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Sample records for spike train statistics

  1. Statistical properties of superimposed stationary spike trains.

    PubMed

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

    2012-06-01

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

  2. Spike train statistics for consonant and dissonant musical accords in a simple auditory sensory model

    NASA Astrophysics Data System (ADS)

    Ushakov, Yuriy V.; Dubkov, Alexander A.; Spagnolo, Bernardo

    2010-04-01

    The phenomena of dissonance and consonance in a simple auditory sensory model composed of three neurons are considered. Two of them, here so-called sensory neurons, are driven by noise and subthreshold periodic signals with different ratio of frequencies, and its outputs plus noise are applied synaptically to a third neuron, so-called interneuron. We present a theoretical analysis with a probabilistic approach to investigate the interspike intervals statistics of the spike train generated by the interneuron. We find that tones with frequency ratios that are considered consonant by musicians produce at the third neuron inter-firing intervals statistics densities that are very distinctive from densities obtained using tones with ratios that are known to be dissonant. In other words, at the output of the interneuron, inharmonious signals give rise to blurry spike trains, while the harmonious signals produce more regular, less noisy, spike trains. Theoretical results are compared with numerical simulations.

  3. A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data

    PubMed Central

    Meng, Liang; Kramer, Mark A.; Middleton, Steven J.; Whittington, Miles A.; Eden, Uri T.

    2014-01-01

    A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data. PMID:24465520

  4. Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains

    PubMed Central

    Picado Muiño, David; Borgelt, Christian

    2015-01-01

    In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which—if it can be demonstrated reliably—would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information). PMID:25866503

  5. Statistical selection of multiple-input multiple-output nonlinear dynamic models of spike train transformation.

    PubMed

    Song, Dong; Chan, Rosa H M; Marmarelis, Vasilis Z; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W

    2007-01-01

    Multiple-input multiple-output nonlinear dynamic model of spike train to spike train transformations was previously formulated for hippocampal-cortical prostheses. This paper further described the statistical methods of selecting significant inputs (self-terms) and interactions between inputs (cross-terms) of this Volterra kernel-based model. In our approach, model structure was determined by progressively adding self-terms and cross-terms using a forward stepwise model selection technique. Model coefficients were then pruned based on Wald test. Results showed that the reduced kernel models, which contained much fewer coefficients than the full Volterra kernel model, gave good fits to the novel data. These models could be used to analyze the functional interactions between neurons during behavior.

  6. Spike train statistics and dynamics with synaptic input from any renewal process: a population density approach.

    PubMed

    Ly, Cheng; Tranchina, Daniel

    2009-02-01

    In the probability density function (PDF) approach to neural network modeling, a common simplifying assumption is that the arrival times of elementary postsynaptic events are governed by a Poisson process. This assumption ignores temporal correlations in the input that sometimes have important physiological consequences. We extend PDF methods to models with synaptic event times governed by any modulated renewal process. We focus on the integrate-and-fire neuron with instantaneous synaptic kinetics and a random elementary excitatory postsynaptic potential (EPSP), A. Between presynaptic events, the membrane voltage, v, decays exponentially toward rest, while s, the time since the last synaptic input event, evolves with unit velocity. When a synaptic event arrives, v jumps by A, and s is reset to zero. If v crosses the threshold voltage, an action potential occurs, and v is reset to v(reset). The probability per unit time of a synaptic event at time t, given the elapsed time s since the last event, h(s, t), depends on specifics of the renewal process. We study how regularity of the train of synaptic input events affects output spike rate, PDF and coefficient of variation (CV) of the interspike interval, and the autocorrelation function of the output spike train. In the limit of a deterministic, clocklike train of input events, the PDF of the interspike interval converges to a sum of delta functions, with coefficients determined by the PDF for A. The limiting autocorrelation function of the output spike train is a sum of delta functions whose coefficients fall under a damped oscillatory envelope. When the EPSP CV, sigma A/mu A, is equal to 0.45, a CV for the intersynaptic event interval, sigma T/mu T = 0.35, is functionally equivalent to a deterministic periodic train of synaptic input events (CV = 0) with respect to spike statistics. We discuss the relevance to neural network simulations. PMID:19431264

  7. Monitoring spike train synchrony.

    PubMed

    Kreuz, Thomas; Chicharro, Daniel; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian

    2013-03-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings. PMID:23221419

  8. Measuring multiple spike train synchrony.

    PubMed

    Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

    2009-10-15

    Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods. PMID:19591867

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

    PubMed Central

    2013-01-01

    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. PMID:24348527

  10. Spiking neural networks for cortical neuronal spike train decoding.

    PubMed

    Fang, Huijuan; Wang, Yongji; He, Jiping

    2010-04-01

    Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications. PMID:19922291

  11. Controlling chaos in balanced neural circuits with input spike trains

    NASA Astrophysics Data System (ADS)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  12. Superposition of many independent spike trains is generally not a Poisson process

    NASA Astrophysics Data System (ADS)

    Lindner, Benjamin

    2006-02-01

    We study the sum of many independent spike trains and ask whether the resulting spike train has Poisson statistics or not. It is shown that for a non-Poissonian statistics of the single spike train, the resulting sum of spikes has exponential interspike interval (ISI) distributions, vanishing the ISI correlation at a finite lag but exhibits exactly the same power spectrum as the original spike train does. This paradox is resolved by considering what happens to ISI correlations in the limit of an infinite number of superposed trains. Implications of our findings for stochastic models in the neurosciences are briefly discussed.

  13. Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.

    PubMed

    Banerjee, Arunava

    2016-05-01

    We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.

  14. Generation of spike trains in CNS neurons.

    PubMed

    Calvin, W H

    1975-01-24

    The membrane potential waveforms to be expected from many asynchronous inputs to CNS neurons are described, along with modes for repetitive firing through which the input waveforms are converted into spike trains. Area beneath a postsynaptic potential (PSP), rather than PSP peak height, is shown to be an important parameter susceptible to modification. Occasional crossings of threshold produce occasional spikes, but a sustained depolarizing waveform which attempts to hold the membrane potential above threshold elicits rhythmic firing. Firing rate is graded with the amount by which the synaptic depolarizing currents exceed the minimum current for rhythmic firing (approximately rheobase). A systematic sequence of alterations in the membrane potential trajectory between spikes, quite different from those of receptors and invertebrate neurons, may control the firing rate and give rise to sudden changes in the "gain" of this conversion of depolarizing current into firing rate. The different implications of synaptic location during the occasional spike mode and the rhythmic firing mode are discussed, as is the role of the antidromic invasion of the soma-dendritic region during rhythmic firing. Less frequently an"extra spike mode" is seen where depolarizing afterpotentials following a spike themselves cross threshold to elicit an extra spike, which may similarly elicit another extra spike, etc., in a regenerative cycle. The character of the underlying depolarizing afterpotentials (or "delayed depolarizations") is reviewed, along with theories for their origin from the antidromic invasion of the dendritic tree. The stereotyped burst firing patterns characteristic of the extra spike mode can also be seen in deafferented neurons and neurons studied in chronic syndromes such as epilepsy and central pain. This raises the question as to whether some disease states may augment extra spike firing, thus multiplying many-fold the response to a normal input. PMID:163121

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

    PubMed

    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.

  16. Fitting Neuron Models to Spike Trains

    PubMed Central

    Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

    2011-01-01

    Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

  17. Fitting neuron models to spike trains.

    PubMed

    Rossant, Cyrille; Goodman, Dan F M; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K; Brette, Romain

    2011-01-01

    Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

  18. Dynamic spiking studies using the DNPH sampling train

    SciTech Connect

    Steger, J.L.; Knoll, J.E.

    1996-12-31

    The proposed aldehyde and ketone sampling method using aqueous 2,4-dinitrophenylhydrazine (DNPH) was evaluated in the laboratory and in the field. The sampling trains studied were based on the train described in SW 846 Method 0011. Nine compounds were evaluated: formaldehyde, acetaldehyde, quinone, acrolein, propionaldeyde, methyl isobutyl ketone, methyl ethyl ketone, acetophenone, and isophorone. In the laboratory, the trains were spiked both statistically and dynamically. Laboratory studies also investigated potential interferences to the method. Based on their potential to hydrolyze in acid solution to form formaldehyde, dimethylolurea, saligenin, s-trioxane, hexamethylenetetramine, and paraformaldehyde were investigated. Ten runs were performed using quadruplicate sampling trains. Two of the four trains were dynamically spiked with the nine aldehydes and ketones. The test results were evaluated using the EPA method 301 criteria for method precision (< + pr - 50% relative standard deviation) and bias (correction factor of 1.00 + or - 0.30).

  19. Temporal Correlations and Neural Spike Train Entropy

    SciTech Connect

    Schultz, Simon R.; Panzeri, Stefano

    2001-06-18

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

  20. Encoding Chaos in Neural Spike Trains

    NASA Astrophysics Data System (ADS)

    Richardson, Kristen A.; Imhoff, Thomas T.; Grigg, Peter; Collins, James J.

    1998-03-01

    Recently, it has been shown that interspike interval (ISI) series from driven model neurons can be used to discriminate between chaotic and stochastic inputs. Here we extend this work to in vitro experimental studies with rat cutaneous mechanoreceptors. For each of the neurons tested, we show that a chaotically driven ISI series can be distinguished from a stochastically driven ISI series on the basis of a nonlinear prediction measure. This work demonstrates that dynamical information can be preserved when an analog chaotic signal is converted into a spike train by a sensory neuron.

  1. Analysis of Neuronal Spike Trains, Deconstructed.

    PubMed

    Aljadeff, Johnatan; Lansdell, Benjamin J; Fairhall, Adrienne L; Kleinfeld, David

    2016-07-20

    As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods. PMID:27477016

  2. Neuronal spike train entropy estimation by history clustering.

    PubMed

    Watters, Nicholas; Reeke, George N

    2014-09-01

    Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.

  3. Spike generation estimated from stationary spike trains in a variety of neurons in vivo.

    PubMed

    Spanne, Anton; Geborek, Pontus; Bengtsson, Fredrik; Jörntell, Henrik

    2014-01-01

    To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H) formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.

  4. Harmony perception and regularity of spike trains in a simple auditory model

    NASA Astrophysics Data System (ADS)

    Spagnolo, B.; Ushakov, Y. V.; Dubkov, A. A.

    2013-01-01

    A probabilistic approach for investigating the phenomena of dissonance and consonance in a simple auditory sensory model, composed by two sensory neurons and one interneuron, is presented. We calculated the interneuron's firing statistics, that is the interspike interval statistics of the spike train at the output of the interneuron, for consonant and dissonant inputs in the presence of additional "noise", representing random signals from other, nearby neurons and from the environment. We find that blurry interspike interval distributions (ISIDs) characterize dissonant accords, while quite regular ISIDs characterize consonant accords. The informational entropy of the non-Markov spike train at the output of the interneuron and its dependence on the frequency ratio of input sinusoidal signals is estimated. We introduce the regularity of spike train and suggested the high or low regularity level of the auditory system's spike trains as an indicator of feeling of harmony during sound perception or disharmony, respectively.

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

    PubMed

    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).

  6. A new multi-neuron spike-train metric

    PubMed Central

    Houghton, Conor; Sen, Kamal

    2009-01-01

    The Victor-Purpura spike-train metric has recently been extended to a family of multi-neuron metrics and used to analyze spike trains recorded simultaneously from pairs of proximate neurons. The Victor-Purpura metric is one of the two metrics commonly used for quantifying the distance between two spike trains, the other is the van Rossum metric. Here, we suggest an extension of the van Rossum metric to a multi-neuron metric. We believe this gives a metric which is both natural and easy to calculate. Both types of multi-neuron metric are applied to simulated data and are compared. PMID:18194108

  7. Statistical evaluation of synchronous spike patterns extracted by frequent item set mining

    PubMed Central

    Torre, Emiliano; Picado-Muiño, David; Denker, Michael; Borgelt, Christian; Grün, Sonja

    2013-01-01

    We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains. PMID:24167487

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

    PubMed Central

    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

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

    PubMed

    Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

    2015-05-01

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

  10. The Structure and Precision of Retinal Spike Trains

    NASA Astrophysics Data System (ADS)

    Berry, Michael J.; Warland, David K.; Meister, Markus

    1997-05-01

    Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.

  11. Transfer of Timing Information from RGC to LGN Spike Trains

    NASA Astrophysics Data System (ADS)

    Teich, Malvin C.; Lowen, Steven B.; Saleh, Bahaa E. A.; Kaplan, Ehud

    1998-03-01

    We have studied the firing patterns of retinal ganglion cells (RGCs) and their target lateral geniculate nucleus (LGN) cells. We find that clusters of spikes in the RGC neural firing pattern appear at the LGN output essentially unchanged, while isolated RGC firing events are more likely to be eliminated; thus the LGN action-potential sequence is therefore not merely a randomly deleted version of the RGC spike train. Employing information-theoretic techniques we developed for point processes,(B. E. A. Saleh and M. C. Teich, Phys. Rev. Lett.) 58, 2656--2659 (1987). we are able to estimate the information efficiency of the LGN neuronal output --- the proportion of the variation in the LGN firing pattern that carries information about its associated RGC input. A suitably modified integrate-and-fire neural model reproduces both the enhanced clustering in the LGN data (which accounts for the increased coefficient of variation) and the measured value of information efficiency, as well as mimicking the results of other observed statistical measures. Reliable information transmission therefore coexists with fractal fluctuations, which appear in RGC and LGN firing patterns.(M. C. Teich, C. Heneghan, S. B. Lowen, T. Ozaki, and E. Kaplan, J. Opt. Soc. Am. A) 14, 529--546 (1997).

