravo, Patricia; Charli, Jean-Louis

    2014-07-07

    Thyrotropin releasing hormone (TRH) is a neuropeptide with a wide neural distribution and a variety of functions. It modulates neuronal electrophysiological properties, including resting membrane potential, as well as excitatory postsynaptic potential and spike frequencies. We explored, with whole-cell patch clamp, TRH effect on action potential shape in pyramidal neurons of the sensorimotor cortex. TRH reduced spike and after hyperpolarization amplitudes, and increased spike half-width. The effect varied with dose, time and cortical layer. In layer V, 0.5µM of TRH induced a small increase in spike half-width, while 1 and 5µM induced a strong but transient change in spike half-width, and amplitude; after hyperpolarization amplitude was modified at 5µM of TRH. Cortical layers III and VI neurons responded intensely to 0.5µM TRH; layer II neurons response was small. The effect of 1µM TRH on action potential shape in layer V neurons was blocked by G-protein inhibition. Inhibition of the activity of the TRH-degrading enzyme pyroglutamyl peptidase II (PPII) reproduced the effect of TRH, with enhanced spike half-width. Many cortical PPII mRNA+ cells were VGLUT1 mRNA+, and some GAD mRNA+. These data show that TRH regulates action potential shape in pyramidal cortical neurons, and are consistent with the hypothesis that PPII controls its action in this region. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Application of constrained deconvolution technique for reconstruction of electron bunch profile with strongly non-Gaussian shape

    International Nuclear Information System (INIS)

    Geloni, G.; Saldin, E.L.; Schneidmiller, E.A.; Yurkov, M.V.

    2004-01-01

    An effective and practical technique based on the detection of the coherent synchrotron radiation (CSR) spectrum can be used to characterize the profile function of ultra-short bunches. The CSR spectrum measurement has an important limitation: no spectral phase information is available, and the complete profile function cannot be obtained in general. In this paper we propose to use constrained deconvolution method for bunch profile reconstruction based on a priori-known information about formation of the electron bunch. Application of the method is illustrated with practically important example of a bunch formed in a single bunch-compressor. Downstream of the bunch compressor the bunch charge distribution is strongly non-Gaussian with a narrow leading peak and a long tail. The longitudinal bunch distribution is derived by measuring the bunch tail constant with a streak camera and by using a priory available information about profile function

  13. Application of constrained deconvolution technique for reconstruction of electron bunch profile with strongly non-Gaussian shape

    Science.gov (United States)

    Geloni, G.; Saldin, E. L.; Schneidmiller, E. A.; Yurkov, M. V.

    2004-08-01

    An effective and practical technique based on the detection of the coherent synchrotron radiation (CSR) spectrum can be used to characterize the profile function of ultra-short bunches. The CSR spectrum measurement has an important limitation: no spectral phase information is available, and the complete profile function cannot be obtained in general. In this paper we propose to use constrained deconvolution method for bunch profile reconstruction based on a priori-known information about formation of the electron bunch. Application of the method is illustrated with practically important example of a bunch formed in a single bunch-compressor. Downstream of the bunch compressor the bunch charge distribution is strongly non-Gaussian with a narrow leading peak and a long tail. The longitudinal bunch distribution is derived by measuring the bunch tail constant with a streak camera and by using a priory available information about profile function.

  14. SEARCHING FOR COOLING SIGNATURES IN STRONG LENSING GALAXY CLUSTERS: EVIDENCE AGAINST BARYONS SHAPING THE MATTER DISTRIBUTION IN CLUSTER CORES

    International Nuclear Information System (INIS)

    Blanchard, Peter K.; Bayliss, Matthew B.; McDonald, Michael; Dahle, Håkon; Gladders, Michael D.; Sharon, Keren; Mushotzky, Richard

    2013-01-01

    The process by which the mass density profile of certain galaxy clusters becomes centrally concentrated enough to produce high strong lensing (SL) cross-sections is not well understood. It has been suggested that the baryonic condensation of the intracluster medium (ICM) due to cooling may drag dark matter to the cores and thus steepen the profile. In this work, we search for evidence of ongoing ICM cooling in the first large, well-defined sample of SL selected galaxy clusters in the range 0.1 0.2 and shows no statistically significant deviation from the total cluster population. Specific star formation rates, as traced by the strength of the 4000 Å break, D 4000 , are also consistent with the general cluster population. Finally, we use optical imaging of the SL clusters to measure the angular separation, R arc , between the arc and the center of mass of each lensing cluster in our sample and test for evidence of changing [O II] emission and D 4000 as a function of R arc , a proxy observable for SL cross-sections. D 4000 is constant with all values of R arc , and the [O II] emission fractions show no dependence on R arc for R arc > 10'' and only very marginal evidence of increased weak [O II] emission for systems with R arc < 10''. These results argue against the ability of baryonic cooling associated with cool core activity in the cores of galaxy clusters to strongly modify the underlying dark matter potential, leading to an increase in SL cross-sections

  15. 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.

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

    Science.gov (United States)

    Swindale, Nicholas V; Spacek, Martin A

    2014-01-01

    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 min. 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 (PCA). Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scalable to larger multi-electrode arrays (MEAs).

  17. LINE SHAPES OF DOPPLER-FREE RESONANCE IN SRFM: STRONG ATOM-WALL INTERACTION AND PRESSURE EFFECT ON THE FREQUENCY SHIFT OF AN ALKALI VAPOR

    Directory of Open Access Journals (Sweden)

    B BOUHAFS

    2003-12-01

    Full Text Available The attractive potential energy between the atoms of rubidium vapor and a dielectric wall has been investigated by monitoring the reflection light at the interface. The atom- wall interaction potential of the form V(z = - C /z3 (z: atom-wall allows to predict experimental results only for weak regime, i.e., where C<< 0.2 kHzmm3. In the strong interaction case, the dispersive line shape is turned into an absorption-type line shape. The influence of atomic density on the shift of  the selective reflection resonance  relatively to the frequency of unperturbed atomic transition is found to be red with a negative slope. This technique opens the way to characterize the windows made of different materials thin films.

  18. 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.

  19. 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.

  20. Wave-dispersive x-ray spectrometer for simultaneous acquisition of several characteristic lines based on strongly and accurately shaped Ge crystal

    International Nuclear Information System (INIS)

    Hayashi, Kouichi; Nakajima, Kazuo; Fujiwara, Kozo; Nishikata, Susumu

    2008-01-01

    Si and Ge are widely used as analyzing crystals for x-rays. Drastic and accurate shaping of Si or Ge gives significant advance in the x-ray field, although covalently bonded Si or Ge crystals have long been believed to be not deformable to various shapes. Recently, we developed a deformation technique for obtaining strongly and accurately shaped Si or Ge wafers of high crystal quality, and the use of the deformed wafer made it possible to produce fine-focused x-rays. In the present study, we prepared a cylindrical Ge wafer with a radius of curvature of 50 mm, and acquired fluorescent x-rays simultaneously from four elements by combining the cylindrical Ge wafer with a position-sensitive detector. The energy resolution of the x-ray fluorescence spectrum was as good as that obtained using a flat single crystal, and its gain was over 100. The demonstration of the simultaneous acquisition of high-resolution x-ray fluorescence spectra indicated various possibilities of x-ray spectrometry, such as one-shot x-ray spectroscopy and highly efficient wave-dispersive x-ray spectrometers

  1. 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

  2. The variational spiked oscillator

    International Nuclear Information System (INIS)

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

    1992-08-01

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

  3. Spike Frequency Adaptation in Neurons of the Central Nervous System.

    Science.gov (United States)

    Ha, Go Eun; Cheong, Eunji

    2017-08-01

    Neuronal firing patterns and frequencies determine the nature of encoded information of the neurons. Here we discuss the molecular identity and cellular mechanisms of spike-frequency adaptation in central nervous system (CNS) neurons. Calcium-activated potassium (K Ca ) channels such as BK Ca and SK Ca channels have long been known to be important mediators of spike adaptation via generation of a large afterhyperpolarization when neurons are hyper-activated. However, it has been shown that a strong hyperpolarization via these K Ca channels would cease action potential generation rather than reducing the frequency of spike generation. In some types of neurons, the strong hyperpolarization is followed by oscillatory activity in these neurons. Recently, spike-frequency adaptation in thalamocortical (TC) and CA1 hippocampal neurons is shown to be mediated by the Ca 2+ -activated Cl- channel (CACC), anoctamin-2 (ANO2). Knockdown of ANO2 in these neurons results in significantly reduced spike-frequency adaptation accompanied by increased number of spikes without shifting the firing mode, which suggests that ANO2 mediates a genuine form of spike adaptation, finely tuning the frequency of spikes in these neurons. Based on the finding of a broad expression of this new class of CACC in the brain, it can be proposed that the ANO2-mediated spike-frequency adaptation may be a general mechanism to control information transmission in the CNS neurons.

  4. Fractal dimension analysis for spike detection in low SNR extracellular signals.

    Science.gov (United States)

    Salmasi, Mehrdad; Büttner, Ulrich; Glasauer, Stefan

    2016-06-01

    Many algorithms have been suggested for detection and sorting of spikes in extracellular recording. Nevertheless, it is still challenging to detect spikes in low signal-to-noise ratios (SNR). We propose a spike detection algorithm that is based on the fractal properties of extracellular signals and can detect spikes in low SNR regimes. Semi-intact spikes are low-amplitude spikes whose shapes are almost preserved. The detection of these spikes can significantly enhance the performance of multi-electrode recording systems. Semi-intact spikes are simulated by adding three noise components to a spike train: thermal noise, inter-spike noise, and spike-level noise. We show that simulated signals have fractal properties which make them proper candidates for fractal analysis. Then we use fractal dimension as the main core of our spike detection algorithm and call it fractal detector. The performance of the fractal detector is compared with three frequently used spike detectors. We demonstrate that in low SNR, the fractal detector has the best performance and results in the highest detection probability. It is shown that, in contrast to the other three detectors, the performance of the fractal detector is independent of inter-spike noise power and that variations in spike shape do not alter its performance. Finally, we use the fractal detector for spike detection in experimental data and similar to simulations, it is shown that the fractal detector has the best performance in low SNR regimes. The detection of low-amplitude spikes provides more information about the neural activity in the vicinity of the recording electrodes. Our results suggest using the fractal detector as a reliable and robust method for detecting semi-intact spikes in low SNR extracellular signals.

  5. 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

  6. Response Features Determining Spike Times

    Directory of Open Access Journals (Sweden)

    Barry J. Richmond

    1999-01-01

    redundant with that carried by the coarse structure. Thus, the existence of precisely timed spike patterns carrying stimulus-related information does not imply control of spike timing at precise time scales.

  7. Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity.

    Science.gov (United States)

    Ocker, Gabriel Koch; Doiron, Brent

    2018-02-03

    The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has often been associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  8. 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

  9. 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)

  10. 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

  11. The grain size dependency of vesicular particle shapes strongly affects the drag of particles. First results from microtomography investigations of Campi Flegrei fallout deposits

    Science.gov (United States)

    Mele, Daniela; Dioguardi, Fabio

    2018-03-01

    Acknowledging the grain size dependency of shape is important in volcanology, in particular when dealing with tephra produced and emplaced during and after explosive volcanic eruptions. A systematic measurement of the tridimensional shape of vesicular pyroclasts of Campi Flegrei fallout deposits (Agnano-Monte Spina, Astroni 6 and Averno 2 eruptions) varying in size from 8.00 to 0.016 mm has been carried out by means of X-Ray Microtomography. Data show that particle shape changes with size, especially for juvenile vesicular clasts, since it is dependent on the distribution and size of vesicles that contour the external clast outline. Two drag laws that include sphericity in the formula were used for estimating the dependency of settling velocity on shape. Results demonstrate that it is not appropriate to assume a size-independent shape for vesicular particles, in contrast with the approach commonly employed when simulating the ash dispersion in the atmosphere.

  12. 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

  13. How the modified method of orbit quality assessment works for Oort spike comets?

    Science.gov (United States)

    Królikowska, Małgorzata; Dybczyński, Piotr A.

    2018-03-01

    We present a brief overview of the effectiveness of the modified method of a quality of orbit estimation proposed by us a few years ago. Having now a complete sample of 100 Oort spike comets with large-perihelion distances, we show that it was justified to introduce more restricted conditions separating the individual quality classes as well as introducing a new quality class containing orbits of the excellent quality, marked by us as 1a+. To enrich the perception, we provided a complete collection of visual time-distributions of positional data sets used by us for an orbit determination (see Appendix). We show that modern positional measurements of large-perihelion Oort spike comets should be carried out for at least three years around perihelion (three-four oppositions) to be almost certain that the derived orbit will be of the highest quality (1a+ class). Our results strongly support an expectation that in a near future it will be possible to study a shape of 1/aori-distribution of the Oort spike comets in a great detail basing only on the highest quality orbits, having 1/aori-uncertainties well below 5 . 10-6 au-1.

  14. 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.

  15. 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.

  16. 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.

  17. 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...

  18. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    International Nuclear Information System (INIS)

    Xue Ming; Wang Jiang; Deng Bin; Wei Xi-Le; Yu Hai-Tao; Chen Ying-Yuan

    2013-01-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture. (interdisciplinary physics and related areas of science and technology)

  19. Phylogenetic assemblage structure of North American trees is more strongly shaped by glacial-interglacial climate variability in gymnosperms than in angiosperms.

    Science.gov (United States)

    Ma, Ziyu; Sandel, Brody; Svenning, Jens-Christian

    2016-05-01

    How fast does biodiversity respond to climate change? The relationship of past and current climate with phylogenetic assemblage structure helps us to understand this question. Studies of angiosperm tree diversity in North America have already suggested effects of current water-energy balance and tropical niche conservatism. However, the role of glacial-interglacial climate variability remains to be determined, and little is known about any of these relationships for gymnosperms. Moreover, phylogenetic endemism, the concentration of unique lineages in restricted ranges, may also be related to glacial-interglacial climate variability and needs more attention. We used a refined phylogeny of both angiosperms and gymnosperms to map phylogenetic diversity, clustering and endemism of North American trees in 100-km grid cells, and climate change velocity since Last Glacial Maximum together with postglacial accessibility to recolonization to quantify glacial-interglacial climate variability. We found: (1) Current climate is the dominant factor explaining the overall patterns, with more clustered angiosperm assemblages toward lower temperature, consistent with tropical niche conservatism. (2) Long-term climate stability is associated with higher angiosperm endemism, while higher postglacial accessibility is linked to to more phylogenetic clustering and endemism in gymnosperms. (3) Factors linked to glacial-interglacial climate change have stronger effects on gymnosperms than on angiosperms. These results suggest that paleoclimate legacies supplement current climate in shaping phylogenetic patterns in North American trees, and especially so for gymnosperms.

  20. A Zero-Dimensional Organic Seesaw-Shaped Tin Bromide with Highly Efficient Strongly Stokes-Shifted Deep-Red Emission

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Chenkun [College of Engineering, Tallahassee, FL (United States). Dept. of Chemical and Biomedical Engineering; Lin, Haoran [College of Engineering, Tallahassee, FL (United States). Dept. of Chemical and Biomedical Engineering; Shi, Hongliang [Beihang Univ., Beijing (China). Dept. of Physics; Tian, Yu [Materials Science and Engineering Program, Florida State University, Tallahassee FL 32306 USA; Pak, Chongin [Florida State Univ., Tallahassee, FL (United States). Dept. of Chemistry and Biochemistry; Shatruk, Michael [Florida State Univ., Tallahassee, FL (United States). Dept. of Chemistry and Biochemistry; Zhou, Yan [Florida State Univ., Tallahassee, FL (United States). Dept. of Chemistry and Biochemistry; Djurovich, Peter [Univ. of Southern California, Los Angeles, CA (United States). Dept. of Chemistry; Du, Mao-Hua [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science and Technology Division, Center for Radiation Detection Materials and Systems; Ma, Biwu [College of Engineering, Tallahassee, FL (United States). Dept. of Chemical and Biomedical Engineering; Beihang Univ., Beijing (China). Dept. of Physics; Florida State Univ., Tallahassee, FL (United States). Dept. of Chemistry and Biochemistry

    2017-12-21

    The synthesis and characterization is reported of (C9NH20)2SnBr4, a novel organic metal halide hybrid with a zero-dimensional (0D) structure, in which individual seesaw-shaped tin (II) bromide anions (SnBr42-) are co-crystallized with 1-butyl-1-methylpyrrolidinium cations (C9NH20+). Upon photoexcitation, the bulk crystals exhibit a highly efficient broadband deep-red emission peaked at 695 nm, with a large Stokes shift of 332 nm and a high quantum efficiency of around 46 %. Furthermore, the unique photophysical properties of this hybrid material are attributed to two major factors: 1) the 0D structure allowing the bulk crystals to exhibit the intrinsic properties of individual SnBr42- species, and 2) the seesaw structure then enables a pronounced excited state structural deformation as confirmed by density functional theory (DFT) calculations.

  1. 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.

  2. Nectar sugars and amino acids in day- and night-flowering Nicotiana species are more strongly shaped by pollinators' preferences than organic acids and inorganic ions.

    Directory of Open Access Journals (Sweden)

    Kira Tiedge

    , nectar sugars and amino acids are more strongly correlated with the preferences of predominant pollinators than organic acids and inorganic ions.

  3. 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

  4. Prospective Coding by Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Johanni Brea

    2016-06-01

    Full Text Available Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron's firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ.

  5. 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)

  6. Caustic-based approach to understanding bunching dynamics and current spike formation in particle bunches

    Directory of Open Access Journals (Sweden)

    T. K. Charles

    2016-10-01

    Full Text Available Current modulations, current spikes, and current horns, are observed in a range of accelerator physics applications including strong bunch compression in Free Electron Lasers and linear colliders, trains of microbunching for terahertz radiation, microbunching instability and many others. This paper considers the fundamental mechanism that drives intense current modulations in dispersive regions, beyond the common explanation of nonlinear and higher-order effects. Under certain conditions, neighboring electron trajectories merge to form caustics, and often result in characteristic current spikes. Caustic lines and surfaces are regions of maximum electron density, and are witnessed in accelerator physics as folds in phase space of accelerated bunches. We identify the caustic phenomenon resulting in cusplike current profiles and derive an expression which describes the conditions needed for particle-bunch caustic formation in dispersive regions. The caustic expression not only reveals the conditions necessary for caustics to form but also where in longitudinal space the caustics will form. Particle-tracking simulations are used to verify these findings. We discuss the broader implications of this work including how to utilize the caustic expression for manipulation of the longitudinal phase space to achieve a desired current profile shape.

  7. 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

  8. Feature Representations for Neuromorphic Audio Spike Streams.

    Science.gov (United States)

    Anumula, Jithendar; Neil, Daniel; Delbruck, Tobi; Liu, Shih-Chii

    2018-01-01

    Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset.

  9. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    DEFF Research Database (Denmark)

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

    2016-01-01

    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...

  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. Stochastic hybrid model of spontaneous dendritic NMDA spikes

    International Nuclear Information System (INIS)

    Bressloff, Paul C; Newby, Jay M

    2014-01-01

    Following recent advances in imaging techniques and methods of dendritic stimulation, active voltage spikes have been observed in thin dendritic branches of excitatory pyramidal neurons, where the majority of synapses occur. The generation of these dendritic spikes involves both Na + ion channels and M-methyl-D-aspartate receptor (NMDAR) channels. During strong stimulation of a thin dendrite, the resulting high levels of glutamate, the main excitatory neurotransmitter in the central nervous system and an NMDA agonist, modify the current-voltage (I–V) characteristics of an NMDAR so that it behaves like a voltage-gated Na + channel. Hence, the NMDARs can fire a regenerative dendritic spike, just as Na + channels support the initiation of an action potential following membrane depolarization. However, the duration of the dendritic spike is of the order 100 ms rather than 1 ms, since it involves slow unbinding of glutamate from NMDARs rather than activation of hyperpolarizing K + channels. It has been suggested that dendritic NMDA spikes may play an important role in dendritic computations and provide a cellular substrate for short-term memory. In this paper, we consider a stochastic, conductance-based model of dendritic NMDA spikes, in which the noise originates from the stochastic opening and closing of a finite number of Na + and NMDA receptor ion channels. The resulting model takes the form of a stochastic hybrid system, in which membrane voltage evolves according to a piecewise deterministic dynamics that is coupled to a jump Markov process describing the opening and closing of the ion channels. We formulate the noise-induced initiation and termination of a dendritic spike in terms of a first-passage time problem, under the assumption that glutamate unbinding is negligible, which we then solve using a combination of WKB methods and singular perturbation theory. Using a stochastic phase-plane analysis we then extend our analysis to take proper account of the

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

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

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

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

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

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845

  15. 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.

  16. 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.

  17. 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

  18. A memristive spiking neuron with firing rate coding.

    Science.gov (United States)

    Ignatov, Marina; Ziegler, Martin; Hansen, Mirko; Petraru, Adrian; Kohlstedt, Hermann

    2015-01-01

    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.

  19. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil

    International Nuclear Information System (INIS)

    Langdon, Kate A.; Warne, Michael StJ.; Smernik, Ronald J.; Shareef, Ali; Kookana, Rai S.

    2013-01-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d 16 and TCS- 13 C 12 ). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d 16 . The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. - Highlights: ► Degradation of indigenous and spiked compounds from biosolids were compared. ► Differences were observed for both the rate and pattern of degradation. ► Spiked bisphenol A entirely degraded however the indigenous compound remained. ► TCS was detectable during the experiment however the degradation patterns varied. ► Spiking experiments may not be suitable to predict degradation of organic compounds

  20. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil

    Energy Technology Data Exchange (ETDEWEB)

    Langdon, Kate A., E-mail: Kate.Langdon@csiro.au [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Warne, Michael StJ. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Smernik, Ronald J. [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Shareef, Ali; Kookana, Rai S. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia)

    2013-03-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d{sub 16} and TCS-{sup 13}C{sub 12}). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d{sub 16}. The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. - Highlights: ► Degradation of indigenous and spiked compounds from biosolids were compared. ► Differences were observed for both the rate and pattern of degradation. ► Spiked bisphenol A entirely degraded however the indigenous compound remained. ► TCS was detectable during the experiment however the degradation patterns varied. ► Spiking experiments may not be suitable to predict degradation of organic compounds.

  1. 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

  2. Robust spike-train learning in spike-event based weight update.

    Science.gov (United States)

    Shrestha, Sumit Bam; Song, Qing

    2017-12-01

    Supervised learning algorithms in a spiking neural network either learn a spike-train pattern for a single neuron receiving input spike-train from multiple input synapses or learn to output the first spike time in a feedforward network setting. In this paper, we build upon spike-event based weight update strategy to learn continuous spike-train in a spiking neural network with a hidden layer using a dead zone on-off based adaptive learning rate rule which ensures convergence of the learning process in the sense of weight convergence and robustness of the learning process to external disturbances. Based on different benchmark problems, we compare this new method with other relevant spike-train learning algorithms. The results show that the speed of learning is much improved and the rate of successful learning is also greatly improved. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Spiking Neuron Network Helmholtz Machine

    Directory of Open Access Journals (Sweden)

    Pavel eSountsov

    2015-04-01

    Full Text Available An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

  4. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil.

    Science.gov (United States)

    Langdon, Kate A; Warne, Michael Stj; Smernik, Ronald J; Shareef, Ali; Kookana, Rai S

    2013-03-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d16 and TCS-(13)C12). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d16. The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. Copyright © 2013 Elsevier B.V. All rights reserved.

  5. 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

    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...

  6. 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

  7. Intracellular calcium spikes in rat suprachiasmatic nucleus neurons induced by BAPTA-based calcium dyes.

    Directory of Open Access Journals (Sweden)

    Jin Hee Hong

    Full Text Available BACKGROUND: Circadian rhythms in spontaneous action potential (AP firing frequencies and in cytosolic free calcium concentrations have been reported for mammalian circadian pacemaker neurons located within the hypothalamic suprachiasmatic nucleus (SCN. Also reported is the existence of "Ca(2+ spikes" (i.e., [Ca(2+](c transients having a bandwidth of 10 approximately 100 seconds in SCN neurons, but it is unclear if these SCN Ca(2+ spikes are related to the slow circadian rhythms. METHODOLOGY/PRINCIPAL FINDINGS: We addressed this issue based on a Ca(2+ indicator dye (fluo-4 and a protein Ca(2+ sensor (yellow cameleon. Using fluo-4 AM dye, we found spontaneous Ca(2+ spikes in 18% of rat SCN cells in acute brain slices, but the Ca(2+ spiking frequencies showed no day/night variation. We repeated the same experiments with rat (and mouse SCN slice cultures that expressed yellow cameleon genes for a number of different circadian phases and, surprisingly, spontaneous Ca(2+ spike was barely observed (<3%. When fluo-4 AM or BAPTA-AM was loaded in addition to the cameleon-expressing SCN cultures, however, the number of cells exhibiting Ca(2+ spikes was increased to 13 approximately 14%. CONCLUSIONS/SIGNIFICANCE: Despite our extensive set of experiments, no evidence of a circadian rhythm was found in the spontaneous Ca(2+ spiking activity of SCN. Furthermore, our study strongly suggests that the spontaneous Ca(2+ spiking activity is caused by the Ca(2+ chelating effect of the BAPTA-based fluo-4 dye. Therefore, this induced activity seems irrelevant to the intrinsic circadian rhythm of [Ca(2+](c in SCN neurons. The problems with BAPTA based dyes are widely known and our study provides a clear case for concern, in particular, for SCN Ca(2+ spikes. On the other hand, our study neither invalidates the use of these dyes as a whole, nor undermines the potential role of SCN Ca(2+ spikes in the function of SCN.

  8. 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.

  9. 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.

  10. The Analysis and Suppression of the spike noise in vibrator record

    Science.gov (United States)

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

    2013-12-01

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

  11. Spiking and LFP activity in PRR during symbolically instructed reaches.

    Science.gov (United States)

    Hwang, Eun Jung; Andersen, Richard A

    2012-02-01

    The spiking activity in the parietal reach region (PRR) represents the spatial goal of an impending reach when the reach is directed toward or away from a visual object. The local field potentials (LFPs) in this region also represent the reach goal when the reach is directed to a visual object. Thus PRR is a candidate area for reading out a patient's intended reach goals for neural prosthetic applications. For natural behaviors, reach goals are not always based on the location of a visual object, e.g., playing the piano following sheet music or moving following verbal directions. So far it has not been directly tested whether and how PRR represents reach goals in such cognitive, nonlocational conditions, and knowing the encoding properties in various task conditions would help in designing a reach goal decoder for prosthetic applications. To address this issue, we examined the macaque PRR under two reach conditions: reach goal determined by the stimulus location (direct) or shape (symbolic). For the same goal, the spiking activity near reach onset was indistinguishable between the two tasks, and thus a reach goal decoder trained with spiking activity in one task performed perfectly in the other. In contrast, the LFP activity at 20-40 Hz showed small but significantly enhanced reach goal tuning in the symbolic task, but its spatial preference remained the same. Consequently, a decoder trained with LFP activity performed worse in the other task than in the same task. These results suggest that LFP decoders in PRR should take into account the task context (e.g., locational vs. nonlocational) to be accurate, while spike decoders can robustly provide reach goal information regardless of the task context in various prosthetic applications.

  12. 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.

  13. Factors correlated with volleyball spike velocity.

    Science.gov (United States)

    Forthomme, Bénédicte; Croisier, Jean-Louis; Ciccarone, Guido; Crielaard, Jean-Michel; Cloes, Marc

    2005-10-01

    Spike effectiveness represents a determining element in volleyball. To compete at a high level, the player must, in particular, produce a spike characterized by a high ball velocity. Some muscular and physical features could influence ball velocity during the volleyball spike. Descriptive laboratory study. A total of 19 male volleyball players from the 2 highest Belgian national divisions underwent an isokinetic assessment of the dominant shoulder and elbow. Ball velocity performance (radar gun) during a spike test, morphological feature, and jump capacity (ergo jump) of the player were measured. We tested the relationship between the isokinetic parameters or physical features and field performances represented by spike velocity. We also compared first-division and second-division player data. Spike velocity correlated significantly with strength performance of the dominant shoulder (internal rotators) and of the dominant elbow (flexors and extensors) in the concentric mode. Negative correlations were established with the concentric external rotator on internal rotator ratio at 400 deg/s and with the mixed ratio (external rotator at 60 deg/s in the eccentric mode on internal rotator at 240 deg/s in the concentric mode). Positive correlations appeared with both the volleyball players' jump capacity and body mass index. First-division players differed from second-division players by higher ball velocity and increased jump capacity. Some specific strength and physical characteristics correlated significantly with spike performance in high-level volleyball practice. Our results could provide useful information for training management and propose some reflections on injury prevention.

  14. 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.

  15. Rate dynamics of leaky integrate-and-fire neurons with strong synapses

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2010-12-01

    Full Text Available Firing-rate models provide a practical tool for studying the dynamics of trial- or population-averaged neuronal signals. A wealth of theoretical and experimental studies has been dedicated to the derivation or extraction of such models by investigating the firing-rate response characteristics of ensembles of neurons. The majority of these studies assumes that neurons receive input spikes at a high rate through weak synapses (diffusion approximation. For many biological neural systems, however, this assumption cannot be justified. So far, it is unclear how time-varying presynaptic firing rates are transmitted by a population of neurons if the diffusion assumption is dropped. Here, we numerically investigate the stationary and non-stationary firing-rate response properties of leaky integrate-and-fire (LIF neurons receiving input spikes through excitatory synapses with alpha-function shaped postsynaptic currents for strong synaptic weights. Input spike trains are modelled by inhomogeneous Poisson point-processes with sinusoidal rate. Average rates, modulation amplitudes and phases of the period-averaged spike responses are measured for a broad range of stimulus, synapse and neuron parameters. Across wide parameter regions, the resulting transfer functions can be approximated by a linear 1st-order low-pass filter. Below a critical synaptic weight, the cutoff frequencies are approximately constant and determined by the synaptic time constants. Only for synapses with unrealistically strong weights are the cutoff frequencies significantly increased. To account for stimuli with larger modulation depths, we combine the measured linear transfer function with the nonlinear response characteristics obtained for stationary inputs. The resulting linear-nonlinear model accurately predicts the population response for a variety of non-sinusoidal stimuli.

  16. Duration of Purkinje cell complex spikes increases with their firing frequency

    NARCIS (Netherlands)

    P.J. Warnaar (Pascal); J. Couto (Joao); M. Negrello (Mario); M. Junker (Marc); A. Smilgin (Aleksandra); A. Ignashchenkova (Alla); M. Giugliano (Michele); P. Thier (Peter); E. de Schutter (Erik)

    2015-01-01

    textabstractClimbing fiber (CF) triggered complex spikes (CS) are massive depolarization bursts in the cerebellar Purkinje cell (PC), showing several high frequency spikelet components (±600 Hz). Since its early observations, the CS is known to vary in shape. In this study we describe CS waveforms,

  17. Frequency Band Separability Feature Extraction Method With Weighted Haar Wavelet Implementation for Implantable Spike Sorting.

    Science.gov (United States)

    Yang, Yuning; Mason, Andrew J

    2017-06-01

    Hardware-efficient feature extraction is an important step for real-time and on-chip spike sorting. Based on an analysis of spike energy spectrum, a new feature set is developed using the positive and negative spike peaks in low and high frequency bands. A separability metric that evaluates the informativeness and noise sensitivity of features is introduced to optimize the cutoff frequency of each band. Haar-based discrete wavelet transform was chosen to implement memory- and hardware-efficient filters for extracting frequency band separability features. Specifically, peaks from the first level detail and the fourth level approximation were used to represent a spike. To improve clustering performance, the detail features were weighted into the same dynamic range as the approximation features. The new feature extraction method was tested at different signal-to-noise ratios using synthesized datasets consisting of considerable and various spike shapes extracted from real neural recordings. The results show that the new method has 3%-10% better spike sorting performance than other hardware-efficient methods while consuming comparable hardware resources.

  18. 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.

  19. 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.