  12. Estimating membrane voltage correlations from extracellular spike trains.

    PubMed

    Dorn, Jessy D; Ringach, Dario L

    2003-04-01

    The cross-correlation coefficient between neural spike trains is a commonly used tool in the study of neural interactions. Two well-known complications that arise in its interpretation are 1) modulations in the correlation coefficient may result solely from changes in the mean firing rate of the cells and 2) the mean firing rates of the neurons impose upper and lower bounds on the correlation coefficient whose absolute values differ by an order of magnitude or more. Here, we propose a model-based approach to the interpretation of spike train correlations that circumvents these problems. The basic idea of our proposal is to estimate the cross-correlation coefficient between the membrane voltages of two cells from their extracellular spike trains and use the resulting value as the degree of correlation (or association) of neural activity. This is done in the context of a model that assumes the membrane voltages of the cells have a joint normal distribution and spikes are generated by a simple thresholding operation. We show that, under these assumptions, the estimation of the correlation coefficient between the membrane voltages reduces to the calculation of a tetrachoric correlation coefficient (a measure of association in nominal data introduced by Karl Pearson) on a contingency table calculated from the spike data. Simulations of conductance-based leaky integrate-and-fire neurons indicate that, despite its simplicity, the technique yields very good estimates of the intracellular membrane voltage correlation from the extracellular spike trains in biologically realistic models. PMID:12686584

  13. The algorithmic complexity of neural spike trains increases during focal seizures.

    PubMed

    Rapp, P E; Zimmerman, I D; Vining, E P; Cohen, N; Albano, A M; Jiménez-Montaño, M A

    1994-08-01

    The interspike interval spike trains of spontaneously active cortical neurons can display nonrandom internal structure. The degree of nonrandom structure can be quantified and was found to decrease during focal epileptic seizures. Greater statistical discrimination between the two physiological conditions (normal vs seizure) was obtained with measurements of context-free grammar complexity than by measures of the distribution of the interspike intervals such as the mean interval, its standard deviation, skewness, or kurtosis. An examination of fixed epoch data sets showed that two factors contribute to the complexity: the firing rate and the internal structure of the spike train. However, calculations with randomly shuffled surrogates of the original data sets showed that the complexity is not completely determined by the firing rate. The sequence-sensitive structure of the spike train is a significant contributor. By combining complexity measurements with statistically related surrogate data sets, it is possible to classify neurons according to the dynamical structure of their spike trains. This classification could not have been made on the basis of conventional distribution-determined measures. Computations with more sophisticated kinds of surrogate data show that the structure observed using complexity measures cannot be attributed to linearly correlated noise or to linearly correlated noise transformed by a static monotonic nonlinearity. The patterns in spike trains appear to reflect genuine nonlinear structure. The limitations of these results are also discussed. The results presented in this article do not, of themselves, establish the presence of a fine-structure encoding of neural information.

  14. Input spike trains suppress chaos in balanced neural circuits

    NASA Astrophysics Data System (ADS)

    Engelken, Rainer; Monteforte, Michael; Wolf, Fred

    2015-03-01

    A longstanding hypothesis claims that structured input in neural circuits enhances reliability of spiking responses. While studies in single neurons well support this hypothesis [Mainen, Sejnowski 1995] the impact of input structure on the dynamics of recurrent networks is not well understood. Earlier studies of the dynamic stability of the balanced state used a constant external input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We generalize the analysis of dynamical stability for balanced networks driven by input spike trains. An analytical expression for the Jacobian enables us to calculate the full Lyapunov spectrum. We solved the dynamics in numerically exact event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We examined the transition from constant to stochastic input in various scenarios. We find a suppression of chaos by input spike trains. We also find that both independent bursty input spike trains and common input more strongly reduces chaos in spiking networks. Our study extends studies of chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of sensory streams in shaping the dynamics of large networks.

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

    PubMed

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

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

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

    PubMed Central

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

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

  19. Estimating nonstationary input signals from a single neuronal spike train

    NASA Astrophysics Data System (ADS)

    Kim, Hideaki; Shinomoto, Shigeru

    2012-11-01

    Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

  20. Estimating nonstationary input signals from a single neuronal spike train.

    PubMed

    Kim, Hideaki; Shinomoto, Shigeru

    2012-11-01

    Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

  1. Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms

    PubMed Central

    Matthews, Brett A.; Clements, Mark A.

    2014-01-01

    This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset. PMID:24829568

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

    PubMed

    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.

  3. Generation of spatiotemporally correlated spike trains and local field potentials using a multivariate autoregressive process.

    PubMed

    Gutnisky, Diego A; Josić, Kresimir

    2010-05-01

    Experimental advances allowing for the simultaneous recording of activity at multiple sites have significantly increased our understanding of the spatiotemporal patterns in neural activity. The impact of such patterns on neural coding is a fundamental question in neuroscience. The simulation of spike trains with predetermined activity patterns is therefore an important ingredient in the study of potential neural codes. Such artificially generated spike trains could also be used to manipulate cortical neurons in vitro and in vivo. Here, we propose a method to generate spike trains with given mean firing rates and cross-correlations. To capture this statistical structure we generate a point process by thresholding a stochastic process that is continuous in space and discrete in time. This stochastic process is obtained by filtering Gaussian noise through a multivariate autoregressive (AR) model. The parameters of the AR model are obtained by a nonlinear transformation of the point-process correlations to the continuous-process correlations. The proposed method is very efficient and allows for the simulation of large neural populations. It can be optimized to the structure of spatiotemporal correlations and generalized to nonstationary processes and spatiotemporal patterns of local field potentials and spike trains. PMID:20032244

  4. Topological analysis of chaos in neural spike train bursts

    SciTech Connect

    Gilmore, R. ); Pei, X.; Moss, F. )

    1999-09-01

    We show how a topological model which describes the stretching and squeezing mechanisms responsible for creating chaotic behavior can be extracted from the neural spike train data. The mechanism we have identified is the same one ([open quotes]gateau roul[acute e],[close quotes] or jelly-roll) which has previously been identified in the Duffing oscillator [Gilmore and McCallum, Phys. Rev. E [bold 51], 935 (1995)] and in a YAG laser [Boulant [ital et al.], Phys. Rev. E [bold 55], 5082 (1997)]. [copyright] [ital 1999 American Institute of Physics.

  5. Training Spiking Neural Models Using Artificial Bee Colony

    PubMed Central

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

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

  6. Analysis of neural spike trains with interspike interval reconstruction.

    PubMed

    Suzuki, H; Aihara, K; Murakami, J; Shimozawa, T

    2000-04-01

    As a method for the analysis of neural spike trains, we examine fundamental characteristics of interspike interval (ISI) reconstruction theoretically with a leaky-integrator neuron model and experimentally with cricket wind receptor cells. Both the input to the leaky integrator and the stimulus to the wind receptor cells are the time series generated from the Rossler system. By numerical analysis of the leaky integrator, it is shown that, even if ISI reconstruction is possible, sometimes the entire structure of the Rössler attractor may not be reconstructed with ISI reconstruction. For analysis of the in vivo physiological responses of cricket wind receptor cells, we apply ISI reconstruction, nonlinear prediction and the surrogate data method to the experimental data. As a result of the analysis, it is found that there is a significant deterministic structure in the spike trains. By this analysis of physiological data, it is also shown that, even if ISI reconstruction is possible, the entire attractor may not be reconstructed. PMID:10804062

  7. Training spiking neural models using artificial bee colony.

    PubMed

    Vazquez, Roberto A; Garro, Beatriz A

    2015-01-01

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

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

    PubMed

    Dummer, Benjamin; Wieland, Stefan; Lindner, Benjamin

    2014-01-01

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

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

    PubMed Central

    Dummer, Benjamin; Wieland, Stefan; Lindner, Benjamin

    2014-01-01

    A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i) a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii) a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, 2000) and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide an excellent approximations to the autocorrelation of spike trains in the recurrent network. PMID:25278869

  10. Short-term synaptic plasticity can enhance weak signal detectability in nonrenewal spike trains.

    PubMed

    Lüdtke, Niklas; Nelson, Mark E

    2006-12-01

    We study the encoding of weak signals in spike trains with interspike interval (ISI) correlations and the signals' subsequent detection in sensory neurons. Motivated by the observation of negative ISI correlations in auditory and electrosensory afferents, we assess the theoretical performance limits of an individual detector neuron receiving a weak signal distributed across multiple afferent inputs. We assess the functional role of ISI correlations in the detection process using statistical detection theory and derive two sequential likelihood ratio detector models: one for afferents with renewal statistics; the other for afferents with negatively correlated ISIs. We suggest a mechanism that might enable sensory neurons to implicitly compute conditional probabilities of presynaptic spikes by means of short-term synaptic plasticity. We demonstrate how this mechanism can enhance a postsynaptic neuron's sensitivity to weak signals by exploiting the correlation structure of the input spike trains. Our model not only captures fundamental aspects of early electrosensory signal processing in weakly electric fish, but may also bear relevance to the mammalian auditory system and other sensory modalities.

  11. Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses.

    PubMed

    Song, Dong; Robinson, Brian S; Hampson, Robert E; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

    2015-01-01

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed match-to-sample task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  12. Regularity of Spike Trains and Harmony Perception in a Model of the Auditory System

    NASA Astrophysics Data System (ADS)

    Ushakov, Yu. V.; Dubkov, A. A.; Spagnolo, B.

    2011-09-01

    Spike train regularity of the noisy neural auditory system model under the influence of two sinusoidal signals with different frequencies is investigated. For the increasing ratio m/n of the input signal frequencies (m, n are natural numbers) the linear growth of the regularity is found at the fixed difference (m-n). It is shown that the spike train regularity in the model is high for harmonious chords of input tones and low for dissonant ones.

  13. Analysis of Rayleigh-Taylor Instability: Statistics on Rising Bubbles and Falling Spikes

    SciTech Connect

    Kamath, C; Gezahegne, A; Miller, P

    2007-10-30

    The analysis of coherent structures in Rayleigh-Taylor simulations is a challenging task as the lack of a precise definition of these structures is compounded by the massive size of the datasets. In an earlier work, we used techniques from image analysis to count these coherent structures in two high-resolution simulations, one a large-eddy simulation with 30 terabytes of analysis data, and the other a direct numerical simulation with 80 terabytes of analysis data. Our analysis indicated that there were four distinct regimes in the process of the mixing of the two fluids, starting from the initial linear stage, followed by the non-linear stage with weak turbulence, the mixing transition stage, and the final stage of strong turbulence. In this paper, we extend our earlier work to focus on only the rising bubbles and the falling spikes. We first consider different ways in which we can constrain the bubble and spike definitions and then extract various statistics on them. Our results on the rising bubble and falling spike counts again show that there are four regimes in the process of fluid mixing, each characterized by an integer-valued slope. Further, the average bubble heights and spike depths are related to similar results obtained using a threshold-based definition. Finally, the ratio of the rising bubbles to all bubbles is very similar in character to the ratio of the falling spikes to all spikes, with near constant values over part of the simulation.

  14. Relation between single neuron and population spiking statistics and effects on network activity.

    PubMed

    Câteau, Hideyuki; Reyes, Alex D

    2006-02-10

    To simplify theoretical analyses of neural networks, individual neurons are often modeled as Poisson processes. An implicit assumption is that even if the spiking activity of each neuron is non-Poissonian, the composite activity obtained by summing many spike trains limits to a Poisson process. Here, we show analytically and through simulations that this assumption is invalid. Moreover, we show with Fokker-Planck equations that the behavior of feedforward networks is reproduced accurately only if the tendency of neurons to fire periodically is incorporated by using colored noise whose autocorrelation has a negative component.

  15. Efficacy of electrical stimulation of retinal ganglion cells with temporal patterns resembling light-evoked spike trains.

    PubMed

    Wong, Raymond C S; Garrett, David J; Grayden, David B; Ibbotson, Michael R; Cloherty, Shaun L

    2014-01-01

    People with degenerative retinal diseases such as retinitis pigmentosa lose most of their photoreceptors but retain a significant proportion (~30%) of their retinal ganglion cells (RGCs). Microelectronic retinal prostheses aim to bypass the lost photoreceptors and restore vision by directly stimulating the surviving RGCs. Here we investigate the extent to which electrical stimulation of RGCs can evoke neural spike trains with statistics resembling those of normal visually-evoked responses. Whole-cell patch clamp recordings were made from individual cat RGCs in vitro. We first recorded the responses of each cell to short sequences of visual stimulation. These responses were converted to trains of electrical stimulation that we then presented to the same cell via an epiretinal stimulating electrode. We then quantified the efficacy of the electrical stimuli and the latency of the evoked spikes. In all cases, spikes were evoked with sub-millisecond latency (0.55 ms, median, ON cells, n = 8; 0.75 ms, median, OFF cells, n = 6) and efficacy ranged from 0.4-1.0 (0.79, median, ON cells; 0.97, median, OFF cells). These data demonstrate that meaningful spike trains, resembling normal responses of RGCs to visual stimulation, can be reliably evoked by epiretinal prostheses. PMID:25570304

  16. Optimal decision making on the basis of evidence represented in spike trains.

    PubMed

    Zhang, Jiaxiang; Bogacz, Rafal

    2010-05-01

    Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

  17. Rate-synchrony relationship between input and output of spike trains in neuronal networks

    NASA Astrophysics Data System (ADS)

    Wang, Sentao; Zhou, Changsong

    2010-01-01

    Neuronal networks interact via spike trains. How the spike trains are transformed by neuronal networks is critical for understanding the underlying mechanism of information processing in the nervous system. Both the rate and synchrony of the spikes can affect the transmission, while the relationship between them has not been fully understood. Here we investigate the mapping between input and output spike trains of a neuronal network in terms of firing rate and synchrony. With large enough input rate, the working mode of the neurons is gradually changed from temporal integrators into coincidence detectors when the synchrony degree of input spike trains increases. Since the membrane potentials of the neurons can be depolarized to near the firing threshold by uncorrelated input spikes, small input synchrony can cause great output synchrony. On the other hand, the synchrony in the output may be reduced when the input rate is too small. The case of the feedforward network can be regarded as iterative process of such an input-output relationship. The activity in deep layers of the feedforward network is in an all-or-none manner depending on the input rate and synchrony.

  18. Thermodynamic order parameters and statistical-mechanical measures for characterization of the burst and spike synchronizations of bursting neurons

    NASA Astrophysics Data System (ADS)

    Kim, Sang-Yoon; Lim, Woochang

    2015-11-01

    We are interested in characterization of population synchronization of bursting neurons which exhibit both the slow bursting and the fast spiking timescales, in contrast to spiking neurons. Population synchronization may be well visualized in the raster plot of neural spikes which can be obtained in experiments. The instantaneous population firing rate (IPFR) R(t) , which may be directly obtained from the raster plot of spikes, is often used as a realistic collective quantity describing population behaviors in both the computational and the experimental neuroscience. For the case of spiking neurons, realistic thermodynamic order parameter and statistical-mechanical spiking measure, based on R(t) , were introduced in our recent work to make practical characterization of spike synchronization. Here, we separate the slow bursting and the fast spiking timescales via frequency filtering, and extend the thermodynamic order parameter and the statistical-mechanical measure to the case of bursting neurons. Consequently, it is shown in explicit examples that both the order parameters and the statistical-mechanical measures may be effectively used to characterize the burst and spike synchronizations of bursting neurons.

  19. [Australian Vocational Education & Training Statistics. Four Reports.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    This item consists of four separate documents covering various aspects of Australian Vocational Education and Training (VET) statistics. The first two documents, "Statistics 1996: Women at a Glance" and "Statistics 1996: Young People at a Glance," provide summary information about women and young people (15- to 24-year-olds) who undertook public…

  20. Detecting dependencies between spike trains of pairs of neurons through copulas.

    PubMed

    Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

    2012-01-24

    The dynamics of a neuron are influenced by the connections with the network where it lies. Recorded spike trains exhibit patterns due to the interactions between neurons. However, the structure of the network is not known. A challenging task is to investigate it from the analysis of simultaneously recorded spike trains. We develop a non-parametric method based on copulas, that we apply to simulated data according to different bivariate Leaky Integrate and Fire models. The method discerns dependencies determined by the surrounding network, from those determined by direct interactions between the two neurons. Furthermore, the method recognizes the presence of delays in the spike propagation. This article is part of a Special Issue entitled "Neural Coding". PMID:21981802

  1. Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

    PubMed Central

    Goldberg, David H.; Victor, Jonathan D.; Gardner, Esther P.

    2009-01-01

    Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org, maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope. PMID:19475519

  2. Calculating mutual information for spike trains and other data with distances but no coordinates

    PubMed Central

    Houghton, Conor

    2015-01-01

    Many important data types, such as the spike trains recorded from neurons in typical electrophysiological experiments, have a natural notion of distance or similarity between data points, even though there is no obvious coordinate system. Here, a simple Kozachenko–Leonenko estimator is derived for calculating the mutual information between datasets of this type. PMID:26064650

  3. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  4. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

    PubMed Central

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

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

    PubMed

    Ramus, Claire; Hovasse, Agnès; Marcellin, Marlène; Hesse, Anne-Marie; Mouton-Barbosa, Emmanuelle; Bouyssié, David; Vaca, Sebastian; Carapito, Christine; Chaoui, Karima; Bruley, Christophe; Garin, Jérôme; Cianférani, Sarah; Ferro, Myriam; Dorssaeler, Alain Van; Burlet-Schiltz, Odile; Schaeffer, Christine; Couté, Yohann; Gonzalez de Peredo, Anne

    2016-03-01

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

  6. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

  7. Non-Markovian spiking statistics of a neuron with delayed feedback in presence of refractoriness.

    PubMed

    Kravchuk, Kseniia; Vidybida, Alexander

    2014-02-01

    Spiking statistics of a self-inhibitory neuron is considered. The neuron receives excitatory input from a Poisson stream and inhibitory impulses through a feedback line with a delay. After triggering, the neuron is in the refractory state for a positive period of time. Recently, [35,6], it was proven for a neuron with delayed feedback and without the refractory state, that the output stream of interspike intervals (ISI) cannot be represented as a Markov process. The refractory state presence, in a sense limits the memory range in the spiking process, which might restore Markov property to the ISI stream. Here we check such a possibility. For this purpose, we calculate the conditional probability density P (tn+1 l tn,...,t1,t0), and prove exactly that it does not reduce to P (tn+1 l tn,...,t1) for any n ⋝0. That means, that activity of the system with refractory state as well cannot be represented as a Markov process of any order. We conclude that it is namely the delayed feedback presence which results in non-Markovian statistics of neuronal firing. As delayed feedback lines are common for any realistic neural network, the non-Markovian statistics of the network activity should be taken into account in processing of experimental data.

  8. Firing statistics and correlations in spiking neurons: a level-crossing approach.

    PubMed

    Badel, Laurent

    2011-10-01

    We present a time-dependent level-crossing theory for linear dynamical systems perturbed by colored Gaussian noise. We apply these results to approximate the firing statistics of conductance-based integrate-and-fire neurons receiving excitatory and inhibitory Poissonian inputs. Analytical expressions are obtained for three key quantities characterizing the neuronal response to time-varying inputs: the mean firing rate, the linear response to sinusoidally modulated inputs, and the pairwise spike correlation for neurons receiving correlated inputs. The theory yields tractable results that are shown to accurately match numerical simulations and provides useful tools for the analysis of interconnected neuronal populations.

  9. Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?

    NASA Astrophysics Data System (ADS)

    Sanli, Ceyda; Lambiotte, Renaud

    2015-09-01

    We study complex time series (spike trains) of online user communication while spreading messages about the discovery of the Higgs boson in Twitter. We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action) and passive (receiving an action) spike trains for each user. The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity. We show that the active spike trains are bursty, independently of their activation frequency. For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random) dynamics. We further characterize the correlations of the local variation in different interactions. We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.

  10. Temporal Features of Spike Trains in the Moth Antennal Lobe Revealed by a Comparative Time-Frequency Analysis

    PubMed Central

    Capurro, Alberto; Baroni, Fabiano; Kuebler, Linda S.; Kárpáti, Zsolt; Dekker, Teun; Lei, Hong; Hansson, Bill S.; Pearce, Timothy C.; Olsson, Shannon B.

    2014-01-01

    The discrimination of complex sensory stimuli in a noisy environment is an immense computational task. Sensory systems often encode stimulus features in a spatiotemporal fashion through the complex firing patterns of individual neurons. To identify these temporal features, we have developed an analysis that allows the comparison of statistically significant features of spike trains localized over multiple scales of time-frequency resolution. Our approach provides an original way to utilize the discrete wavelet transform to process instantaneous rate functions derived from spike trains, and select relevant wavelet coefficients through statistical analysis. Our method uncovered localized features within olfactory projection neuron (PN) responses in the moth antennal lobe coding for the presence of an odor mixture and the concentration of single component odorants, but not for compound identities. We found that odor mixtures evoked earlier responses in biphasic response type PNs compared to single components, which led to differences in the instantaneous firing rate functions with their signal power spread across multiple frequency bands (ranging from 0 to 45.71 Hz) during a time window immediately preceding behavioral response latencies observed in insects. Odor concentrations were coded in excited response type PNs both in low frequency band differences (2.86 to 5.71 Hz) during the stimulus and in the odor trace after stimulus offset in low (0 to 2.86 Hz) and high (22.86 to 45.71 Hz) frequency bands. These high frequency differences in both types of PNs could have particular relevance for recruiting cellular activity in higher brain centers such as mushroom body Kenyon cells. In contrast, neurons in the specialized pheromone-responsive area of the moth antennal lobe exhibited few stimulus-dependent differences in temporal response features. These results provide interesting insights on early insect olfactory processing and introduce a novel comparative approach for

  11. Deriving functional structure of neuronal networks from spike train data

    NASA Astrophysics Data System (ADS)

    Feldt, Sarah; Hetrick, Vaughn; Berke, Joshua; Zochowski, Michal

    2009-03-01

    We present a novel algorithm for the detection of functional clusters in neural data. In contrast to many clustering techniques which convert functional interactions to topological distances to determine groupings, our algorithm directly utilizes the dynamics of the neurons to obtain functional groupings. No prior knowledge of the number of groups is needed, as the algorithm determines statistically significant clusters through a comparison to surrogate data sets. Additionally, we introduce a new synchronization measure and use this measure in the algorithm to observe known groupings in simulated data. We then apply our algorithm to experimental data obtained from the hippocampus of a freely moving mouse and show that it detects known changes in neural states associated with exploration and slow wave sleep. Finally, we show that the new synchronization measure can detect changes which are consistent with known neurophysiological processes involved in memory consolidation.

  12. Input-output mapping reconstruction of spike trains at dorsal horn evoked by manual acupuncture

    NASA Astrophysics Data System (ADS)

    Wei, Xile; Shi, Dingtian; Yu, Haitao; Deng, Bin; Lu, Meili; Han, Chunxiao; Wang, Jiang

    2016-12-01

    In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the “Zusanli” point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture.

  13. Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.

    PubMed

    Lyamzin, Dmitry R; Macke, Jakob H; Lesica, Nicholas A

    2010-01-01

    As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic "signal" that is repeated on each trial and a Gaussian random "noise" that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single-cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Torre, Emiliano; Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz; Grün, Sonja

    2016-07-01

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

  16. The Episodic Nature of Spike Trains in the Early Visual Pathway

    PubMed Central

    Desbordes, Gaëlle; Weng, Chong; Jin, Jianzhong; Alonso, Jose-Manuel; Stanley, Garrett B.