  20. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

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

    2016-01-01

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

  1. Towards statistical summaries of spike train data.

    Science.gov (United States)

    Wu, Wei; Srivastava, Anuj

    2011-01-30

    Statistical inference has an important role in analysis of neural spike trains. While current approaches are mostly model-based, and designed for capturing the temporal evolution of the underlying stochastic processes, we focus on a data-driven approach where statistics are defined and computed in function spaces where individual spike trains are viewed as points. The first contribution of this paper is to endow spike train space with a parameterized family of metrics that takes into account different time warpings and generalizes several currently used metrics. These metrics are essentially penalized L(p) norms, involving appropriate functions of spike trains, with penalties associated with time-warpings. The second contribution of this paper is to derive a notion of a mean spike train in the case when p=2. We present an efficient recursive algorithm, termed Matching-Minimization algorithm, to compute the sample mean of a set of spike trains. The proposed metrics as well as the mean computations are demonstrated using an experimental recording from the motor cortex. Copyright © 2010 Elsevier B.V. All rights reserved.

  2. Macroscopic Description for Networks of Spiking Neurons

    Science.gov (United States)

    Montbrió, Ernest; Pazó, Diego; Roxin, Alex

    2015-04-01

    A major goal of neuroscience, statistical physics, and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here, we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally, we show that the firing-rate description is related, via a conformal map, to a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate that our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain.

  3. Codeine-spiked beer in a date rape case?

    Science.gov (United States)

    Havig, Stine Marie; Wiik, Elisabeth; Karinen, Ritva; Brochmann, Gerd Wenche; Vevelstad, Merete

    2016-11-01

    A case of suspected drug-facilitated sexual assault, involving codeine and acetaminophen, possibly mixed in beer, was recently addressed at the Norwegian Institute of Public Health. To examine the case, a small study was performed, spiking beer with preparations containing codeine and acetaminophen and observing the concentrations, appearance, and taste of the solutions. The study revealed the majority of the preparations to be quickly soluble in beer, achieving high concentrations, but at the expense of strong taste and drastic visible changes in the beer.

  4. Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.

    Science.gov (United States)

    Gangopadhyay, Ahana; Chakrabartty, Shantanu

    2017-04-27

    This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of ΣΔ modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.

  5. Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise.

    Science.gov (United States)

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-08-01

    Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex.

  6. 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

  7. In the Crosshair: Astrometric Exoplanet Detection with WFIRST's Diffraction Spikes

    Science.gov (United States)

    Melchior, Peter; Spergel, David; Lanz, Arianna

    2018-02-01

    WFIRST will conduct a coronagraphic program that characterizes the atmospheres of planets around bright nearby stars. When observed with the WFIRST Wide Field Camera, these stars will saturate the detector and produce very strong diffraction spikes. In this paper, we forecast the astrometric precision that WFIRST can achieve by centering on the diffraction spikes of highly saturated stars. This measurement principle is strongly facilitated by the WFIRST H4RG detectors, which confine excess charges within the potential well of saturated pixels. By adopting a simplified analytical model of the diffraction spike caused by a single support strut obscuring the telescope aperture, integrated over the WFIRST pixel size, we predict the performance of this approach with the Fisher-matrix formalism. We discuss the validity of the model and find that 10 μ {as} astrometric precision is achievable with a single 100 s exposure of an {R}{AB}=6 or a {J}{AB}=5 star. We discuss observational limitations from the optical distortion correction and pixel-level artifacts, which need to be calibrated at the level of 10{--}20 μ {as} so as to not dominate the error budget. To suppress those systematics, we suggest a series of short exposures, dithered by at least several hundred pixels, to reach an effective per-visit astrometric precision better than 10 μ {as}. If this can be achieved, a dedicated WFIRST GO program will be able to detect Earth-mass exoplanets with orbital periods of ≳ 1 {year} around stars within a few pc as well as Neptune-like planets with shorter periods or around more massive or distant stars. Such a program will also enable mass measurements of many anticipated direct-imaging exoplanet targets of the WFIRST coronagraph and a “starshade” occulter.

  8. Photospheric Current Spikes And Their Possible Association With Flares - Results from an HMI Data Driven Model

    Science.gov (United States)

    Goodman, M. L.; Kwan, C.; Ayhan, B.; Eric, S. L.

    2016-12-01

    A data driven, near photospheric magnetohydrodynamic model predicts spikes in the horizontal current density, and associated resistive heating rate. The spikes appear as increases by orders of magnitude above background values in neutral line regions (NLRs) of active regions (ARs). The largest spikes typically occur a few hours to a few days prior to M or X flares. The spikes correspond to large vertical derivatives of the horizontal magnetic field. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. This 2.5 D field is used to determine an analytic expression for a 3 D magnetic field, from which the current density, vector potential, and electric field are computed in every AR pixel for 14 ARs. The field is not assumed to be force-free. The spurious 6, 12, and 24 hour Doppler periods due to SDO orbital motion are filtered out of the time series of the HMI magnetic field for each pixel. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in ARs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares. The energy to drive the heating associated with the largest current spikes comes from bulk flow kinetic energy, not the electromagnetic field, and the current density is highly non-force free. The results suggest that, in combination with the model, HMI is revealing strong, convection driven, non-force free heating events on granulation scales, and it is plausible these events are correlated with subsequent M or X flares. More and longer time series need to be analyzed to determine if such a correlation exists.

  9. Spike train encoding by regular-spiking cells of the visual cortex.

    Science.gov (United States)

    Carandini, M; Mechler, F; Leonard, C S; Movshon, J A

    1996-11-01

    1. To study the encoding of input currents into output spike trains by regular-spiking cells, we recorded intracellularly from slices of the guinea pig visual cortex while injecting step, sinusoidal, and broadband noise currents. 2. When measured with sinusoidal currents, the frequency tuning of the spike responses was markedly band-pass. The preferred frequency was between 8 and 30 Hz, and grew with stimulus amplitude and mean intensity. 3. Stimulation with broadband noise currents dramatically enhanced the gain of the spike responses at low and high frequencies, yielding an essentially flat frequency tuning between 0.1 and 130 Hz. 4. The averaged spike responses to sinusoidal currents exhibited two nonlinearities: rectification and spike synchronization. By contrast, no nonlinearity was evident in the averaged responses to broadband noise stimuli. 5. These properties of the spike responses were not present in the membrane potential responses. The latter were roughly linear, and their frequency tuning was low-pass and well fit by a single-compartment passive model of the cell membrane composed of a resistance and a capacitance in parallel (RC circuit). 6. To account for the spike responses, we used a "sandwich model" consisting of a low-pass linear filter (the RC circuit), a rectification nonlinearity, and a high-pass linear filter. The model is described by six parameters and predicts analog firing rates rather than discrete spikes. It provided satisfactory fits to the firing rate responses to steps, sinusoids, and broadband noise currents. 7. The properties of spike encoding are consistent with temporal nonlinearities of the visual responses in V1, such as the dependence of response frequency tuning and latency on stimulus contrast and bandwidth. We speculate that one of the roles of the high-frequency membrane potential fluctuations observed in vivo could be to amplify and linearize the responses to lower, stimulus-related frequencies.

  10. 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...... fashion. The CNN has a convolutional architecture with filters of various sizes applied to the input layer, leaky ReLUs as activation functions, and a sigmoid output layer. Balanced mini-batches were applied to handle the imbalance in the data set. Leave-one-patient-out cross-validation was carried out...... to test the CNN and benchmark models on EEG data of five epilepsy patients. We achieved 0.947 AUC for the CNN, while the best performing benchmark model, Support Vector Machines with Gaussian kernel, achieved an AUC of 0.912....

  11. 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.

  12. Gap junctions are essential for generating the correlated spike activity of neighboring retinal ganglion cells.

    Directory of Open Access Journals (Sweden)

    Béla Völgyi

    Full Text Available Neurons throughout the brain show spike activity that is temporally correlated to that expressed by their neighbors, yet the generating mechanism(s remains unclear. In the retina, ganglion cells (GCs show robust, concerted spiking that shapes the information transmitted to central targets. Here we report the synaptic circuits responsible for generating the different types of concerted spiking of GC neighbors in the mouse retina. The most precise concerted spiking was generated by reciprocal electrical coupling of GC neighbors via gap junctions, whereas indirect electrical coupling to a common cohort of amacrine cells generated the correlated activity with medium precision. In contrast, the correlated spiking with the lowest temporal precision was produced by shared synaptic inputs carrying photoreceptor noise. Overall, our results demonstrate that different synaptic circuits generate the discrete types of GC correlated activity. Moreover, our findings expand our understanding of the roles of gap junctions in the retina, showing that they are essential for generating all forms of concerted GC activity transmitted to central brain targets.

  13. Is the Fundamental Electrical Response of the Single Heart Muscle Cell a Spike Potential?

    Science.gov (United States)

    Churney, Leon; Ohshima, Hisashi

    1963-01-01

    The urodele amphibians, Amphiuma and Necturus, provide heart fibers large enough to serve for microelectrode recording under visual control with the microscope. Bundles containing as few as 5 to 10 fibers yield spike potentials, rather than the plateau forms generally considered to be characteristic of heart muscle. These spikes fail to overshoot. The plateau form, and only the plateau form, is recorded exclusively from large tissue masses. An intermingling of spikes and plateau-shaped action potentials is obtained from bundles of intermediate size. These data are confirmed in experiments in which the myocardium is sliced into adhering strips of unequal sizes. The conclusion is drawn that the configuration of the recorded action potential curve is contingent upon the mass and geometry of the tissue impaled by the microelectrode. The crucial experiment of recording from an isolated single heart fiber is not possible, because of the attendant injury. Our proposal that the spike form is the elemental heart action potential is, to this extent, an extrapolation. Attempts to explain the nature of the spike along classical lines are not entirely satisfactory. Other theories are considered which, in their turn, are generally unacceptable. Evidently only further experimentation can clarify the situation. PMID:14021260

  14. 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.

  15. 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.

  16. Emergence of slow collective oscillations in neural networks with spike-timing dependent plasticity

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

    2013-01-01

    The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain...

  17. 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.

  18. 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?

  19. Investment spikes in Dutch greenhouse horticulture

    NARCIS (Netherlands)

    Goncharova, N.; Oskam, A.; Oude Lansink, A.G.J.M.; Vlist, van der A.J.; Verstegen, J.A.A.M.

    2008-01-01

    The presence of investment cycles demonstrates the long-run policy of firms investing in particular periods (investment spikes) with lower or zero investment levels in between, which contradicts the smooth pattern predicted by a convex adjustment model. This paper investigates the spells between

  20. Food Price Spikes, Price Insulation, and Poverty

    OpenAIRE

    Anderson, Kym; Ivanic, Maros; Martin, Will

    2013-01-01

    This paper has two purposes. It first considers the impact on world food prices of the changes in restrictions on trade in staple foods during the 2008 world food price crisis. Those changes -- reductions in import protection or increases in export restraints -- were meant to partially insulate domestic markets from the spike in international prices. The authors find that this insulation a...

  1. Gymnosporia montana Benth.(Mountain Spike Thorn)

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 23; Issue 2. Gymnosporia montana Benth. (Mountain Spike Thorn). Flowering Trees Volume 23 Issue 2 February 2018 pp 245-245. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/023/02/0245-0245 ...

  2. 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.

  3. 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.

  4. Encoding noxious heat by spike bursts of antennal bimodal hygroreceptor (dry) neurons in the carabid Pterostichus oblongopunctatus.

    Science.gov (United States)

    Must, Anne; Merivee, Enno; Nurme, Karin; Sibul, Ivar; Muzzi, Maurizio; Di Giulio, Andrea; Williams, Ingrid; Tooming, Ene

    2017-04-01

    Despite thermosensation being crucial in effective thermoregulation behaviour, it is poorly studied in insects. Very little is known about encoding of noxious high temperatures by peripheral thermoreceptor neurons. In carabids, thermo- and hygrosensitive neurons innervate antennal dome-shaped sensilla (DSS). In this study, we demonstrate that several essential fine structural features of dendritic outer segments of the sensory neurons in the DSS and the classical model of insect thermo- and hygrosensitive sensilla differ fundamentally. Here, we show that spike bursts produced by the bimodal dry neurons in the antennal DSS may contribute to the sensation of noxious heat in P. oblongopunctatus. Our electrophysiological experiments showed that, at temperatures above 25 °C, these neurons switch from humidity-dependent regular spiking to temperature-dependent spike bursting. Five out of seven measured parameters of the bursty spike trains, the percentage of bursty dry neurons, the CV of ISIs in a spike train, the percentage of bursty spikes, the number of spikes in a burst and the ISIs in a burst, are unambiguously dependent on temperature and thus may precisely encode both noxious high steady temperatures up to 45 °C as well as rapid step-changes in it. The cold neuron starts to produce temperature-dependent spike bursts at temperatures above 30-35 °C. Thus, the two neurons encode different but largely overlapping ranges in noxious heat. The extent of dendritic branching and lamellation of the neurons largely varies in different DSS, which might be the structural basis for their variation in threshold temperatures for spike bursting.

  5. Spike detection algorithm improvement, spike waveforms projections with PCA and hierarchical classification

    OpenAIRE

    Biffi, Emilia; Ghezzi, Diego; Pedrocchi, Alessandra; Ferrigno, Giancarlo

    2008-01-01

    Definition of single spikes from multiunit spike trains plays a critical role in neurophysiology and in neuroengineering. Moreover, long period analysis are needed to study synaptic plasticity effects and observe the long and medium term development on which all central nervous system (CNS) learning functions are based. Therefore, the increasing importance of long period recordings makes necessary on-line and real time analysis, memory use optimization and data transmission rate improvement. ...

  6. Theory of input spike auto- and cross-correlations and their effect on the response of spiking neurons.

    Science.gov (United States)

    Moreno-Bote, Rubén; Renart, Alfonso; Parga, Néstor

    2008-07-01

    Spike correlations between neurons are ubiquitous in the cortex, but their role is not understood. Here we describe the firing response of a leaky integrate-and-fire neuron (LIF) when it receives a temporarily correlated input generated by presynaptic correlated neuronal populations. Input correlations are characterized in terms of the firing rates, Fano factors, correlation coefficients, and correlation timescale of the neurons driving the target neuron. We show that the sum of the presynaptic spike trains cannot be well described by a Poisson process. In fact, the total input current has a nontrivial two-point correlation function described by two main parameters: the correlation timescale (how precise the input correlations are in time) and the correlation magnitude (how strong they are). Therefore, the total current generated by the input spike trains is not well described by a white noise gaussian process. Instead, we model the total current as a colored gaussian process with the same mean and two-point correlation function, leading to the formulation of the problem in terms of a Fokker-Planck equation. Solutions of the output firing rate are found in the limit of short and long correlation timescales. The solutions described here expand and improve on our previous results (Moreno, de la Rocha, Renart, & Parga, 2002) by presenting new analytical expressions for the output firing rate for general IF neurons, extending the validity of the results for arbitrarily large correlation magnitude, and by describing the differential effect of correlations on the mean-driven or noise-dominated firing regimes. Also the details of this novel formalism are given here for the first time. We employ numerical simulations to confirm the analytical solutions and study the firing response to sudden changes in the input correlations. We expect this formalism to be useful for the study of correlations in neuronal networks and their role in neural processing and information

  7. Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2018-01-01

    We consider the Watts-Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a "Matthew" effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation of synaptic strengths, while bad synchronization gets worse via long-term depression. Emergences of long-term potentiation and long-term depression of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on states) on the SSS are studied and discussed in comparison with the case of additive STDP (independent of states). These effects of STDP on the SSS in the SWN are also compared with those in the regular lattice and the random graph. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. High frequency electric field spikes formed by electron beam-plasma interaction in plasma density gradients

    International Nuclear Information System (INIS)

    Gunell, H.; Loefgren, T.

    1997-02-01

    In the electron beam-plasma interaction at an electric double layer the beam density is much higher than in the classical beam-plasma experiments. The wave propagation takes place along the density gradient, that is present at the high potential side of the double layer. Such a case is studied experimentally by injecting the electron beam from a plane cathode, without any grids suppressing the gradient, and by particle simulations. The high frequency field concentrates in a sharp 'spike' with a half width of the order of one wavelength. The spike is found to be a standing wave surrounded by regions dominated by propagating waves. It forms at a position where its frequency is close to the local plasma frequency. The spike forms also when the electric field is well below the threshold for modulational instability, and long before a density cavity is formed in the simulations. Particle simulations reveal that, at the spike, there is a backward travelling wave that, when it is strongly damped, accelerates electrons back towards the cathode. In a simulation of a homogeneous plasma without the density gradient no spike is seen, and the wave is purely travelling instead of standing. 9 refs

  9. 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...

  10. Thermal impact on spiking properties in Hodgkin–Huxley neuron ...

    Indian Academy of Sciences (India)

    Abstract. The effect of environmental temperature on neuronal spiking behaviors is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin–Huxley neuron subject to synaptic stimulus. We find that the spiking threshold exhibits a global minimum in a specific temperature range ...

  11. Cytoplasmic tail of Coronavirus spike protein has intracellular ...

    Indian Academy of Sciences (India)

    58

    Transfection ability of YFP tagged spike protein constructs are much more efficient. 220 compared to wild type spike construct, the reasons for which are unclear (data not. 221 shown). Because of efficient detection of YFP fluorescence and the limitations of spike. 222 specific antibodies, we decided to use the YFP tagged ...

  12. Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate.

    Science.gov (United States)

    Graupner, Michael; Wallisch, Pascal; Ostojic, Srdjan

    2016-11-02

    Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions. Copyright © 2016 Graupner et al.

  13. 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.

  14. Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model.

    Science.gov (United States)

    Teka, Wondimu; Stockton, David; Santamaria, Fidel

    2016-03-01

    We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.

  15. 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.

  16. Spiking Neural P Systems With Scheduled Synapses.

    Science.gov (United States)

    Cabarle, Francis George C; Adorna, Henry N; Jiang, Min; Zeng, Xiangxiang

    2017-12-01

    Spiking neural P systems (SN P systems) are models of computation inspired by biological spiking neurons. SN P systems have neurons as spike processors, which are placed on the nodes of a directed and static graph (the edges in the graph are the synapses). In this paper, we introduce a variant called SN P systems with scheduled synapses (SSN P systems). SSN P systems are inspired and motivated by the structural dynamism of biological synapses, while incorporating ideas from nonstatic (i.e., dynamic) graphs and networks. In particular, synapses in SSN P systems are available only at specific durations according to their schedules. The SSN P systems model is a response to the problem of introducing durations to synapses of SN P systems. Since SN P systems are in essence static graphs, it is natural to consider them for dynamic graphs also. We introduce local and global schedule types, also taking inspiration from the above-mentioned sources. We prove that SSN P systems are computationally universal as number generators and acceptors for both schedule types, under a normal form (i.e., a simplifying set of restrictions). The introduction of synapse schedules for either schedule type proves useful in programming the system, despite restrictions in the normal form.

  17. Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market

    International Nuclear Information System (INIS)

    Higgs, Helen; Worthington, Andrew

    2008-01-01

    It is commonly known that wholesale spot electricity markets exhibit high price volatility, strong mean-reversion and frequent extreme price spikes. This paper employs a basic stochastic model, a mean-reverting model and a regime-switching model to capture these features in the Australian national electricity market (NEM), comprising the interconnected markets of New South Wales, Queensland, South Australia and Victoria. Daily spot prices from 1 January 1999 to 31 December 2004 are employed. The results show that the regime-switching model outperforms the basic stochastic and mean-reverting models. Electricity prices are also found to exhibit stronger mean-reversion after a price spike than in the normal period, and price volatility is more than fourteen times higher in spike periods than in normal periods. The probability of a spike on any given day ranges between 5.16% in NSW and 9.44% in Victoria

  18. 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. PMID:24223789

  19. 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.

  20. 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.

  1. 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.

  2. Diffraction of sonic booms around buildings resulting in the building spiking effect.

    Science.gov (United States)

    Cho, Sang-Ik T; Sparrow, Victor W

    2011-03-01

    The diffraction of a sonic boom around a building of finite dimensions yields amplification of the front shock and a positive spike that follows the tail shock in the pressure waveform recorded at the incident side of the building's exterior surface. This physical phenomenon is consistently found both in the data obtained from a 2006 NASA flight test and field experiment, and in the finite-difference time-domain simulation that models this particular experiment, and the authors call it the "building spiking" effect. This paper presents an analysis of the numerical and the accompanying experimental results used to investigate the cause of this effect. The simulation assumes linear acoustics only, which sufficiently describes the physics of interest. Separating the low and high frequency components of boom recordings using optimal finite impulse response filters with complementary magnitude responses shows that the building spiking effect can be attributed to the frequency dependent nature of diffraction. A comparison of the building spiking effect of a conventional N-wave and a low-amplitude sonic boom shows that a longer shock rise time leads to less pronounced amplification of the exterior pressure loading on buildings, and thus reveals an advantage of shaping a boom to elongate its rise time. © 2011 Acoustical Society of America

  3. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2015-08-01

    Full Text Available The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

  4. Kv1.2 Channels Promote Nonlinear Spiking Motoneurons for Powering Up Locomotion

    Directory of Open Access Journals (Sweden)

    Rémi Bos

    2018-03-01

    Full Text Available Summary: Spinal motoneurons are endowed with nonlinear spiking behaviors manifested by a spike acceleration whose functional significance remains uncertain. Here, we show in rodent lumbar motoneurons that these nonlinear spiking properties do not rely only on activation of dendritic nifedipine-sensitive L-type Ca2+ channels, as assumed for decades, but also on the slow inactivation of a nifedipine-sensitive K+ current mediated by Kv1.2 channels that are highly expressed in axon initial segments. Specifically, the pharmacological and computational inhibition of Kv1.2 channels occluded the spike acceleration of rhythmically active motoneurons and the correlated slow buildup of rhythmic motor output recorded at the onset of locomotor-like activity. This study demonstrates that slow inactivation of Kv1.2 channels provides a potent gain control mechanism in mammalian spinal motoneurons and has a behavioral role in enhancing locomotor drive during the transition from immobility to steady-state locomotion. : Bos et al. demonstrate that slow inactivation of Kv1.2 channels is critical in shaping nonlinear firing properties in mammalian spinal cord. It provides a potent gain control mechanism in spinal motoneurons and has a behavioral role in enhancing locomotor drive during the transition from immobility to steady-state locomotion. Keywords: locomotion, spinal cord, motoneuron, bistability, potassium channels, Kv1.2

  5. Automated spike sorting using density grid contour clustering and subtractive waveform decomposition.

    Science.gov (United States)

    Vargas-Irwin, Carlos; Donoghue, John P

    2007-08-15

    In multiple cell recordings identifying the number of neurons and assigning each action potential to a particular source, commonly referred to as 'spike sorting', is a highly non-trivial problem. Density grid contour clustering provides a computationally efficient way of locating high-density regions of arbitrary shape in low-dimensional space. When applied to waveforms projected onto their first two principal components, the algorithm allows the extraction of templates that provide high-dimensional reference points that can be used to perform accurate spike sorting. Template matching using subtractive waveform decomposition can locate these templates in waveform samples despite the influence of noise, spurious threshold crossing and waveform overlap. Tests with a large synthetic dataset incorporating realistic challenges faced during spike sorting (including overlapping and phase-shifted spikes) reveal that this strategy can consistently yield results with less than 6% false positives and false negatives (and less than 2% for high signal-to-noise ratios) at processing speeds exceeding those previously reported for similar algorithms by more than an order of magnitude.

  6. 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.

  7. 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...

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

    African Journals Online (AJOL)

    Five wheat genotypes were crossed in complete diallel fashion for gene action studies of spike length, spikelets per spike, grains per spike, grain weight per spike ... High magnitude of narrow sense heritability (h2n.s) was noticed for spikelets per spike (79%), and grains per spike (88%) thus illustrated fixable and additive ...

  9. Extraction of spiked metals from contaminated coastal sediments: a comparison of different methods.

    Science.gov (United States)

    Fan, Wenhong; Wang, Wen-Xiong

    2003-11-01

    Various extraction methods have been developed to assess metal bioavailability from sediments. In this study, we compared the extraction of Cd, Cr, and Zn from contaminated sediments using different extractants (normal seawater, acidic seawater of pH = 5. seawater with 1% sodium dodecyl sulfate [SDS], and gut digestive fluids collected in vitro from deposit-feeding peanut worm Sipunculus nudus) coupled with concurrent metal speciation measurements. The influences of sediment aging on metal extraction were also examined using radiotracer-spiked techniques. Sediments aging up to 100 d did not significantly affect the partitioning of spiked Cd and Cr in different geochemical phases, but the spiked Zn was partitioned more into the reducible fraction and less into the carbonate phase with increasing sediment aging. There was a major difference in the partitioning into different geochemical phases between the spiked metals and the native metals within the 100-d sediment aging. The difference between the spiked and native Cd and Zn extraction using gut juices was somewhat smaller than the strong geochemical contrast. Metals bound with the anoxic sediments were hardly extracted by different extractants. There was a significant relationship between the extraction of spiked Cd and its distribution in the exchangeable phase (positive correlation) or in the reducible phase (negative correlation). For Cr and Zn, extraction was not correlated with their partitioning in any of the geochemical phases. Further, extraction of all three metals by digestive gut fluids was not correlated with the concentrations of simultaneously extractable metals (SEM), nor with the difference between SEM and acid volatile sulfide (AVS). Our study suggests that there were large differences in extraction among metals using different extractants and only Cd extraction was significantly related to its geochemical speciation in sediments.

  10. Fluctuating inhibitory inputs promote reliable spiking at theta frequencies in hippocampal interneurons

    Directory of Open Access Journals (Sweden)

    Duluxan eSritharan

    2012-05-01

    Full Text Available Theta frequency (4-12 Hz rhythms in the hippocampus play important roles in learning and memory. CA1 interneurons located at the stratum lacunosum-moleculare and radiatum junction (LM/RAD are thought to contribute to hippocampal theta population activities by rhythmically pacing pyramidal cells with inhibitory postsynaptic potentials. This implies that LM/RAD cells need to fire reliably at theta frequencies in vivo. To determine whether this could occur, we use biophysically-based LM/RAD model cells and apply different cholinergic and synaptic inputs to simulate in vivo-like network environments. We assess spike reliabilities and spiking frequencies, identifying biophysical properties and network conditions that best promote reliable theta spiking. We find that synaptic background activities that feature large inhibitory, but not excitatory, fluctuations are essential. This suggests that strong inhibitory input to these cells is vital for them to be able to contribute to population theta activities. Furthermore, we find that Type I-like oscillator models produced by augmented persistent sodium currents (INap or diminished A type potassium currents (IA enhance reliable spiking at lower theta frequencies. These Type I-like models are also the most responsive to large inhibitory fluctuations and can fire more reliably under such conditions. In previous work, we showed that INap and IA are largely responsible for establishing LM/RAD cells’ subthreshold activities. Taken together with this study, we see that while both these currents are important for subthreshold theta fluctuations and reliable theta spiking, they contribute in different ways – INap to reliable theta spiking and subthreshold activity generation, and IA to subthreshold activities at theta frequencies. This suggests that linking subthreshold and suprathreshold activities should be done with consideration of both in vivo contexts and biophysical specifics.

  11. Serotonin inhibits low-threshold spike interneurons in the striatum

    Science.gov (United States)

    Cains, Sarah; Blomeley, Craig P; Bracci, Enrico

    2012-01-01

    Low-threshold spike interneurons (LTSIs) are important elements of the striatal architecture and the only known source of nitric oxide in this nucleus, but their rarity has so far prevented systematic studies. Here, we used transgenic mice in which green fluorescent protein is expressed under control of the neuropeptide Y (NPY) promoter and striatal NPY-expressing LTSIs can be easily identified, to investigate the effects of serotonin on these neurons. In sharp contrast with its excitatory action on other striatal interneurons, serotonin (30 μm) strongly inhibited LTSIs, reducing or abolishing their spontaneous firing activity and causing membrane hyperpolarisations. These hyperpolarisations persisted in the presence of tetrodotoxin, were mimicked by 5-HT2C receptor agonists and reversed by 5-HT2C antagonists. Voltage-clamp slow-ramp experiments showed that serotonin caused a strong increase in an outward current activated by depolarisations that was blocked by the specific M current blocker XE 991. In current-clamp experiments, XE 991 per se caused membrane depolarisations in LTSIs and subsequent application of serotonin (in the presence of XE 991) failed to affect these neurons. We concluded that serotonin strongly inhibits striatal LTSIs acting through postsynaptic 5-HT2C receptors and increasing an M type current. PMID:22495583

  12. Spike-timing-dependent learning rule to encode spatiotemporal patterns in a network of spiking neurons

    Science.gov (United States)

    Yoshioka, Masahiko

    2002-01-01

    We study associative memory neural networks based on the Hodgkin-Huxley type of spiking neurons. We introduce the spike-timing-dependent learning rule, in which the time window with the negative part as well as the positive part is used to describe the biologically plausible synaptic plasticity. The learning rule is applied to encode a number of periodical spatiotemporal patterns, which are successfully reproduced in the periodical firing pattern of spiking neurons in the process of memory retrieval. The global inhibition is incorporated into the model so as to induce the gamma oscillation. The occurrence of gamma oscillation turns out to give appropriate spike timings for memory retrieval of discrete type of spatiotemporal pattern. The theoretical analysis to elucidate the stationary properties of perfect retrieval state is conducted in the limit of an infinite number of neurons and shows the good agreement with the result of numerical simulations. The result of this analysis indicates that the presence of the negative and positive parts in the form of the time window contributes to reduce the size of crosstalk term, implying that the time window with the negative and positive parts is suitable to encode a number of spatiotemporal patterns. We draw some phase diagrams, in which we find various types of phase transitions with change of the intensity of global inhibition.

  13. Spike-timing dependent plasticity and the cognitive map

    Directory of Open Access Journals (Sweden)

    Daniel eBush

    2010-10-01

    Full Text Available Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesised that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modelled using auto-associative networks, which utilise rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighbouring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post- synaptic firing according to a spike-timing dependent plasticity (STDP rule. Furthermore, electrophysiology studies have identified persistent ‘theta-coded’ temporal correlations in place cell activity in vivo, characterised by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post- synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilises this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

  14. 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.

  15. Eliminating thermal violin spikes from LIGO noise

    Energy Technology Data Exchange (ETDEWEB)

    Santamore, D. H.; Levin, Yuri

    2001-08-15

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

  16. Eliminating thermal violin spikes from LIGO noise

    International Nuclear Information System (INIS)

    Santamore, D. H.; Levin, Yuri

    2001-01-01

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

  17. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

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

  18. Spiking Neural Network in Precision Agriculture

    Directory of Open Access Journals (Sweden)

    Nadia Adnan Shiltagh

    2015-07-01

    Full Text Available In this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN. Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN. In this work, the precision agriculture system is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce power consumption of sensor nodes Modified Chain-Cluster based Mixed (MCCM routing algorithm is used. According to MCCM, the sensors will send their packets that are less than threshold moisture level to the sink. The SNN with Modified Spike-Prop (MSP training algorithm is capable of identifying soil, irrigation periods and monitoring the soil moisture level, this means that SNN has the ability to be an identifier and monitor. By applying this system the particular agriculture area reaches to the desired moisture level.

  19. Application of magnetoencephalography in epilepsy patients with widespread spike or slow-wave activity.

    Science.gov (United States)

    Shiraishi, Hideaki; Ahlfors, Seppo P; Stufflebeam, Steven M; Takano, Kyoko; Okajima, Maki; Knake, Susanne; Hatanaka, Keisaku; Kohsaka, Shinobu; Saitoh, Shinji; Dale, Anders M; Halgren, Eric

    2005-08-01

    To examine whether magnetoencephalography (MEG) can be used to determine patterns of brain activity underlying widespread paroxysms of epilepsy patients, thereby extending the applicability of MEG to a larger population of epilepsy patients. We studied two children with symptomatic localization-related epilepsy. Case 1 had widespread spikes in EEG with an operation scar from a resection of a brain tumor; Case 2 had hemispheric slow-wave activity in EEG with sensory auras. MEG was collected with a 204-channel helmet-shaped sensor array. Dynamic statistical parametric maps (dSPMs) were constructed to estimate the cortical distribution of interictal discharges for these patients. Equivalent current dipoles (ECDs) also were calculated for comparison with the results of dSPM. In case 1 with widespread spikes, dSPM presented the major activity at the vicinity of the operation scar in the left frontal lobe at the peak of the spikes, and some activities were detected in the left temporal lobe just before the peak in some spikes. In case 2 with hemispheric slow waves, the most active area was located in the left parietal lobe, and additional activity was seen at the ipsilateral temporal and frontal lobes in dSPM. The source estimates correlated well with the ictal manifestation and interictal single-photon emission computed tomography (SPECT) findings for this patient. In comparison with the results of ECDs, ECDs could not express a prior activity at the left temporal lobe in case 1 and did not model well the MEG data in case 2. We suggest that by means of dSPM, MEG is useful for presurgical evaluation of patients, not only with localized epileptiform activity, but also with widespread spikes or slow waves, because it requires no selections of channels and no time-point selection.