    2010-01-01

    An understanding of the neural code in a given visual area is often confounded by the immense complexity of visual stimuli combined with the number of possible meaningful patterns that comprise the response spike train. In the lateral geniculate nucleus (LGN), visual stimulation generates spike trains comprised of short spiking episodes (“events”) separated by relatively long intervals of silence, which establishes a basis for in-depth analysis of the neural code. By studying this event structure in both artificial and natural visual stimulus contexts and at different contrasts, we are able to describe the dependence of event structure on stimulus class and discern which aspects generalize. We find that the event structure on coarse time scales is robust across stimulus and contrast and can be explained by receptive field processing. However, the relationship between the stimulus and fine-time-scale features of events is less straightforward, partially due to a significant amount of trial-to-trial variability. A new measure called “label information” identifies structural elements of events that can contain ≤30% more information in the context of natural movies compared with what is available from the overall event timing. The first interspike interval of an event most robustly conveys additional information about the stimulus and is somewhat more informative than the event spike count and much more informative than the presence of bursts. Nearly every event is preserved across contrast despite changes in their fine-time-scale features, suggesting that—at least on a coarse level—the stimulus selectivity of LGN neurons is contrast invariant. Event-based analysis thus casts previously studied elements of LGN coding such as contrast adaptation and receptive field processing in a new light and leads to broad conclusions about the composition of the LGN neuronal code. PMID:20926615

  17. Interareal Spike-Train Correlations of Anterior Cingulate and Dorsal Prefrontal Cortex during Attention Shifts.

    PubMed

    Oemisch, Mariann; Westendorff, Stephanie; Everling, Stefan; Womelsdorf, Thilo

    2015-09-23

    The anterior cingulate cortex (ACC) and prefrontal cortex (PFC) are believed to coactivate during goal-directed behavior to identify, select, and monitor relevant sensory information. Here, we tested whether coactivation of neurons across macaque ACC and PFC would be evident at the level of pairwise neuronal correlations during stimulus selection in a spatial attention task. We found that firing correlations emerged shortly after an attention cue, were evident for 50-200 ms time windows, were strongest for neuron pairs in area 24 (ACC) and areas 8 and 9 (dorsal PFC), and were independent of overall firing rate modulations. For a subset of cell pairs from ACC and dorsal PFC, the observed functional spike-train connectivity carried information about the direction of the attention shift. Reliable firing correlations were evident across area boundaries for neurons with broad spike waveforms (putative excitatory neurons) as well as for pairs of putative excitatory neurons and neurons with narrow spike waveforms (putative interneurons). These findings reveal that stimulus selection is accompanied by slow time scale firing correlations across those ACC/PFC subfields implicated to control and monitor attention. This functional coupling was informative about which stimulus was selected and thus indexed possibly the exchange of task-relevant information. We speculate that interareal, transient firing correlations reflect the transient coordination of larger, reciprocally interacting brain networks at a characteristic 50-200 ms time scale. Significance statement: Our manuscript identifies interareal spike-train correlations between primate anterior cingulate and dorsal prefrontal cortex during a period where attentional stimulus selection is likely controlled by these very same circuits. Interareal correlations emerged during the covert attention shift to one of two peripheral stimuli, proceeded on a slow 50-200 ms time scale, and occurred between putative pyramidal and

  18. Interareal Spike-Train Correlations of Anterior Cingulate and Dorsal Prefrontal Cortex during Attention Shifts.

    PubMed

    Oemisch, Mariann; Westendorff, Stephanie; Everling, Stefan; Womelsdorf, Thilo

    2015-09-23

    The anterior cingulate cortex (ACC) and prefrontal cortex (PFC) are believed to coactivate during goal-directed behavior to identify, select, and monitor relevant sensory information. Here, we tested whether coactivation of neurons across macaque ACC and PFC would be evident at the level of pairwise neuronal correlations during stimulus selection in a spatial attention task. We found that firing correlations emerged shortly after an attention cue, were evident for 50-200 ms time windows, were strongest for neuron pairs in area 24 (ACC) and areas 8 and 9 (dorsal PFC), and were independent of overall firing rate modulations. For a subset of cell pairs from ACC and dorsal PFC, the observed functional spike-train connectivity carried information about the direction of the attention shift. Reliable firing correlations were evident across area boundaries for neurons with broad spike waveforms (putative excitatory neurons) as well as for pairs of putative excitatory neurons and neurons with narrow spike waveforms (putative interneurons). These findings reveal that stimulus selection is accompanied by slow time scale firing correlations across those ACC/PFC subfields implicated to control and monitor attention. This functional coupling was informative about which stimulus was selected and thus indexed possibly the exchange of task-relevant information. We speculate that interareal, transient firing correlations reflect the transient coordination of larger, reciprocally interacting brain networks at a characteristic 50-200 ms time scale. Significance statement: Our manuscript identifies interareal spike-train correlations between primate anterior cingulate and dorsal prefrontal cortex during a period where attentional stimulus selection is likely controlled by these very same circuits. Interareal correlations emerged during the covert attention shift to one of two peripheral stimuli, proceeded on a slow 50-200 ms time scale, and occurred between putative pyramidal and

  19. Causal pattern recovery from neural spike train data using the Snap Shot Score.

    PubMed

    Echtermeyer, Christoph; Smulders, Tom V; Smith, V Anne

    2010-08-01

    We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.

  20. Estimating the directed information to infer causal relationships in ensemble neural spike train recordings.

    PubMed

    Quinn, Christopher J; Coleman, Todd P; Kiyavash, Negar; Hatsopoulos, Nicholas G

    2011-02-01

    Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering

  1. What can spike train distances tell us about the neural code?

    PubMed

    Chicharro, Daniel; Kreuz, Thomas; Andrzejak, Ralph G

    2011-07-15

    Time scale parametric spike train distances like the Victor and the van Rossum distances are often applied to study the neural code based on neural stimuli discrimination. Different neural coding hypotheses, such as rate or coincidence coding, can be assessed by combining a time scale parametric spike train distance with a classifier in order to obtain the optimal discrimination performance. The time scale for which the responses to different stimuli are distinguished best is assumed to be the discriminative precision of the neural code. The relevance of temporal coding is evaluated by comparing the optimal discrimination performance with the one achieved when assuming a rate code. We here characterize the measures quantifying the discrimination performance, the discriminative precision, and the relevance of temporal coding. Furthermore, we evaluate the information these quantities provide about the neural code. We show that the discriminative precision is too unspecific to be interpreted in terms of the time scales relevant for encoding. Accordingly, the time scale parametric nature of the distances is mainly an advantage because it allows maximizing the discrimination performance across a whole set of measures with different sensitivities determined by the time scale parameter, but not due to the possibility to examine the temporal properties of the neural code.

  2. Response to best-frequency tone bursts in the ventral cochlear nucleus is governed by ordered inter-spike interval statistics.

    PubMed

    Wright, M C M; Winter, I M; Forster, J J; Bleeck, S

    2014-11-01

    The spike trains generated by short constant-amplitude constant-frequency tone bursts in the ventral cochlear nucleus of the anaesthetised guinea pig are examined. Spikes are grouped according to the order in which they occur following the onset of the stimulus. It is found that successive inter-spike intervals have low statistical dependence according to information-theoretic measures. This is in contrast to previous observations with long-duration tone bursts in the cat dorsal and posteroventral cochlear nuclei and lateral superior olive, where it was found that long intervals tended to be followed by shorter ones and vice versa. The interval distributions can also be reasonably modelled by a shifted Gamma distribution parameterised by the dead-time and the mean and coefficient of variation of the dead-time corrected ISI distribution. Knowledge of those three parameters for each interval is sufficient to determine the peri-stimulus time histogram and the regularity measures used to classify these neurons. PMID:25261771

  3. A compound memristive synapse model for statistical learning through STDP in spiking neural networks

    PubMed Central

    Bill, Johannes; Legenstein, Robert

    2014-01-01

    Memristors have recently emerged as promising circuit elements to mimic the function of biological synapses in neuromorphic computing. The fabrication of reliable nanoscale memristive synapses, that feature continuous conductance changes based on the timing of pre- and postsynaptic spikes, has however turned out to be challenging. In this article, we propose an alternative approach, the compound memristive synapse, that circumvents this problem by the use of memristors with binary memristive states. A compound memristive synapse employs multiple bistable memristors in parallel to jointly form one synapse, thereby providing a spectrum of synaptic efficacies. We investigate the computational implications of synaptic plasticity in the compound synapse by integrating the recently observed phenomenon of stochastic filament formation into an abstract model of stochastic switching. Using this abstract model, we first show how standard pulsing schemes give rise to spike-timing dependent plasticity (STDP) with a stabilizing weight dependence in compound synapses. In a next step, we study unsupervised learning with compound synapses in networks of spiking neurons organized in a winner-take-all architecture. Our theoretical analysis reveals that compound-synapse STDP implements generalized Expectation-Maximization in the spiking network. Specifically, the emergent synapse configuration represents the most salient features of the input distribution in a Mixture-of-Gaussians generative model. Furthermore, the network's spike response to spiking input streams approximates a well-defined Bayesian posterior distribution. We show in computer simulations how such networks learn to represent high-dimensional distributions over images of handwritten digits with high fidelity even in presence of substantial device variations and under severe noise conditions. Therefore, the compound memristive synapse may provide a synaptic design principle for future neuromorphic architectures. PMID

  4. Sparse decoding of multiple spike trains for brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Tankus, Ariel; Fried, Itzhak; Shoham, Shy

    2012-10-01

    Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.

  5. nSTAT: Open-Source Neural Spike Train Analysis Toolbox for Matlab

    PubMed Central

    Cajigas, I.; Malik, W.Q.; Brown, E.N.

    2012-01-01

    Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - Generalized Linear Model (PPGLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT – an open source neural spike train analysis toolbox for Matlab®. By adopting an Object-Oriented Programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems. PMID:22981419

  6. Reconstruction of sparse connectivity in neural networks from spike train covariances

    NASA Astrophysics Data System (ADS)

    Pernice, Volker; Rotter, Stefan

    2013-03-01

    The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous-irregular state, where spike train covariances are well described by a linear model.

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

    PubMed Central

    Rallapalli, Varsha H.

    2016-01-01

    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.

  8. nSTAT: open-source neural spike train analysis toolbox for Matlab.

    PubMed

    Cajigas, I; Malik, W Q; Brown, E N

    2012-11-15

    Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - generalized linear model (PP-GLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT--an open source neural spike train analysis toolbox for Matlab®. By adopting an object-oriented programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems. PMID:22981419

  9. Variability of spike trains and the processing of temporal patterns of acoustic signals-problems, constraints, and solutions.

    PubMed

    Ronacher, B; Franz, A; Wohlgemuth, S; Hennig, R M

    2004-04-01

    Object recognition and classification by sensory pathways is rooted in spike trains provided by sensory neurons. Nervous systems had to evolve mechanisms to extract information about relevant object properties, and to separate these from spurious features. In this review, problems caused by spike train variability and counterstrategies are exemplified for the processing of acoustic signals in orthopteran insects. Due to size limitations of their nervous system we expect to find solutions that are stripped to the computational basics. A key feature of auditory systems is temporal resolution, which is likely limited by spike train variability. Basic strategies to reduce such variability are to integrate over time, or to average across several neurons. The first strategy is constrained by its possible interference with temporal resolution. Grasshoppers do not seem to explore temporal integration much, in spite of the repetitive structure of their songs, which invites for 'multiple looks' at the signal. The benefits of averaging across neurons depend on uncorrelated responses, a factor that may be crucial for the performance and evolution of small nervous systems. In spite of spike train variability the temporal information necessary for the recognition of conspecifics is preserved to a remarkable degree in the auditory pathway.

  10. The resemblance of an autocorrelation function to a power spectrum density for a spike train of an auditory model

    NASA Astrophysics Data System (ADS)

    Ushakov, Y. V.; Dubkov, A. A.; Spagnolo, B.

    2013-01-01

    In this work we develop an analytical approach for calculation of the all-order interspike interval density (AOISID), show its connection with the autocorrelation function, and try to explain the discovered resemblance of AOISID to the power spectrum of the same spike train.

  11. Improved statistical analysis of budding yeast TAG microarrays revealed by defined spike-in pools.

    PubMed

    Peyser, Brian D; Irizarry, Rafael A; Tiffany, Carol W; Chen, Ou; Yuan, Daniel S; Boeke, Jef D; Spencer, Forrest A

    2005-09-15

    Saccharomyces cerevisiae knockout collection TAG microarrays are an emergent platform for rapid, genome-wide functional characterization of yeast genes. TAG arrays report abundance of unique oligonucleotide 'TAG' sequences incorporated into each deletion mutation of the yeast knockout collection, allowing measurement of relative strain representation across experimental conditions for all knockout mutants simultaneously. One application of TAG arrays is to perform genome-wide synthetic lethality screens, known as synthetic lethality analyzed by microarray (SLAM). We designed a fully defined spike-in pool to resemble typical SLAM experiments and performed TAG microarray hybridizations. We describe a method for analyzing two-color array data to efficiently measure the differential knockout strain representation across two experimental conditions, and use the spike-in pool to show that the sensitivity and specificity of this method exceed typical current approaches.

  12. Stochastic variational learning in recurrent spiking networks

    PubMed Central

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about “novelty” on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal. PMID:24772078

  13. Optimal control of laser plasma instabilities using Spike Trains of Uneven Duration and Delay (STUD pulses) for ICF and IFE

    NASA Astrophysics Data System (ADS)

    Afeyan, Bedros; Hüller, Stefan

    2013-11-01

    An adaptive method of controlling parametric instabilities in laser produced plasmas is proposed. It involves fast temporal modulation of a laser pulse on the fastest instability's amplification time scale, adapting to changing and unknown plasma conditions. These pulses are comprised of on and off sequences having at least one or two orders of magnitude contrast between them. Such laser illumination profiles are called STUD pulses for Spike Trains of Uneven Duration and Delay. The STUD pulse program includes scrambling the speckle patterns spatially in between the laser spikes. The off times allow damping of driven waves. The scrambling of the hot spots allows tens of damping times to elapse before hot spot locations experience recurring high intensity spikes. Damping in the meantime will have healed the scars of past growth. Another unique feature of STUD pulses on crossing beams is that their temporal profiles can be interlaced or staggered, and their interactions thus controlled with an on-off switch and a dimmer.

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

    PubMed

    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

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

    PubMed Central

    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

  16. Relation Between Firing Statistics of Spiking Neuron with Instantaneous Feedback and Without Feedback

    NASA Astrophysics Data System (ADS)

    Vidybida, Alexander

    2015-09-01

    We consider a class of spiking neuron models, defined by a set of conditions which are typical for basic threshold-type models like leaky integrate-and-fire, or binding neuron model and also for some artificial neurons. A neuron is fed with a point renewal process. A relation between the three probability density functions (PDF): (i) PDF of input interspike intervals ISIs, (ii) PDF of output interspike intervals of a neuron with a feedback and (iii) PDF for that same neuron without feedback is derived. This allows to calculate any one of the three PDFs provided the remaining two are given. Similar relation between corresponding means and variances is derived. The relations are checked exactly for the binding neuron model stimulated with Poisson stream.

  17. Mode-Locked Spike Trains in Responses of Ventral Cochlear Nucleus Chopper and Onset Neurons to Periodic Stimuli

    PubMed Central

    Laudanski, Jonathan; Coombes, Stephen; Palmer, Alan R.

    2010-01-01

    We report evidence of mode-locking to the envelope of a periodic stimulus in chopper units of the ventral cochlear nucleus (VCN). Mode-locking is a generalized description of how responses in periodically forced nonlinear systems can be closely linked to the input envelope, while showing temporal patterns of higher order than seen during pure phase-locking. Re-analyzing a previously unpublished dataset in response to amplitude modulated tones, we find that of 55% of cells (6/11) demonstrated stochastic mode-locking in response to sinusoidally amplitude modulated (SAM) pure tones at 50% modulation depth. At 100% modulation depth SAM, most units (3/4) showed mode-locking. We use interspike interval (ISI) scattergrams to unravel the temporal structure present in chopper mode-locked responses. These responses compared well to a leaky integrate-and-fire model (LIF) model of chopper units. Thus the timing of spikes in chopper unit responses to periodic stimuli can be understood in terms of the complex dynamics of periodically forced nonlinear systems. A larger set of onset (33) and chopper units (24) of the VCN also shows mode-locked responses to steady-state vowels and cosine-phase harmonic complexes. However, while 80% of chopper responses to complex stimuli meet our criterion for the presence of mode-locking, only 40% of onset cells show similar complex-modes of spike patterns. We found a correlation between a unit's regularity and its tendency to display mode-locked spike trains as well as a correlation in the number of spikes per cycle and the presence of complex-modes of spike patterns. These spiking patterns are sensitive to the envelope as well as the fundamental frequency of complex sounds, suggesting that complex cell dynamics may play a role in encoding periodic stimuli and envelopes in the VCN. PMID:20042702

  18. Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis

    PubMed Central

    2014-01-01

    When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323–330, 1984; Brown et al. in Neural Comput. 14(2):325–346, 2002; Pouzat and Chaffiol in Technical report, http://arxiv.org/abs/arXiv:0909.2785, 2009). In doing so, there is a fundamental plug-in step, where the parameters of the supposed underlying model are estimated. The aim of this article is to show that plug-in has sometimes very undesirable effects. We propose a new method based on subsampling to deal with those plug-in issues in the case of the Kolmogorov–Smirnov test of uniformity. The method relies on the plug-in of good estimates of the underlying model that have to be consistent with a controlled rate of convergence. Some nonparametric estimates satisfying those constraints in the Poisson or in the Hawkes framework are highlighted. Moreover, they share adaptive properties that are useful from a practical point of view. We show the performance of those methods on simulated data. We also provide a complete analysis with these tools on single unit activity recorded on a monkey during a sensory-motor task. Electronic Supplementary Material The online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material. PMID:24742008

  19. Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods

    PubMed Central

    Koepcke, Lena; Ashida, Go; Kretzberg, Jutta

    2016-01-01

    In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets. PMID:27445714

  20. Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods.

    PubMed

    Koepcke, Lena; Ashida, Go; Kretzberg, Jutta

    2016-01-01

    In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets.

  1. Single and Multiple Change Point Detection in Spike Trains: Comparison of Different CUSUM Methods.

    PubMed

    Koepcke, Lena; Ashida, Go; Kretzberg, Jutta

    2016-01-01

    In a natural environment, sensory systems are faced with ever-changing stimuli that can occur, disappear or change their properties at any time. For the animal to react adequately the sensory systems must be able to detect changes in external stimuli based on its neuronal responses. Since the nervous system has no prior knowledge of the stimulus timing, changes in stimulus need to be inferred from the changes in neuronal activity, in particular increase or decrease of the spike rate, its variability, and shifted response latencies. From a mathematical point of view, this problem can be rephrased as detecting changes of statistical properties in a time series. In neuroscience, the CUSUM (cumulative sum) method has been applied to recorded neuronal responses for detecting a single stimulus change. Here, we investigate the applicability of the CUSUM approach for detecting single as well as multiple stimulus changes that induce increases or decreases in neuronal activity. Like the nervous system, our algorithm relies exclusively on previous neuronal population activities, without using knowledge about the timing or number of external stimulus changes. We apply our change point detection methods to experimental data obtained by multi-electrode recordings from turtle retinal ganglion cells, which react to changes in light stimulation with a range of typical neuronal activity patterns. We systematically examine how variations of mathematical assumptions (Poisson, Gaussian, and Gamma distributions) used for the algorithms may affect the detection of an unknown number of stimulus changes in our data and compare these CUSUM methods with the standard Rate Change method. Our results suggest which versions of the CUSUM algorithm could be useful for different types of specific data sets. PMID:27445714

  2. Undersampled critical branching processes on small-world and random networks fail to reproduce the statistics of spike avalanches.

    PubMed

    Ribeiro, Tiago L; Ribeiro, Sidarta; Belchior, Hindiael; Caixeta, Fábio; Copelli, Mauro

    2014-01-01

    The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent [Formula: see text]. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

  3. Reliable detection of predator cues in afferent spike trains of a katydid under high background noise levels

    PubMed Central

    Hartbauer, Manfred; Radspieler, Gerald; Römer, Heiner

    2014-01-01

    SUMMARY Katydid receivers face the problem of detecting behaviourally relevant predatory cues from echolocating bats in the same frequency domain as their own conspecific mating signals. We therefore tested the hypothesis that katydids are able to detect the presence of insectivorous bats in spike discharges at early stages of nervous processing in the auditory pathway by using the temporal details characteristic for responses to echolocation sequences. Spike activity was recorded from an identified nerve cell (omega neuron) under both laboratory and field conditions. In the laboratory, the preparation was stimulated with sequences of bat calls at different repetition rates typical for the guild of insectivorous bats, in the presence of background noise. The omega cell fired brief high-frequency bursts of action potentials in response to each bat sound pulse. Repetition rates of 18 and 24 Hz of these pulses resulted in a suppression of activity resulting from background noise, thus facilitating the detection of bat calls. The spike activity typical for responses to bat echolocation contrasts to responses to background noise, producing different distributions of inter-spike intervals. This allowed development of a ‘neuronal bat detector’ algorithm, optimized to detect responses to bats in afferent spike trains. The algorithm was applied to more than 24 hours of outdoor omega-recordings performed either at a rainforest clearing with high bat activity or in rainforest understory, where bat activity was low. In 95% of cases, the algorithm detected a bat reliably, even under high background noise, and correctly rejected responses when an electronic bat detector showed no response. PMID:20709932

  4. Australian Vocational Education and Training Statistics 1998: Financial Data.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    This publication presents some of the highlights of financial information summarized from the national collections of vocational education and training (VET) data in Australia for 1998. The report includes detailed statistics for Australia, its eight states and territories, and the Australian National Training Authority. The financial information…

  5. Australian Vocational Education and Training Statistics Pocket Guide, Issued 2011

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    This handy, pocket-sized booklet summarises information from the National Centre for Vocational Education Research's (NCVER's) current statistical publications. It presents statistics about: Australia's public vocational education and training (VET) system (which includes activity undertaken at technical and further education [TAFE] institutes,…

  6. Control of Stimulated Raman Scattering in the Strongly Nonlinear and Kinetic Regime Using Spike Trains of Uneven Duration and Delay

    NASA Astrophysics Data System (ADS)

    Albright, B. J.; Yin, L.; Afeyan, B.

    2014-07-01

    Stimulated Raman scattering (SRS) in its strongly nonlinear, kinetic regime is controlled by a technique of deterministic, strong temporal modulation and spatial scrambling of laser speckle patterns, called spike trains of uneven duration and delay (STUD) pulses [B. Afeyan and S. Hüller (unpublished)]. Kinetic simulations show that the proper use of STUD pulses decreases SRS reflectivity by more than an order of magnitude over random-phase-plate or induced-spatial-incoherence beams of the same average intensity and comparable bandwidth.

  7. Efficient spike-sorting of multi-state neurons using inter-spike intervals information.

    PubMed

    Delescluse, Matthieu; Pouzat, Christophe

    2006-01-15

    We demonstrate the efficacy of a new spike-sorting method based on a Markov chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge states, giving rise to multi-modal inter-spike interval (ISI) histograms and to correlations between successive ISIs. The amplitude of the spikes generated by a PC in an "active" state decreases, a feature typical of many neurons from both vertebrates and invertebrates. These two features constitute a major and recurrent problem for all the presently available spike-sorting methods. We first show that a hidden Markov model with three log-normal states provides a flexible and satisfying description of the complex firing of single PCs. We then incorporate this model into our previous MCMC based spike-sorting algorithm [Pouzat C, Delescluse M, Viot P, Diebolt J. Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach. J Neurophysiol 2004;91:2910-28] and test this new algorithm on multi-unit recordings of bursting PCs. We show that our method successfully classifies the bursty spike trains fired by PCs by using an independent single unit recording from a patch-clamp pipette. PMID:16085317

  8. Functional differences between statistical learning with and without explicit training

    PubMed Central

    Reber, Paul J.; Paller, Ken A.

    2015-01-01

    Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644

  9. Methods of Optimal Control of Laser-Plasma Instabilities Using Spike Trains of Uneven Duration and Delay (STUD Pulses)

    NASA Astrophysics Data System (ADS)

    Afeyan, Bedros

    2013-10-01

    We have recently introduced and extensively studied a new adaptive method of LPI control. It promises to extend the effectiveness of laser as inertial fusion drivers by allowing active control of stimulated Raman and Brillouin scattering and crossed beam energy transfer. It breaks multi-nanosecond pulses into a series of picosecond (ps) time scale spikes with comparable gaps in between. The height and width of each spike as well as their separations are optimization parameters. In addition, the spatial speckle patterns are changed after a number of successive spikes as needed (from every spike to never). The combination of these parameters allows the taming of parametric instabilities to conform to any desired reduced reflectivity profile, within the bounds of the performance limitations of the lasers. Instead of pulse shaping on hydrodynamical time scales, far faster (from 1 ps to 10 ps) modulations of the laser profile will be needed to implement the STUD pulse program for full LPI control. We will show theoretical and computational evidence for the effectiveness of the STUD pulse program to control LPI. The physics of why STUD pulses work and how optimization can be implemented efficiently using statistical nonlinear optical models and techniques will be explained. We will also discuss a novel diagnostic system employing STUD pulses that will allow the boosted measurement of velocity distribution function slopes on a ps time scale in the small crossing volume of a pump and a probe beam. Various regimes from weak to strong coupling and weak to strong damping will be treated. Novel pulse modulation schemes and diagnostic tools based on time-lenses used in both microscope and telescope modes will be suggested for the execution of the STUD pule program. Work Supported by the DOE NNSA-OFES Joint Program on HEDLP and DOE OFES SBIR Phase I Grants.

  10. Australian Vocational Education and Training Statistics Pocket Guide, Issued 2012

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    This pocket guide presents statistics about: (1) the public vocational education and training (VET) system, which includes activity undertaken at technical and further education (TAFE) institutes, other government providers, community education providers and publicly funded delivery by private providers; (2) apprentices and trainees, who are…

  11. Spike train auto-structure impacts post-synaptic firing and timing-based plasticity.

    PubMed

    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

  12. Compressed and Distributed Sensing of Neuronal Activity for Real Time Spike Train Decoding

    PubMed Central

    Aghagolzadeh, Mehdi; Oweiss, Karim

    2009-01-01

    Multivariate point processes are increasingly being used to model neuronal response properties in the cortex. Estimating the conditional intensity functions underlying these processes is important to characterize and decode the firing patterns of cortical neurons. This paper proposes a new approach for estimating these intensity functions directly from a compressed representation of the neurons’ extracellular recordings. The approach is based on exploiting a sparse representation of the extracellular spike waveforms, previously demonstrated to yield near-optimal denoising and compression properties. We show that by restricting this sparse representation to a subset of projections that simultaneously preserve features of the spike waveforms in addition to the temporal characteristics of the underlying intensity functions, we can reasonably approximate the instantaneous firing rates of the recorded neurons with variable tuning characteristics across a multitude of time scales. Such feature is highly desirable to detect subtle temporal differences in neuronal firing characteristics from single-trial data. An added advantage of this approach is that it eliminates multiple steps from the typical processing path of neural signals that are customarily performed for instantaneous neural decoding. We demonstrate the decoding performance of the approach using a stochastic cosine tuning model of motor cortical activity during a natural, nongoal-directed 2-D arm movement. PMID:19193517

  13. Simulations of drastically reduced SBS with laser pulses composed of a Spike Train of Uneven Duration and Delay (STUD pulses)

    NASA Astrophysics Data System (ADS)

    Hüller, Stefan; Afeyan, Bedros

    2013-11-01

    By comparing the impact of established laser smoothing techniques like Random Phase Plates (RPP) and Smoothing by Spectral Dispersion (SSD) to the concept of "Spike Trains of Uneven Duration and Delay" (STUD pulses) on the amplification of parametric instabilities in laser-produced plasmas, we show with the help of numerical simulations, that STUD pulses can drastically reduce instability growth by orders of magnitude. The simulation results, obtained with the code Harmony in a nonuniformly flowing mm-size plasma for the Stimulated Brillouin Scattering (SBS) instability, show that the efficiency of the STUD pulse technique is due to the fact that successive re-amplification in space and time of parametrically excited plasma waves inside laser hot spots is minimized. An overall mean fluctuation level of ion acoustic waves at low amplitude is established because of the frequent change of the speckle pattern in successive spikes. This level stays orders of magnitude below the levels of ion acoustic waves excited in hot spots of RPP and SSD laser beams.