  20. Topographic prominence discriminator for the detection of short-latency spikes of retinal ganglion cells

    Science.gov (United States)

    Choi, Myoung-Hwan; Ahn, Jungryul; Park, Dae Jin; Lee, Sang Min; Kim, Kwangsoo; Cho, Dong-il Dan; Senok, Solomon S.; Koo, Kyo-in; Goo, Yong Sook

    2017-02-01

    Objective. Direct stimulation of retinal ganglion cells in degenerate retinas by implanting epi-retinal prostheses is a recognized strategy for restoration of visual perception in patients with retinitis pigmentosa or age-related macular degeneration. Elucidating the best stimulus-response paradigms in the laboratory using multielectrode arrays (MEA) is complicated by the fact that the short-latency spikes (within 10 ms) elicited by direct retinal ganglion cell (RGC) stimulation are obscured by the stimulus artifact which is generated by the electrical stimulator. Approach. We developed an artifact subtraction algorithm based on topographic prominence discrimination, wherein the duration of prominences within the stimulus artifact is used as a strategy for identifying the artifact for subtraction and clarifying the obfuscated spikes which are then quantified using standard thresholding. Main results. We found that the prominence discrimination based filters perform creditably in simulation conditions by successfully isolating randomly inserted spikes in the presence of simple and even complex residual artifacts. We also show that the algorithm successfully isolated short-latency spikes in an MEA-based recording from degenerate mouse retinas, where the amplitude and frequency characteristics of the stimulus artifact vary according to the distance of the recording electrode from the stimulating electrode. By ROC analysis of false positive and false negative first spike detection rates in a dataset of one hundred and eight RGCs from four retinal patches, we found that the performance of our algorithm is comparable to that of a generally-used artifact subtraction filter algorithm which uses a strategy of local polynomial approximation (SALPA). Significance. We conclude that the application of topographic prominence discrimination is a valid and useful method for subtraction of stimulation artifacts with variable amplitudes and shapes. We propose that our algorithm

  1. Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Bin Min

    2018-01-01

    Full Text Available Synaptic plasticity is believed to be the biological substrate underlying learning and memory. One of the most widespread forms of synaptic plasticity, spike-timing-dependent plasticity (STDP, uses the spike timing information of presynaptic and postsynaptic neurons to induce synaptic potentiation or depression. An open question is how STDP organizes the connectivity patterns in neuronal circuits. Previous studies have placed much emphasis on the role of firing rate in shaping connectivity patterns. Here, we go beyond the firing rate description to develop a self-consistent linear response theory that incorporates the information of both firing rate and firing variability. By decomposing the pairwise spike correlation into one component associated with local direct connections and the other associated with indirect connections, we identify two distinct regimes regarding the network structures learned through STDP. In one regime, the contribution of the direct-connection correlations dominates over that of the indirect-connection correlations in the learning dynamics; this gives rise to a network structure consistent with the firing rate description. In the other regime, the contribution of the indirect-connection correlations dominates in the learning dynamics, leading to a network structure different from the firing rate description. We demonstrate that the heterogeneity of firing variability across neuronal populations induces a temporally asymmetric structure of indirect-connection correlations. This temporally asymmetric structure underlies the emergence of the second regime. Our study provides a new perspective that emphasizes the role of high-order statistics of spiking activity in the spike-correlation-sensitive learning dynamics.

  2. Spike-timing computation properties of a feed-forward neural network model

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

  3. 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.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

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

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

    Science.gov (United States)

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

    2012-06-01

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

  7. Spike timing precision in the visual front-end

    OpenAIRE

    Borghuis, B.G. (Bart Gerard)

    2003-01-01

    This thesis describes a series of investigations into the reliability of neural responses in the primary visual pathway. The results described in subsequent chapters are primarily based on extracellular recordings from single neurons in anaesthetized cats and area MT of an awake monkey, and computational model analysis. Comparison of spike timing precision in recorded and Poisson-simulated spike trains shows that spike timing in the front-end visual system is considerably more precise than on...

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

    OpenAIRE

    Swindale, Nicholas V.; Spacek, Martin A.

    2014-01-01

    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 rec...

  9. Electric field spikes formed by electron beam endash plasma interaction in plasma density gradients

    International Nuclear Information System (INIS)

    Gunell, H.; Loefgren, T.

    1997-01-01

    In the electron beam endash plasma interaction at an electric double layer the beam density is much higher than in the classical beam endash plasma experiments. The wave propagation takes place along the density gradient that is present at the high potential side of the double layer. Such a case is studied experimentally by injecting the electron beam from a plane cathode, without any grids suppressing the gradient, and by particle simulations. The high frequency field concentrates in a sharp open-quotes spikeclose quotes with a half width of the order of one wavelength. The spike is found to be a standing wave surrounded by regions dominated by propagating waves. It forms at a position where its frequency is close to the local plasma frequency. The spike forms also when the electric field is well below the threshold for modulational instability, and long before a density cavity is formed in the simulations. Particle simulations reveal that, at the spike, there is a backward traveling wave that, when it is strongly damped, accelerates electrons back towards the cathode. In a simulation of a homogeneous plasma without the density gradient no spike is seen, and the wave is purely travelling instead of standing. copyright 1997 American Institute of Physics

  10. A Model of Electrically Stimulated Auditory Nerve Fiber Responses with Peripheral and Central Sites of Spike Generation

    DEFF Research Database (Denmark)

    Joshi, Suyash Narendra; Dau, Torsten; Epp, Bastian

    2017-01-01

    A computational model of cat auditory nerve fiber (ANF) responses to electrical stimulation is presented. The model assumes that (1) there exist at least two sites of spike generation along the ANF and (2) both an anodic (positive) and a cathodic (negative) charge in isolation can evoke a spike...... of facilitation, accommodation, refractoriness, and spike-rate adaptation in ANF. Although the model is parameterized using data for either single or paired pulse stimulation with monophasic rectangular pulses, it correctly predicts effects of various stimulus pulse shapes, stimulation pulse rates, and level...... on the neural response statistics. The model may serve as a framework to explore the effects of different stimulus parameters on psychophysical performance measured in cochlear implant listeners....

  11. 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

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

    OpenAIRE

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    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...

  13. Schapiro Shapes

    Science.gov (United States)

    O'Connell, Emily

    2009-01-01

    This article describes a lesson on Schapiro Shapes. Schapiro Shapes is based on the art of Miriam Schapiro, who created a number of works of figures in action. Using the basic concepts of this project, students learn to create their own figures and styles. (Contains 1 online resource.)

  14. [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.

  15. 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.

  16. A robust and biologically plausible spike pattern recognition network.

    Science.gov (United States)

    Larson, Eric; Perrone, Ben P; Sen, Kamal; Billimoria, Cyrus P

    2010-11-17

    The neural mechanisms that enable recognition of spiking patterns in the brain are currently unknown. This is especially relevant in sensory systems, in which the brain has to detect such patterns and recognize relevant stimuli by processing peripheral inputs; in particular, it is unclear how sensory systems can recognize time-varying stimuli by processing spiking activity. Because auditory stimuli are represented by time-varying fluctuations in frequency content, it is useful to consider how such stimuli can be recognized by neural processing. Previous models for sound recognition have used preprocessed or low-level auditory signals as input, but complex natural sounds such as speech are thought to be processed in auditory cortex, and brain regions involved in object recognition in general must deal with the natural variability present in spike trains. Thus, we used neural recordings to investigate how a spike pattern recognition system could deal with the intrinsic variability and diverse response properties of cortical spike trains. We propose a biologically plausible computational spike pattern recognition model that uses an excitatory chain of neurons to spatially preserve the temporal representation of the spike pattern. Using a single neural recording as input, the model can be trained using a spike-timing-dependent plasticity-based learning rule to recognize neural responses to 20 different bird songs with >98% accuracy and can be stimulated to evoke reverse spike pattern playback. Although we test spike train recognition performance in an auditory task, this model can be applied to recognize sufficiently reliable spike patterns from any neuronal system.

  17. A review on cluster estimation methods and their applications to neural spike data.

    Science.gov (United States)

    Zhang, James; Nguyen, Thanh; Cogill, Steven; Bhatti, Asim; Luo, Lingkun; Yang, Samuel; Nahavandi, Saeid

    2018-03-02

    The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons, "spike sorting", is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. With the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and the most challenging part due to the existence of background noise and the overlap and interactions among neurons in the neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to process the ever-growing vast amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices were comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against the pre-existing ground truth labels. The results showed that the top five validity indices work consistently well across the variations in noise levels both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process. © 2018 IOP Publishing Ltd.

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

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

    Upward spikes in the international price of food in recent years led some countries to raise export barriers, thereby exacerbating both the price spike and reducing the terms of trade for food-importing countries (beggaring their neighbors). At the same time, and for similar political...

  19. 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.

  20. Interictal spike EEG source analysis in hypothalamic hamartoma epilepsy.

    Science.gov (United States)

    Leal, Alberto J R; Passão, Vitorina; Calado, Eulália; Vieira, José P; Silva Cunha, João P

    2002-12-01

    The epilepsy associated with the hypothalamic hamartomas constitutes a syndrome with peculiar seizures, usually refractory to medical therapy, mild cognitive delay, behavioural problems and multifocal spike activity in the scalp electroencephalogram (EEG). The cortical origin of spikes has been widely assumed but not specifically demonstrated. We present results of a source analysis of interictal spikes from 4 patients (age 2-25 years) with epilepsy and hypothalamic hamartoma, using EEG scalp recordings (32 electrodes) and realistic boundary element models constructed from volumetric magnetic resonance imaging (MRIs). Multifocal spike activity was the most common finding, distributed mainly over the frontal and temporal lobes. A spike classification based on scalp topography was done and averaging within each class performed to improve the signal to noise ratio. Single moving dipole models were used, as well as the Rap-MUSIC algorithm. All spikes with good signal to noise ratio were best explained by initial deep sources in the neighbourhood of the hamartoma, with late sources located in the cortex. Not a single patient could have his spike activity explained by a combination of cortical sources. Overall, the results demonstrate a consistent origin of spike activity in the subcortical region in the neighbourhood of the hamartoma, with late spread to cortical areas.

  1. Spike protection device for electronics and communication appliances

    African Journals Online (AJOL)

    Experience shows that most failures of electronic and communication equipment result from damage caused by external electrical disturbances in the form of overvoltage, undervoltage, surge, sag, spike, or voltage dropout (blackout), the status of which is determined by the amplitude and duration of the disturbance. Spikes ...

  2. Diagrammatic scale for the assessment of blast on wheat spikes

    Directory of Open Access Journals (Sweden)

    João Leodato Nunes Maciel

    2013-09-01

    Full Text Available The correct quantification of blast caused by the fungus Magnaporthe oryzae on wheat (Triticum aestivum spikes is an important component to understand the development of this disease aimed at its control. Visual quantification based on a diagrammatic scale can be a practical and efficient strategy that has already proven to be useful against several plant pathosystems, including diseases affecting wheat spikes like glume blotch and fusarium head blight. Spikes showing different disease severity values were collected from a wheat field with the aim of elaborating a diagrammatic scale to quantify blast severity on wheat spikes. The spikes were photographed and blast severity was determined by using resources of the software ImageJ. A diagrammatic scale was developed with the following disease severity values: 3.7, 7.5, 21.4, 30.5, 43.8, 57.3, 68.1, 86.0, and 100.0%. An asymptomatic spike was added to the scale. Scale validation was performed by eight people who estimated blast severity by using digitalized images of 40 wheat spikes. The precision and the accuracy of the evaluations varied according to the rater (0.82spikes.

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

    Science.gov (United States)

    Mulansky, Mario; Bozanic, Nebojsa

    2015-01-01

    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. PMID:25744888

  4. Cytoplasmic tail of coronavirus spike protein has intracellular

    Indian Academy of Sciences (India)

    Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is localized in the ER or ERGIC compartment and OC43 spike protein is predominantly localized in thelysosome. Differential localization can be explained by signal sequence. The sequence alignment ...

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

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

    We show here, by means of evolutionary spectral analysis and synthesis of cytosolic Ca2+ ([Ca2+]c) spiking observed at the single cell level using digital imaging fluorescence microscopy of fura-2-loaded mouse cerebellar granule cells in culture, that [Ca2+]c spiking can be resolved into evolutio......We show here, by means of evolutionary spectral analysis and synthesis of cytosolic Ca2+ ([Ca2+]c) spiking observed at the single cell level using digital imaging fluorescence microscopy of fura-2-loaded mouse cerebellar granule cells in culture, that [Ca2+]c spiking can be resolved...... into evolutionary spectra of a characteristic set of frequencies. Non-delayed small spikes on top of sustained [Ca2+]c were synthesized by a main component frequency, 0.132+/-0.012 Hz, showing its maximal amplitude in phase with the start of depolarization (25 mM KCI) combined with caffeine (10 mM) application...

  6. Spike detection II: automatic, perception-based detection and clustering.

    Science.gov (United States)

    Wilson, S B; Turner, C A; Emerson, R G; Scheuer, M L

    1999-03-01

    We developed perception-based spike detection and clustering algorithms. The detection algorithm employs a novel, multiple monotonic neural network (MMNN). It is tested on two short-duration EEG databases containing 2400 spikes from 50 epilepsy patients and 10 control subjects. Previous studies are compared for database difficulty and reliability and algorithm accuracy. Automatic grouping of spikes via hierarchical clustering (using topology and morphology) is visually compared with hand marked grouping on a single record. The MMNN algorithm is found to operate close to the ability of a human expert while alleviating problems related to overtraining. The hierarchical and hand marked spike groupings are found to be strikingly similar. An automatic detection algorithm need not be as accurate as a human expert to be clinically useful. A user interface that allows the neurologist to quickly delete artifacts and determine whether there are multiple spike generators is sufficient.

  7. 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.

  8. <strong>Neuroeconomics and Health Economicsstrong>/>

    DEFF Research Database (Denmark)

    Larsen, Torben

    2009-01-01

    activation of Amygdala - a key center in our emotional arousal (limbic system) - as shaped in the elder stone-age with many acute threats. II. In general, the Hawthorne-effect of management is explained as the result of supportive job-relations reinforcing the homeostatic properties of the limbic system...... with de-stressing benefits as reduced anxiety, less use of stimulants and a reduction of blood pressure which in all increase life-expectancy. Conclusion: Neuroeconomics helps economists to identify dominant health economic interventions that may be overlooked by traditional discipålines   [i] This part...

  9. Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness.

    Science.gov (United States)

    Toyoizumi, Taro; Rad, Kamiar Rahnama; Paninski, Liam

    2009-05-01

    There has recently been a great deal of interest in inferring network connectivity from the spike trains in populations of neurons. One class of useful models that can be fit easily to spiking data is based on generalized linear point process models from statistics. Once the parameters for these models are fit, the analyst is left with a nonlinear spiking network model with delays, which in general may be very difficult to understand analytically. Here we develop mean-field methods for approximating the stimulus-driven firing rates (in both the time-varying and steady-state cases), auto- and cross-correlations, and stimulus-dependent filtering properties of these networks. These approximations are valid when the contributions of individual network coupling terms are small and, hence, the total input to a neuron is approximately gaussian. These approximations lead to deterministic ordinary differential equations that are much easier to solve and analyze than direct Monte Carlo simulation of the network activity. These approximations also provide an analytical way to evaluate the linear input-output filter of neurons and how the filters are modulated by network interactions and some stimulus feature. Finally, in the case of strong refractory effects, the mean-field approximations in the generalized linear model become inaccurate; therefore, we introduce a model that captures strong refractoriness, retains all of the easy fitting properties of the standard generalized linear model, and leads to much more accurate approximations of mean firing rates and cross-correlations that retain fine temporal behaviors.

  10. Controlled Growth of Gold Nanostars: Effect of Spike Length on SERS Signal Enhancement.

    Science.gov (United States)

    Sheen Mers, S V; Umadevi, S; Ganesh, V

    2017-05-19

    Two different types of gold nanostars (Au NS), namely, short-spiked nanostars (SSNS) and long-spiked nanostars (LSNS), are prepared by using a hexagonal lyotropic liquid-crystalline (LLC) phase as a template. The formation, size and length of spikes or arms of the resultant Au NS are controlled by preparation in either a hexagonal LLC phase or an isotropic phase. These NS are anchored onto indium tin oxide (ITO) electrodes through a self-assembled monolayer of 3-mercaptopropyltrimethoxysilane, which acts as a linker molecule. Structural and morphological characterisations of SSNS- and LSNS-anchored ITO electrodes are performed by means of microscopic and spectroscopic analyses. Further electrochemical techniques, namely, cyclic voltammetry and electrochemical impedance spectroscopy, are also used to confirm the immobilisation of these Au NS on ITO electrodes and to study the electrochemical characteristics. These studies clearly reveal the formation of star-shaped, branched, anisotropic nanostructures of gold during the template preparation method and these Au NS are successfully anchored onto ITO electrodes through a covalent immobilisation strategy. Furthermore, the SERS activity of these Au NS is analysed by using glutathione and crystal violet as analytes and by employing glass and ITO as substrates. It is interesting to note that SSNS show a significant enhancement in SERS signals relative to those of LSNS. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Integrative spike dynamics of rat CA1 neurons: a multineuronal imaging study.

    Science.gov (United States)

    Sasaki, Takuya; Kimura, Rie; Tsukamoto, Masako; Matsuki, Norio; Ikegaya, Yuji

    2006-07-01

    The brain operates through a coordinated interplay of numerous neurons, yet little is known about the collective behaviour of individual neurons embedded in a huge network. We used large-scale optical recordings to address synaptic integration in hundreds of neurons. In hippocampal slice cultures bolus-loaded with Ca2+ fluorophores, we stimulated the Schaffer collaterals and monitored the aggregate presynaptic activity from the stratum radiatum and individual postsynaptic spikes from the CA1 stratum pyramidale. Single neurons responded to varying synaptic inputs with unreliable spikes, but at the population level, the networks stably output a linear sum of synaptic inputs. Nonetheless, the network activity, even though given constant stimuli, varied from trial to trial. This variation emerged through time-varying recruitment of different neuron subsets, which were shaped by correlated background noise. We also mapped the input-frequency preference in spiking activity and found that the majority of CA1 neurons fired in response to a limited range of presynaptic firing rates (20-40 Hz), acting like a band-pass filter, although a few neurons had high pass-like or low pass-like characteristics. This frequency selectivity depended on phasic inhibitory transmission. Thus, our imaging approach enables the linking of single-cell behaviours to their communal dynamics, and we discovered that, even in a relatively simple CA1 circuit, neurons could be engaged in concordant information processing.

  12. Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages

    Directory of Open Access Journals (Sweden)

    Adam Sol Shai

    2014-08-01

    Full Text Available Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (~ hundreds of milliseconds and strong (~50 mV from rest depolarizations. Such plateau events rely on clustered glutamatergic input, can be mediated by calcium and NMDA currents, and often generate somatic depolarizations that last for the time course of the dendritic plateau event. We address the computational significance of such single-neuron processing via reduced but biophysically realistic modeling. We introduce a model based on two discrete integration zones, a somatic and a dendritic one, that communicate via a long plateau-conductance. We show principled differences in the way dendritic versus somatic inhibition controls spike timing, and demonstrate how this could implement a mechanism of spike time control in the face of barrages of synaptic inputs.

  13. Transient electroluminescence spikes in small molecular organic light-emitting diodes

    Science.gov (United States)

    Liu, Rui; Gan, Zhengqing; Shinar, Ruth; Shinar, Joseph

    2011-06-01

    We present a comprehensive study of transient nanosecond electroluminescence (EL) spikes that exceed the dc level and microseconds-long EL tails following a bias pulse in guest-host small molecular organic light-emitting diodes (SMOLEDs), including relatively efficient devices, which elucidates carrier and exciton dynamics in such devices. The transient EL is strongly dependent, among other parameters, on device materials and structure. At low temperatures, all measured devices, with the exception of Pt octaethylporphyrin (PtOEP)-doped tris(8-hydroxyquinoline) Al (Alq3) SMOLEDs, exhibit the spikes at ˜70-300 ns. At room temperature (RT), however, only those with a hole injection barrier, carrier-trapping guest-host emitting layer, and no strong electron-transporting and hole-blocking layer [such as 4,7-diphenyl-1,10-phenanthroline (BPhen)] exhibit strong spikes. These narrow and appear earlier under postpulse reverse bias. To further elucidate the origin of the spikes, we monitored their dependence on the pulsed bias width and voltage, the doped layer thickness, and its location within the OLED structure. The characteristics of the microseconds-long tails were also evaluated through the effect of the postpulse voltage. A model based on the recombination of correlated charge pairs (CCPs) and on charge detrapping is presented; the model agrees well with the experimental data. The results suggest that reduced electric-field-induced dissociative quenching of singlet excitons is responsible for the spikes’ amplitude exceeding the on-pulse dc EL level. The long tails are attributed to recombination of charges detrapped from a distribution of shallow, mostly host, sites, reminiscent of the detrapping and recombination processes that yield the thermally stimulated luminescence of such materials. The comprehensive transient EL measurements in guest-host devices demonstrate the generality of the strong spike phenomenon in devices with charge trapping in the emitting guest

  14. <strong>Neuroeconomics and behavioral health economicsstrong>/>

    DEFF Research Database (Denmark)

    Larsen, Torben

    2009-01-01

    dissemination of relaxation procedures is evident in industrialized countries since about 1970 both inside the medical healthcare system and as NGO-settings in a market-alike competition. However, a serious barrier to the dissemination of meditative de-stressing is the lack of general knowledge of the action...... for explanation of the neural dynamics of normal decision making. Secondly, the literature is reviewed for evidence on hypothesized applications of NeM in behavioral health. Results I. The present bias as documented by neuroeconomic game-trials is explained by NeM as rooted in the basal activation of Amygdala...... - a key center in our emotional arousal (limbic system) - as shaped in the elder stone-age with many acute threats. II. In general, the Hawthorne-effect of human-relations management is explained as the result of supportive job-relations relaxing Amygdala for better emotional integration...

  15. <strong>Neuroeconomics and behavioral health economicsstrong>/>

    DEFF Research Database (Denmark)

    Larsen, Torben

    2009-01-01

    - a key center in our emotional arousal (limbic system) - as shaped in the elder stone-age with many acute threats. II. In general, the Hawthorne-effect of human-relations management is explained as the result of supportive job-relations relaxing Amygdala for better emotional integration...... some are rooted in the religious tradition while other aim to be post-religious. Medical meditation across settings combines savings on health care costs with de-stressing benefits as reduced anxiety, less use of stimulants and a reduction of blood pressure which in all increase life...... is met by a meso-strategy aiming the formation of an international, multidisciplinary network which might organize regional workshops for representatives for all involved parties in order to prepare local implementation projects.   Regarding de-stressing by medical meditation a relatively fast...

  16. Uroguanylin induces electroencephalographic spikes in rats

    Directory of Open Access Journals (Sweden)

    MDA. Teixeira

    Full Text Available Uroguanylin (UGN is an endogenous peptide that acts on membrane-bound guanylate cyclase receptors of intestinal and renal cells increasing cGMP production and regulating electrolyte and water epithelial transport. Recent research works demonstrate the expression of this peptide and its receptor in the central nervous system. The current work was undertaken in order to evaluate modifications of electroencephalographic spectra (EEG in anesthetized Wistar rats, submitted to intracisternal infusion of uroguanylin (0.0125 nmoles/min or 0.04 nmoles/min. The current observations demonstrate that 0.0125 nmoles/min and 0.04 nmoles/min intracisternal infusion of UGN significantly enhances amplitude and frequency of sharp waves and evoked spikes (p = 0.03. No statistical significance was observed on absolute alpha and theta spectra amplitude. The present data suggest that UGN acts on bioelectrogenesis of cortical cells by inducing hypersynchronic firing of neurons. This effect is blocked by nedocromil, suggesting that UGN acts by increasing the activity of chloride channels.

  17. 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.

  18. Generalized analog thresholding for spike acquisition at ultralow sampling rates.

    Science.gov (United States)

    He, Bryan D; Wein, Alex; Varshney, Lav R; Kusuma, Julius; Richardson, Andrew G; Srinivasan, Lakshminarayan

    2015-07-01

    Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. Copyright © 2015 the American Physiological Society.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. Cigar-shaped quarkonia under strong magnetic field

    Science.gov (United States)

    Suzuki, Kei; Yoshida, Tetsuya

    2016-03-01

    Heavy quarkonia in a homogeneous magnetic field are analyzed by using a potential model with constituent quarks. To obtain anisotropic wave functions and corresponding eigenvalues, the cylindrical Gaussian expansion method is applied, where the anisotropic wave functions are expanded by a Gaussian basis in the cylindrical coordinates. Deformation of the wave functions and the mass shifts of the S-wave heavy quarkonia (ηc, J /ψ , ηc(2 S ), ψ (2 S ) and bottomonia) are examined for the wide range of external magnetic field. The spatial structure of the wave functions changes drastically as adjacent energy levels cross each other. Possible observables in heavy-ion collision experiments and future lattice QCD simulations are also discussed.

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

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

    Upward spikes in the international price of food in recent years led some countries to raise export barriers, thereby exacerbating both the price spike and reducing the terms of trade for food-importing countries (beggaring their neighbors). At the same time, and for similar political......-economy reasons, numerous food-importing countries reduced or suspended their import tariffs, and some even provided food import subsidies -- which also exacerbated the international price spike, thus turning the terms of trade even further against food-importing countries. This issue became a major item...

  5. 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 ...

  6. Electroencephalographic precursors of spike-wave discharges in a genetic rat model of absence epilepsy: Power spectrum and coherence EEG analyses.

    Science.gov (United States)

    Sitnikova, Evgenia; van Luijtelaar, Gilles

    2009-04-01

    Periods in the electroencephalogram (EEG) that immediately precede the onset of spontaneous spike-wave discharges (SWD) were examined in WAG/Rij rat model of absence epilepsy. Precursors of SWD (preSWD) were classified based on the distribution of EEG power in delta-theta-alpha frequency bands as measured in the frontal cortex. In 95% of preSWD, an elevation of EEG power was detected in delta band (1-4Hz). 73% of preSWD showed high power in theta frequencies (4.5-8Hz); these preSWD might correspond to 5-9Hz oscillations that were found in GAERS before SWD onset [Pinault, D., Vergnes, M., Marescaux, C., 2001. Medium-voltage 5-9Hz oscillations give rise to spike-and-wave discharges in a genetic model of absence epilepsy: in vivo dual extracellular recording of thalamic relay and reticular neurons. Neuroscience 105, 181-201], however, theta component of preSWD in our WAG/Rij rats was not shaped into a single rhythm. It is concluded that a coalescence of delta and theta in the cortex is favorable for the occurrence of SWD. The onset of SWD was associated with strengthening of intracortical and thalamo-cortical coherence in 9.5-14Hz and in double beta frequencies. No features of EEG coherence can be considered as unique for any of preSWD subtype. Reticular and ventroposteromedial thalamic nuclei were strongly coupled even before the onset of SWD. All this suggests that SWD derive from an intermixed delta-theta EEG background; seizure onset associates with reinforcement of intracortical and cortico-thalamic associations.

  7. Testing strong interaction theories

    International Nuclear Information System (INIS)

    Ellis, J.

    1979-01-01

    The author discusses possible tests of the current theories of the strong interaction, in particular, quantum chromodynamics. High energy e + e - interactions should provide an excellent means of studying the strong force. (W.D.L.)

  8. 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

  9. 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.

  10. Experimental demonstration of a single-spike hard-X-ray free-electron laser starting from noise

    International Nuclear Information System (INIS)

    Marinelli, A.; MacArthur, J.; Emma, P.; Guetg, M.; Field, C.

    2017-01-01

    In this letter, we report the experimental demonstration of single-spike hard-X-ray free-electron laser pulses starting from noise with multi-eV bandwidth. Here, this is accomplished by shaping a low-charge electron beam with a slotted emittance spoiler and by adjusting the transport optics to optimize the beam-shaping accuracy. Based on elementary free-electron laser scaling laws, we estimate the pulse duration to be less than 1 fs full-width at half-maximum.

  11. Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams

    Directory of Open Access Journals (Sweden)

    Nicolangelo L Iannella

    2010-07-01

    Full Text Available Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP. Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibres with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organisation in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite.Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a dendritic efficacy mosaic. Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite.

  12. 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...... 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...... into account physiological constraints on the control. A precise and robust targeting of neural activity based on stochastic optimal control has great potential for regulating neural activity in e.g. prosthetic applications and to improve our understanding of the basic mechanisms by which neuronal firing...

  13. 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.

  14. Sonar target localization based on spike coded spectrograms

    Directory of Open Access Journals (Sweden)

    Bertrand FONTAINE

    2006-12-01

    Full Text Available Target location is coded into the pattern of spikes that run up the auditory nerve to the bat's brain. Realistic scenes containing multiple, closely spaced, reflectors give rise to complex echo signals consisting of multiple filtered copies of the bat's own vocalisation. Some of this filtering is due to the directivity of the bat’s reception system i.e., the outer ears, and some of it is due to sound absorption and the reflection process. The analysis below concentrates on the conspicuous ridges (notches these filter operations give rise to in the time-frequency representation of the echo as produced by the bat's inner ear. Assuming multiple threshold detecting neurons for each frequency channel it is shown how the distribution of spike times within the generated spike bursts is linked to the presence and characteristics of these notches. A neural network decoding the spike bursts in terms of target location is described.

  15. Cellular Origin of Spontaneous Ganglion Cell Spike Activity in Animal Models of Retinitis Pigmentosa

    Directory of Open Access Journals (Sweden)

    David J. Margolis

    2011-01-01

    Full Text Available Here we review evidence that loss of photoreceptors due to degenerative retinal disease causes an increase in the rate of spontaneous ganglion spike discharge. Information about persistent spike activity is important since it is expected to add noise to the communication between the eye and the brain and thus impact the design and effective use of retinal prosthetics for restoring visual function in patients blinded by disease. Patch-clamp recordings from identified types of ON and OFF retinal ganglion cells in the adult (36–210 d old rd1 mouse show that the ongoing oscillatory spike activity in both cell types is driven by strong rhythmic synaptic input from presynaptic neurons that is blocked by CNQX. The recurrent synaptic activity may arise in a negative feedback loop between a bipolar cell and an amacrine cell that exhibits resonant behavior and oscillations in membrane potential when the normal balance between excitation and inhibition is disrupted by the absence of photoreceptor input.

  16. Independent fissile inventory verification in a large tank employing lutetium double spikes

    International Nuclear Information System (INIS)

    Carter, J.A.; Walker, R.L.; May, M.P.; Smith, D.H.; Hebble, T.L.

    1987-01-01

    A 3000-liter feed adjustment tank containing over 2400 L of uranium solution was assayed for its contents using the double spiking technique of isotope dilution mass spectrometry. Lutetium was the double spike, with the natural element used as the initial spike and enriched 176-Lu as the second. The ability of a remote sampling system was evaluated for its ability to introduce the lutetium and also to produce homogeneous sample solutions. The system was found to be satisfactory. Volumes of the tank can be measured to a precision of about 0.2%. The concentration of uranium was measured as 154.5 g/L uranium, thus giving a total of 382.3 kg in the tank as compared to the plant's best estimate of 383 kg. Uranium measurements were subjected to internal calibration calculations, with 233-U and 236-U being used as the reference isotopes. A diversion of 5% of the tank contents was simulated to evaluate the method's sensitivity in this regard. The ability of this method to give timely results of good precision makes it a strong candidate for use in material balance and inventory accountability applications; it also has potential use in quality assurance areas

  17. 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.

  18. Spike Neural Models Part II: Abstract Neural Models

    OpenAIRE

    Johnson, Melissa G.; Chartier, Sylvain

    2018-01-01

    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 whic...

  19. Characteristics of Spike motion in World top lever volleyball players

    OpenAIRE

    黒川, 貞生; 森田, 恭光; 亀ヶ谷, 純一; 加藤, 浩人; 松井, 泰二; 鈴木, 陽一; 矢島, 忠明

    2008-01-01

    The purpose of this study was to investigate the characteristics of spike motion in world top level volleyball players. Front- and back-spike motions were recorded by high-speed video camera system operating at 250Hz to obtain three dimensional coordinates of the body segments and the center of the ball. The velocity of the ball, wrist, shoulder and trunk twist angle was calculated using motion analyzer. There is no significant relationship between the initial ball velocity and the velocity o...