  14. Australian Vocational Education and Training Statistics: Young People in Education and Training, 2011

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    The Australian education and training system offers a range of options for young people. This publication provides a summary of the statistics relating to young people aged 15 to 19 years who participated in an education and training activity during 2011. Information on participation is presented for VET in Schools students, school students,…

  15. Australian Vocational Education and Training Statistics: Young People in Education and Training 2014

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    The Australian education and training system offers a range of options for young people. This publication provides a summary of the statistics relating to young people aged 15 to 19 years who participated in an education and training activity during 2014. Information on participation is presented for school students, VET in Schools students,…

  16. Training and technology statistical report, October 1979-September 1980

    SciTech Connect

    Not Available

    1981-01-01

    A total of 839 trainees were enrolled at TAT during the 1979 to 1980 training year. Section One of this statistical report includes information on only those 613 trainees who exited training between October 1, 1979, and September 30, 1980. Demographic, educational, and employment data on the 613 exiting trainees - graduates and nongraduates - are summarized. There were 478 graduates (78% of concluding trainees), of whom 459 were available for placement. Profile summaries of graduates and nongraduates are tabulated. Of the 459 available for placement, 432 were placed in jobs with beginning wages averaging $6.34 per hour. The estimated annual income for those who were placed, assuming 2080 h/y, was $13,187. The majority of graduates, 85.8%, were unemployed at the time they entered TAT. The remainder, 14.2% of graduates, reported wages averaging $3.62 per hour at entry to training. Projected on an annual basis, those graduates employed at entry earned $7529. Compared to the average starting wage of placed TAT trainees on their first jobs after graduation, $13,187, their increased earnings were $5658 or a 75% increase after training. During the training year there were 135 trainees who did not graduate. Exit information on these nongraduates is presented. In addition to industrial skills training, TAT offers trainees who do not have a high school diploma or its equivalent the opportunity to work on the General Education Development (GED) by studying at TAT. Thirty-five trainees received their GED certification during the 1979 to 1980 training year. Supplementary statistical data on TAT enrollments, training and placement from 1966 to 1980 is provided.

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

    PubMed

    Goense, J B M; Ratnam, R

    2003-10-01

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

  18. Hot gas ingestion effects on fuel control surge recovery and AH-1 rotor drive train torque spikes

    NASA Technical Reports Server (NTRS)

    Tokarski, Frank; Desai, Mihir; Books, Martin; Zagranski, Raymond

    1994-01-01

    This report summarizes the work accomplished through computer simulation to understand the impact of the hydromechanical turbine assembly (TA) fuel control on rocket gas ingestion induced engine surges on the AH-1 (Cobra) helicopter. These surges excite the lightly damped torsional modes of the Cobra rotor drive train and can cause overtorqueing of the tail rotor shaft. The simulation studies show that the hydromechanical TA control has a negligible effect on drive train resonances because its response is sufficiently attenuated at the resonant frequencies. However, a digital electronic control working through the TA control's separate, emergency fuel metering system has been identified as a solution to the overtorqueing problem. State-of-the-art software within the electronic control can provide active damping of the rotor drive train to eliminate excessive torque spikes due to any disturbances including engine surges and aggressive helicopter maneuvers. Modifications to the existing TA hydromechanical control are relatively minor, and existing engine sensors can be utilized by the electronic control. Therefore, it is concluded that the combination of full authority digital electronic control (FADEC) with hydromechanical backup using the existing TA control enhances flight safety, improves helicopter performance, reduces pilot workload, and provides a substantial payback for very little investment.

  19. A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks

    PubMed Central

    Charlesworth, Paul; Thomas, Christopher W.; Paulsen, Ole

    2016-01-01

    Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide “perfect” burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques. PMID:27098024

  20. A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks.

    PubMed

    Cotterill, Ellese; Charlesworth, Paul; Thomas, Christopher W; Paulsen, Ole; Eglen, Stephen J

    2016-08-01

    Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.

  1. Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

    PubMed Central

    Schuch, Klaus; Logothetis, Nikos K.; Maass, Wolfgang

    2011-01-01

    A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N-methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network

  2. Australian Vocational Education and Training Statistics: Young People in Education and Training, 2010

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    This publication provides a summary of statistics relating to young people aged 15 to 19 years who participated in an education and training activity during 2010. Information on participation is presented for VET in Schools students, school students, higher education students, apprentices and trainees, and other vocational education and training…

  3. Experimental analysis of accuracy in the identification of motor unit spike trains from high-density surface EMG.

    PubMed

    Holobar, Ales; Minetto, Marco Alessandro; Botter, Alberto; Negro, Francesco; Farina, Dario

    2010-06-01

    The aim of this study was to compare the decomposition results obtained from high-density surface electromyography (EMG) and concurrently recorded intramuscular EMG. Surface EMG signals were recorded with electrode grids from the tibialis anterior, biceps brachii, and abductor digiti minimi muscles of twelve healthy men during isometric contractions ranging between 5% and 20% of the maximal force. Bipolar intramuscular EMG signals were recorded with pairs of wire electrodes. Surface and intramuscular EMG were independently decomposed into motor unit spike trains. When averaged over all the contractions of the same contraction force, the percentage of discharge times of motor units identified by both decompositions varied in the ranges 84%-87% (tibialis anterior), 84%-86% (biceps brachii), and 87%-92% (abductor digiti minimi) across the force levels analyzed. This index of agreement between the two decompositions was linearly correlated with a self-consistency measure of motor unit discharge pattern that was based on coefficient of variation for the interspike interval (R(2) = 0.68 for tibialis anterior, R(2) = 0.56 for biceps brachii, and R(2) = 0.38 for abductor digiti minimi). These results constitute an important contribution to the validation of the noninvasive approach for the investigation of motor unit behavior in isometric low-force tasks.

  4. Consensus-Based Sorting of Neuronal Spike Waveforms.

    PubMed

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

    2016-01-01

    Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

  5. Consensus-Based Sorting of Neuronal Spike Waveforms

    PubMed Central

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

    2016-01-01

    Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

  6. Seven Pervasive Statistical Flaws in Cognitive Training Interventions

    PubMed Central

    Moreau, David; Kirk, Ian J.; Waldie, Karen E.

    2016-01-01

    The prospect of enhancing cognition is undoubtedly among the most exciting research questions currently bridging psychology, neuroscience, and evidence-based medicine. Yet, convincing claims in this line of work stem from designs that are prone to several shortcomings, thus threatening the credibility of training-induced cognitive enhancement. Here, we present seven pervasive statistical flaws in intervention designs: (i) lack of power; (ii) sampling error; (iii) continuous variable splits; (iv) erroneous interpretations of correlated gain scores; (v) single transfer assessments; (vi) multiple comparisons; and (vii) publication bias. Each flaw is illustrated with a Monte Carlo simulation to present its underlying mechanisms, gauge its magnitude, and discuss potential remedies. Although not restricted to training studies, these flaws are typically exacerbated in such designs, due to ubiquitous practices in data collection or data analysis. The article reviews these practices, so as to avoid common pitfalls when designing or analyzing an intervention. More generally, it is also intended as a reference for anyone interested in evaluating claims of cognitive enhancement. PMID:27148010

  7. Statistical mentoring at early training and career stages

    DOE PAGES

    Anderson-Cook, Christine M.; Hamada, Michael S.; Moore, Leslie M.; Wendelberger, Joanne R.

    2016-06-27

    At Los Alamos National Laboratory (LANL), statistical scientists develop solutions for a variety of national security challenges through scientific excellence, typically as members of interdisciplinary teams. At LANL, mentoring is actively encouraged and practiced to develop statistical skills and positive career-building behaviors. Mentoring activities targeted at different career phases from student to junior staff are an important catalyst for both short and long term career development. This article discusses mentoring strategies for undergraduate and graduate students through internships as well as for postdoctoral research associates and junior staff. Topics addressed include project selection, progress, and outcome; intellectual and social activitiesmore » that complement the student internship experience; key skills/knowledge not typically obtained in academic training; and the impact of such internships on students’ careers. Experiences and strategies from a number of successful mentorships are presented. Feedback from former mentees obtained via a questionnaire is incorporated. As a result, these responses address some of the benefits the respondents received from mentoring, helpful contributions and advice from their mentors, key skills learned, and how mentoring impacted their later careers.« less

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

    PubMed Central

    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 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 work

  9. Australian Vocational Education & Training Statistics: Apprentices and Trainees, 2011--Annual

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    This annual publication provides a summary of training activity in apprenticeships and traineeships in Australia for the period 2001 to 2011. It includes information on training rates, individual completion rates, and duration of training. Highlights include: (1) 3.9% of Australian workers were employed as an apprentice or trainee as at December…

  10. Statistics of Nurse Training Schools 1926-1927. Bulletin, 1928, No. 2

    ERIC Educational Resources Information Center

    Phillips, Frank M.

    1928-01-01

    This report contains statistics of nurse-training schools for the year 1926-27. The principal items included are: Number of schools; number of nurse-training pupils; number of graduates; bed capacity of the hospitals maintaining the schools; average number of patients in these hospitals, length of the nurse-training course; admission requirements,…

  11. Training in Statistical Reasoning Inhibits the Formation of Erroneous Group Stereotypes.

    ERIC Educational Resources Information Center

    Schaller, Mark; And Others

    1996-01-01

    Tested the hypothesis that training in statistical reasoning inhibits erroneous group stereotypes. In study one, 60 students were assigned to a control or one of two training conditions focused on training in the logic of analysis of covariance. Study two (N=82) replicated study one. Study three (N=44) tested an alternative explanation, providing…

  12. Functional Differences between Statistical Learning with and without Explicit Training

    ERIC Educational Resources Information Center

    Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.

    2015-01-01

    Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…

  13. Reflection on Training, Experience, and Introductory Statistics: A Mini-Survey of Tertiary Level Statistics Instructors

    ERIC Educational Resources Information Center

    Hassad, Rossi A.

    2006-01-01

    Instructors of statistics who teach non-statistics majors possess varied academic backgrounds, and hence it is reasonable to expect variability in their content knowledge, and pedagogical approach. The aim of this study was to determine the specific course(s) that contributed mostly to instructors' understanding of statistics. Courses reported…

  14. VET in Schools 2015. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2016

    2016-01-01

    This report presents information on VET in Schools, the vocational education and training (VET) undertaken by school students as part of their senior secondary certificate. The VET in Schools arrangement offers two main options: students can undertake school-based apprenticeships and traineeships; or they can take VET subjects and courses as part…

  15. Australian Vocational Education and Training Statistics: Financial Information 2007

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2008

    2008-01-01

    This publication details the financial operations of Australia's public vocational education and training (VET) system for 2007. The information presented covers revenues and expenses; assets, liabilities and equities; cash flows; and trends in total revenues and expenses. The scope of the financial data collection covers all transactions that…

  16. Australian Vocational Education and Training Statistics. VET in Schools, 2009

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2010

    2010-01-01

    This publication presents information on vocational education and training (VET) undertaken by school students as part of their senior secondary certificate, known as VET in Schools. The VET in Schools arrangement offers two main options: students can undertake school-based apprenticeships and traineeships; or VET subjects and courses (the latter…

  17. Australian Vocational Education and Training Statistics: VET in Schools, 2008

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2010

    2010-01-01

    This report presents information about senior secondary school students undertaking vocational education and training (VET) through the program known as "VET in Schools" during 2008. It includes information on participation, students, courses and qualifications, and subjects. The information on key performance measures and program measures for VET…

  18. Australian Vocational Education and Training Statistics, 2001: Financial Data.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    In presenting highlights of vocational education and training (VET) finances for 2001, this publication provides insight into how publicly funded VET in Australia is financed and where the money is spent. Information includes primary summaries focusing on revenues and expenses (to show financial performance); assets and liabilities (to show…

  19. Spike oscillations

    NASA Astrophysics Data System (ADS)

    Heinzle, J. Mark; Uggla, Claes; Lim, Woei Chet

    2012-11-01

    According to Belinskiǐ, Khalatnikov and Lifshitz (BKL), a generic spacelike singularity is characterized by asymptotic locality: Asymptotically, toward the singularity, each spatial point evolves independently from its neighbors, in an oscillatory manner that is represented by a sequence of Bianchi type I and II vacuum models. Recent investigations support this conjecture but with a modification: Apart from local BKL behavior there also exists formation of spatial structures (“spikes”) at, and in the neighborhood of, certain spatial surfaces that break asymptotic locality; the complete description of a generic spacelike singularity involves spike oscillations, which are described by sequences of Bianchi type I and certain inhomogeneous vacuum models. In this paper we describe how BKL and spike oscillations arise from concatenations of exact solutions in a Hubble-normalized state space setting, suggesting the existence of hidden symmetries and showing that the results of BKL are part of a greater picture.

  20. Expanding National Vocational Education and Training Statistical Collections: Private Provider Engagement

    ERIC Educational Resources Information Center

    Smith, Andrew C.; Potter, Rosemary; Smith, Peter J.

    2010-01-01

    The research reported was intended to identify the barriers and facilitators to the participation of private education and training providers in the supply of data to the national vocational education and training (VET) statistical collection. In addition, the research process developed a number of strategies to assist private registered training…

  1. Spike history neural response model.

    PubMed

    Kameneva, Tatiana; Abramian, Miganoosh; Zarelli, Daniele; Nĕsić, Dragan; Burkitt, Anthony N; Meffin, Hamish; Grayden, David B

    2015-06-01

    There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

    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.

  3. Wheat signature modeling and analysis for improved training statistics

    NASA Technical Reports Server (NTRS)

    Nalepka, R. F. (Principal Investigator); Malila, W. A.; Cicone, R. C.; Gleason, J. M.

    1976-01-01

    The author has identified the following significant results. The spectral, spatial, and temporal characteristics of wheat and other signatures in LANDSAT multispectral scanner data were examined through empirical analysis and simulation. Irrigation patterns varied widely within Kansas; 88 percent of wheat acreage in Finney was irrigated and 24 percent in Morton, as opposed to less than 3 percent for western 2/3's of the State. The irrigation practice was definitely correlated with the observed spectral response; wheat variety differences produced observable spectral differences due to leaf coloration and different dates of maturation. Between-field differences were generally greater than within-field differences, and boundary pixels produced spectral features distinct from those within field centers. Multiclass boundary pixels contributed much of the observed bias in proportion estimates. The variability between signatures obtained by different draws of training data decreased as the sample size became larger; also, the resulting signatures became more robust and the particular decision threshold value became less important.

  4. Shifting Spike Times or Adding and Deleting Spikes-How Different Types of Noise Shape Signal Transmission in Neural Populations.

    PubMed

    Voronenko, Sergej O; Stannat, Wilhelm; Lindner, Benjamin

    2015-12-01

    We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal-output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically. PMID:26458900

  5. Time resolution dependence of information measures for spiking neurons: scaling and universality

    PubMed Central

    Marzen, Sarah E.; DeWeese, Michael R.; Crutchfield, James P.

    2015-01-01

    The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step toward that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), predictable information (excess entropy), and active information accumulation (bound information rate). We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., τ-entropy rates that diverge less quickly than the firing rate indicated by interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes. PMID:26379538

  6. A point process approach to identifying and tracking transitions in neural spiking dynamics in the subthalamic nucleus of Parkinson's patients

    NASA Astrophysics Data System (ADS)

    Deng, Xinyi; Eskandar, Emad N.; Eden, Uri T.

    2013-12-01

    Understanding the role of rhythmic dynamics in normal and diseased brain function is an important area of research in neural electrophysiology. Identifying and tracking changes in rhythms associated with spike trains present an additional challenge, because standard approaches for continuous-valued neural recordings—such as local field potential, magnetoencephalography, and electroencephalography data—require assumptions that do not typically hold for point process data. Additionally, subtle changes in the history dependent structure of a spike train have been shown to lead to robust changes in rhythmic firing patterns. Here, we propose a point process modeling framework to characterize the rhythmic spiking dynamics in spike trains, test for statistically significant changes to those dynamics, and track the temporal evolution of such changes. We first construct a two-state point process model incorporating spiking history and develop a likelihood ratio test to detect changes in the firing structure. We then apply adaptive state-space filters and smoothers to track these changes through time. We illustrate our approach with a simulation study as well as with experimental data recorded in the subthalamic nucleus of Parkinson's patients performing an arm movement task. Our analyses show that during the arm movement task, neurons underwent a complex pattern of modulation of spiking intensity characterized initially by a release of inhibitory control at 20-40 ms after a spike, followed by a decrease in excitatory influence at 40-60 ms after a spike.

  7. Spike Detection Based on Normalized Correlation with Automatic Template Generation

    PubMed Central

    Hwang, Wen-Jyi; Wang, Szu-Huai; Hsu, Ya-Tzu

    2014-01-01

    A novel feedback-based spike detection algorithm for noisy spike trains is presented in this paper. It uses the information extracted from the results of spike classification for the enhancement of spike detection. The algorithm performs template matching for spike detection by a normalized correlator. The detected spikes are then sorted by the OSortalgorithm. The mean of spikes of each cluster produced by the OSort algorithm is used as the template of the normalized correlator for subsequent detection. The automatic generation and updating of templates enhance the robustness of the spike detection to input trains with various spike waveforms and noise levels. Experimental results show that the proposed algorithm operating in conjunction with OSort is an efficient design for attaining high detection and classification accuracy for spike sorting. PMID:24960082

  8. People with a Disability in Vocational Education and Training: A Statistical Compendium

    ERIC Educational Resources Information Center

    Cavallaro, Toni; Foley, Paul; Saunders, John; Bowman, Kaye

    2005-01-01

    This statistical compendium examines, firstly, vocational education and training (VET) students with a disability as a whole group, focusing on their participation levels, achievements and outcomes from VET, and identifies gaps and/or issues with the existing data. This is followed by a section dealing with people with different types of…

  9. Statistical and optimization methods to expedite neural network training for transient identification

    SciTech Connect

    Reifman, J. . Reactor Analysis Div.); Vitela, E.J. . Inst. de Ciencias Nucleares); Lee, J.C. . Dept. of Nuclear Engineering)

    1993-01-01

    Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.

  10. Statistical and optimization methods to expedite neural network training for transient identification

    SciTech Connect

    Reifman, J.; Vitela, E.J.; Lee, J.C.

    1993-03-01

    Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.

  11. Methods of artificial enlargement of the training set for statistical shape models.

    PubMed

    Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki

    2008-11-01

    Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.

  12. Spiking dynamics of interacting oscillatory neurons

    NASA Astrophysics Data System (ADS)

    Kazantsev, V. B.; Nekorkin, V. I.; Binczak, S.; Jacquir, S.; Bilbault, J. M.

    2005-06-01

    Spiking sequences emerging from dynamical interaction in a pair of oscillatory neurons are investigated theoretically and experimentally. The model comprises two unidirectionally coupled FitzHugh-Nagumo units with modified excitability (MFHN). The first (master) unit exhibits a periodic spike sequence with a certain frequency. The second (slave) unit is in its excitable mode and responds on the input signal with a complex (chaotic) spike trains. We analyze the dynamic mechanisms underlying different response behavior depending on interaction strength. Spiking phase maps describing the response dynamics are obtained. Complex phase locking and chaotic sequences are investigated. We show how the response spike trains can be effectively controlled by the interaction parameter and discuss the problem of neuronal information encoding.

  13. How Can Monosynaptic Spike Transmission Be So Fast?

    NASA Astrophysics Data System (ADS)

    Platkiewicz, Jonathan; Amarasingham, Asohan

    There has been recently a great deal of interest in ``mapping the brain'', namely in establishing the precise structural organization of neural microcircuits. High-density extracellular recordings offer the unique opportunity to observe simultaneously the activity of hundreds of neurons with millisecond precision in the behaving mammal. Neural connectivity is typically inferred from this recording type by seeking the cell pairs that exhibit finely-timed spike correlation. There is however no widely-accepted biophysical justification for this procedure, nor is there much in the way of ``ground truth'' data that might validate these inferences. First, we showed that a millisecond spike correlation can be observed between monosynaptically connected neurons regardless of the timescale of the postsynaptic potential response. The demonstration is based on the theory of stochastic processes - in particular on an escape noise model - and numerical simulations of biophysical models of monosynaptic spike transfer. Second, using the developed biophysical models, we highlighted the relevance of nonparametric statistical methods, called ``jitter methods'', in connectivity analysis from spike trains, even in the face of extreme firing nonstationarity. Supported by NIH Grant R01MH102840 and DoD (HBCU/MI) Grant W911NF-15-R-0002.

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

    PubMed

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

    2014-02-01

    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.

  15. Learning in neural networks by reinforcement of irregular spiking

    NASA Astrophysics Data System (ADS)

    Xie, Xiaohui; Seung, H. Sebastian

    2004-04-01

    Artificial neural networks are often trained by using the back propagation algorithm to compute the gradient of an objective function with respect to the synaptic strengths. For a biological neural network, such a gradient computation would be difficult to implement, because of the complex dynamics of intrinsic and synaptic conductances in neurons. Here we show that irregular spiking similar to that observed in biological neurons could be used as the basis for a learning rule that calculates a stochastic approximation to the gradient. The learning rule is derived based on a special class of model networks in which neurons fire spike trains with Poisson statistics. The learning is compatible with forms of synaptic dynamics such as short-term facilitation and depression. By correlating the fluctuations in irregular spiking with a reward signal, the learning rule performs stochastic gradient ascent on the expected reward. It is applied to two examples, learning the XOR computation and learning direction selectivity using depressing synapses. We also show in simulation that the learning rule is applicable to a network of noisy integrate-and-fire neurons.

  16. Statistics

    Cancer.gov

    Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.

  17. Statistical-Mechanical Analysis of Pre-training and Fine Tuning in Deep Learning

    NASA Astrophysics Data System (ADS)

    Ohzeki, Masayuki

    2015-03-01

    In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.

  18. Methods for obtaining 3D training images for multiple-point statistics simulations: a comparative study

    NASA Astrophysics Data System (ADS)

    Jha, S. K.; Comunian, A.; Mariethoz, G.; Kelly, B. F.

    2013-12-01

    In recent years, multiple-point statistics (MPS) has been used in several studies for characterizing facies heterogeneity in geological formations. MPS uses a conceptual representation of the expected facies distribution, called a Training image (TI), to generate patterns of facies heterogeneity. In two-dimensional (2D) simulations the TI can be a hand-drawn image, an analogue outcrop image, or derived from geological reconstructions using a combination of geological analogues and geophysical data. However, obtaining suitable TI in three-dimensions (3D) from geological analogues or geophysical data is harder and has limited the use of MPS for simulating facies heterogeneity in 3D. There have been attempts to generate 3D training images using object-based simulation (OBS). However, determining suitable values for the large number of parameters required by OBS is often challenging. In this study, we compare two approaches for generating three-dimensional training images to model a valley filling sequence deposited by meandering rivers. The first approach is based on deriving statistical information from two-dimensional TIs. The 3D domain is simulated with a sequence of 2D MPS simulation steps, performed along different directions on slices of the 3D domain. At each 2D simulation step, the facies simulated at the previous steps that lie on the current 2D slice are used as conditioning data. The second approach uses hand-drawn two-dimensional TIs and produces complex patterns resembling the geological structures by applying rotation and affinity transformations in the facies simulation. The two techniques are compared using transition probabilities, facies proportions, and connectivity metrics. In the presentation we discuss the benefits of each approach for generating three-dimensional facies models.

  19. Spikes in Brewer spectroradiometer UV spectra

    NASA Astrophysics Data System (ADS)

    Meinander, O.; Josefsson, W.; Kaurola, J.; Koskela, T.; Lakkala, K.

    2003-04-01

    The occurrence of spikes in Brewer UV spectra has been studied. By a spike we mean an anomalous number of counts recorded in one wavelength channel causing an abrupt upwards or downwards change in value that does not originate from the true radiation signal. We have recorded downward spikes in lamp scans measured in the darkroom, and spikes occur in sky measurements as well. We analyzed continuous measurement data over several years, with more than 90 000 spectra, from one single monochromator and two double monochromator Brewers. We found that especially the double monochromators may suffer from more than 200 spikes per ~5000 annual spectra. The spikes were not always randomly distributed over the wavelength range. The single monochromator was found to have a significant number of spikes at wavelengths below 300 nm, indicating possible bias in the stray light correction unless taken into consideration. The error caused by non-corrected spikes varied greatly from case to case. For example, the effect of one moderate-size spiked was found to be more than 5 % on a DNA action dose rate and close to 1 % on a DNA action daily dose. When high accuracy of the in situ UV measurements is required, our results suggest a need to remove spikes from the spectra. We used a simple statistical approach. Other slightly different approaches exist as well. Our data showed that ancillary radiation measurements may be necessary to interpret the data correctly. Under rapidly-changing cloudiness it can be difficult to distinguish between noise spikes and the variation in irradiance due to changes in the state of the sky.