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

    OpenAIRE

    E. D. Belousova

    2012-01-01

    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, clini...

  1. 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)

  2. 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.

  3. 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.

  4. 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.

  5. Cellular and circuit mechanisms maintain low spike co-variability and enhance population coding in somatosensory cortex

    Directory of Open Access Journals (Sweden)

    Cheng eLy

    2012-03-01

    Full Text Available The responses of cortical neurons are highly variable across repeated presentations of a stimulus. Understanding this variability is critical for theories of both sensory and motor processing, since response variance affects the accuracy of neural codes. Despite this influence, the cellular and circuit mechanisms that shape the trial-to-trial variability of population responses remain poorly understood. We used a combination of experimental and computational techniques to uncover the mechanisms underlying response variability of populations of pyramidal (E cells in layer 2/3 of rat whisker barrel cortex. Spike trains recorded from pairs of E-cells during either spontaneous activity or whisker deflected responses show similarly low levels of spiking co-variability, despite large differences in network activation between the two states. We developed network models that show how spike threshold nonlinearities dilutes E-cell spiking co-variability during spontaneous activity and low velocity whisker deflections. In contrast, during high velocity whisker deflections, cancelation mechanisms mediated by feedforward inhibition maintain low E-cell pairwise co-variability. Thus, the combination of these two mechanisms ensure low E-cell population variability over a wide range of whisker deflection velocities. Finally, we show how this active decorrelation of population variability leads to a drastic increase in the population information about whisker velocity. The canonical cellular and circuit components of our study suggest that low network variability over a broad range of neural states may generalize across the nervous system.

  6. Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks.

    Directory of Open Access Journals (Sweden)

    Nafise Erfanian Saeedi

    2016-04-01

    Full Text Available Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons' action potentials (spikes as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

  7. Joint statistics of strongly correlated neurons via dimensionality reduction

    Science.gov (United States)

    Deniz, Taşkın; Rotter, Stefan

    2017-06-01

    The relative timing of action potentials in neurons recorded from local cortical networks often shows a non-trivial dependence, which is then quantified by cross-correlation functions. Theoretical models emphasize that such spike train correlations are an inevitable consequence of two neurons being part of the same network and sharing some synaptic input. For non-linear neuron models, however, explicit correlation functions are difficult to compute analytically, and perturbative methods work only for weak shared input. In order to treat strong correlations, we suggest here an alternative non-perturbative method. Specifically, we study the case of two leaky integrate-and-fire neurons with strong shared input. Correlation functions derived from simulated spike trains fit our theoretical predictions very accurately. Using our method, we computed the non-linear correlation transfer as well as correlation functions that are asymmetric due to inhomogeneous intrinsic parameters or unequal input.

  8. Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact

    Directory of Open Access Journals (Sweden)

    Andreas eKlaus

    2011-07-01

    Full Text Available In the striatal microcircuit, fast-spiking (FS interneurons have an important role in mediating inhibition onto neighboring medium spiny (MS projection neurons. In this study, we combined computational modeling with in vitro and in vivo electrophysiological measurements to investigate FS cells in terms of their discharge properties and their synaptic efficacies onto MS neurons. In vivo firing of striatal FS interneurons is characterized by a high firing variability. It is not known, however, if this variability results from the input that FS cells receive, or if it is promoted by the stuttering spike behavior of these neurons. Both our model and measurements in vitro show that FS neurons that exhibit random stuttering discharge in response to steady depolarization, do not show the typical stuttering behavior when they receive fluctuating input. Importantly, our model predicts that electrically coupled FS cells show substantial spike synchronization only when they are in the stuttering regime. Therefore, together with the lack of synchronized firing of striatal FS interneurons that has been reported in vivo, these results suggest that neighboring FS neurons are not in the stuttering regime simultaneously and that in vivo FS firing variability is more likely determined by the input fluctuations. Furthermore, the variability in FS firing is translated to variability in the postsynaptic amplitudes in MS neurons due to the strong synaptic depression of the FS-to-MS synapse. Our results support the idea that these synapses operate over a wide range from strongly depressed to almost fully recovered. The strong inhibitory effects that FS cells can impose on their postsynaptic targets, and the fact that the FS-to-MS synapse model showed substantial depression over extended periods of time might indicate the importance of cooperative effects of multiple presynaptic FS interneurons and the precise orchestration of their activity.

  9. Weighted spiking neural P systems with structural plasticity working in sequential mode based on maximum spike number

    Science.gov (United States)

    Sun, Mingming; Qu, Jianhua

    2017-10-01

    Spiking neural P systems (SNP systems, in short) are a group of parallel and distributed computing devices inspired by the function and structure of spiking neurons. Recently, a new variant of SNP systems, called SNP systems with structural plasticity (SNPSP systems, in short) was proposed. In SNPSP systems, neuron can use plasticity ru les to create and delete synapses. In this work, we consider many restrictions sequentiality on SNPSP systems: (1) neuron with the maximum number of spikes is chosen to fire; (2) we use the weighted synapses. Specifically, we investigate the computational power of weighted SNPSP systems working in the sequential mode based on maximum spike number (WSNPSPM systems, in short) and we proved that SNPSP systems with these new restrictions are universal as generating devices.

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

    Science.gov (United States)

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    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). PMID:27555816

  11. 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.

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

    OpenAIRE

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    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-wis...

  13. 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

  14. The epileptic syndromes with continuous spikes and waves during slow sleep: definition and management guidelines.

    Science.gov (United States)

    Van Bogaert, P; Aeby, A; De Borchgrave, V; De Cocq, C; Deprez, M; De Tiège, X; de Tourtchaninoff, M; Dubru, J M; Foulon, M; Ghariani, S; Grisar, T; Legros, B; Ossemann, M; Tugendhaft, P; van Rijckevorsel, K; Verheulpen, D

    2006-06-01

    The authors propose to define the epileptic syndromes with continuous spikes and waves during slow sleep (CSWS) as a cognitive or behavioral impairment acquired during childhood, associated with a strong activation of the interictal epileptiform discharges during NREM sleep--whatever focal or generalized--and not related to another factor than the presence of CSWS. The type of syndrome will be defined according to the neurological and neuropsychological deficit. These syndromes have to be classified among the localization-related epileptic syndromes. Some cases are idiopathic and others are symptomatic. Guidelines for work-up and treatment are proposed.

  15. The Holocene Geomagnetic Field: Spikes, Low Field Anomalies, and Asymmetries

    Science.gov (United States)

    Constable, C.

    2017-12-01

    Our understanding of the Holocene magnetic field is constrained by individual paleomagnetic records of variable quality and resolution, composite regional secular variation curves, and low resolution global time-varying geomagnetic field models. Although spatial and temporal data coverages have greatly improved in recent years, typical views of millennial-scale secular variation and the underlying physical processes continue to be heavily influenced by more detailed field structure and short term variability inferred from the historical record and modern observations. Recent models of gyre driven decay of the geomagnetic dipole on centennial time scales, and studies of the evolution of the South Atlantic Anomaly provide one prominent example. Since 1840 dipole decay has largely been driven by meridional flux advection, with generally smaller fairly steady contributions from magnetic diffusion. The decay is dominantly associated with geomagnetic activity in the Southern Hemisphere. In contrast to the present decay, dipole strength generally grew between 1500 and 1000 BC, sustaining high but fluctuating values around 90-100 ZAm2 until after 1500 AD. Thus high dipole moments appear to have been present shortly after 1000 AD at the time of the Levantine spikes, which represent extreme variations in regional geomagnetic field strength. It has been speculated that the growth in dipole moment originated from a strong flux patch near the equatorial region at the core-mantle boundary that migrated north and west to augment the dipole strength, suggesting the presence of a large-scale anticyclonic gyre in the northern hemisphere, not totally unlike the southern hemisphere flow that dominates present day dipole decay. The later brief episodes of high field strength in the Levant may have contributed to prolonged values of high dipole strength until the onset of dipole decay in the late second millennium AD. This could support the concept of a large-scale stable flow

  16. 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.

  17. Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation

    Science.gov (United States)

    Liu, Qian; Pineda-García, Garibaldi; Stromatias, Evangelos; Serrano-Gotarredona, Teresa; Furber, Steve B.

    2016-01-01

    Today, increasing attention is being paid to research into spike-based neural computation both to gain a better understanding of the brain and to explore biologically-inspired computation. Within this field, the primate visual pathway and its hierarchical organization have been extensively studied. Spiking Neural Networks (SNNs), inspired by the understanding of observed biological structure and function, have been successfully applied to visual recognition and classification tasks. In addition, implementations on neuromorphic hardware have enabled large-scale networks to run in (or even faster than) real time, making spike-based neural vision processing accessible on mobile robots. Neuromorphic sensors such as silicon retinas are able to feed such mobile systems with real-time visual stimuli. A new set of vision benchmarks for spike-based neural processing are now needed to measure progress quantitatively within this rapidly advancing field. We propose that a large dataset of spike-based visual stimuli is needed to provide meaningful comparisons between different systems, and a corresponding evaluation methodology is also required to measure the performance of SNN models and their hardware implementations. In this paper we first propose an initial NE (Neuromorphic Engineering) dataset based on standard computer vision benchmarksand that uses digits from the MNIST database. This dataset is compatible with the state of current research on spike-based image recognition. The corresponding spike trains are produced using a range of techniques: rate-based Poisson spike generation, rank order encoding, and recorded output from a silicon retina with both flashing and oscillating input stimuli. In addition, a complementary evaluation methodology is presented to assess both model-level and hardware-level performance. Finally, we demonstrate the use of the dataset and the evaluation methodology using two SNN models to validate the performance of the models and their hardware

  18. 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.

  19. A proteomic study of spike development inhibition in bread wheat.

    Science.gov (United States)

    Zheng, Yong-Sheng; Guo, Jun-Xian; Zhang, Jin-Peng; Gao, Ai-Nong; Yang, Xin-Ming; Li, Xiu-Quan; Liu, Wei-Hua; Li, Li-Hui

    2013-09-01

    Spike development in wheat is a complicated development process and determines the wheat propagation and survival. We report herein a proteomic study on the bread wheat mutant strain 5660M underlying spike development inhibition. A total of 121 differentially expressed proteins, which were involved in cold stress response, protein folding and assembly, cell-cycle regulation, scavenging of ROS, and the autonomous pathway were identified using MS/MS and database searching. We found that cold responsive proteins were highly expressed in the mutant in contrast to those expressed in the wild-type line. Particularly, the autonomous pathway protein FVE, which modulates flowering, was dramatically downregulated and closely related to the spike development inhibition phenotype of 5660M. A quantitative RT-PCR study demonstrated that the transcription of the FVE and other six genes in the autonomous pathway and downstream flowering regulators were all markedly downregulated. The results indicate that spike development of 5660M cannot complete the floral transition. FVE might play an important role in the spikes development of the wheat. Our results provide the theory basis for studying floral development and transition in the reproductive growth period, and further analysis of wheat yield formation. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. 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.

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

    OpenAIRE

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

    2015-01-01

    Background: New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times.New method: We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms...

  2. 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

  3. A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons” [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Rainer Engelken

    2016-08-01

    Full Text Available Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

  4. Mouse neuroblastoma cell based model and the effect of epileptic events on calcium oscillations and neural spikes

    Science.gov (United States)

    Kim, Suhwan; Baek, Juyeong; Jung, Unsang; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

    2013-05-01

    Recently, Mouse neuroblastoma cells are considered as an attractive model for the study of human neurological and prion diseases, and intensively used as a model system in different areas. Among those areas, differentiation of neuro2a (N2A) cells, receptor mediated ion current, and glutamate induced physiological response are actively investigated. The reason for the interest to mouse neuroblastoma N2A cells is that they have a fast growing rate than other cells in neural origin with a few another advantages. This study evaluated the calcium oscillations and neural spikes recording of mouse neuroblastoma N2A cells in an epileptic condition. Based on our observation of neural spikes in mouse N2A cell with our proposed imaging modality, we report that mouse neuroblastoma N2A cells can be an important model related to epileptic activity studies. It is concluded that the mouse neuroblastoma N2A cells produce the epileptic spikes in vitro in the same way as produced by the neurons or the astrocytes. This evidence advocates the increased and strong level of neurotransmitters release by enhancement in free calcium using the 4-aminopyridine which causes the mouse neuroblastoma N2A cells to produce the epileptic spikes and calcium oscillation.

  5. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Science.gov (United States)

    Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

    2012-01-01

    Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  6. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Directory of Open Access Journals (Sweden)

    George L Chadderdon

    Full Text Available Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1, no learning (0, or punishment (-1, corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  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. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  9. 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...... have assessed the extent to which those policies contributed to the 2006–08 international price rises but only by focusing on one commodity or by using a back-of-the envelope (BOTE) method. The present more comprehensive analysis uses 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...

  10. 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...

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

    DEFF Research Database (Denmark)

    Proietti, Tomasso; Haldrup, Niels; Knapik, Oskar

    from the normal price, where the latter is defined as the expectation arising from a model accounting for long memory at the zero and at the weekly seasonal frequencies, given the knowledge of the past realizations. Hence, a spike is associated to a time series innovation with size larger than......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...... a specified threshold. The latter regulates the robustness of the estimates of the underlying price level and it is chosen by a data driven procedure that focuses on the ability to predict future prices. The normal price is computed by a modified Kalman filter, which robustifies the inferences by cleaning...

  12. 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.

  13. 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

  14. 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

  15. 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.

  16. Strongly Correlated Topological Insulators

    Science.gov (United States)

    2016-02-03

    Strongly Correlated Topological Insulators In the past year, the grant was used for work in the field of topological phases, with emphasis on finding...surface of topological insulators. In the past 3 years, we have started a new direction, that of fractional topological insulators. These are materials...in which a topologically nontrivial quasi-flat band is fractionally filled and then subject to strong interactions. The views, opinions and/or

  17. Upper limb biomechanics during the volleyball serve and spike.

    Science.gov (United States)

    Reeser, Jonathan C; Fleisig, Glenn S; Bolt, Becky; Ruan, Mianfang

    2010-09-01

    The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Significant kinetic differences exist among specific types of volleyball serves and spikes. Controlled laboratory study. Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were competent in jump serves (n, 5) performed 5 trials of that skill. A 240-Hz 3-dimensional automatic digitizing system captured each trial. Multivariate analysis of variance and post hoc paired t tests were used to compare kinetic parameters for the shoulder and elbow across all the skills (except the jump serve). A similar statistical analysis was performed for upper extremity kinematics. Forces, torques, and angular velocities at the shoulder and elbow were lowest for the roll shot and second-lowest for the float serve. No differences were detected between the cross-body and straight-ahead spikes. Although there was an insufficient number of participants to statistically analyze the jump serve, the data for it appear similar to those of the cross-body and straight-ahead spikes. Shoulder abduction at the instant of ball contact was approximately 130° for all skills, which is substantially greater than that previously reported for female athletes performing tennis serves or baseball pitches. Because shoulder kinetics were greatest during spiking, the volleyball player with symptoms of shoulder overuse may wish to reduce the number of repetitions performed during practice. Limiting the number of jump serves may also reduce the athlete's risk of overuse-related shoulder dysfunction. Volleyball-specific overhead skills, such as the spike and serve, produce considerable upper extremity force and torque, which may contribute to the risk of shoulder injury.

  18. Strong Cosmic Censorship

    Science.gov (United States)

    Isenberg, James

    2017-01-01

    The Hawking-Penrose theorems tell us that solutions of Einstein's equations are generally singular, in the sense of the incompleteness of causal geodesics (the paths of physical observers). These singularities might be marked by the blowup of curvature and therefore crushing tidal forces, or by the breakdown of physical determinism. Penrose has conjectured (in his `Strong Cosmic Censorship Conjecture`) that it is generically unbounded curvature that causes singularities, rather than causal breakdown. The verification that ``AVTD behavior'' (marked by the domination of time derivatives over space derivatives) is generically present in a family of solutions has proven to be a useful tool for studying model versions of Strong Cosmic Censorship in that family. I discuss some of the history of Strong Cosmic Censorship, and then discuss what is known about AVTD behavior and Strong Cosmic Censorship in families of solutions defined by varying degrees of isometry, and discuss recent results which we believe will extend this knowledge and provide new support for Strong Cosmic Censorship. I also comment on some of the recent work on ``Weak Null Singularities'', and how this relates to Strong Cosmic Censorship.

  19. 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...... Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using...

  20. A Spiking Neural Network in sEMG Feature Extraction.

    Science.gov (United States)

    Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor

    2015-11-03

    We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

  1. 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...

  2. Stable Learning of Functional Maps in Self-Organizing Spiking Neural Networks with Continuous Synaptic Plasticity

    Directory of Open Access Journals (Sweden)

    Narayan eSrinivasa

    2013-02-01

    Full Text Available This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1. This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through +/- 180 along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.

  3. Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity.

    Science.gov (United States)

    Srinivasa, Narayan; Jiang, Qin

    2013-01-01

    This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN) to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through ±180° along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.

  4. Characteristics of fast-spiking neurons in the striatum of behaving monkeys.

    Science.gov (United States)

    Yamada, Hiroshi; Inokawa, Hitoshi; Hori, Yukiko; Pan, Xiaochuan; Matsuzaki, Ryuichi; Nakamura, Kae; Samejima, Kazuyuki; Shidara, Munetaka; Kimura, Minoru; Sakagami, Masamichi; Minamimoto, Takafumi

    2016-04-01

    Inhibitory interneurons are the fundamental constituents of neural circuits that organize network outputs. The striatum as part of the basal ganglia is involved in reward-directed behaviors. However, the role of the inhibitory interneurons in this process remains unclear, especially in behaving monkeys. We recorded the striatal single neuron activity while monkeys performed reward-directed hand or eye movements. Presumed parvalbumin-containing GABAergic interneurons (fast-spiking neurons, FSNs) were identified based on narrow spike shapes in three independent experiments, though they were a small population (4.2%, 42/997). We found that FSNs are characterized by high-frequency and less-bursty discharges, which are distinct from the basic firing properties of the presumed projection neurons (phasically active neurons, PANs). Besides, the encoded information regarding actions and outcomes was similar between FSNs and PANs in terms of proportion of neurons, but the discharge selectivity was higher in PANs than that of FSNs. The coding of actions and outcomes in FSNs and PANs was consistently observed under various behavioral contexts in distinct parts of the striatum (caudate nucleus, putamen, and anterior striatum). Our results suggest that FSNs may enhance the discharge selectivity of postsynaptic output neurons (PANs) in encoding crucial variables for a reward-directed behavior. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  5. Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

    Science.gov (United States)

    Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

    2011-01-01

    Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

  6. Shape coexistence and strong shape changes in very neutron deficient platinum isotopes

    International Nuclear Information System (INIS)

    Cederwall, B.; Wyss, R.; Johnson, A.; Nyberg, J.; Fant, B.; Chapman, R.; Clarke, D.; Khazaie, F.; Lisle, J.C.; Mo, J.N.; Simpson, J.; Thorslund, I.

    1990-01-01

    High spin states of 175,176 Pt have been populated in 144 Sm( 35 Cl, pxn) reactions at beam energies of 175-185 MeV. In-beam γ-ray spectroscopic techniques using the ESSA30 spectrometer array were adopted. Levels up to spin 26 in 176 Pt and tentatively up to spin 45/2 in 175 Pt have been identified. The data are interpreted within the framework of Cranked Shell model calculations using the deformed Woods-Saxon potential and including monopole pairing. (orig.)

  7. Optimal stimulus shapes for neuronal excitation.

    Directory of Open Access Journals (Sweden)

    Daniel B Forger

    2011-07-01

    Full Text Available An important problem in neuronal computation is to discern how features of stimuli control the timing of action potentials. One aspect of this problem is to determine how an action potential, or spike, can be elicited with the least energy cost, e.g., a minimal amount of applied current. Here we show in the Hodgkin & Huxley model of the action potential and in experiments on squid giant axons that: 1 spike generation in a neuron can be highly discriminatory for stimulus shape and 2 the optimal stimulus shape is dependent upon inputs to the neuron. We show how polarity and time course of post-synaptic currents determine which of these optimal stimulus shapes best excites the neuron. These results are obtained mathematically using the calculus of variations and experimentally using a stochastic search methodology. Our findings reveal a surprising complexity of computation at the single cell level that may be relevant for understanding optimization of signaling in neurons and neuronal networks.

  8. 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/.

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

    Science.gov (United States)

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

    2009-12-01

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

  10. Computational modeling of spike generation in serotonergic neurons of the dorsal raphe nucleus.

    Science.gov (United States)

    Tuckwell, Henry C; Penington, Nicholas J

    2014-07-01

    Serotonergic neurons of the dorsal raphe nucleus, with their extensive innervation of limbic and higher brain regions and interactions with the endocrine system have important modulatory or regulatory effects on many cognitive, emotional and physiological processes. They have been strongly implicated in responses to stress and in the occurrence of major depressive disorder and other psychiatric disorders. In order to quantify some of these effects, detailed mathematical models of the activity of such cells are required which describe their complex neurochemistry and neurophysiology. We consider here a single-compartment model of these neurons which is capable of describing many of the known features of spike generation, particularly the slow rhythmic pacemaking activity often observed in these cells in a variety of species. Included in the model are 11 kinds of ion channels: a fast sodium current INa, a delayed rectifier potassium current IKDR, a transient potassium current IA, a slow non-inactivating potassium current IM, a low-threshold calcium current IT, two high threshold calcium currents IL and IN, small and large conductance potassium currents ISK and IBK, a hyperpolarization-activated cation current IH and a leak current ILeak. In Sections 3-8, each current type is considered in detail and parameters estimated from voltage clamp data where possible. Three kinds of model are considered for the BK current and two for the leak current. Intracellular calcium ion concentration Cai is an additional component and calcium dynamics along with buffering and pumping is discussed in Section 9. The remainder of the article contains descriptions of computed solutions which reveal both spontaneous and driven spiking with several parameter sets. Attention is focused on the properties usually associated with these neurons, particularly long duration of action potential, steep upslope on the leading edge of spikes, pacemaker-like spiking, long-lasting afterhyperpolarization

  11. Shaping 3-D boxes

    DEFF Research Database (Denmark)

    Stenholt, Rasmus; Madsen, Claus B.

    2011-01-01

    Enabling users to shape 3-D boxes in immersive virtual environments is a non-trivial problem. In this paper, a new family of techniques for creating rectangular boxes of arbitrary position, orientation, and size is presented and evaluated. These new techniques are based solely on position data......F) docking experiment against an existing technique, which requires the user to perform the rotation and scaling of the box explicitly. The precision of the users' box construction is evaluated by a novel error metric measuring the difference between two boxes. The results of the experiment strongly indicate...

  12. Double Dissociation of Spike Timing-Dependent Potentiation and Depression by Subunit-Preferring NMDA Receptor Antagonists in Mouse Barrel Cortex

    NARCIS (Netherlands)

    Banerjee, A.; Meredith, R.M.; Rodriguez-Moreno, A.; Mierau, S.B.; Auberson, Y.P.; Paulsen, O.

    2009-01-01

    Spike timing-dependent plasticity (STDP) is a strong candidate for an N-methyl-D-aspartate (NMDA) receptor-dependent form of synaptic plasticity that could underlie the development of receptive field properties in sensory neocortices. Whilst induction of timing-dependent long-term potentiation

  13. CLASSIFICATION OF GENUS Triticum, SENSU LATO AND SENSU STRICTO, BASED ON SPIKE AND GRAIN MORPHOLOGY

    Directory of Open Access Journals (Sweden)

    Hristo P. STOYANOV

    2015-10-01

    Full Text Available The formulation of the present classifications of species of the genus Triticum associates mainly with several plant morphological factors such as fragility of the spikes spindle, grains threshability, grain sphericity, shape and position of glumes, lemmas and paleas and awns, compactness, etc. Special attention is paid to the factor "cultural/wild" form, the ploidy and the genomic constitution of the species, often supported by molecular data which provides considerable comfort in disclosing phylogenetic features in a particular taxonomic unit. Such taxonomic determination is associated with certain disadvantages. It is not sufficiently focused on the spike morphology related to the reproductive apparatus of the plant, and also the causes of phylogenetic differentiation of certain parameters, such as spike branching, multiple spikelets, as well as the ratios of quantitative properties. The existing classifications do not give a precise answer to the taxonomic position of amphidiploids in the genus Triticum, and also for those obtained from hybrid combinations with genera Aegilops, Secale, Haynaldia, Hordeum, Elymus, Leymus, Elytrigia, Agropyron, as transitional and similar forms. Based on studies of spike and grain morphology of a large number of representatives of the genus Triticum and other interspecific and intergeneric amphidiploid forms, a classification of the genus sensu lato and sensu stricto is composed. Sensu stricto, genus Triticum covers all existing wild and cultivated known wheat forms, together with interspecific artificial synthetic forms. Sensu lato, the genus includes intergeneric hybrids, for which a specific generic epithet was coined - ×Triticum, and also a specific epithet, consistent with the originator of the amphidiploid. Special attention was paid to species and amphidiploids with the genus Aegilops. Classification sensu strictissimo was also formulated where the genus Triticum brings together only diploid species

  14. Strong Arcwise Connectedness

    OpenAIRE

    Espinoza, Benjamin; Gartside, Paul; Kovan-Bakan, Merve; Mamatelashvili, Ana

    2012-01-01

    A space is `n-strong arc connected' (n-sac) if for any n points in the space there is an arc in the space visiting them in order. A space is omega-strong arc connected (omega-sac) if it is n-sac for all n. We study these properties in finite graphs, regular continua, and rational continua. There are no 4-sac graphs, but there are 3-sac graphs and graphs which are 2-sac but not 3-sac. For every n there is an n-sac regular continuum, but no regular continuum is omega-sac. There is an omega-sac ...

  15. Abortion: Strong's counterexamples fail

    DEFF Research Database (Denmark)

    Di Nucci, Ezio

    2009-01-01

    This paper shows that the counterexamples proposed by Strong in 2008 in the Journal of Medical Ethics to Marquis's argument against abortion fail. Strong's basic idea is that there are cases--for example, terminally ill patients--where killing an adult human being is prima facie seriously morally......'s scenarios have some valuable future or admitted that killing them is not seriously morally wrong. Finally, if "valuable future" is interpreted as referring to objective standards, one ends up with implausible and unpalatable moral claims....

  16. CD-SEM metrology of spike detection on sub-40 nm contact holes

    Science.gov (United States)

    Momonoi, Yoshinori; Osabe, Taro; Yamaguchi, Atsuko; Mclellan Martin, Erin; Koyanagi, Hajime; Colburn, Matthew E.; Torii, Kazuyoshi

    2010-03-01

    In this work, for the purpose of contact-hole process control, new metrics for contact-hole edge roughness (CER) are being proposed. The metrics are correlated to lithographic process variation which result in increased electric fields; a primary driver of time-dependent dielectric breakdown (TDDB). Electric field strength at the tip of spoke-shaped CER has been simulated; and new hole-feature metrics have been introduced. An algorithm for defining critical features like spoke angle, spoke length, etc has been defined. In addition, a method for identifying at-risk holes has been demonstrated. The number of spike holes can determine slight defocus conditions that are not detected though the conventional CER metrics. The newly proposed metrics can identify contact holes with a propensity for increased electric field concentration and are expected to improve contact-hole reliability in the sub-40-nm contact-hole process.

  17. 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.

  18. 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...

  19. 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-methyl,...

  20. 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.

  1. 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.

  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. 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.

  4. Thermal impact on spiking properties in Hodgkin-Huxley neuron ...

    Indian Academy of Sciences (India)

    Thermal impact on spiking properties in Hodgkin-Huxley neuron with synaptic stimulus. Shenbing ... Department of Physical Science and Technology, Wuhan University of Technology, Wuhan, 430070, China; State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan, 430070, China ...

  5. Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

    Directory of Open Access Journals (Sweden)

    Emre eNeftci

    2014-01-01

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

  6. Stochastic resonance in noisy spiking retinal and sensory neuron models.

    Science.gov (United States)

    Patel, Ashok; Kosko, Bart

    2005-01-01

    Two new theorems show that small amounts of additive white noise can improve the bit count or mutual information of several popular models of spiking retinal neurons and spiking sensory neurons. The first theorem gives necessary and sufficient conditions for this noise benefit or stochastic resonance (SR) effect for subthreshold signals in a standard family of Poisson spiking models of retinal neurons. The result holds for all types of finite-variance noise and for all types of infinite-variance stable noise: SR occurs if and only if a sum of noise means or location parameters falls outside a 'forbidden interval' of values. The second theorem gives a similar forbidden-interval sufficient condition for the SR effect for several types of spiking sensory neurons that include the Fitzhugh-Nagumo neuron, the leaky integrate-and-fire neuron, and the reduced Type I neuron model if the additive noise is Gaussian white noise. Simulations show that neither the forbidden-interval condition nor Gaussianity is necessary for the SR effect.

  7. 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.

  8. A strong comeback

    International Nuclear Information System (INIS)

    Marier, D.

    1992-01-01

    This article presents the results of a financial rankings survey which show a strong economic activity in the independent energy industry. The topics of the article include advisor turnover, overseas banks, and the increase in public offerings. The article identifies the top project finance investors for new projects and restructurings and rankings for lenders

  9. The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

    Science.gov (United States)

    Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

    2017-01-01

    Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

  10. Critical slowing down governs the transition to neuron spiking.

    Directory of Open Access Journals (Sweden)

    Christian Meisel

    2015-02-01

    Full Text Available Many complex systems have been found to exhibit critical transitions, or so-called tipping points, which are sudden changes to a qualitatively different system state. These changes can profoundly impact the functioning of a system ranging from controlled state switching to a catastrophic break-down; signals that predict critical transitions are therefore highly desirable. To this end, research efforts have focused on utilizing qualitative changes in markers related to a system's tendency to recover more slowly from a perturbation the closer it gets to the transition--a phenomenon called critical slowing down. The recently studied scaling of critical slowing down offers a refined path to understand critical transitions: to identify the transition mechanism and improve transition prediction using scaling laws. Here, we outline and apply this strategy for the first time in a real-world system by studying the transition to spiking in neurons of the mammalian cortex. The dynamical system approach has identified two robust mechanisms for the transition from subthreshold activity to spiking, saddle-node and Hopf bifurcation. Although theory provides precise predictions on signatures of critical slowing down near the bifurcation to spiking, quantitative experimental evidence has been lacking. Using whole-cell patch-clamp recordings from pyramidal neurons and fast-spiking interneurons, we show that 1 the transition to spiking dynamically corresponds to a critical transition exhibiting slowing down, 2 the scaling laws suggest a saddle-node bifurcation governing slowing down, and 3 these precise scaling laws can be used to predict the bifurcation point from a limited window of observation. To our knowledge this is the first report of scaling laws of critical slowing down in an experiment. They present a missing link for a broad class of neuroscience modeling and suggest improved estimation of tipping points by incorporating scaling laws of critical slowing

  11. 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.

  12. Linear shaped charge

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, David; Stofleth, Jerome H.; Saul, Venner W.

    2017-07-11

    Linear shaped charges are described herein. In a general embodiment, the linear shaped charge has an explosive with an elongated arrowhead-shaped profile. The linear shaped charge also has and an elongated v-shaped liner that is inset into a recess of the explosive. Another linear shaped charge includes an explosive that is shaped as a star-shaped prism. Liners are inset into crevices of the explosive, where the explosive acts as a tamper.

  13. 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

  14. Contributions of adaptation currents to dynamic spike threshold on slow timescales: Biophysical insights from conductance-based models

    Science.gov (United States)

    Yi, Guosheng; Wang, Jiang; Wei, Xile; Deng, Bin; Li, Huiyan; Che, Yanqiu

    2017-06-01

    Spike-frequency adaptation (SFA) mediated by various adaptation currents, such as voltage-gated K+ current (IM), Ca2+-gated K+ current (IAHP), or Na+-activated K+ current (IKNa), exists in many types of neurons, which has been shown to effectively shape their information transmission properties on slow timescales. Here we use conductance-based models to investigate how the activation of three adaptation currents regulates the threshold voltage for action potential (AP) initiation during the course of SFA. It is observed that the spike threshold gets depolarized and the rate of membrane depolarization (dV/dt) preceding AP is reduced as adaptation currents reduce firing rate. It is indicated that the presence of inhibitory adaptation currents enables the neuron to generate a dynamic threshold inversely correlated with preceding dV/dt on slower timescales than fast dynamics of AP generation. By analyzing the interactions of ionic currents at subthreshold potentials, we find that the activation of adaptation currents increase the outward level of net membrane current prior to AP initiation, which antagonizes inward Na+ to result in a depolarized threshold and lower dV/dt from one AP to the next. Our simulations demonstrate that the threshold dynamics on slow timescales is a secondary effect caused by the activation of adaptation currents. These findings have provided a biophysical interpretation of the relationship between adaptation currents and spike threshold.

  15. Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Panda, Priyadarshini; Roy, Kaushik

    2017-01-01

    Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

  16. Strong Electroweak Symmetry Breaking

    CERN Document Server

    Grinstein, Benjamin

    2011-01-01

    Models of spontaneous breaking of electroweak symmetry by a strong interaction do not have fine tuning/hierarchy problem. They are conceptually elegant and use the only mechanism of spontaneous breaking of a gauge symmetry that is known to occur in nature. The simplest model, minimal technicolor with extended technicolor interactions, is appealing because one can calculate by scaling up from QCD. But it is ruled out on many counts: inappropriately low quark and lepton masses (or excessive FCNC), bad electroweak data fits, light scalar and vector states, etc. However, nature may not choose the minimal model and then we are stuck: except possibly through lattice simulations, we are unable to compute and test the models. In the LHC era it therefore makes sense to abandon specific models (of strong EW breaking) and concentrate on generic features that may indicate discovery. The Technicolor Straw Man is not a model but a parametrized search strategy inspired by a remarkable generic feature of walking technicolor,...

  17. Shape language - How people describe shapes and shape operations

    NARCIS (Netherlands)

    Wiegers, T.; Langeveld, L.H.; Vergeest, J.S.M.

    2001-01-01

    Many designers do not use CAD tools for shape ideation. They consider CAD systems not appropriate for the ideation phase. This research investigates how designers ideate shape, in particular which terms they use to exteriorize shape. The goal is to be able to propose digital tools that are useful

  18. Plasmons in strong superconductors

    International Nuclear Information System (INIS)

    Baldo, M.; Ducoin, C.

    2011-01-01

    We present a study of the possible plasmon excitations that can occur in systems where strong superconductivity is present. In these systems the plasmon energy is comparable to or smaller than the pairing gap. As a prototype of these systems we consider the proton component of Neutron Star matter just below the crust when electron screening is not taken into account. For the realistic case we consider in detail the different aspects of the elementary excitations when the proton, electron components are considered within the Random-Phase Approximation generalized to the superfluid case, while the influence of the neutron component is considered only at qualitative level. Electron screening plays a major role in modifying the proton spectrum and spectral function. At the same time the electron plasmon is strongly modified and damped by the indirect coupling with the superfluid proton component, even at moderately low values of the gap. The excitation spectrum shows the interplay of the different components and their relevance for each excitation modes. The results are relevant for neutrino physics and thermodynamical processes in neutron stars. If electron screening is neglected, the spectral properties of the proton component show some resemblance with the physical situation in high-T c superconductors, and we briefly discuss similarities and differences in this connection. In a general prospect, the results of the study emphasize the role of Coulomb interaction in strong superconductors.

  19. Investigation of Current Spike Phenomena During Heavy Ion Irradiation of NAND Flash Memories

    Science.gov (United States)

    Oldham, Timothy R.; Berg, Melanie; Friendlich, Mark; Wilcox, Ted; Seidleck, Christina; LaBel, Kenneth A.; Irom, Farokh; Buchner, Steven P.; McMorrow, Dale; Mavis, David G.; hide

    2011-01-01

    A series of heavy ion and laser irradiations were performed to investigate previously reported current spikes in flash memories. High current events were observed, however, none matches the previously reported spikes. Plausible mechanisms are discussed.

  20. Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.

    Science.gov (United States)

    Ly, Cheng

    2015-12-01

    Heterogeneity of neural attributes has recently gained a lot of attention and is increasing recognized as a crucial feature in neural processing. Despite its importance, this physiological feature has traditionally been neglected in theoretical studies of cortical neural networks. Thus, there is still a lot unknown about the consequences of cellular and circuit heterogeneity in spiking neural networks. In particular, combining network or synaptic heterogeneity and intrinsic heterogeneity has yet to be considered systematically despite the fact that both are known to exist and likely have significant roles in neural network dynamics. In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a combination of Monte Carlo simulations and probability density function equations. We find that the relationship between intrinsic and network heterogeneity has a strong effect on the overall level of heterogeneity of the firing rates. Specifically, this relationship can lead to amplification or attenuation of firing rate heterogeneity, and these effects depend on whether the recurrent network is firing asynchronously or rhythmically firing. These observations are captured with the aforementioned reduction method, and furthermore simpler analytic descriptions based on this dimension reduction method are developed. The final analytic descriptions provide compact and descriptive formulas for how the relationship between intrinsic and network heterogeneity determines the firing rate heterogeneity dynamics in various settings.

  1. Sensitive spectrofluorimetric method of analysis for venlafaxine in spiked rat plasma and formulations.

    Science.gov (United States)

    Shahnawaz, Sheikh; Siddiqui, Zaki; Hoda, Quaisul

    2010-07-01

    A simple, sensitive, accurate and affordable spectrofluorimetric method was developed and validated for the determination of venlafaxine, both in marketed preparations as well as in spiked rat plasma. Venlafaxine depicted strong native fluorescence property in freshly prepared 0.05 M sulphuric acid. The excitation and emission wavelengths were found to be 237.0 nm and 301.0 respectively. Effect of variations in pH, temperature, concentration, change in molarities of different solvents, and effect of excipients were studied. The calibration graph in case of dosage forms and in spiked plasma was found to be rectilinear in the concentrations of 15-600 ng/ml and 20-650 ng/ml respectively. The intra- day and inter-day accuracy measurements of VEN in formulations ranged from 0.29 to 0.44% and 0.27 to 0.49%, respectively. The intra-day and inter-day accuracy in measurement of VEN in plasma ranged from 0.062 to 2.26% and 0.52 to 2.32%, respectively. The limit of detection (LOD) was found to be 6.0 ng/mL and 4.0 ng/mL in plasma and formulations respectively. The mean recovery of VEN from plasma was 97.46.

  2. Memory-induced resonancelike suppression of spike generation in a resonate-and-fire neuron model

    Science.gov (United States)

    Mankin, Romi; Paekivi, Sander

    2018-01-01

    The behavior of a stochastic resonate-and-fire neuron model based on a reduction of a fractional noise-driven generalized Langevin equation (GLE) with a power-law memory kernel is considered. The effect of temporally correlated random activity of synaptic inputs, which arise from other neurons forming local and distant networks, is modeled as an additive fractional Gaussian noise in the GLE. Using a first-passage-time formulation, in certain system parameter domains exact expressions for the output interspike interval (ISI) density and for the survival probability (the probability that a spike is not generated) are derived and their dependence on input parameters, especially on the memory exponent, is analyzed. In the case of external white noise, it is shown that at intermediate values of the memory exponent the survival probability is significantly enhanced in comparison with the cases of strong and weak memory, which causes a resonancelike suppression of the probability of spike generation as a function of the memory exponent. Moreover, an examination of the dependence of multimodality in the ISI distribution on input parameters shows that there exists a critical memory exponent αc≈0.402 , which marks a dynamical transition in the behavior of the system. That phenomenon is illustrated by a phase diagram describing the emergence of three qualitatively different structures of the ISI distribution. Similarities and differences between the behavior of the model at internal and external noises are also discussed.

  3. Snout shape in extant ruminants.

    Directory of Open Access Journals (Sweden)

    Jonathan P Tennant

    Full Text Available Snout shape is a prominent aspect of herbivore feeding ecology, interacting with both forage selectivity and intake rate. Previous investigations have suggested ruminant feeding styles can be discriminated via snout shape, with grazing and browsing species characterised by 'blunt' and 'pointed' snouts respectively, often with specification of an 'intermediate' sub-grouping to represent ambiguous feeding styles and/or morphologies. Snout shape morphology is analysed here using a geometric morphometric approach to compare the two-dimensional profiles of the premaxilla in ventral aspect for a large sample of modern ruminant species, for which feeding modes are known from secondary criteria. Results suggest that, when browsing and grazing ruminants are classified ecologically based on a range of feeding style indicators, they cannot be discriminated unambiguously on the basis of snout profile shape alone. Profile shapes in our sample form a continuum with substantial overlap between groupings and a diverse range of morphologies. Nevertheless, we obtained an 83.8 percent ratio of correct post hoc feeding style categorisations based on the proximity of projected profile shapes to group centroids in the discriminant space. Accordingly, this procedure for identifying species whose feeding strategy is 'unknown' can be used with a reasonable degree of confidence, especially if backed-up by additional information. Based on these results we also refine the definitions of snout shape varieties, taking advantage of the descriptive power that geometric morphometrics offers to characterize the morphological disparities observed. The shape variance exhibited by both browsing and grazing ruminants corresponds strongly to body mass, providing further evidence for an interaction between snout shape, feeding style, and body size evolution. Finally, by exploring the role of phylogenetic similarity in snout shape, we find a slight increase in successful categorisation

  4. Strong Plate, Weak Slab Dichotomy

    Science.gov (United States)

    Petersen, R. I.; Stegman, D. R.; Tackley, P.

    2015-12-01

    Models of mantle convection on Earth produce styles of convection that are not observed on Earth.Moreover non-Earth-like modes, such as two-sided downwellings, are the de facto mode of convection in such models.To recreate Earth style subduction, i.e. one-sided asymmetric recycling of the lithosphere, proper treatment of the plates and plate interface are required. Previous work has identified several model features that promote subduction. A free surface or pseudo-free surface and a layer of material with a relatively low strength material (weak crust) allow downgoing plates to bend and slide past overriding without creating undue stress at the plate interface. (Crameri, et al. 2012, GRL)A low viscosity mantle wedge, possibly a result of slab dehydration, decouples the plates in the system. (Gerya et al. 2007, Geo)Plates must be composed of material which, in the case of the overriding plate, are is strong enough to resist bending stresses imposed by the subducting plate and yet, as in the case of the subducting plate, be weak enough to bend and subduct when pulled by the already subducted slab. (Petersen et al. 2015, PEPI) Though strong surface plates are required for subduction such plates may present a problem when they encounter the lower mantle.As the subducting slab approaches the higher viscosity, lower mantle stresses are imposed on the tip.Strong slabs transmit this stress to the surface.There the stress field at the plate interface is modified and potentially modifies the style of convection. In addition to modifying the stress at the plate interface, the strength of the slab affects the morphology of the slab at the base of the upper mantle. (Stegman, et al 2010, Tectonophysics)Slabs that maintain a sufficient portion of their strength after being bent require high stresses to unbend or otherwise change their shape.On the other hand slabs that are weakened though the bending process are more amenable to changes in morphology. We present the results of

  5. Estimating short-term synaptic plasticity from pre- and postsynaptic spiking

    Science.gov (United States)

    Malyshev, Aleksey; Stevenson, Ian H.

    2017-01-01

    Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful

  6. Strong-coupling approximations

    International Nuclear Information System (INIS)

    Abbott, R.B.

    1984-03-01

    Standard path-integral techniques such as instanton calculations give good answers for weak-coupling problems, but become unreliable for strong-coupling. Here we consider a method of replacing the original potential by a suitably chosen harmonic oscillator potential. Physically this is motivated by the fact that potential barriers below the level of the ground-state energy of a quantum-mechanical system have little effect. Numerically, results are good, both for quantum-mechanical problems and for massive phi 4 field theory in 1 + 1 dimensions. 9 references, 6 figures

  7. Strong interaction and QFD

    International Nuclear Information System (INIS)

    Ebata, T.

    1981-01-01

    With an assumed weak multiplet structure for bosonic hadrons, which is consistent with the ΔI = 1/2 rule, it is shown that the strong interaction effective hamiltonian is compatible with the weak SU(2) x U(1) gauge transformation. Especially the rho-meson transforms as a triplet under SU(2)sub(w), and this is the origin of the rho-photon analogy. It is also shown that the existence of the non-vanishing Cabibbo angle is a necessary condition for the absence of the exotic hadrons. (orig.)

  8. A 41 μW real-time adaptive neural spike classifier

    NARCIS (Netherlands)

    Zjajo, A.; van Leuken, T.G.R.M.

    2016-01-01

    Robust, power- and area-efficient spike classifier, capable of accurate identification of the neural spikes even for low SNR, is a prerequisite for the real-time, implantable, closed-loop brain-machine interface. In this paper, we propose an easily-scalable, 128-channel, programmable, neural spike

  9. Intracellular transport of recombinant coronavirus spike proteins: implications for virus assembly

    NARCIS (Netherlands)

    Horzinek, M.C.; Vennema, H.; Heijnen, L.; Zijderveld, A.; Spaan, W.J.M.

    1990-01-01

    Coronavirus spike protein genes were expressed in vitro by using the recombinant vaccinia virus expression system. Recombinant spike proteins were expressed at the cell surface and induced cell fusion in a host-cell-dependent fashion. The intracellular transport of recombinant spike proteins was

  10. Strong Coupling Holography

    CERN Document Server

    Dvali, Gia

    2009-01-01

    We show that whenever a 4-dimensional theory with N particle species emerges as a consistent low energy description of a 3-brane embedded in an asymptotically-flat (4+d)-dimensional space, the holographic scale of high-dimensional gravity sets the strong coupling scale of the 4D theory. This connection persists in the limit in which gravity can be consistently decoupled. We demonstrate this effect for orbifold planes, as well as for the solitonic branes and string theoretic D-branes. In all cases the emergence of a 4D strong coupling scale from bulk holography is a persistent phenomenon. The effect turns out to be insensitive even to such extreme deformations of the brane action that seemingly shield 4D theory from the bulk gravity effects. A well understood example of such deformation is given by large 4D Einstein term in the 3-brane action, which is known to suppress the strength of 5D gravity at short distances and change the 5D Newton's law into the four-dimensional one. Nevertheless, we observe that the ...

  11. 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.

  12. Prolonging the postcomplex spike pause speeds eyeblink conditioning.

    Science.gov (United States)

    Maiz, Jaione; Karakossian, Movses H; Pakaprot, Narawut; Robleto, Karla; Thompson, Richard F; Otis, Thomas S

    2012-10-09

    Climbing fiber input to the cerebellum is believed to serve as a teaching signal during associative, cerebellum-dependent forms of motor learning. However, it is not understood how this neural pathway coordinates changes in cerebellar circuitry during learning. Here, we use pharmacological manipulations to prolong the postcomplex spike pause, a component of the climbing fiber signal in Purkinje neurons, and show that these manipulations enhance the rate of learning in classical eyelid conditioning. Our findings elucidate an unappreciated aspect of the climbing fiber teaching signal, and are consistent with a model in which convergent postcomplex spike pauses drive learning-related plasticity in the deep cerebellar nucleus. They also suggest a physiological mechanism that could modulate motor learning rates.

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

    Directory of Open Access Journals (Sweden)

    E. D. Belousova

    2012-01-01

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

  14. Harmonics of Solar Radio Spikes at Metric Wavelengths

    Science.gov (United States)

    Feng, S. W.; Chen, Y.; Li, C. Y.; Wang, B.; Wu, Z.; Kong, X. L.; Du, Q. F.; Zhang, J. R.; Zhao, G. Q.

    2018-03-01

    This paper presents the latest observations from the newly built solar radio spectrograph at the Chashan Solar Observatory. On July 18, 2016, the spectrograph records a solar spike burst event, which has several episodes showing harmonic structures, with the second, third, and fourth harmonics. The lower harmonic radio spike emissions are observed later than the higher harmonic bands, and the temporal delay of the second (third) harmonic relative to the fourth harmonic is about 30 - 40 (10) ms. Based on the electron cyclotron maser emission mechanism, we analyze possible causes of the temporal delay and further infer relevant coronal parameters, such as the magnetic field strength and the electron density at the radio source.

  15. 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.

  16. 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.

  17. Origin of the spike-timing-dependent plasticity rule

    Science.gov (United States)

    Cho, Myoung Won; Choi, M. Y.

    2016-08-01

    A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

  18. Spin-orbit torque induced spike-timing dependent plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Abhronil, E-mail: asengup@purdue.edu; Al Azim, Zubair; Fong, Xuanyao; Roy, Kaushik [School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States)

    2015-03-02

    Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work, we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement spike-timing dependent plasticity. The proposed device offers the advantage of decoupled spike transmission and programming current paths, thereby leading to reliable operation during online learning. Possible arrangement of such devices in a crosspoint architecture can pave the way for ultra-dense neural networks. Simulation studies indicate that the device has the potential of achieving pico-Joule level energy consumption (maximum 2 pJ per synaptic event) which is comparable to the energy consumption for synaptic events in biological synapses.

  19. 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

    2017-06-22

    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 powerContributes 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. © The Author 2017. Published by Oxford University Press on behalf of the Society for Research

  20. 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.

  1. Simulating large-scale spiking neuronal networks with NEST

    OpenAIRE

    Senk, Johanna; Diesmann, Markus

    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...

  2. Special Electrophysiological Tests: Brain Spiking, EEG Spectral Coherence

    Science.gov (United States)

    1983-01-01

    Mental Sciences 9TIC E-~ MAY (In Press) Handbook of Diagnostic Procedifre, Spectrum Pblications, Jamaica , New York. > This wk was pa -tially supported...likelihood is sufficiently small, then we reject the hypothesis that an EEG record containing N spikes in a time t is a normal record, - ,- - Flor several...significantly from zero, one may concl de that the two signal processes in question are related through ,o linear transformation over the spectral

  3. Stiff fluid spike solutions from Bianchi type V seed solutions

    Science.gov (United States)

    Gregoris, D.; Lim, W. C.; Coley, A. A.

    2017-12-01

    In this paper we expand upon our previous work Coley et al (2016 Class. Quantum Grav. 33 215010) by using the entire family of Bianchi type V stiff fluid solutions as seed solutions of the Stephani transformation. Among the new exact solutions generated, we observe a number of important physical phenomena. The most interesting phenomenon is exact solutions with intersecting spikes. Other interesting phenomena are solutions with saddle states and a close-to-FL epoch.

  4. 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.

  5. 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...

  6. Spiking neural network-based control chart pattern recognition

    Directory of Open Access Journals (Sweden)

    Medhat H.A. Awadalla

    2012-03-01

    Full Text Available Due to an increasing competition in products, consumers have become more critical in choosing products. The quality of products has become more important. Statistical Process Control (SPC is usually used to improve the quality of products. Control charting plays the most important role in SPC. Control charts help to monitor the behavior of the process to determine whether it is stable or not. Unnatural patterns in control charts mean that there are some unnatural causes for variations in SPC. Spiking neural networks (SNNs are the third generation of artificial neural networks that consider time as an important feature for information representation and processing. In this paper, a spiking neural network architecture is proposed to be used for control charts pattern recognition (CCPR. Furthermore, enhancements to the SpikeProp learning algorithm are proposed. These enhancements provide additional learning rules for the synaptic delays, time constants and for the neurons thresholds. Simulated experiments have been conducted and the achieved results show a remarkable improvement in the overall performance compared with artificial neural networks.

  7. Poisson-Like Spiking in Circuits with Probabilistic Synapses

    Science.gov (United States)

    Moreno-Bote, Rubén

    2014-01-01

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

  8. 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.

  9. 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

  10. 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

  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. 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.

  13. 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.

  14. LIGO: The strong belief

    CERN Multimedia

    Antonella Del Rosso

    2016-01-01

    Twenty years of designing, building and testing a number of innovative technologies, with the strong belief that the endeavour would lead to a historic breakthrough. The Bulletin publishes an abstract of the Courier’s interview with Barry Barish, one of the founding fathers of LIGO.   The plots show the signals of gravitational waves detected by the twin LIGO observatories at Livingston, Louisiana, and Hanford, Washington. (Image: Caltech/MIT/LIGO Lab) On 11 February, the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo collaborations published a historic paper in which they showed a gravitational signal emitted by the merger of two black holes. These results come after 20 years of hard work by a large collaboration of scientists operating the two LIGO observatories in the US. Barry Barish, Linde Professor of Physics, Emeritus at the California Institute of Technology and former Director of the Global Design Effort for the Internat...

  15. Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.

    Science.gov (United States)

    Zenke, Friedemann; Agnes, Everton J; Gerstner, Wulfram

    2015-04-21

    Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, when complemented with slower homeostatic changes and consolidation, sufficient for assembly formation and memory recall in a spiking recurrent network model of excitatory and inhibitory neurons. In the model, assemblies were formed during repeated sensory stimulation and characterized by strong recurrent excitatory connections. Even days after formation, and despite ongoing network activity and synaptic plasticity, memories could be recalled through selective delay activity following the brief stimulation of a subset of assembly neurons. Blocking any component of plasticity prevented stable functioning as a memory network. Our modelling results suggest that the diversity of plasticity phenomena in the brain is orchestrated towards achieving common functional goals.

  16. Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images

    Science.gov (United States)

    Zhang, Xuming; Li, Liu; Zhu, Fei; Hou, Wenguang; Chen, Xinjian

    2014-06-01

    Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.

  17. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems

    Energy Technology Data Exchange (ETDEWEB)

    Hurtado, Antonio, E-mail: antonio.hurtado@strath.ac.uk [Institute of Photonics, SUPA Department of Physics, University of Strathclyde, TIC Centre, 99 George Street, Glasgow G1 1RD (United Kingdom); Javaloyes, Julien [Departament de Fisica, Universitat de les Illes Balears, c/Valldemossa km 7.5, 07122 Mallorca (Spain)

    2015-12-14

    Multiple controllable spiking patterns are achieved in a 1310 nm Vertical-Cavity Surface Emitting Laser (VCSEL) in response to induced perturbations and for two different cases of polarized optical injection, namely, parallel and orthogonal. Furthermore, reproducible spiking responses are demonstrated experimentally at sub-nanosecond speed resolution and with a controlled number of spikes fired. This work opens therefore exciting research avenues for the use of VCSELs in ultrafast neuromorphic photonic systems for non-traditional computing applications, such as all-optical binary-to-spiking format conversion and spiking information encoding.

  18. Detecting spikes of wheat plants using neural networks with Laws texture energy.

    Science.gov (United States)

    Qiongyan, Li; Cai, Jinhai; Berger, Bettina; Okamoto, Mamoru; Miklavcic, Stanley J

    2017-01-01

    The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential. We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper. Our highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.

  19. Effect of defects, magnetocrystalline anisotropy, and shape anisotropy on magnetic structure of iron thin films by magnetic force microscopy

    Directory of Open Access Journals (Sweden)

    Ke Xu

    2017-05-01

    Full Text Available Microstructures of magnetic materials, including defects and crystallographic orientations, are known to strongly influence magnetic domain structures. Measurement techniques such as magnetic force microscopy (MFM thus allow study of correlations between microstructural and magnetic properties. The present work probes effects of anisotropy and artificial defects on the evolution of domain structure with applied field. Single crystal iron thin films on MgO substrates were milled by Focused Ion Beam (FIB to create different magnetically isolated squares and rectangles in [110] crystallographic orientations, having their easy axis 45° from the sample edge. To investigate domain wall response on encountering non-magnetic defects, a 150 nm diameter hole was created in the center of some samples. By simultaneously varying crystal orientation and shape, both magnetocrystalline anisotropy and shape anisotropy, as well as their interaction, could be studied. Shape anisotropy was found to be important primarily for the longer edge of rectangular samples, which exaggerated the FIB edge effects and provided nucleation sites for spike domains in non-easy axis oriented samples. Center holes acted as pinning sites for domain walls until large applied magnetic fields. The present studies are aimed at deepening the understanding of the propagation of different types of domain walls in the presence of defects and different crystal orientations.

  20. Ion channel density regulates switches between regular and fast spiking in soma but not in axons.

    Directory of Open Access Journals (Sweden)

    Hugo Zeberg

    2010-04-01

    Full Text Available The threshold firing frequency of a neuron is a characterizing feature of its dynamical behaviour, in turn determining its role in the oscillatory activity of the brain. Two main types of dynamics have been identified in brain neurons. Type 1 dynamics (regular spiking shows a continuous relationship between frequency and stimulation current (f-I(stim and, thus, an arbitrarily low frequency at threshold current; Type 2 (fast spiking shows a discontinuous f-I(stim relationship and a minimum threshold frequency. In a previous study of a hippocampal neuron model, we demonstrated that its dynamics could be of both Type 1 and Type 2, depending on ion channel density. In the present study we analyse the effect of varying channel density on threshold firing frequency on two well-studied axon membranes, namely the frog myelinated axon and the squid giant axon. Moreover, we analyse the hippocampal neuron model in more detail. The models are all based on voltage-clamp studies, thus comprising experimentally measurable parameters. The choice of analysing effects of channel density modifications is due to their physiological and pharmacological relevance. We show, using bifurcation analysis, that both axon models display exclusively Type 2 dynamics, independently of ion channel density. Nevertheless, both models have a region in the channel-density plane characterized by an N-shaped steady-state current-voltage relationship (a prerequisite for Type 1 dynamics and associated with this type of dynamics in the hippocampal model. In summary, our results suggest that the hippocampal soma and the two axon membranes represent two distinct kinds of membranes; membranes with a channel-density dependent switching between Type 1 and 2 dynamics, and membranes with a channel-density independent dynamics. The difference between the two membrane types suggests functional differences, compatible with a more flexible role of the soma membrane than that of the axon membrane.

  1. A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking

    Directory of Open Access Journals (Sweden)

    Sushmita Lakshmi Allam

    2012-10-01

    Full Text Available Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

  2. John Strong (1941 - 2006)

    CERN Multimedia

    Wickens, F

    Our friend and colleague John Strong was cruelly taken from us by a brain tumour on Monday 31st July, a few days before his 65th birthday John started his career working with a group from Westfield College, under the leadership of Ted Bellamy. He obtained his PhD and spent the early part of his career on experiments at Rutherford Appleton Laboratory (RAL), but after the early 1970s his research was focussed on experiments in CERN. Over the years he made a number of notable contributions to experiments in CERN: The Omega spectrometer adopted a system John had originally developed for experiments at RAL using vidicon cameras to record the sparks in the spark chambers; He contributed to the success of NA1 and NA7, where he became heavily involved in the electronic trigger systems; He was responsible for the second level trigger system for the ALEPH detector and spent five years leading a team that designed and built the system, which ran for twelve years with only minor interventions. Following ALEPH he tur...

  3. Stirring Strongly Coupled Plasma

    CERN Document Server

    Fadafan, Kazem Bitaghsir; Rajagopal, Krishna; Wiedemann, Urs Achim

    2009-01-01

    We determine the energy it takes to move a test quark along a circle of radius L with angular frequency w through the strongly coupled plasma of N=4 supersymmetric Yang-Mills (SYM) theory. We find that for most values of L and w the energy deposited by stirring the plasma in this way is governed either by the drag force acting on a test quark moving through the plasma in a straight line with speed v=Lw or by the energy radiated by a quark in circular motion in the absence of any plasma, whichever is larger. There is a continuous crossover from the drag-dominated regime to the radiation-dominated regime. In the crossover regime we find evidence for significant destructive interference between energy loss due to drag and that due to radiation as if in vacuum. The rotating quark thus serves as a model system in which the relative strength of, and interplay between, two different mechanisms of parton energy loss is accessible via a controlled classical gravity calculation. We close by speculating on the implicati...

  4. Strong-interaction nonuniversality

    International Nuclear Information System (INIS)

    Volkas, R.R.; Foot, R.; He, X.; Joshi, G.C.

    1989-01-01

    The universal QCD color theory is extended to an SU(3) 1 direct product SU(3) 2 direct product SU(3) 3 gauge theory, where quarks of the ith generation transform as triplets under SU(3)/sub i/ and singlets under the other two factors. The usual color group is then identified with the diagonal subgroup, which remains exact after symmetry breaking. The gauge bosons associated with the 16 broken generators then form two massive octets under ordinary color. The interactions between quarks and these heavy gluonlike particles are explicitly nonuniversal and thus an exploration of their physical implications allows us to shed light on the fundamental issue of strong-interaction universality. Nonuniversality and weak flavor mixing are shown to generate heavy-gluon-induced flavor-changing neutral currents. The phenomenology of these processes is studied, as they provide the major experimental constraint on the extended theory. Three symmetry-breaking scenarios are presented. The first has color breaking occurring at the weak scale, while the second and third divorce the two scales. The third model has the interesting feature of radiatively induced off-diagonal Kobayashi-Maskawa matrix elements

  5. Confinement optimisation by plasma shaping on TCV

    International Nuclear Information System (INIS)

    Moret, J.M.; Behn, R.; Franke, S.; Hofmann, F.; Weisen, H.

    1997-01-01

    Any improvement in the energy confinement time of a tokamak reactor may facilitate its access to ignition. TCV has the unique capability of creating a wide variety of plasma shapes and can therefore investigate to which extent an appropriate choice of the plasma shape can improve the energy confinement time. For simple shapes defined only by their elongation and triangularity, it has already been observed on TCV that the confinement properties of the plasma depend strongly on the shape. This previous work has now been extended to include more complex shapes and higher elongations, in order firstly to test the applicability of the previously proposed explanation for the shape dependence of the confinement time and secondly to propose new shapes which offer a substantial gain on their confinement characteristics. (author) 4 figs., 1 tab., 2 refs

  6. Spike Train Similarity Space (SSIMS) Method Detects Effects of Obstacle Proximity and Experience on Temporal Patterning of Bat Biosonar

    Science.gov (United States)

    Accomando, Alyssa W.; Vargas-Irwin, Carlos E.; Simmons, James A.

    2018-01-01

    Bats emit biosonar pulses in complex temporal patterns that change to accommodate dynamic surroundings. Efforts to quantify these patterns have included analyses of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters such as duration or frequency. Here, the similarity in temporal structure between trains of biosonar pulses is assessed. The spike train similarity space (SSIMS) algorithm, originally designed for neural activity pattern analysis, was applied to determine which features of the environment influence temporal patterning of pulses emitted by flying big brown bats, Eptesicus fuscus. In these laboratory experiments, bats flew down a flight corridor through an obstacle array. The corridor varied in width (100, 70, or 40 cm) and shape (straight or curved). Using a relational point-process framework, SSIMS was able to discriminate between echolocation call sequences recorded from flights in each of the corridor widths. SSIMS was also able to tell the difference between pulse trains recorded during flights where corridor shape through the obstacle array matched the previous trials (fixed, or expected) as opposed to those recorded from flights with randomized corridor shape (variable, or unexpected), but only for the flight path shape in which the bats had previous training. The results show that experience influences the temporal patterns with which bats emit their echolocation calls. It is demonstrated that obstacle proximity to the bat affects call patterns more dramatically than flight path shape. PMID:29472848

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

    International Nuclear Information System (INIS)

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

    2004-01-01

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

  8. A stimulus-dependent spike threshold is an optimal neural coder

    Directory of Open Access Journals (Sweden)

    Douglas L Jones

    2015-06-01

    Full Text Available A neural code based on sequences of spikes can consume a significant portion of the brain’s energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding and fidelity (decoding. The threshold mimics a post-synaptic membrane (a low-pass filter and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint. The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.

  9. GABA(B) receptors inhibit backpropagating dendritic spikes in hippocampal CA1 pyramidal cells in vivo.

    Science.gov (United States)

    Leung, L Stan; Peloquin, Pascal

    2006-01-01

    Spike backpropagation has been proposed to enhance dendritic depolarization and synaptic plasticity. However, relatively little is known about the inhibitory control of spike backpropagation in vivo. In this study, the backpropagation of the antidromic spike into the dendrites of CA1 pyramidal cells was studied by extracellular recording in urethane-anesthetized rats. The population antidromic spike (pAS) in CA1 following stimulation of the alveus was recorded simultaneously with a 16-channel silicon probe and analyzed as current source density (CSD). The pAS current sink was shown to sequentially invade the soma and then the apical and basal dendrites. When the pAS was preceded sinks were reduced and delayed. Dendritic spike suppression was large after a high-intensity CA3 conditioning stimulus that evoked a population spike, small after a low-intensity CA3 conditioning stimulus, and weak after conditioning by another pAS. The late (150-400 ms latency) inhibition of the backpropagating pAS at the apical and basal dendrites was partially relieved by a GABA(B) receptor antagonist, CGP35348 or CGP56999A, given intracerebroventricularly (icv). CGP35348 icv also decreased the latency of the antidromic spike sinks at all depths. A compartment cable model of a CA1 pyramidal cell with excitable dendrites, combined with a model of extracellular potential generation, confirms that GABA(B) receptor activation delays a backpropagating spike and blocks distal dendritic spikes. GABA(B) receptor-mediated conductance increase and hyperpolarization, amplified by removing dendritic I(A) inactivation, contribute to conditioned dendritic spike suppression. In addition, the model shows that slow Na(+) channel inactivation also participates in conditioned spike suppression, which may partly explain the small dendritic spike suppression after conditioning with a weak orthodromic stimulus or another antidromic spike. Thus, both theory and experiment confirm an important role of the GABA

  10. Event shape engineering with ALICE

    CERN Document Server

    Dobrin, A

    2013-01-01

    The strong fluctuations in the initial energy density of heavy-ion collisions allow an efficient selection of events corresponding to a specific initial geometry. For such "shape engineered events", the elliptic flow coefficient, $v_2$, of unidentified charged particles, pions and (anti-)protons in Pb-Pb collisions at $\\snn = 2.76$ TeV is measured by the ALICE collaboration. $v_2$ obtained with the event plane method at mid-rapidity, $|\\eta|<0.8$, is reported for different collision centralities as a function of transverse momentum, $\\pt$, out to $\\pt=20$ GeV/$c$. The measured $v_2$ for the shape engineered events is significantly larger or smaller than the average which demonstrates the ability to experimentally select events with the desired shape of the initial spatial asymmetry.

  11. 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.

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

    Directory of Open Access Journals (Sweden)

    Srdjan Ostojic

    Full Text Available 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.

  13. Self-organization of spiking neurons using action potential timing.

    Science.gov (United States)

    Ruf, B; Schmitt, M

    1998-01-01

    We propose a mechanism for unsupervised learning in networks of spiking neurons which is based on the timing of single firing events. Our results show that a topology preserving behavior quite similar to that of Kohonen's self-organizing map can be achieved using temporal coding. In contrast to previous approaches, which use rate coding, the winner among competing neurons can be determined fast and locally. Our model is a further step toward a more realistic description of unsupervised learning in biological neural systems. Furthermore, it may provide a basis for fast implementations in pulsed VLSI (very large scale integration).

  14. First-spike latency in the presence of spontaneous activity

    Czech Academy of Sciences Publication Activity Database

    Pawlas, Z.; Klebanov, L. B.; Beneš, V.; Prokešová, M.; Popelář, Jiří; Lánský, Petr

    2010-01-01

    Roč. 22, č. 7 (2010), s. 1675-1693 ISSN 0899-7667 R&D Projects: GA ČR(CZ) GA309/07/1336; GA MŠk(CZ) LC554 Grant - others:GA AVCR(CZ) IAA101120604; GA ČR(CZ) GP201/08/P100 Program:IA Institutional research plan: CEZ:AV0Z50390703; CEZ:AV0Z50110509 Keywords : spike trans * inferior colliculus * response latency Subject RIV: FH - Neurology Impact factor: 2.290, year: 2010

  15. On the Non-Learnability of a Single Spiking Neuron

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří; Sgall, Jiří

    2005-01-01

    Roč. 17, č. 12 (2005), s. 2635-2647 ISSN 0899-7667 R&D Projects: GA ČR GA201/02/1456; GA AV ČR 1ET100300517; GA MŠk LN00A056; GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10300504; CEZ:AV0Z10190503 Keywords : spiking neuron * consistency problem * NP-completness * PAC model * robust learning * representation problem Subject RIV: BA - General Mathematics Impact factor: 2.591, year: 2005

  16. Propagation of epileptic spikes reconstructed from spatiotemporal magnetoencephalographic and electroencephalographic source analysis.

    Science.gov (United States)

    Tanaka, Naoaki; Hämäläinen, Matti S; Ahlfors, Seppo P; Liu, Hesheng; Madsen, Joseph R; Bourgeois, Blaise F; Lee, Jong Woo; Dworetzky, Barbara A; Belliveau, John W; Stufflebeam, Steven M

    2010-03-01

    The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient's MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes. Copyright (c) 2009 Elsevier Inc. All rights reserved.

  17. Changes in Purkinje Cell Simple Spike Encoding of Reach Kinematics during Adaption to a Mechanical Perturbation

    Science.gov (United States)

    Hewitt, Angela L.; Popa, Laurentiu S.

    2015-01-01

    The cerebellum is essential in motor learning. At the cellular level, changes occur in both the simple spike and complex spike firing of Purkinje cells. Because simple spike discharge reflects the main output of the cerebellar cortex, changes in simple spike firing likely reflect the contribution of the cerebellum to the adapted behavior. Therefore, we investigated in Rhesus monkeys how the representation of arm kinematics in Purkinje cell simple spike discharge changed during adaptation to mechanical perturbations of reach movements. Monkeys rapidly adapted to a novel assistive or resistive perturbation along the direction of the reach. Adaptation consisted of matching the amplitude and timing of the perturbation to minimize its effect on the reach. In a majority of Purkinje cells, simple spike firing recorded before and during adaptation demonstrated significant changes in position, velocity, and acceleration sensitivity. The timing of the simple spike representations change within individual cells, including shifts in predictive versus feedback signals. At the population level, feedback-based encoding of position increases early in learning and velocity decreases. Both timing changes reverse later in learning. The complex spike discharge was only weakly modulated by the perturbations, demonstrating that the changes in simple spike firing can be independent of climbing fiber input. In summary, we observed extensive alterations in individual Purkinje cell encoding of reach kinematics, although the movements were nearly identical in the baseline and adapted states. Therefore, adaption to mechanical perturbation of a reaching movement is accompanied by widespread modifications in the simple spike encoding. PMID:25609626

  18. 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.

  19. Combining abilities for spike traits in a diallel cross of barley

    Directory of Open Access Journals (Sweden)

    Milomirka Madić

    2014-03-01

    Full Text Available Five two-row winter barley (Hordeum vulgare L. cultivars divergent in spike traits were crossed in all possible combinations excluding reciprocals to produce 10 F1 and F2 hybrids for analysis of combining abilities. The analysis of variance of combining abilities showed significant differences for GCA and SCA in F1 hybrids and F2 generation, suggesting additive and non-additive gene action. The GCA/SCA ratio in F1 and F2 indicated the prevalence of the additive component of genetic variance for spike length, grain weight per spike and spike harvest index. By contrast, the SCA variance for grain weight per spike was higher than the GCA variance, indicating the dominance of non-additive gene action. Good GCAs were found in parents having high values for spike length (Djerdap, NS-293, grain number per spike (Vada, Jagodinac, grain weight per spike (Vada, NS-293 and spike harvest index (Djerdap, Jagodinac. None of the parents had good GCA for all traits, suggesting a potential increase in combining abilities for spike traits. The best SCA were obtained mostly from crosses between parents having high x low, high x high or average x low GCA values. Parents having high GCA values may be used to produce improved lines in hybridisation programmes. Combinations with high SCA values may yield good segregating lines in further selection programmes.

  20. 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.

  1. 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.

  2. Optimum shapes for pump limiters

    International Nuclear Information System (INIS)

    Ulrickson, M.

    1982-05-01

    The design of a pump limiter depends strongly on the details of the plasma scrapeoff zone. A model has been developed which allows the transport coefficients in the scrapeoff to be functions of n and t. This model has been used to predict scrapeoff profiles for FED/INTOR. The profiles are used to find and analyze limiter profiles. The results suggest the use of limiter shapes which curve toward the plasma

  3. 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.

  4. Shape memory polymers

    Science.gov (United States)

    Wilson, Thomas S.; Bearinger, Jane P.

    2015-06-09

    New shape memory polymer compositions, methods for synthesizing new shape memory polymers, and apparatus comprising an actuator and a shape memory polymer wherein the shape memory polymer comprises at least a portion of the actuator. A shape memory polymer comprising a polymer composition which physically forms a network structure wherein the polymer composition has shape-memory behavior and can be formed into a permanent primary shape, re-formed into a stable secondary shape, and controllably actuated to recover the permanent primary shape. Polymers have optimal aliphatic network structures due to minimization of dangling chains by using monomers that are symmetrical and that have matching amine and hydroxyl groups providing polymers and polymer foams with clarity, tight (narrow temperature range) single transitions, and high shape recovery and recovery force that are especially useful for implanting in the human body.

  5. 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.

  6. Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.

    Science.gov (United States)

    Jovanović, Stojan; Rotter, Stefan

    2016-06-01

    The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs) are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

  7. Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks.

    Directory of Open Access Journals (Sweden)

    Stojan Jovanović

    2016-06-01

    Full Text Available The study of processes evolving on networks has recently become a very popular research field, not only because of the rich mathematical theory that underpins it, but also because of its many possible applications, a number of them in the field of biology. Indeed, molecular signaling pathways, gene regulation, predator-prey interactions and the communication between neurons in the brain can be seen as examples of networks with complex dynamics. The properties of such dynamics depend largely on the topology of the underlying network graph. In this work, we want to answer the following question: Knowing network connectivity, what can be said about the level of third-order correlations that will characterize the network dynamics? We consider a linear point process as a model for pulse-coded, or spiking activity in a neuronal network. Using recent results from theory of such processes, we study third-order correlations between spike trains in such a system and explain which features of the network graph (i.e. which topological motifs are responsible for their emergence. Comparing two different models of network topology-random networks of Erdős-Rényi type and networks with highly interconnected hubs-we find that, in random networks, the average measure of third-order correlations does not depend on the local connectivity properties, but rather on global parameters, such as the connection probability. This, however, ceases to be the case in networks with a geometric out-degree distribution, where topological specificities have a strong impact on average correlations.

  8. Neural Spike-Train Analyses of the Speech-Based Envelope Power Spectrum Model

    Directory of Open Access Journals (Sweden)

    Varsha H. Rallapalli

    2016-10-01

    Full Text Available Diagnosing and treating hearing impairment is challenging because people with similar degrees of sensorineural hearing loss (SNHL often have different speech-recognition abilities. The speech-based envelope power spectrum model (sEPSM has demonstrated that the signal-to-noise ratio (SNRENV from a modulation filter bank provides a robust speech-intelligibility measure across a wider range of degraded conditions than many long-standing models. In the sEPSM, noise (N is assumed to: (a reduce S + N envelope power by filling in dips within clean speech (S and (b introduce an envelope noise floor from intrinsic fluctuations in the noise itself. While the promise of SNRENV has been demonstrated for normal-hearing listeners, it has not been thoroughly extended to hearing-impaired listeners because of limited physiological knowledge of how SNHL affects speech-in-noise envelope coding relative to noise alone. Here, envelope coding to speech-in-noise stimuli was quantified from auditory-nerve model spike trains using shuffled correlograms, which were analyzed in the modulation-frequency domain to compute modulation-band estimates of neural SNRENV. Preliminary spike-train analyses show strong similarities to the sEPSM, demonstrating feasibility of neural SNRENV computations. Results suggest that individual differences can occur based on differential degrees of outer- and inner-hair-cell dysfunction in listeners currently diagnosed into the single audiological SNHL category. The predicted acoustic-SNR dependence in individual differences suggests that the SNR-dependent rate of susceptibility could be an important metric in diagnosing individual differences. Future measurements of the neural SNRENV in animal studies with various forms of SNHL will provide valuable insight for understanding individual differences in speech-in-noise intelligibility.

  9. Neurobiologically Realistic Determinants of Self-Organized Criticality in Networks of Spiking Neurons

    Science.gov (United States)

    Rubinov, Mikail; Sporns, Olaf; Thivierge, Jean-Philippe; Breakspear, Michael

    2011-01-01

    Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for

  10. Spike timing of distinct types of GABAergic interneuron during hippocampal gamma oscillations in vitro.

    Science.gov (United States)

    Hájos, Norbert; Pálhalmi, János; Mann, Edward O; Németh, Beáta; Paulsen, Ole; Freund, Tamas F

    2004-10-13

    Gamma frequency (30-100 Hz) network oscillations occur in the intact hippocampus during awake, attentive behavior. Here, we explored the underlying cellular mechanisms in an in vitro model of persistent gamma-frequency oscillations, induced by bath application of 20 microm carbachol in submerged hippocampal slices at 30 +/- 1 degrees C. Current-source density analysis of the field oscillation revealed a prominent alternating sink-source pair in the perisomatic and apical dendritic regions of CA3. To elucidate the active events generating these extracellular dipoles, we examined the firing properties of distinct neuron types. Visually guided unit recordings were obtained from individual CA3 neurons followed by intracellular labeling for anatomical identification. Pyramidal cells fired at 2.82 +/- 0.7 Hz, close to the negative peak of the oscillation (0.03 +/- 0.65 msec), and often in conjunction with a negative spike-like component of the field potential. In contrast, all phase-coupled interneurons fired after this negative peak. Perisomatic inhibitory interneurons fired at high frequency (18.1 +/- 2.7 Hz), shortly after the negative peak (1.97 +/- 0.95 msec) and were strongly phase-coupled. Dendritic inhibitory interneurons fired at lower frequency (8.4 +/- 2.4 Hz) and with less fidelity and a longer delay after the negative peak (4.3 +/- 1.1 msec), whereas interneurons with cell body in the stratum radiatum often showed no phase relationship with the field oscillation. The phase and spike time data of individual neurons, together with the current-source density analysis, support a synaptic feedback model of gamma oscillations primarily involving pyramidal cells and inhibitory cells targeting their perisomatic region.

  11. 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.

  12. Approximate, computationally efficient online learning in Bayesian spiking neurons.

    Science.gov (United States)

    Kuhlmann, Levin; Hauser-Raspe, Michael; Manton, Jonathan H; Grayden, David B; Tapson, Jonathan; van Schaik, André

    2014-03-01

    Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

  13. 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

  14. 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

  15. SWAT: a spiking neural network training algorithm for classification problems.

    Science.gov (United States)

    Wade, John J; McDaid, Liam J; Santos, Jose A; Sayers, Heather M

    2010-11-01

    This paper presents a synaptic weight association training (SWAT) algorithm for spiking neural networks (SNNs). SWAT merges the Bienenstock-Cooper-Munro (BCM) learning rule with spike timing dependent plasticity (STDP). The STDP/BCM rule yields a unimodal weight distribution where the height of the plasticity window associated with STDP is modulated causing stability after a period of training. The SNN uses a single training neuron in the training phase where data associated with all classes is passed to this neuron. The rule then maps weights to the classifying output neurons to reflect similarities in the data across the classes. The SNN also includes both excitatory and inhibitory facilitating synapses which create a frequency routing capability allowing the information presented to the network to be routed to different hidden layer neurons. A variable neuron threshold level simulates the refractory period. SWAT is initially benchmarked against the nonlinearly separable Iris and Wisconsin Breast Cancer datasets. Results presented show that the proposed training algorithm exhibits a convergence accuracy of 95.5% and 96.2% for the Iris and Wisconsin training sets, respectively, and 95.3% and 96.7% for the testing sets, noise experiments show that SWAT has a good generalization capability. SWAT is also benchmarked using an isolated digit automatic speech recognition (ASR) system where a subset of the TI46 speech corpus is used. Results show that with SWAT as the classifier, the ASR system provides an accuracy of 98.875% for training and 95.25% for testing.

  16. 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.

  17. 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.

  18. Information filtering by synchronous spikes in a neural population.

    Science.gov (United States)

    Sharafi, Nahal; Benda, Jan; Lindner, Benjamin

    2013-04-01

    Information about time-dependent sensory stimuli is encoded by the spike trains of neurons. Here we consider a population of uncoupled but noisy neurons (each subject to some intrinsic noise) that are driven by a common broadband signal. We ask specifically how much information is encoded in the synchronous activity of the population and how this information transfer is distributed with respect to frequency bands. In order to obtain some insight into the mechanism of information filtering effects found previously in the literature, we develop a mathematical framework to calculate the coherence of the synchronous output with the common stimulus for populations of simple neuron models. Within this frame, the synchronous activity is treated as the product of filtered versions of the spike trains of a subset of neurons. We compare our results for the simple cases of (1) a Poisson neuron with a rate modulation and (2) an LIF neuron with intrinsic white current noise and a current stimulus. For the Poisson neuron, formulas are particularly simple but show only a low-pass behavior of the coherence of synchronous activity. For the LIF model, in contrast, the coherence function of the synchronous activity shows a clear peak at high frequencies, comparable to recent experimental findings. We uncover the mechanism for this shift in the maximum of the coherence and discuss some biological implications of our findings.

  19. Dynamics of Monoterpene Formation in Spike Lavender Plants

    Science.gov (United States)

    Kutzner, Erika; Huber, Claudia; Segura, Juan; Arrillaga, Isabel

    2017-01-01

    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. PMID:29257083

  20. 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.

  1. Automatic detection of interictal spikes using data mining models.

    Science.gov (United States)

    Valenti, Pablo; Cazamajou, Enrique; Scarpettini, Marcelo; Aizemberg, Ariel; Silva, Walter; Kochen, Silvia

    2006-01-15

    A prospective candidate for epilepsy surgery is studied both the ictal and interictal spikes (IS) to determine the localization of the epileptogenic zone. In this work, data mining (DM) classification techniques were utilized to build an automatic detection model. The selected DM algorithms are: Decision Trees (J 4.8), and Statistical Bayesian Classifier (naïve model). The main objective was the detection of IS, isolating them from the EEG's base activity. On the other hand, DM has an attractive advantage in such applications, in that the recognition of epileptic discharges does not need a clear definition of spike morphology. Furthermore, previously 'unseen' patterns could be recognized by the DM with proper 'training'. The results obtained showed that the efficacy of the selected DM algorithms is comparable to the current visual analysis used by the experts. Moreover, DM is faster than the time required for the visual analysis of the EEG. So this tool can assist the experts by facilitating the analysis of a patient's information, and reducing the time and effort required in the process.

  2. 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.

  3. Membrane-mediated interaction between strongly anisotropic protein scaffolds.

    Directory of Open Access Journals (Sweden)

    Yonatan Schweitzer

    2015-02-01

    Full Text Available Specialized proteins serve as scaffolds sculpting strongly curved membranes of intracellular organelles. Effective membrane shaping requires segregation of these proteins into domains and is, therefore, critically dependent on the protein-protein interaction. Interactions mediated by membrane elastic deformations have been extensively analyzed within approximations of large inter-protein distances, small extents of the protein-mediated membrane bending and small deviations of the protein shapes from isotropic spherical segments. At the same time, important classes of the realistic membrane-shaping proteins have strongly elongated shapes with large and highly anisotropic curvature. Here we investigated, computationally, the membrane mediated interaction between proteins or protein oligomers representing membrane scaffolds with strongly anisotropic curvature, and addressed, quantitatively, a specific case of the scaffold geometrical parameters characterizing BAR domains, which are crucial for membrane shaping in endocytosis. In addition to the previously analyzed contributions to the interaction, we considered a repulsive force stemming from the entropy of the scaffold orientation. We computed this interaction to be of the same order of magnitude as the well-known attractive force related to the entropy of membrane undulations. We demonstrated the scaffold shape anisotropy to cause a mutual aligning of the scaffolds and to generate a strong attractive interaction bringing the scaffolds close to each other to equilibrium distances much smaller than the scaffold size. We computed the energy of interaction between scaffolds of a realistic geometry to constitute tens of kBT, which guarantees a robust segregation of the scaffolds into domains.

  4. Reduction of the fast electrons preheating by changing the spike launch time in shock ignition approach

    Science.gov (United States)

    Jafar Jafari, Mohammad; Farahbod, Amir Hossein; Rezaei, Somayeh

    2016-01-01

    Target characteristic parameters in shock ignition approach before launching the spike pulse are studied using a 1-D hydrodynamic simulation code. By delaying the spike launch time, the shell areal density, ρR, is increased. The enhanced shell areal density prevents the hot electrons preheating of main fuel which in turn is generated from the intense laser plasma interaction with corona. To consider the effect of the spike launch time on the target performance, the target gain for a wide range of spike powers and launch times are computed. It is noticed that for HiPER reference target, few tenth nanoseconds displacement of spike launch time increases the areal density, ρR, value up to 30-70 percent. Furthermore, by choosing an appropriate spike energy and peak power, the optimum target gain is achieved in which the total driver energy is reduced.

  5. Principal cell spiking, postsynaptic excitation, and oxygen consumption in the rat cerebellar cortex

    DEFF Research Database (Denmark)

    Thomsen, Kirsten; Piilgaard, Henning; Gjedde, Albert

    2009-01-01

    . In contrast, PC spiking was largely responsible for the increase in CMRO2 when ongoing neuronal activity was increased by gamma-aminobutyric acid type A receptor blockade. In this case, CMRO2 increased equally during PC spiking with excitatory synaptic activity as during PC pacemaker spiking without......One contention within the field of neuroimaging concerns the character of the depicted activity: Does it represent neuronal action potential generation (i.e., spiking) or postsynaptic excitation? This question is related to the metabolic costs of different aspects of neurosignaling. The cerebellar...... cortex is well suited for addressing this problem because synaptic input to and spiking of the principal cell, the Purkinje cell (PC), are spatially segregated. Also, PCs are pacemakers, able to generate spikes endogenously. We examined the contributions to cerebellar cortical oxygen consumption (CMRO2...

  6. On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

    Science.gov (United States)

    Koyama, Shinsuke

    2015-07-01

    We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

  7. SPAN: spike pattern association neuron for learning spatio-temporal sequences

    OpenAIRE

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

    2012-01-01

    Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

  8. Spike Pattern Structure Influences Efficacy Variability under STDP and Synaptic Homeostasis

    OpenAIRE

    Bi, Zedong; Zhou, Changsong; Zhou, Hai-Jun

    2015-01-01

    In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons, synapses and networks, spike trains typically exhibit externally uncontrollable variability such as spatial heterogeneity and temporal stochasticity, resulting in variability of synapses, which we call efficacy variability. Spike patterns with the same population rate but inducing different efficacy variability may result in neuronal networks with sharply different structures and f...

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

    Directory of Open Access Journals (Sweden)

    Luis I Angel-Chavez

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

  10. Exploration of the Use of Spiking Detectors to Solve GNC Problems

    Data.gov (United States)

    National Aeronautics and Space Administration — This task is evaluating spiking sensor technology for Guidance, Navigation and Control applications, which includes detailed study, analysis and test for...

  11. Voltage-spike analysis for a free-running parallel inverter

    Science.gov (United States)

    Lee, F. C. Y.; Wilson, T. G.

    1974-01-01

    Unwanted and sometimes damaging high-amplitude voltage spikes occur during each half cycle in many transistor saturable-core inverters at the moment when the core saturates and the transistors switch. The analysis shows that spikes are an intrinsic characteristic of certain types of inverters even with negligible leakage inductance and purely resistive load. The small but unavoidable after-saturation inductance of the saturable-core transformer plays an essential role in creating these undesired thigh-voltage spikes. State-plane analysis provides insight into the complex interaction between core and transistors, and shows the circuit parameters upon which the magnitude of these spikes depends.

  12. Multineuron spike train analysis with R-convolution linear combination kernel.

    Science.gov (United States)

    Tezuka, Taro

    2018-06-01

    A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Strong Josephson Coupling in Planar Graphene Junctions

    Science.gov (United States)

    Park, Jinho; Lee, Gil-Ho; Lee, Jae Hyeong; Takane, Yositake; Imura, Ken-Ichiro; Taniguchi, Takashi; Watanabe, Kenji; Lee, Hu-Jong

    A recent breakthrough of processing graphene, employing encapsulation by hexagonal boron nitride layers (BGB structure), allows realizing the ballistic carrier transport in graphene. Thereafter, ballistic Josephson coupling has been studied by closely edge-contacted BGB structure with two superconducting electrodes. Here, we report on the strong Josephson coupling with planar graphene junction in truly short and ballistic regime. Our device showed high transmission probability and the junction critical current (IC) oscillating for sweeping the gate voltage along with the normal conductance oscillation (Fabry-Perot oscillations), providing a direct evidence for the ballistic nature of the junction pair current. We also observed the convex-upward shape of decreasing critical currents with increasing temperature, canonical properties of the short Josephson coupling. By fitting these curves into theoretical models, we demonstrate the strong Josephson coupling in our devices, which is also supported by the exceptionally large value of ICRN ( 2 Δ / e RNis the normal resistance).

  14. Analysis of synonymous codon usage in spike protein gene of infectious bronchitis virus.

    Science.gov (United States)

    Makhija, Aditi; Kumar, Sachin

    2015-12-01

    Infectious bronchitis virus (IBV) is responsible for causing respiratory, renal, and urogenital diseases in poultry. IBV infection in poultry leads to high mortality rates in affected flocks and to severe economic losses due to a drop in egg production and a reduced gain in live weight of the broiler birds. IBV-encoded spike protein (S) is the major protective immunogen for the host. Although the functions of the S protein have been well studied, the factors shaping synonymous codon usage bias and nucleotide composition in the S gene have not been reported yet. In the present study, we analyzed the relative synonymous codon usage and effective number of codons (Nc) using the 53 IBV S genes. The major trend in codon usage variation was studied using correspondence analysis. The plot of Nc values against GC3 as well as the correlation between base composition and codon usage bias suggest that mutational pressure rather than natural selection is the main factor that determines the codon usage bias in the S gene. Interestingly, no association of aromaticity, degree of hydrophobicity, and aliphatic index was observed with the codon usage variation in IBV S genes. The study represents a comprehensive analysis of IBV S gene codon usage patterns and provides a basic understanding of the codon usage bias.

  15. Shape-changing interfaces:

    DEFF Research Database (Denmark)

    Rasmussen, Majken Kirkegård; Pedersen, Esben Warming; Petersen, Marianne Graves

    2015-01-01

    Shape change is increasingly used in physical user interfaces, both as input and output. Yet, the progress made and the key research questions for shape-changing interfaces are rarely analyzed systematically. We review a sample of existing work on shape-changing interfaces to address these shortc......Shape change is increasingly used in physical user interfaces, both as input and output. Yet, the progress made and the key research questions for shape-changing interfaces are rarely analyzed systematically. We review a sample of existing work on shape-changing interfaces to address...... shape-changing interfaces be used for, (b) which parts of the design space are not well understood, and (c) why studying user experience with shape-changing interfaces is important....

  16. Self-erecting shapes

    Science.gov (United States)

    Reading, Matthew W.

    2017-07-04

    Technologies for making self-erecting structures are described herein. An exemplary self-erecting structure comprises a plurality of shape-memory members that connect two or more hub components. When forces are applied to the self-erecting structure, the shape-memory members can deform, and when the forces are removed the shape-memory members can return to their original pre-deformation shape, allowing the self-erecting structure to return to its own original shape under its own power. A shape of the self-erecting structure depends on a spatial orientation of the hub components, and a relative orientation of the shape-memory members, which in turn depends on an orientation of joining of the shape-memory members with the hub components.

  17. Nuclear shapes: from earliest ideas to multiple shape coexisting structures

    International Nuclear Information System (INIS)

    Heyde, K; Wood, J L

    2016-01-01

    The concept of the atomic nucleus being characterized by an intrinsic property such as shape came as a result of high precision hyperfine studies in the field of atomic physics, which indicated a non-spherical nuclear charge distribution. Herein, we describe the various steps taken through ingenious experimentation and bold theoretical suggestions that mapped the way for later work in the early 50s by Aage Bohr, Ben Mottelson and James Rainwater. We lay out a long and winding road that marked, in the period of 50s to 70s, the way shell-model and collective-model concepts were reconciled. A rapid increase in both accelerator and detection methods (70s towards the early 2000s) opened new vistas into nuclear shapes, and their coexistence, in various regions of the nuclear mass table. Next, we outline a possible unified view of nuclear shapes: emphasizing decisive steps taken as well as questions remaining, next to the theoretical efforts that could result in an emerging understanding of nuclear shapes, building on the nucleus considered as a strongly interacting system of nucleons as the microscopic starting point. (invited comment)

  18. Non-Euclidean properties of spike train metric spaces.

    Science.gov (United States)

    Aronov, Dmitriy; Victor, Jonathan D

    2004-06-01

    Quantifying the dissimilarity (or distance) between two sequences is essential to the study of action potential (spike) trains in neuroscience and genetic sequences in molecular biology. In neuroscience, traditional methods for sequence comparisons rely on techniques appropriate for multivariate data, which typically assume that the space of sequences is intrinsically Euclidean. More recently, metrics that do not make this assumption have been introduced for comparison of neural activity patterns. These metrics have a formal resemblance to those used in the comparison of genetic sequences. Yet the relationship between such metrics and the traditional Euclidean distances has remained unclear. We show, both analytically and computationally, that the geometries associated with metric spaces of event sequences are intrinsically non-Euclidean. Our results demonstrate that metric spaces enrich the study of neural activity patterns, since accounting for perceptual spaces requires a non-Euclidean geometry.

  19. Variational and perturbative schemes for a spiked harmonic oscillator

    International Nuclear Information System (INIS)

    Aguilera-Navarro, V.C.; Estevez, G.A.; Guardiola, R.

    1989-01-01

    A variational analysis of the spiked harmonic-oscillator Hamiltonian operator -d 2 /dx 2 + x 2 + l(l+1)/x 2 + λ |x| -α , where α is a real positive parameter, is reported in this work. The formalism makes use of the functional space spanned by the solutions of the Schroedinger equation for the linear harmonic-oscillator Hamiltonian supplemented by a Dirichlet boundary condition, and a standard procedure for diagonalizing symmetric matrices. The eigenvalues obtained by increasing the dimension of the basis set provides accurate approximations for the ground-state energy of the model system, valid for positive and relatively large values of the coupling parameter λ. Additionally, a large-coupling pertubative-expansion is carried out and the contributions up to fourth order to the ground-state energy are explicitly evaluated. Numerical results are compared for the special case α=5/2. (author) [pt

  20. A Model of Fast Hebbian Spike Latency Normalization

    Directory of Open Access Journals (Sweden)

    Hafsteinn Einarsson

    2017-05-01

    Full Text Available Hebbian changes of excitatory synapses are driven by and enhance correlations between pre- and postsynaptic neuronal activations, forming a positive feedback loop that can lead to instability in simulated neural networks. Because Hebbian learning may occur on time scales of seconds to minutes, it is conjectured that some form of fast stabilization of neural firing is necessary to avoid runaway of excitation, but both the theoretical underpinning and the biological implementation for such homeostatic mechanism are to be fully investigated. Supported by analytical and computational arguments, we show that a Hebbian spike-timing-dependent metaplasticity rule, accounts for inherently-stable, quick tuning of the total input weight of a single neuron in the general scenario of asynchronous neural firing characterized by UP and DOWN states of activity.

  1. Brian: a simulator for spiking neural networks in Python

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2008-11-01

    Full Text Available Brian is a new simulator for spiking neural networks, written in Python (http://brian.di.ens.fr. It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

    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......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...

  3. A self-resetting spiking phase-change neuron.

    Science.gov (United States)

    Cobley, R A; Hayat, H; Wright, C D

    2018-05-11

    Neuromorphic, or brain-inspired, computing applications of phase-change devices have to date concentrated primarily on the implementation of phase-change synapses. However, the so-called accumulation mode of operation inherent in phase-change materials and devices can also be used to mimic the integrative properties of a biological neuron. Here we demonstrate, using physical modelling of nanoscale devices and SPICE modelling of associated circuits, that a single phase-change memory cell integrated into a comparator type circuit can deliver a basic hardware mimic of an integrate-and-fire spiking neuron with self-resetting capabilities. Such phase-change neurons, in combination with phase-change synapses, can potentially open a new route for the realisation of all-phase-change neuromorphic computing.

  4. Double spike with isotope pattern deconvolution for mercury speciation

    International Nuclear Information System (INIS)

    Castillo, A.; Rodriguez-Gonzalez, P.; Centineo, G.; Roig-Navarro, A.F.; Garcia Alonso, J.I.

    2009-01-01

    Full text: A double-spiking approach, based on an isotope pattern deconvolution numerical methodology, has been developed and applied for the accurate and simultaneous determination of inorganic mercury (IHg) and methylmercury (MeHg). Isotopically enriched mercury species ( 199 IHg and 201 MeHg) are added before sample preparation to quantify the extent of methylation and demethylation processes. Focused microwave digestion was evaluated to perform the quantitative extraction of such compounds from solid matrices of environmental interest. Satisfactory results were obtained in different certificated reference materials (dogfish liver DOLT-4 and tuna fish CRM-464) both by using GC-ICPMS and GC-MS, demonstrating the suitability of the proposed analytical method. (author)

  5. A simultaneous comparison of acupuncture needle and insulated needle sphenoidal electrodes for detection of anterior temporal spikes.

    Science.gov (United States)

    Chu, N S

    1992-01-01

    Uninsulated acupuncture needles have been used as sphenoidal electrodes, but the issue of insulation has not been adequately addressed. In this report, acupuncture needles and insulated needle sphenoidal electrodes were simultaneously used to compare the rate of spike detection, spike amplitude and distribution of maximal spikes from eight spike foci in seven patients with temporal lobe epilepsy. When compared to the insulated needle electrode, the acupuncture needle electrode was equally effective in spike detection, but spike amplitudes tended to be smaller and maximal spikes were less frequently encountered. Thus, insulation has an influence on the spikes recorded by the acupuncture needle sphenoidal electrode. However, the overall effect appears to be not sufficiently different from the insulated needle electrode for the purpose of detecting anterior temporal spikes in outpatient EEG recordings for the diagnosis of temporal lobe epilepsy.

  6. The Hue of Shapes

    Science.gov (United States)

    Albertazzi, Liliana; Da Pos, Osvaldo; Canal, Luisa; Micciolo, Rocco; Malfatti, Michela; Vescovi, Massimo

    2013-01-01

    This article presents an experimental study on the naturally biased association between shape and color. For each basic geometric shape studied, participants were asked to indicate the color perceived as most closely related to it, choosing from the Natural Color System Hue Circle. Results show that the choices of color for each shape were not…

  7. Building with shapes

    CERN Document Server

    Mooney, Carla

    2014-01-01

    There are shapes everywhere you look. You can put shapes together or build with them. What can you build with three circles? In this title, students will explore and understand that certain attributes define what a shape is called. This title will allow students to identify the main purpose of a text, including what the author wants to answer, explain, or describe.

  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. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

    Science.gov (United States)

    Covi, Erika; Brivio, Stefano; Serb, Alexander; Prodromakis, Themis; Fanciulli, Marco; Spiga, Sabina

    2016-01-01

    Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%. PMID:27826226

  10. Relationship of Sphincter of Oddi Spike Bursts to Gastrointestinal Myoelectric Activity in Conscious Opossums

    Science.gov (United States)

    Honda, Ryuichi; Toouli, James; Dodds, Wylie J.; Sarna, Sushil; Hogan, Walter J.; Itoh, Zen

    1982-01-01

    The oppossum sphincter of Oddi (SO) exhibits peristaltic spike bursts with accompanying contraction waves that originate proximally in the sphincter of Oddi and propagate toward the duodenum. In this study we recorded myoelectrical activity of the opossum SO and upper gastrointestinal tract in six conscious animals using chronically implanted electrodes. Biopolar electrodes were implanted in the gastric antrum, duodenum, SO segment, jejunum, and ileum. During fasting the frequency of SO spike bursts, scored as number per minute, showed a cyclic pattern consisting of four phases (A to D). Phase A had a low spike burst frequency of ∼2/min that lasted ∼20 min. In phase B, the spike burst frequency increased progressively during a 40-45 min interval culminating in a short interval of phase C activity characterized by a maximal spike burst frequency of ∼5/min. During phase D, the spike bursts decreased over 15 min to merge with the low frequency of phase A and the cycle repeated. Cycle length of the interdigestive SO cycle, 87±11 SD min, was virtually identical with that of the interdigestive migrating myoelectric complex (MMC) of the upper gastrointestinal tract. The onset of phase C activity in the SO began 1-2 min before phase III of the MMC activity in the duodenum. Feeding abolished the cyclic pattern of spike burst activity in the SO as well as in the upper gastrointestinal tract. After feeding the SO spike bursts occurred at a frequency of 5-6/min for at least 3 h. We conclude that: (a) During fasting, the oppossum SO exhibits cyclic changes in its spike burst frequency; (b) Maximal spike burst frequency of the SO occurs virtually concurrent with passage of phase III MMC activity through the duodenum and; (c) Feeding abolishes the interdigestive cyclic spike burst pattern of the SO as well as that of the gastrointestinal tract. PMID:7076847

  11. Extracting information in spike time patterns with wavelets and information theory.

    Science.gov (United States)

    Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

    2015-02-01

    We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

  12. Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

    Science.gov (United States)

    Maisel, Brenton; Lindenberg, Katja

    2017-02-01

    While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.

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

    Directory of Open Access Journals (Sweden)

    K. U. Kurkiev

    2016-01-01

    Full Text Available Aim. The aim is to study the effect of different environmental conditions on the morphological traits of the spike of hexaploid triticale varieties.Methods. We analyzed 507 samples of triticale of various eco-geographical origins, in different years of study and at different seeding times. To investigate the influence of environmental conditions on the phenotypic expression of the studied traits we held a comparative analysis of the spike of two years and, in addition, of spring triticale during winter and spring crops. Analysis on the features was carried out on the main spikes. We studied the following morphological characteristics of the spike: length, number of spikelets and density.Results and discussion. The study of differences in individual variety samples showed that more than 60% triticale samples had significant differences in the length of the spike, depending on the weather conditions of the year – with the winter crops number of spikelets per spike was significantly higher than with the spring crops. A comparative analysis of the impact of the weather conditions of the year on triticale showed that significant differences in the density of the spike were observed in less than 30%.Conclusion. Study of the influence of conditions of the year and sowing dates on the main features of the spike of triticale showed that the density of the spike is the least affected by the external environment. The length of the spikes and the number of spikelets per spike differed significantly when growing in a various conditions.

  14. Alpha Shapes and Proteins

    DEFF Research Database (Denmark)

    Winter, Pawel; Sterner, Henrik; Sterner, Peter

    2009-01-01

    We provide a unified description of (weighted) alpha shapes, beta shapes and the corresponding simplicialcomplexes. We discuss their applicability to various protein-related problems. We also discuss filtrations of alpha shapes and touch upon related persistence issues.We claim that the full...... potential of alpha-shapes and related geometrical constructs in protein-related problems yet remains to be realized and verified. We suggest parallel algorithms for (weighted) alpha shapes, and we argue that future use of filtrations and kinetic variants for larger proteins will need such implementation....

  15. Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

    Directory of Open Access Journals (Sweden)

    Fernando Perez-Peña

    2013-11-01

    Full Text Available In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM. All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6 and power requirements (3.4 W to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6. It also evidences the suitable use of AER as a communication protocol between processing and actuation.

  16. Transforming shape in design

    DEFF Research Database (Denmark)

    Prats, Miquel; Lim, Sungwoo; Jowers, Iestyn

    2009-01-01

    This paper is concerned with how design shapes are generated and explored by means of sketching. It presents research into the way designers transform shapes from one state to another using sketch representations. An experimental investigation of the sketching processes of designers is presented...... phenomenon of ‘subshape' and suggests that a computational mechanism for detecting sub-shapes in design sketches might augment explorative sketching by providing important opportunities for manipulating and generating shape in design........ Connections between sketches are defined in terms of shape transformations and described according to shape rules. These rules provide a formal description of the shape exploration process and develop understanding of the mechanics of sketching in design. The paper concludes by discussing the important...

  17. Learning of spiking networks with different forms of long-term synaptic plasticity

    International Nuclear Information System (INIS)

    Vlasov, D.S.; Sboev, A.G.; Serenko, A.V.; Rybka, R.B.; Moloshnikov, I.A.

    2016-01-01

    The possibility of modeling the learning process based on different forms of spike timing-dependent plasticity (STDP) has been studied. It has been shown that the learnability depends on the choice of the spike pairing scheme in the STDP rule and the type of the input signal used during learning [ru

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

    NARCIS (Netherlands)

    Bosch, B.J.

    2004-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Laureline Logiaco

    2015-08-01

    Full Text Available The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

  20. Reconstruction of audio waveforms from spike trains of artificial cochlea models

    Science.gov (United States)

    Zai, Anja T.; Bhargava, Saurabh; Mesgarani, Nima; Liu, Shih-Chii

    2015-01-01

    Spiking cochlea models describe the analog processing and spike generation process within the biological cochlea. Reconstructing the audio input from the artificial cochlea spikes is therefore useful for understanding the fidelity of the information preserved in the spikes. The reconstruction process is challenging particularly for spikes from the mixed signal (analog/digital) integrated circuit (IC) cochleas because of multiple non-linearities in the model and the additional variance caused by random transistor mismatch. This work proposes an offline method for reconstructing the audio input from spike responses of both a particular spike-based hardware model called the AEREAR2 cochlea and an equivalent software cochlea model. This method was previously used to reconstruct the auditory stimulus based on the peri-stimulus histogram of spike responses recorded in the ferret auditory cortex. The reconstructed audio from the hardware cochlea is evaluated against an analogous software model using objective measures of speech quality and intelligibility; and further tested in a word recognition task. The reconstructed audio under low signal-to-noise (SNR) conditions (SNR < –5 dB) gives a better classification performance than the original SNR input in this word recognition task. PMID:26528113

  1. Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes

    Directory of Open Access Journals (Sweden)

    Takashi eTakekawa

    2012-03-01

    Full Text Available This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term multimodality-weighted principal component analysis (mPCA, and a clustering method by variational Bayes for Student’s t mixture model (SVB. The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP inference for the degree of freedom parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these difficult neurons. A parallelized implementation of the proposed algorithm (EToS version 3 is available as open-source code at http://etos.sourceforge.net/.

  2. Mutations in GRIN2A cause idiopathic focal epilepsy with rolandic spikes

    DEFF Research Database (Denmark)

    Lemke, Johannes R; Lal, Dennis; Reinthaler, Eva M

    2013-01-01

    Idiopathic focal epilepsy (IFE) with rolandic spikes is the most common childhood epilepsy, comprising a phenotypic spectrum from rolandic epilepsy (also benign epilepsy with centrotemporal spikes, BECTS) to atypical benign partial epilepsy (ABPE), Landau-Kleffner syndrome (LKS) and epileptic enc...

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

    Science.gov (United States)

    Matsubara, Takashi

    2017-01-01

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

  4. Reconstruction of audio waveforms from spike trains of artificial cochlea models

    Directory of Open Access Journals (Sweden)

    Anja eZai

    2015-10-01

    Full Text Available Spiking cochlea models describe the analog processing and spike generation process within the biological cochlea. Reconstructing the audio input from the artificial cochlea spikes is therefore useful for understanding the fidelity of the information preserved in the spikes. The reconstruction process is challenging particularly for spikes from the mixed signal (analog/digital integrated circuit (IC cochleas because of multiple nonlinearities in the model and the additional variance caused by random transistor mismatch. This work proposes an offline method for reconstructing the audio input from spike responses of both a particular spike-based hardware model called the AEREAR2 cochlea and an equivalent software cochlea model. This method was previously used to reconstruct the auditory stimulus based on the peri-stimulus histogram of spike responses recorded in the ferret auditory cortex. The reconstructed audio from the hardware cochlea is evaluated against an analogous software model using objective measures of speech quality and intelligibility; and further tested in a word recognition task. The reconstructed audio under low signal-to-noise (SNR conditions (SNR < -5 dB gives a better classification performance than the original SNR input in this word recognition task.

  5. Ictal source imaging and electroclinical correlation in self-limited epilepsy with centrotemporal spikes

    DEFF Research Database (Denmark)

    Alving, Jørgen; Fabricius, Martin; Rosenzweig, Ivana

    2017-01-01

    PURPOSE: To elucidate the localization of ictal EEG activity, and correlate it to semiological features in self-limited epilepsy with centrotemporal spikes (formerly called "benign epilepsy with centrotemporal spikes"). METHODS: We have performed ictal electric source imaging, and we analysed...

  6. New explicit spike solution -- non-local component of the generalized Mixmaster attractor

    OpenAIRE

    Lim, Woei Chet

    2007-01-01

    By applying a standard solution-generating transformation to an arbitrary vacuum Bianchi type II solution, one generates a new solution with spikes commonly observed in numerical simulations. It is conjectured that the spike solution is part of the generalized Mixmaster attractor.

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

    DEFF Research Database (Denmark)

    Grønlykke, L; Knudsen, M L; Høgenhaven, H

    2008-01-01

    To assess the value of remifentanil in intraoperative evaluation of spike activity in patients undergoing surgery for mesial temporal lobe epilepsy (MTLE).......To assess the value of remifentanil in intraoperative evaluation of spike activity in patients undergoing surgery for mesial temporal lobe epilepsy (MTLE)....

  8. Regulation of granule cell excitability by a low-threshold calcium spike in turtle olfactory bulb

    DEFF Research Database (Denmark)

    Pinato, Giulietta; Midtgaard, Jens

    2003-01-01

    Granule cells excitability in the turtle olfactory bulb was analyzed using whole cell recordings in current- and voltage-clamp mode. Low-threshold spikes (LTSs) were evoked at potentials that are subthreshold for Na spikes in normal medium. The LTSs were evoked from rest, but hyperpolarization...

  9. VARIABILITY OF NUMBER OF KERNELS PER SPIKE IN WHEAT CULTIVARS (Triticum aestivum L.

    Directory of Open Access Journals (Sweden)

    Desimir KNEZEVIC

    2012-09-01

    Full Text Available In this paper was analyzed number of kernels per spike in 20 genetically divergent wheat cultivars originated from different breeding centers in Serbia. Investigation conducted during two seasons which characterized different climatic condition. For analysis used samples of 60 wheat plants (20 plants in 3 replications which were harvested in full maturity stage. The differences in average values for number of kernels per spike in studied cultivars were determined. The variability of number of kernels per spike was established. In average, number of kernels per spike for all cultivars was higher in second year 72.22 than in first experimental year 68.73. The highest number of kernels/spike in both year expressed Tanjugovka cultivar and the lowest Yugoslavia cultivar. Average value of coefficientvariation for all cultivars varied from 14.19 in first year to 12.92 in second year. Average number of kernels per spike for both year of growing, varied from 54.56 in cultivar Yugoslavia to 77.83 in cultivar Tanjugovka. Significant differences for number of kernels/spike were found among cultivars in both years as well between years. Heritability in wide sense for number of kernels/spike was 79.13%.

  10. Spike detection from noisy neural data in linear-probe recordings.

    Science.gov (United States)

    Takekawa, Takashi; Ota, Keisuke; Murayama, Masanori; Fukai, Tomoki

    2014-06-01

    Simultaneous recordings of multiple neuron activities with multi-channel extracellular electrodes are widely used for studying information processing by the brain's neural circuits. In this method, the recorded signals containing the spike events of a number of adjacent or distant neurons must be correctly sorted into spike trains of individual neurons, and a variety of methods have been proposed for this spike sorting. However, spike sorting is computationally difficult because the recorded signals are often contaminated by biological noise. Here, we propose a novel method for spike detection, which is the first stage of spike sorting and hence crucially determines overall sorting performance. Our method utilizes a model of extracellular recording data that takes into account variations in spike waveforms, such as the widths and amplitudes of spikes, by detecting the peaks of band-pass-filtered data. We show that the new method significantly improves the cost-performance of multi-channel electrode recordings by increasing the number of cleanly sorted neurons. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

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

    Directory of Open Access Journals (Sweden)

    Takashi Matsubara

    2017-11-01

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

  12. Neuronal Networks in Children with Continuous Spikes and Waves during Slow Sleep

    Science.gov (United States)

    Siniatchkin, Michael; Groening, Kristina; Moehring, Jan; Moeller, Friederike; Boor, Rainer; Brodbeck, Verena; Michel, Christoph M.; Rodionov, Roman; Lemieux, Louis; Stephani, Ulrich

    2010-01-01

    Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least greater than 85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and…

  13. Calcium spikes and calcium plateaux evoked by differential polarization in dendrites of turtle motoneurones in vitro

    DEFF Research Database (Denmark)

    Hounsgaard, J; Kiehn, O

    1993-01-01

    The ability of dendrites in turtle motoneurones to support calcium spikes and calcium plateaux was investigated using differential polarization by applied electric fields. 2. Electric fields were generated by passing current through transverse slices of the turtle spinal cord between two plate......+ spikes and Ca2+ plateaux are present in dendrites of spinal motoneurones of the turtle....

  14. Network oscillations drive correlated spiking of ON and OFF ganglion cells in the rd1 mouse model of retinal degeneration.

    Directory of Open Access Journals (Sweden)

    David J Margolis

    Full Text Available Following photoreceptor degeneration, ON and OFF retinal ganglion cells (RGCs in the rd-1/rd-1 mouse receive rhythmic synaptic input that elicits bursts of action potentials at ∼ 10 Hz. To characterize the properties of this activity, RGCs were targeted for paired recording and morphological classification as either ON alpha, OFF alpha or non-alpha RGCs using two-photon imaging. Identified cell types exhibited rhythmic spike activity. Cross-correlation of spike trains recorded simultaneously from pairs of RGCs revealed that activity was correlated more strongly between alpha RGCs than between alpha and non-alpha cell pairs. Bursts of action potentials in alpha RGC pairs of the same type, i.e. two ON or two OFF cells, were in phase, while bursts in dissimilar alpha cell types, i.e. an ON and an OFF RGC, were 180 degrees out of phase. This result is consistent with RGC activity being driven by an input that provides correlated excitation to ON cells and inhibition to OFF cells. A2 amacrine cells were investigated as a candidate cellular mechanism and found to display 10 Hz oscillations in membrane voltage and current that persisted in the presence of antagonists of fast synaptic transmission and were eliminated by tetrodotoxin. Results support the conclusion that the rhythmic RGC activity originates in a presynaptic network of electrically coupled cells including A2s via a Na(+-channel dependent mechanism. Network activity drives out of phase oscillations in ON and OFF cone bipolar cells, entraining similar frequency fluctuations in RGC spike activity over an area of retina that migrates with changes in the spatial locus of the cellular oscillator.

  15. Network oscillations drive correlated spiking of ON and OFF ganglion cells in the rd1 mouse model of retinal degeneration.

    Science.gov (United States)

    Margolis, David J; Gartland, Andrew J; Singer, Joshua H; Detwiler, Peter B

    2014-01-01

    Following photoreceptor degeneration, ON and OFF retinal ganglion cells (RGCs) in the rd-1/rd-1 mouse receive rhythmic synaptic input that elicits bursts of action potentials at ∼ 10 Hz. To characterize the properties of this activity, RGCs were targeted for paired recording and morphological classification as either ON alpha, OFF alpha or non-alpha RGCs using two-photon imaging. Identified cell types exhibited rhythmic spike activity. Cross-correlation of spike trains recorded simultaneously from pairs of RGCs revealed that activity was correlated more strongly between alpha RGCs than between alpha and non-alpha cell pairs. Bursts of action potentials in alpha RGC pairs of the same type, i.e. two ON or two OFF cells, were in phase, while bursts in dissimilar alpha cell types, i.e. an ON and an OFF RGC, were 180 degrees out of phase. This result is consistent with RGC activity being driven by an input that provides correlated excitation to ON cells and inhibition to OFF cells. A2 amacrine cells were investigated as a candidate cellular mechanism and found to display 10 Hz oscillations in membrane voltage and current that persisted in the presence of antagonists of fast synaptic transmission and were eliminated by tetrodotoxin. Results support the conclusion that the rhythmic RGC activity originates in a presynaptic network of electrically coupled cells including A2s via a Na(+)-channel dependent mechanism. Network activity drives out of phase oscillations in ON and OFF cone bipolar cells, entraining similar frequency fluctuations in RGC spike activity over an area of retina that migrates with changes in the spatial locus of the cellular oscillator.

  16. Temporal succession of soil antibiotic resistance genes following application of swine, cattle and poultry manures spiked with or without antibiotics.

    Science.gov (United States)

    Zhang, Yu-Jing; Hu, Hang-Wei; Gou, Min; Wang, Jun-Tao; Chen, Deli; He, Ji-Zheng

    2017-12-01

    Land application of animal manure is a common agricultural practice potentially leading to dispersal and propagation of antibiotic resistance genes (ARGs) in environmental settings. However, the fate of resistome in agro-ecosystems over time following application of different manure sources has never been compared systematically. Here, soil microcosm incubation was conducted to compare effects of poultry, cattle and swine manures spiked with or without the antibiotic tylosin on the temporal changes of soil ARGs. The high-throughput quantitative PCR detected a total of 185 unique ARGs, with Macrolide-Lincosamide-Streptogramin B resistance as the most frequently encountered ARG type. The diversity and abundance of ARGs significantly increased following application of manure and manure spiked with tylosin, with more pronounced effects observed in the swine and poultry manure treatments than in the cattle manure treatment. The level of antibiotic resistance gradually decreased over time in all manured soils but was still significantly higher in the soils treated with swine and poultry manures than in the untreated soils after 130 days' incubation. Tylosin-amended soils consistently showed higher abundances of ARGs than soils treated with manure only, suggesting a strong selection pressure of antibiotic-spiked manure on soil ARGs. The relative abundance of ARGs had significantly positive correlations with integrase and transposase genes, indicative of horizontal transfer potential of ARGs in manure and tylosin treated soils. Our findings provide evidence that application of swine and poultry manures might enrich more soil ARGs than cattle manure, which necessitates the appropriate treatment of raw animal manures prior to land application to minimise the spread of environmental ARGs. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. To sort or not to sort: the impact of spike-sorting on neural decoding performance

    Science.gov (United States)

    Todorova, Sonia; Sadtler, Patrick; Batista, Aaron; Chase, Steven; Ventura, Valérie

    2014-10-01

    Objective. Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients. Spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity. Approach. We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expert-sorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step. Main results. Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior. Significance. Our results indicate that simple automated spike-sorting performs as well as the more computationally or manually intensive

  18. Quantum electrodynamics of strong fields

    International Nuclear Information System (INIS)

    Greiner, W.

    1983-01-01

    Quantum Electrodynamics of Strong Fields provides a broad survey of the theoretical and experimental work accomplished, presenting papers by a group of international researchers who have made significant contributions to this developing area. Exploring the quantum theory of strong fields, the volume focuses on the phase transition to a charged vacuum in strong electric fields. The contributors also discuss such related topics as QED at short distances, precision tests of QED, nonperturbative QCD and confinement, pion condensation, and strong gravitational fields In addition, the volume features a historical paper on the roots of quantum field theory in the history of quantum physics by noted researcher Friedrich Hund

  19. No bacterial growth found in spiked intravenous fluids over an 8-hour period.

    Science.gov (United States)

    Haas, Richard E; Beitz, Edwin; Reed, Amy; Burtnett, Howard; Lowe, Jason; Crist, Arthur E; Stierer, Kevin A; Birenberg, Allan M

    2017-04-01

    Protocol changes prompted by the Joint Commission mandating intravenous (IV) fluid bags to be used within 1 hour of spiking because of possible bacterial contamination have sparked clinical and economic concerns. This study investigated the degree of bacterial growth in which samples were obtained from spiked IV fluid bags at the time of spiking and 1, 2, 4, and 8 hours after spiking. No bacterial growth occurred in any of the 80 bags of Lactated Ringer's (LR) IV solutions sampled. This study demonstrated that LR IV bags do not support any bacterial growth for up to 8 hours after spiking. Copyright © 2017 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

  20. 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.

  1. Approach for domestic preparation of standard material (LSD spike) for isotope dilution mass spectrometry

    International Nuclear Information System (INIS)

    Ishikawa, Fumitaka; Sumi, Mika; Chiba, Masahiko; Suzuki, Toru; Abe, Tomoyuki; Kuno, Yusuke

    2008-01-01

    The accountancy analysis of the nuclear fuel material at Plutonium Fuel Development Center of JAEA is performed by isotope dilution mass spectrometry (IDMS; Isotope Dilution Mass Spectrometry). IDMS requires the standard material called LSD spike (Large Size Dried spike) which is indispensable for the accountancy in the facilities where the nuclear fuel materials are handled. Although the LSD spike and Pu source material have been supplied from foreign countries, the transportation for such materials has been getting more difficult recently. This difficulty may affect the operation of nuclear facilities in the future. Therefore, research and development of the domestic LSD spike and base material has been performed at JAEA. Certification for such standard nuclear materials including spikes produced in Japan is being studied. This report presents the current status and the future plan for the technological development. (author)

  2. 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.

  3. Shape memory alloys

    International Nuclear Information System (INIS)

    Kaszuwara, W.

    2004-01-01

    Shape memory alloys (SMA), when deformed, have the ability of returning, in certain circumstances, to their initial shape. Deformations related to this phenomenon are for polycrystals 1-8% and up to 15% for monocrystals. The deformation energy is in the range of 10 6 - 10 7 J/m 3 . The deformation is caused by martensitic transformation in the material. Shape memory alloys exhibit one directional or two directional shape memory effect as well as pseudoelastic effect. Shape change is activated by temperature change, which limits working frequency of SMA to 10 2 Hz. Other group of alloys exhibit magnetic shape memory effect. In these alloys martensitic transformation is triggered by magnetic field, thus their working frequency can be higher. Composites containing shape memory alloys can also be used as shape memory materials (applied in vibration damping devices). Another group of composite materials is called heterostructures, in which SMA alloys are incorporated in a form of thin layers The heterostructures can be used as microactuators in microelectromechanical systems (MEMS). Basic SMA comprise: Ni-Ti, Cu (Cu-Zn,Cu-Al, Cu-Sn) and Fe (Fe-Mn, Fe-Cr-Ni) alloys. Shape memory alloys find applications in such areas: automatics, safety and medical devices and many domestic appliances. Currently the most important appears to be research on magnetic shape memory materials and high temperature SMA. Vital from application point of view are composite materials especially those containing several intelligent materials. (author)

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

    Directory of Open Access Journals (Sweden)

    Guo-Sheng eYi

    2015-05-01

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

  5. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

    Science.gov (United States)

    Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

    2016-01-01

    Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image

  6. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

    Directory of Open Access Journals (Sweden)

    Arno Onken

    2016-11-01

    Full Text Available Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations, in their temporal dimension (temporal neural response variations, or in their combination (temporally coordinated neural population firing. Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together, temporal firing patterns (temporal activation of these groups of neurons and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial. We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine

  7. The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns

    Science.gov (United States)

    Florian, Răzvan V.

    2012-01-01

    In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm. PMID:22879876

  8. iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

    Science.gov (United States)

    Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

    2010-12-15

    Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis). Copyright © 2010

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

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  12. Strong WW Interaction at LHC

    Energy Technology Data Exchange (ETDEWEB)

    Pelaez, Jose R

    1998-12-14

    We present a brief pedagogical introduction to the Effective Electroweak Chiral Lagrangians, which provide a model independent description of the WW interactions in the strong regime. When it is complemented with some unitarization or a dispersive approach, this formalism allows the study of the general strong scenario expected at the LHC, including resonances.

  13. Strong-back safety latch

    International Nuclear Information System (INIS)

    DeSantis, G.N.

    1995-01-01

    The calculation decides the integrity of the safety latch that will hold the strong-back to the pump during lifting. The safety latch will be welded to the strong-back and will latch to a 1.5-in. dia cantilever rod welded to the pump baseplate. The static and dynamic analysis shows that the safety latch will hold the strong-back to the pump if the friction clamps fail and the pump become free from the strong-back. Thus, the safety latch will meet the requirements of the Lifting and Rigging Manual for under the hook lifting for static loading; it can withstand shock loads from the strong-back falling 0.25 inch

  14. Strong-back safety latch

    Energy Technology Data Exchange (ETDEWEB)

    DeSantis, G.N.

    1995-03-06

    The calculation decides the integrity of the safety latch that will hold the strong-back to the pump during lifting. The safety latch will be welded to the strong-back and will latch to a 1.5-in. dia cantilever rod welded to the pump baseplate. The static and dynamic analysis shows that the safety latch will hold the strong-back to the pump if the friction clamps fail and the pump become free from the strong-back. Thus, the safety latch will meet the requirements of the Lifting and Rigging Manual for under the hook lifting for static loading; it can withstand shock loads from the strong-back falling 0.25 inch.

  15. Perspectives in shape analysis

    CERN Document Server

    Bruckstein, Alfred; Maragos, Petros; Wuhrer, Stefanie

    2016-01-01

    This book presents recent advances in the field of shape analysis. Written by experts in the fields of continuous-scale shape analysis, discrete shape analysis and sparsity, and numerical computing who hail from different communities, it provides a unique view of the topic from a broad range of perspectives. Over the last decade, it has become increasingly affordable to digitize shape information at high resolution. Yet analyzing and processing this data remains challenging because of the large amount of data involved, and because modern applications such as human-computer interaction require real-time processing. Meeting these challenges requires interdisciplinary approaches that combine concepts from a variety of research areas, including numerical computing, differential geometry, deformable shape modeling, sparse data representation, and machine learning. On the algorithmic side, many shape analysis tasks are modeled using partial differential equations, which can be solved using tools from the field of n...

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

    Science.gov (United States)

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

    2015-04-30

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

  17. Shaping of planetary nebulae

    International Nuclear Information System (INIS)

    Balick, B.

    1987-01-01

    The phases of stellar evolution and the development of planetary nebulae are examined. The relation between planetary nebulae and red giants is studied. Spherical and nonspherical cases of shaping planetaries with stellar winds are described. CCD images of nebulae are analyzed, and it is determined that the shape of planetary nebulae depends on ionization levels. Consideration is given to calculating the distances of planetaries using radio images, and molecular hydrogen envelopes which support the wind-shaping model of planetary nebulae

  18. Geomagnetic intensity spike recorded in high resolution slag deposit in southern Jordan

    Science.gov (United States)

    Ben-Yosef, E.; Tauxe, L.; Levy, T. E.; Shaar, R.; Ron, H.; Najjar, M.

    2009-12-01

    In paleomagnetism, periods of high field intensity have been largely ignored in favor of the more spectacular directional changes associated with low field intensity periods of excursions and reversals. Hence, questions such as how strong the field can get and how fast changes occur are still open. In this paper we report on data obtained from an archaeometallurgical excavation in the Middle East, designed specifically for archaeomagnetic sampling. We measured 342 specimens from 72 samples collected from a 6.1 meter mound of well stratified copper production debris at the early Iron Age (12th-9th centuries BCE) site of Khirbat en-Nahas in southern Jordan. Seventeen samples spanning 200 years yielded excellent archaeointensity results that demonstrate rapid changes in field intensity in a period of overall high field values. The results display a remarkable spike in field strength, with sample means values of over 120 μT (compared to the current field strength of 44 μT). A suite of 13 radiocarbon dates intimately associated with our samples, tight control of sample location and relative stratigraphy provide tight constraints on the rate and magnitude of changes in archaeomagnetic field intensities.

  19. Layer Specific Development of Neocortical Pyramidal to Fast Spiking Cells Synapses.

    Directory of Open Access Journals (Sweden)

    Olga eVoinova

    2016-01-01

    Full Text Available All cortical neurons are engaged in inhibitory feedback loops which ensure excitation-inhibition balance and are key elements for the development of coherent network activity. The resulting network patterns are strongly dependent on the strength and dynamic properties of these excitatory-inhibitory loops which show pronounced regional and developmental diversity. We therefore compared the properties and postnatal maturation of two different synapses between rat neocortical pyramidal cells (layer 2/3 and layer 5, respectively and fast spiking (FS interneurons in the corresponding layer. At P14, both synapses showed synaptic depression upon repetitive activation. Synaptic release properties between layer 2/3 pyramidal cells and FS cells were stable from P14 to P28. In contrast, layer 5 pyramidal to FS cell connections showed a significant increase in paired pulse ratio by P28. Presynaptic calcium dynamics did also change at these synapses, including the sensitivity to exogenously loaded calcium buffers and expression of presynaptic calcium channels subtypes. These results underline the large variety of properties at different, yet similar, synapses in the neocortex. They also suggest that postnatal maturation of the brain goes along with increasing differences between synaptically driven network activity in layer 5 and layer 2/3.

  20. Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

    Science.gov (United States)

    Han, Bing; Taha, Tarek M

    2010-04-01

    There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

  1. Local changes in the excitability of the cerebellar cortex produce spatially restricted changes in complex spike synchrony.

    Science.gov (United States)

    Marshall, Sarah P; Lang, Eric J

    2009-11-11

    Complex spike (CS) synchrony patterns are modulated by the release of GABA within the inferior olive (IO). The GABAergic projection to most of the IO arises from the cerebellar nuclei, which are themselves subject to strong inhibitory control by Purkinje cells in the overlying cortex. Moreover, the connections between the IO and cerebellum are precisely aligned, raising the possibility that each cortical region controls its own CS synchrony distribution. This possibility was tested using multielectrode recordings of CSs and simple spikes (SSs) in crus 2a of anesthetized rats. Picrotoxin or muscimol was applied to the cerebellar cortex at the borders of the recording array. These drugs induced significant changes in CS synchrony and in CS and SS firing rates and changes in post-CS pauses and modulation of SS activity. The level of CS synchrony was correlated with SS firing rate in control, and application of picrotoxin increased both. In contrast, muscimol decreased CS synchrony. Furthermore, when picrotoxin was applied only at the lateral edge of the array, changes in CS synchrony occurred sequentially across the recording array, with cells located in the lateral half of the array having earlier and larger changes in CS synchrony than cells in the medial half. The results indicate that a double-inhibitory feedback circuit from Purkinje cells to the IO provides a mechanism by which SS activity may regulate CS synchrony. Thus, CS synchrony may be a physiologically controlled parameter of cerebellar activity, with the cerebellum and IO comprising a series of self-updating circuits.

  2. Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons.

    Science.gov (United States)

    Balachandar, Arjun; Prescott, Steven A

    2018-01-20

    Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of K V 1- and A-type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking-pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co-occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by

  3. Detection of hidden structures in nonstationary spike trains.

    Science.gov (United States)

    Takiyama, Ken; Okada, Masato

    2011-05-01

    We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into a quadratic form. The transformed observation process enables us to construct a variational Bayes algorithm that can determine the number of neural states based on automatic relevance determination. Additionally, our algorithm can estimate model parameters from single-trial data using a priori knowledge about state transitions and firing rates. Synthetic data analysis reveals that our algorithm has higher performance for estimating nonstationary firing rates than previous methods. The analysis also confirms that our algorithm can detect state transitions on the basis of discontinuous changes in temporal correlation, which are transitions that previous hidden Markov models could not detect. We also analyze neural data recorded from the medial temporal area. The statistically detected neural states probably coincide with transient and sustained states that have been detected heuristically. Estimated parameters suggest that our algorithm detects the state transitions on the basis of discontinuous changes in the temporal correlation of firing rates. These results suggest that our algorithm is advantageous in real-data analysis.

  4. Dynamics of spiking neurons: between homogeneity and synchrony.

    Science.gov (United States)

    Rangan, Aaditya V; Young, Lai-Sang

    2013-06-01

    Randomly connected networks of neurons driven by Poisson inputs are often assumed to produce "homogeneous" dynamics, characterized by largely independent firing and approximable by diffusion processes. At the same time, it is well known that such networks can fire synchronously. Between these two much studied scenarios lies a vastly complex dynamical landscape that is relatively unexplored. In this paper, we discuss a phenomenon which commonly manifests in these intermediate regimes, namely brief spurts of spiking activity which we call multiple firing events (MFE). These events do not depend on structured network architecture nor on structured input; they are an emergent property of the system. We came upon them in an earlier modeling paper, in which we discovered, through a careful benchmarking process, that MFEs are the single most important dynamical mechanism behind many of the V1 phenomena we were able to replicate. In this paper we explain in a simpler setting how MFEs come about, as well as their potential dynamic consequences. Although the mechanism underlying MFEs cannot easily be captured by current population dynamics models, this phenomena should not be ignored during analysis; there is a growing body of evidence that such collaborative activity may be a key towards unlocking the possible functional properties of many neuronal networks.

  5. U.S. Virgin Islands Petroleum Price-Spike Preparation

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, C.

    2012-06-01

    This NREL technical report details a plan for the U.S. Virgin Islands (USVI) to minimize the economic damage caused by major petroleum price increases. The assumptions for this plan are that the USVI will have very little time and money to implement it and that the population will be highly motivated to follow it because of high fuel prices. The plan's success, therefore, is highly dependent on behavior change. This plan was derived largely from a review of the actions taken and behavior changes made by companies and commuters throughout the United States in response to the oil price spike of 2008. Many of these solutions were coordinated by or reported through the 88 local representatives of the U.S. Department of Energy's Clean Cities program. The National Renewable Energy Laboratory provides technical and communications support for the Clean Cities program and therefore serves as a de facto repository of these solutions. This plan is the first publication that has tapped this repository.

  6. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  7. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  8. Symbol manipulation and rule learning in spiking neuronal networks.

    Science.gov (United States)

    Fernando, Chrisantha

    2011-04-21

    It has been claimed that the productivity, systematicity and compositionality of human language and thought necessitate the existence of a physical symbol system (PSS) in the brain. Recent discoveries about temporal coding suggest a novel type of neuronal implementation of a physical symbol system. Furthermore, learning classifier systems provide a plausible algorithmic basis by which symbol re-write rules could be trained to undertake behaviors exhibiting systematicity and compositionality, using a kind of natural selection of re-write rules in the brain, We show how the core operation of a learning classifier system, namely, the replication with variation of symbol re-write rules, can be implemented using spike-time dependent plasticity based supervised learning. As a whole, the aim of this paper is to integrate an algorithmic and an implementation level description of a neuronal symbol system capable of sustaining systematic and compositional behaviors. Previously proposed neuronal implementations of symbolic representations are compared with this new proposal. Copyright © 2011 Elsevier Ltd. All rights reserved.

  9. Recurrent coupling improves discrimination of temporal spike patterns

    Directory of Open Access Journals (Sweden)

    Chun-Wei eYuan

    2012-05-01

    Full Text Available Despite the ubiquitous presence of recurrent synaptic connections insensory neuronal systems, their general functional purpose is not wellunderstood. A recent conceptual advance has been achieved by theoriesof reservoir computing in which recurrent networks have been proposedto generate short-term memory as well as to improve neuronalrepresentation of the sensory input for subsequent computations.Here, we present a numerical study on the distinct effects ofinhibitory and excitatory recurrence in a canonical linearclassification task. It is found that both types of coupling improvethe ability to discriminate temporal spike patterns as compared to apurely feed-forward system, although in different ways. For a largeclass of inhibitory networks, the network's performance is optimal aslong as a fraction of roughly 50% of neurons per stimulus is activein the resulting population code. Thereby the contribution of inactiveneurons to the neural code is found to be even more informative thanthat of the active neurons, generating an inherent robustness ofclassification performance against temporal jitter of the inputspikes. Excitatory couplings are found to not only produce ashort-term memory buffer but also to improve linear separability ofthe population patterns by evoking more irregular firing as comparedto the purely inhibitory case. As the excitatory connectivity becomesmore sparse, firing becomes more variable and pattern separabilityimproves. We argue that the proposed paradigm is particularlywell-suited as a conceptual framework for processing of sensoryinformation in the auditory pathway.

  10. Dynamic finite size effects in spiking neural networks.

    Directory of Open Access Journals (Sweden)

    Michael A Buice

    Full Text Available We investigate the dynamics of a deterministic finite-sized network of synaptically coupled spiking neurons and present a formalism for computing the network statistics in a perturbative expansion. The small parameter for the expansion is the inverse number of neurons in the network. The network dynamics are fully characterized by a neuron population density that obeys a conservation law analogous to the Klimontovich equation in the kinetic theory of plasmas. The Klimontovich equation does not possess well-behaved solutions but can be recast in terms of a coupled system of well-behaved moment equations, known as a moment hierarchy. The moment hierarchy is impossible to solve but in the mean field limit of an infinite number of neurons, it reduces to a single well-behaved conservation law for the mean neuron density. For a large but finite system, the moment hierarchy can be truncated perturbatively with the inverse system size as a small parameter but the resulting set of reduced moment equations that are still very difficult to solve. However, the entire moment hierarchy can also be re-expressed in terms of a functional probability distribution of the neuron density. The moments can then be computed perturbatively using methods from statistical field theory. Here we derive the complete mean field theory and the lowest order second moment corrections for physiologically relevant quantities. Although we focus on finite-size corrections, our method can be used to compute perturbative expansions in any parameter.

  11. Population spikes in cortical networks during different functional states.

    Directory of Open Access Journals (Sweden)

    Shirley eMark

    2012-07-01

    Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.

  12. Mixed-mode bursting oscillations: Dynamics created by a slow passage through spike-adding canard explosion in a square-wave burster

    Science.gov (United States)

    Desroches, Mathieu; Kaper, Tasso J.; Krupa, Martin

    2013-12-01

    This article concerns the phenomenon of Mixed-Mode Bursting Oscillations (MMBOs). These are solutions of fast-slow systems of ordinary differential equations that exhibit both small-amplitude oscillations (SAOs) and bursts consisting of one or multiple large-amplitude oscillations (LAOs). The name MMBO is given in analogy to Mixed-Mode Oscillations, which consist of alternating SAOs and LAOs, without the LAOs being organized into burst events. In this article, we show how MMBOs are created naturally in systems that have a spike-adding bifurcation or spike-adding mechanism, and in which the dynamics of one (or more) of the slow variables causes the system to pass slowly through that bifurcation. Canards are central to the dynamics of MMBOs, and their role in shaping the MMBOs is two-fold: saddle-type canards are involved in the spike-adding mechanism of the underlying burster and permit one to understand the number of LAOs in each burst event, and folded-node canards arise due to the slow passage effect and control the number of SAOs. The analysis is carried out for a prototypical fourth-order system of this type, which consists of the third-order Hindmarsh-Rose system, known to have the spike-adding mechanism, and in which one of the key bifurcation parameters also varies slowly. We also include a discussion of the MMBO phenomenon for the Morris-Lecar-Terman system. Finally, we discuss the role of the MMBOs to a biological modeling of secreting neurons.

  13. Titanium: light, strong, and white

    Science.gov (United States)

    Woodruff, Laurel; Bedinger, George

    2013-01-01

    Titanium (Ti) is a strong silver-gray metal that is highly resistant to corrosion and is chemically inert. It is as strong as steel but 45 percent lighter, and it is twice as strong as aluminum but only 60 percent heavier. Titanium dioxide (TiO2) has a very high refractive index, which means that it has high light-scattering ability. As a result, TiO2 imparts whiteness, opacity, and brightness to many products. ...Because of the unique physical properties of titanium metal and the whiteness provided by TiO2, titanium is now used widely in modern industrial societies.

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

    Directory of Open Access Journals (Sweden)

    Gregor Strobbe

    2016-01-01

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

  15. Comments on the variation of spike morphology in selected species of Elytrigia and Elymus (Triticeae

    Directory of Open Access Journals (Sweden)

    Romuald Kosina

    2014-01-01

    Full Text Available The structure of spikes of Elytrigia repens, E. intermedia and Elymus caninus was investigated. The number of spikelets per spike reveals the weakest correlations with other characters of the spike. The same concerns some character ratios. The correlations provide information about the segmented structure (metamers of the spike. There is a great difference between matrices of correlation coefficients for E. repens and E. intermedia related to the development and structure of spike. Characters important for the description of the spike were chosen - in five-character set these are among others: length of glume awn in median spikelet, length of lemma awn in the first floret of the median spikelet, number of spikelets per spike. Length of lemma awn and mean length of the rachis segment were recognized as the best discriminants for species. Ordination of forms along axes of canonical variates does not indicate the subunits within E. repens. Intermediate forms between E. repens and Elymus caninus have not been found. Between E. repens and E. intermedia there exists some proximity. Heteromorphic individuals were described by means of cluster analysis. They prove the mobility of the genome in ramets of a single genet.

  16. Energetics based spike generation of a single neuron: simulation results and analysis

    Directory of Open Access Journals (Sweden)

    Nagarajan eVenkateswaran

    2012-02-01

    Full Text Available Existing current based models that capture spike activity, though useful in studying information processing capabilities of neurons, fail to throw light on their internal functioning. It is imperative to develop a model that captures the spike train of a neuron as a function of its intra cellular parameters for non-invasive diagnosis of diseased neurons. This is the first ever article to present such an integrated model that quantifies the inter-dependency between spike activity and intra cellular energetics. The generated spike trains from our integrated model will throw greater light on the intra-cellular energetics than existing current models. Now, an abnormality in the spike of a diseased neuron can be linked and hence effectively analyzed at the energetics level. The spectral analysis of the generated spike trains in a time-frequency domain will help identify abnormalities in the internals of a neuron. As a case study, the parameters of our model are tuned for Alzheimer disease and its resultant spike trains are studied and presented.

  17. Biophysical properties and computational modeling of calcium spikes in serotonergic neurons of the dorsal raphe nucleus.

    Science.gov (United States)

    Tuckwell, Henry C

    2013-06-01

    Serotonergic neurons of the dorsal raphe nuclei, with their extensive innervation of nearly the whole brain have important modulatory effects on many cognitive and physiological processes. They play important roles in clinical depression and other psychiatric disorders. In order to quantify the effects of serotonergic transmission on target cells it is desirable to construct computational models and to this end these it is necessary to have details of the biophysical and spike properties of the serotonergic neurons. Here several basic properties are reviewed with data from several studies since the 1960s to the present. The quantities included are input resistance, resting membrane potential, membrane time constant, firing rate, spike duration, spike and afterhyperpolarization (AHP) amplitude, spike threshold, cell capacitance, soma and somadendritic areas. The action potentials of these cells are normally triggered by a combination of sodium and calcium currents which may result in autonomous pacemaker activity. We here analyse the mechanisms of high-threshold calcium spikes which have been demonstrated in these cells the presence of TTX (tetrodotoxin). The parameters for calcium dynamics required to give calcium spikes are quite different from those for regular spiking which suggests the involvement of restricted parts of the soma-dendritic surface as has been found, for example, in hippocampal neurons. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Bertrand Fontaine

    2014-04-01

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

  19. Electricity market price spike analysis by a hybrid data model and feature selection technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2010-01-01

    In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

  20. 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...... interference on Pt from Os. The Pt-Pt double-spike does not use Pt in the double-spike inversion and is unaffected by Os contamination, and is our recommended double-spike for use with natural samples. As part of this study, we re-determined the natural Pt isotopic composition of IRMM-010 by MC-ICPMS using...

  1. Neonatal NMDA receptor blockade disrupts spike timing and glutamatergic synapses in fast spiking interneurons in a NMDA receptor hypofunction model of schizophrenia.

    Directory of Open Access Journals (Sweden)

    Kevin S Jones

    Full Text Available The dysfunction of parvalbumin-positive, fast-spiking interneurons (FSI is considered a primary contributor to the pathophysiology of schizophrenia (SZ, but deficits in FSI physiology have not been explicitly characterized. We show for the first time, that a widely-employed model of schizophrenia minimizes first spike latency and increases GluN2B-mediated current in neocortical FSIs. The reduction in FSI first-spike latency coincides with reduced expression of the Kv1.1 potassium channel subunit which provides a biophysical explanation for the abnormal spiking behavior. Similarly, the increase in NMDA current coincides with enhanced expression of the GluN2B NMDA receptor subunit, specifically in FSIs. In this study mice were treated with the NMDA receptor antagonist, MK-801, during the first week of life. During adolescence, we detected reduced spike latency and increased GluN2B-mediated NMDA current in FSIs, which suggests transient disruption of NMDA signaling during neonatal development exerts lasting changes in the cellular and synaptic physiology of neocortical FSIs. Overall, we propose these physiological disturbances represent a general impairment to the physiological maturation of FSIs which may contribute to schizophrenia-like behaviors produced by this model.

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

    Directory of Open Access Journals (Sweden)

    Xin-Xin Zhu

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

    Satuvuori, Eero; Kreuz, Thomas

    2018-04-01

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

  5. Discriminative Shape Alignment

    DEFF Research Database (Denmark)

    Loog, M.; de Bruijne, M.

    2009-01-01

    , not taking into account that eventually the shapes are to be assigned to two or more different classes. This work introduces a discriminative variation to well-known Procrustes alignment and demonstrates its benefit over this classical method in shape classification tasks. The focus is on two...

  6. Shape from touch

    NARCIS (Netherlands)

    Kappers, A.M.L.; Bergmann Tiest, W.M.

    2014-01-01

    The shape of objects cannot only be recognized by vision, but also by touch. Vision has the advantage that shapes can be seen at a distance, but touch has the advantage that during exploration many additional object properties become available, such as temperature (Jones, 2009), texture (Bensmaia,

  7. The Site of Spontaneous Ectopic Spike Initiation Facilitates Signal Integration in a Sensory Neuron.

    Science.gov (United States)

    Städele, Carola; Stein, Wolfgang

    2016-06-22

    Essential to understanding the process of neuronal signal integration is the knowledge of where within a neuron action potentials (APs) are generated. Recent studies support the idea that the precise location where APs are initiated and the properties of spike initiation zones define the cell's information processing capabilities. Notably, the location of spike initiation can be modified homeostatically within neurons to adjust neuronal activity. Here we show that this potential mechanism for neuronal plasticity can also be exploited in a rapid and dynamic fashion. We tested whether dislocation of the spike initiation zone affects signal integration by studying ectopic spike initiation in the anterior gastric receptor neuron (AGR) of the stomatogastric nervous system of Cancer borealis Like many other vertebrate and invertebrate neurons, AGR can generate ectopic APs in regions distinct from the axon initial segment. Using voltage-sensitive dyes and electrophysiology, we determined that AGR's ectopic spike activity was consistently initiated in the neuropil region of the stomatogastric ganglion motor circuits. At least one neurite branched off the AGR axon in this area; and indeed, we found that AGR's ectopic spike activity was influenced by local motor neurons. This sensorimotor interaction was state-dependent in that focal axon modulation with the biogenic amine octopamine, abolished signal integration at the primary spike initiation zone by dislocating spike initiation to a distant region of the axon. We demonstrate that the site of ectopic spike initiation is important for signal integration and that axonal neuromodulation allows for a dynamic adjustment of signal integration. Although it is known that action potentials are initiated at specific sites in the axon, it remains to be determined how the precise location of action potential initiation affects neuronal activity and signal integration. We addressed this issue by studying ectopic spiking in the axon of

  8. Polychlorinated biphenyl (PCB) recovery from spiked organic matrix using accelerated solvent extraction (ASE) and Soxhlet extraction.

    Science.gov (United States)

    Abrha, Y; Raghavan, D

    2000-12-30

    The recovery of five PCB congeners from PCB spiked organic matrices was studied using Accelerated solvent extraction (ASE) and Soxhlet extraction (SE). The chromatogram of ASE extract was found to be relatively clean and similar to that of SE extract. ASE extraction efficiency was dependent on the operation temperature and sample size loading. ASE showed extraction efficiency comparable or slightly higher to that of SE for the PCB spiked organic matrix. PCB recovery from spiked matrix was dependent on the type and molecular weight of congener, and nature of matrix. For some selected PCB congeners, ortho-substitution did influence the PCB recovery from graphite matrix.

  9. Immunogenic Display of Purified Chemically Cross-Linked HIV-1 Spikes

    Science.gov (United States)

    Leaman, Daniel P.; Lee, Jeong Hyun; Ward, Andrew B.

    2015-01-01

    ABSTRACT HIV-1 envelope glycoprotein (Env) spikes are prime vaccine candidates, at least in principle, but suffer from instability, molecular heterogeneity and a low copy number on virions. We anticipated that chemical cross-linking of HIV-1 would allow purification and molecular characterization of trimeric Env spikes, as well as high copy number immunization. Broadly neutralizing antibodies bound tightly to all major quaternary epitopes on cross-linked spikes. Covalent cross-linking of the trimer also stabilized broadly neutralizing epitopes, although surprisingly some individual epitopes were still somewhat sensitive to heat or reducing agent. Immunodepletion using non-neutralizing antibodies to gp120 and gp41 was an effective method for removing non-native-like Env. Cross-linked spikes, purified via an engineered C-terminal tag, were shown by negative stain EM to have well-ordered, trilobed structure. An immunization was performed comparing a boost with Env spikes on virions to spikes cross-linked and captured onto nanoparticles, each following a gp160 DNA prime. Although differences in neutralization did not reach statistical significance, cross-linked Env spikes elicited a more diverse and sporadically neutralizing antibody response against Tier 1b and 2 isolates when displayed on nanoparticles, despite attenuated binding titers to gp120 and V3 crown peptides. Our study demonstrates display of cross-linked trimeric Env spikes on nanoparticles, while showing a level of control over antigenicity, purity and density of virion-associated Env, which may have relevance for Env based vaccine strategies for HIV-1. IMPORTANCE The envelope spike (Env) is the target of HIV-1 neutralizing antibodies, which a successful vaccine will need to elicit. However, native Env on virions is innately labile, as well as heterogeneously and sparsely displayed. We therefore stabilized Env spikes using a chemical cross-linker and removed non-native Env by immunodepletion with non

  10. Investigation of Supply Current Spikes in Flash Memories Using Ion-Electron Emission Microscopy

    Science.gov (United States)

    Gerardin, S.; Bagatin, M.; Paccagnella, A.; Bisello, D.; Giubilato, P.; Mattiazzo, S.; Pantano, D.; Silvestrin, L.; Tessaro, M.; Wyss, J.; Ferlet-Cavrois, V.

    2013-12-01

    We studied the occurrence of supply current spikes and destructive events in NAND flash memories under heavy-ion exposure. In addition to broad-beam experiments, we used collimated beams and ion-electron emission microscopy to investigate the phenomena on two types of memories with different feature size. Current spikes on the supply current were observed in both devices, also with collimated beams, whereas destructive events occurred only with broad beam. We show that current spikes do not originate from charge-pump capacitors, as previously suggested, and propose that destructive events are due to the effects of temporally close heavy-ion hits on distinct areas of the tested chips.

  11. Power corrections and event shapes at LEP

    CERN Document Server

    Sanders, Michiel P

    2000-01-01

    Measurements of event shape variables from hadronic events collected by the LEP experiments, corresponding to hadronic center of mass energies between 30 GeV and 202 GeV are presented. Fits are performed to extract a, and the effective infrared strong coupling o with the power correction ansatz. Universality is observed for the effective coupling and comparisons are made with fragmentation models.

  12. Just-in-time connectivity for large spiking networks.

    Science.gov (United States)

    Lytton, William W; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

    2008-11-01

    The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.

  13. Colored noise and memory effects on formal spiking neuron models

    Science.gov (United States)

    da Silva, L. A.; Vilela, R. D.

    2015-06-01

    Simplified neuronal models capture the essence of the electrical activity of a generic neuron, besides being more interesting from the computational point of view when compared to higher-dimensional models such as the Hodgkin-Huxley one. In this work, we propose a generalized resonate-and-fire model described by a generalized Langevin equation that takes into account memory effects and colored noise. We perform a comprehensive numerical analysis to study the dynamics and the point process statistics of the proposed model, highlighting interesting new features such as (i) nonmonotonic behavior (emergence of peak structures, enhanced by the choice of colored noise characteristic time scale) of the coefficient of variation (CV) as a function of memory characteristic time scale, (ii) colored noise-induced shift in the CV, and (iii) emergence and suppression of multimodality in the interspike interval (ISI) distribution due to memory-induced subthreshold oscillations. Moreover, in the noise-induced spike regime, we study how memory and colored noise affect the coherence resonance (CR) phenomenon. We found that for sufficiently long memory, not only is CR suppressed but also the minimum of the CV-versus-noise intensity curve that characterizes the presence of CR may be replaced by a maximum. The aforementioned features allow to interpret the interplay between memory and colored noise as an effective control mechanism to neuronal variability. Since both variability and nontrivial temporal patterns in the ISI distribution are ubiquitous in biological cells, we hope the present model can be useful in modeling real aspects of neurons.

  14. Physiological Ripples (± 100 Hz) in Spike-Free Scalp EEGs of Children With and Without Epilepsy.

    Science.gov (United States)

    Mooij, Anne H; Raijmann, Renee C M A; Jansen, Floor E; Braun, Kees P J; Zijlmans, Maeike

    2017-11-01

    Pathological high frequency oscillations (HFOs, >80 Hz) are considered new biomarkers for epilepsy. They have mostly been recorded invasively, but pathological ripples (80-250 Hz) can also be found in scalp EEGs with frequent epileptiform spikes. Physiological HFOs also exist. They have been recorded invasively in hippocampus and neocortex. There are no reports of spontaneously occurring physiological HFOs recorded with scalp EEG. We aimed to study ripples in spike-free scalp EEGs. We included 23 children (6 with, 17 without epilepsy) who had an EEG without interictal epileptiform spikes recorded during sleep. We differentiated true ripples from spurious ripples such as filtering effects of sharp artifacts and high frequency components of muscle artifacts by viewing ripples simultaneously in bipolar and average montage and double-checking the unfiltered signal. We calculated mean frequency, duration and root mean square amplitude of the ripples, and studied their shape and distribution. We found ripples in EEGs of 20 out of 23 children (4 with, 16 without epilepsy). Ripples had a regular shape and occurred mostly on central and midline channels. Mean frequency was 102 Hz, mean duration 70 ms, mean root mean square amplitude 0.95 µV. Ripples occurring in normal EEGs of children without epilepsy were considered physiological; the similarity in appearance suggested that the ripples occurring in normal EEGs of children with epilepsy were also physiological. The finding that it is possible to study physiological neocortical ripples in scalp EEG paves the way for investigating their occurrence during brain development and their relation with cognitive functioning.

  15. The exchangeability of shape

    Directory of Open Access Journals (Sweden)

    Kaba Dramane

    2010-10-01

    Full Text Available Abstract Background Landmark based geometric morphometrics (GM allows the quantitative comparison of organismal shapes. When applied to systematics, it is able to score shape changes which often are undetectable by traditional morphological studies and even by classical morphometric approaches. It has thus become a fast and low cost candidate to identify cryptic species. Due to inherent mathematical properties, shape variables derived from one set of coordinates cannot be compared with shape variables derived from another set. Raw coordinates which produce these shape variables could be used for data exchange, however they contain measurement error. The latter may represent a significant obstacle when the objective is to distinguish very similar species. Results We show here that a single user derived dataset produces much less classification error than a multiple one. The question then becomes how to circumvent the lack of exchangeability of shape variables while preserving a single user dataset. A solution to this question could lead to the creation of a relatively fast and inexpensive systematic tool adapted for the recognition of cryptic species. Conclusions To preserve both exchangeability of shape and a single user derived dataset, our suggestion is to create a free access bank of reference images from which one can produce raw coordinates and use them for comparison with external specimens. Thus, we propose an alternative geometric descriptive system that separates 2-D data gathering and analyzes.

  16. The SNAP Strong Lens Survey

    Energy Technology Data Exchange (ETDEWEB)

    Marshall, P.

    2005-01-03

    Basic considerations of lens detection and identification indicate that a wide field survey of the types planned for weak lensing and Type Ia SNe with SNAP are close to optimal for the optical detection of strong lenses. Such a ''piggy-back'' survey might be expected even pessimistically to provide a catalogue of a few thousand new strong lenses, with the numbers dominated by systems of faint blue galaxies lensed by foreground ellipticals. After sketching out our strategy for detecting and measuring these galaxy lenses using the SNAP images, we discuss some of the scientific applications of such a large sample of gravitational lenses: in particular we comment on the partition of information between lens structure, the source population properties and cosmology. Understanding this partitioning is key to assessing strong lens cosmography's value as a cosmological probe.

  17. Strong coupling phase in QED

    International Nuclear Information System (INIS)

    Aoki, Ken-ichi

    1988-01-01

    Existence of a strong coupling phase in QED has been suggested in solutions of the Schwinger-Dyson equation and in Monte Carlo simulation of lattice QED. In this article we recapitulate the previous arguments, and formulate the problem in the modern framework of the renormalization theory, Wilsonian renormalization. This scheme of renormalization gives the best understanding of the basic structure of a field theory especially when it has a multi-phase structure. We resolve some misleading arguments in the previous literature. Then we set up a strategy to attack the strong phase, if any. We describe a trial; a coupled Schwinger-Dyson equation. Possible picture of the strong coupling phase QED is presented. (author)

  18. Attention Induced Gain Stabilization in Broad and Narrow-Spiking Cells in the Frontal Eye-Field of Macaque Monkeys

    Science.gov (United States)

    Brandt, Christian; Dasilva, Miguel; Gotthardt, Sascha; Chicharro, Daniel; Panzeri, Stefano; Distler, Claudia

    2016-01-01

    Top-down attention increases coding abilities by altering firing rates and rate variability. In the frontal eye field (FEF), a key area enabling top-down attention, attention induced firing rate changes are profound, but its effect on different cell types is unknown. Moreover, FEF is the only cortical area investigated in which attention does not affect rate variability, as assessed by the Fano factor, suggesting that task engagement affects cortical state nonuniformly. We show that putative interneurons in FEF of Macaca mulatta show stronger attentional rate modulation than putative pyramidal cells. Partitioning rate variability reveals that both cell types reduce rate variability with attention, but more strongly so in narrow-spiking cells. The effects are captured by a model in which attention stabilizes neuronal excitability, thereby reducing the expansive nonlinearity that links firing rate and variance. These results show that the effect of attention on different cell classes and different coding properties are consistent across the cortical hierarchy, acting through increased and stabilized neuronal excitability. SIGNIFICANCE STATEMENT Cortical processing is critically modulated by attention. A key feature of this influence is a modulation of “cortical state,” resulting in increased neuronal excitability and resilience of the network against perturbations, lower rate variability, and an increased signal-to-noise ratio. In the frontal eye field (FEF), an area assumed to control spatial attention in human and nonhuman primates, firing rate changes with attention occur, but rate variability, quantified by the Fano factor, appears to be unaffected by attention. Using recently developed analysis tools and models to quantify attention effects on narrow- and broad-spiking cell activity, we show that attention alters cortical state strongly in the FEF, demonstrating that its effect on the neuronal network is consistent across the cortical hierarchy. PMID

  19. Shaping light with MOEMS

    Science.gov (United States)

    Noell, W.; Weber, S.; Masson, J.; Extermann, J.; Bonacina, L.; Bich, A.; Bitterli, R.; Herzig, H. P.; Kiselev, D.; Scharf, T.; Voelkel, R.; Weible, K. J.; Wolf, J.-P.; de Rooij, N. F.

    2011-03-01

    Shaping light with microtechnology components has been possible for many years. The Texas Instruments digital micromirror device (DMD) and all types of adaptive optics systems are very sophisticated tools, well established and widely used. Here we present, however, two very dedicated systems, where one is an extremely simple MEMS-based tunable diffuser, while the second device is complex micromirror array with new capabilities for femtosecond laser pulse shaping. Showing the two systems right next to each other demonstrates the vast options and versatility of MOEMS for shaping light in the space and time domain.

  20. Volatile organic compound matrix spike recoveries for ground- and surface-water samples, 1997-2001

    Science.gov (United States)

    Rowe, Barbara L.; Delzer, Gregory C.; Bender, David A.; Zogorski, John S.

    2005-01-01

    The U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program used field matrix spikes (FMSs), field matrix spike replicates (FMSRs), laboratory matrix spikes (LMSs), and laboratory reagent spikes (LRSs), in part, to assess the quality of volatile organic compound (VOC) data from water samples collected and analyzed in more than 50 of the Nation's largest river basins and aquifers (Study Units). The data-quality objectives of the NAWQA Program include estimating the extent to which variability, degradation, and matrix effects, if any, may affect the interpretation of chemical analyses of ground- and surface-water samples. In order to help meet these objectives, a known mass of VOCs was added (spiked) to water samples collected in 25 Study Units. Data within this report include recoveries from 276 ground- and surface-water samples spiked with a 25-microliter syringe with a spike solution containing 85 VOCs to achieve a concentration of 0.5 microgram per liter. Combined recoveries for 85 VOCs from spiked ground- and surface-water samples and reagent water were used to broadly characterize the overall recovery of VOCs. Median recoveries for 149 FMSs, 107 FMSRs, 20 LMSs, and 152 LRSs were 79.9, 83.3, 113.1, and 103.5 percent, respectively. Spike recoveries for 85 VOCs also were calculated individually. With the exception of a few VOCs, the median percent recoveries determined from each spike type for individual VOCs followed the same pattern as for all VOC recoveries combined, that is, listed from least to greatest recovery-FMSs, FMSRs, LRSs, and LMSs. The median recoveries for individual VOCs ranged from 63.7 percent to 101.5 percent in FMSs; 63.1 percent to 101.4 percent in FMSRs; 101.7 percent to 135.0 percent in LMSs; and 91.0 percent to 118.7 percent in LRSs. Additionally, individual VOC recoveries were compared among paired spike types, and these recoveries were used to evaluate potential bias in the method. Variability associated with field