  20. On the Way to 2020: Data for Vocational Education and Training Policies. Country Statistical Overviews. Update 2013

    ERIC Educational Resources Information Center

    Cedefop - European Centre for the Development of Vocational Training, 2014

    2014-01-01

    This report provides an updated statistical overview of vocational education and training (VET) and lifelong learning in European countries. These country statistical snapshots illustrate progress on indicators selected for their policy relevance and contribution to Europe 2020 objectives. The indicators take 2010 as the baseline year and present…

  1. Parameterization of training images for aquifer 3-D facies modeling integrating geological interpretations and statistical inference

    NASA Astrophysics Data System (ADS)

    Jha, Sanjeev Kumar; Comunian, Alessandro; Mariethoz, Gregoire; Kelly, Bryce F. J.

    2014-10-01

    We develop a stochastic approach to construct channelized 3-D geological models constrained to borehole measurements as well as geological interpretation. The methodology is based on simple 2-D geologist-provided sketches of fluvial depositional elements, which are extruded in the 3rd dimension. Multiple-point geostatistics (MPS) is used to impair horizontal variability to the structures by introducing geometrical transformation parameters. The sketches provided by the geologist are used as elementary training images, whose statistical information is expanded through randomized transformations. We demonstrate the applicability of the approach by applying it to modeling a fluvial valley filling sequence in the Maules Creek catchment, Australia. The facies models are constrained to borehole logs, spatial information borrowed from an analogue and local orientations derived from the present-day stream networks. The connectivity in the 3-D facies models is evaluated using statistical measures and transport simulations. Comparison with a statistically equivalent variogram-based model shows that our approach is more suited for building 3-D facies models that contain structures specific to the channelized environment and which have a significant influence on the transport processes.

  2. Spike Bursts from an Excitable Optical System

    NASA Astrophysics Data System (ADS)

    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.

  3. Mapping Spikes to Sensations

    PubMed Central

    Stüttgen, Maik C.; Schwarz, Cornelius; Jäkel, Frank

    2011-01-01

    Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research. PMID:22084627

  4. Supervised learning in multilayer spiking neural networks.

    PubMed

    Sporea, Ioana; Grüning, André

    2013-02-01

    We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.

  5. Summary statistics from training images as prior information in probabilistic inversion

    NASA Astrophysics Data System (ADS)

    Lochbühler, Tobias; Vrugt, Jasper A.; Sadegh, Mojtaba; Linde, Niklas

    2015-04-01

    A strategy is presented to incorporate prior information from conceptual geological models in probabilistic inversion of geophysical data. The conceptual geological models are represented by multiple-point statistics training images (TIs) featuring the expected lithological units and structural patterns. Information from an ensemble of TI realizations is used in two different ways. First, dominant modes are identified by analysis of the frequency content in the realizations, which drastically reduces the model parameter space in the frequency-amplitude domain. Second, the distributions of global, summary metrics (e.g. model roughness) are used to formulate a prior probability density function. The inverse problem is formulated in a Bayesian framework and the posterior pdf is sampled using Markov chain Monte Carlo simulation. The usefulness and applicability of this method is demonstrated on two case studies in which synthetic crosshole ground-penetrating radar traveltime data are inverted to recover 2-D porosity fields. The use of prior information from TIs significantly enhances the reliability of the posterior models by removing inversion artefacts and improving individual parameter estimates. The proposed methodology reduces the ambiguity inherent in the inversion of high-dimensional parameter spaces, accommodates a wide range of summary statistics and geophysical forward problems.

  6. Comparison of Grammar-Based and Statistical Language Models Trained on the Same Data

    NASA Technical Reports Server (NTRS)

    Hockey, Beth Ann; Rfayner, Manny

    2005-01-01

    This paper presents a methodologically sound comparison of the performance of grammar-based (GLM) and statistical-based (SLM) recognizer architectures using data from the Clarissa procedure navigator domain. The Regulus open source packages make this possible with a method for constructing a grammar-based language model by training on a corpus. We construct grammar-based and statistical language models from the same corpus for comparison, and find that the grammar-based language models provide better performance in this domain. The best SLM version has a semantic error rate of 9.6%, while the best GLM version has an error rate of 6.0%. Part of this advantage is accounted for by the superior WER and Sentence Error Rate (SER) of the GLM (WER 7.42% versus 6.27%, and SER 12.41% versus 9.79%). The rest is most likely accounted for by the fact that the GLM architecture is able to use logical-form-based features, which permit tighter integration of recognition and semantic interpretation.

  7. Removal of spurious correlations between spikes and local field potentials.

    PubMed

    Zanos, Theodoros P; Mineault, Patrick J; Pack, Christopher C

    2011-01-01

    Single neurons carry out important sensory and motor functions related to the larger networks in which they are embedded. Understanding the relationships between single-neuron spiking and network activity is therefore of great importance and the latter can be readily estimated from low-frequency brain signals known as local field potentials (LFPs). In this work we examine a number of issues related to the estimation of spike and LFP signals. We show that spike trains and individual spikes contain power at the frequencies that are typically thought to be exclusively related to LFPs, such that simple frequency-domain filtering cannot be effectively used to separate the two signals. Ground-truth simulations indicate that the commonly used method of estimating the LFP signal by low-pass filtering the raw voltage signal leads to artifactual correlations between spikes and LFPs and that these correlations exert a powerful influence on popular metrics of spike-LFP synchronization. Similar artifactual results were seen in data obtained from electrophysiological recordings in macaque visual cortex, when low-pass filtering was used to estimate LFP signals. In contrast LFP tuning curves in response to sensory stimuli do not appear to be affected by spike contamination, either in simulations or in real data. To address the issue of spike contamination, we devised a novel Bayesian spike removal algorithm and confirmed its effectiveness in simulations and by applying it to the electrophysiological data. The algorithm, based on a rigorous mathematical framework, outperforms other methods of spike removal on most metrics of spike-LFP correlations. Following application of this spike removal algorithm, many of our electrophysiological recordings continued to exhibit spike-LFP correlations, confirming previous reports that such relationships are a genuine aspect of neuronal activity. Overall, these results show that careful preprocessing is necessary to remove spikes from LFP

  8. Spike Code Flow in Cultured Neuronal Networks.

    PubMed

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

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network. PMID:27217825

  9. Spike Code Flow in Cultured Neuronal Networks.

    PubMed

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

    2016-01-01

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

  10. Australian Vocational Education and Training Statistics: Apprentices and Trainees. Annual, 2009

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2010

    2010-01-01

    This annual publication provides a summary of training activity in apprenticeships and traineeships in Australia, including information on training rates, completion rates, attrition rates, training within the trades and duration of training. The figures in this publication are derived from the National Apprentice and Trainee Collection no.63…

  11. Australian Vocational Education and Training Statistics: The Likelihood of Completing a VET Qualification, 2005-08

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    The Australian vocational education and training (VET) system provides training across a wide range of subject areas and is delivered through a variety of training institutions and enterprises (including to apprentices and trainees). The system provides training for students of all ages and backgrounds. Students may study individual subjects or…

  12. Australian Vocational Education and Training Statistics: The Likelihood of Completing a VET Qualification, 2006-09

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    The Australian vocational education and training (VET) system provides training across a wide range of subject areas and is delivered through a variety of training institutions and enterprises (including to apprentices and trainees). The system provides training for students of all ages and backgrounds. Students may study individual subjects or…

  13. Spiking the Geomagnetic Field

    NASA Astrophysics Data System (ADS)

    Constable, C.; Davies, C. J.

    2015-12-01

    Geomagnetic field intensities corresponding to virtual axial dipole moments of up to 200 ZAm2, more than twice the modern value, have been inferred from archeomagnetic measurements on artifacts dated at or shortly after 1000 BC. Anomalously high values occur in the Levant and Georgia, but not in Bulgaria. The origin of this spike is believed to lie in Earth's core: however, its spatio-temporal characteristics and the geomagnetic processes responsible for such a feature remain a mystery. We show that a localized spike in the radial magnetic field at the core-mantle boundary (CMB) must necessarily contribute to the largest scale changes in Earth's surface field, namely the dipole. Even the limiting spike of a delta function at the CMB produces a minimum surface cap size of 60 degrees for a factor of two increase in paleointensity. Combined evidence from modern satellite and millennial scale field modeling suggests that the Levantine Spike is intimately associated with a strong increase in dipole moment prior to 1000 BC and likely the product of north-westward motion of concentrated near equatorial Asian flux patches like those seen in the modern field. New archeomagnetic studies are needed to confirm this interpretation. Minimum estimates of the power dissipated by the spike are comparable to independent estimates of the dissipation associated with the entire steady state geodynamo. This suggests that geomagnetic spikes are either associated with rapid changes in magnetic energy or strong Lorentz forces.

  14. The Application of Strength of Association Statistics to the Item Analysis of an In-Training Examination in Diagnostic Radiology.

    ERIC Educational Resources Information Center

    Diamond, James J.; McCormick, Janet

    1986-01-01

    Using item responses from an in-training examination in diagnostic radiology, the application of a strength of association statistic to the general problem of item analysis is illustrated. Criteria for item selection, general issues of reliability, and error of measurement are discussed. (Author/LMO)

  15. Competitive STDP-based spike pattern learning.

    PubMed

    Masquelier, Timothée; Guyonneau, Rudy; Thorpe, Simon J

    2009-05-01

    Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons "listening" to the incoming spike trains. These "listening" neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them. PMID:19718815

  16. Detection of spikes with artificial neural networks using raw EEG.

    PubMed

    Ozdamar, O; Kalayci, T

    1998-04-01

    Artificial neural networks (ANN) using raw electroencephalogram (EEG) data were developed and tested off-line to detect transient epileptiform discharges (spike and spike/wave) and EMG activity in an ongoing EEG. In the present study, a feedforward ANN with a variable number of input and hidden layer units and two output units was used to optimize the detection system. The ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. The effects of different EEG time windows and the number of hidden layer neurons were examined using rigorous statistical tests for optimum detection sensitivity and selectivity. The best ANN configuration occurred with an input time window of 150 msec (30 input units) and six hidden layer neurons. This input interval contained information on the wave component of the epileptiform discharge which improved detection. Two-dimensional receiver operating curves were developed to define the optimum threshold parameters for best detection. Comparison with previous networks using raw EEG showed improvement in both sensitivity and selectivity. This study showed that raw EEG can be successfully used to train ANNs to detect epileptogenic discharges with a high success rate without resorting to experimenter-selected parameters which may limit the efficiency of the system.

  17. Apprentices and Trainees 2014. Annual. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2014

    2014-01-01

    This annual publication provides a summary of training activity in apprenticeships and traineeships in Australia, including information on training rates and duration of training, from 2004 to 2014. The figures in this publication are derived from the National Apprentice and Trainee Collection no. 83 (March, 2015 estimates), which is compiled…

  18. Macroscopic Description for Networks of Spiking Neurons

    NASA Astrophysics Data System (ADS)

    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.

  19. Hierarchical spike clustering analysis for investigation of interneuron heterogeneity.

    PubMed

    Boehlen, Anne; Heinemann, Uwe; Henneberger, Christian

    2016-04-21

    Action potentials represent the output of a neuron. Especially interneurons display a variety of discharge patterns ranging from regular action potential firing to prominent spike clustering or stuttering. The mechanisms underlying this heterogeneity remain incompletely understood. We established hierarchical cluster analysis of spike trains as a measure of spike clustering. A clustering index was calculated from action potential trains recorded in the whole-cell patch clamp configuration from hippocampal (CA1, stratum radiatum) and entorhinal (medial entorhinal cortex, layer 2) interneurons in acute slices and simulated data. Prominent, region-dependent, but also variable spike clustering was detected using this measure. Further analysis revealed a strong positive correlation between spike clustering and membrane potentials oscillations but an inverse correlation with neuronal resonance. Furthermore, clustering was more pronounced when the balance between fast-activating K(+) currents, assessed by the spike repolarisation time, and hyperpolarization-activated currents, gauged by the size of the sag potential, was shifted in favour of fast K(+) currents. Simulations of spike clustering confirmed that variable ratios of fast K(+) and hyperpolarization-activated currents could underlie different degrees of spike clustering and could thus be crucial for temporally structuring interneuron spike output. PMID:26987719

  20. Government-Funded Students and Courses: January to September 2015. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    This publication provides a summary of data relating to students, programs, training providers and funding in Australia's government-funded vocational education and training (VET) system (broadly defined as all activity delivered by government providers and government-funded activity delivered by community education and private training…

  1. Government-Funded Students and Courses: January to March 2015. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    This publication provides a summary of data relating to students, programs, training providers, and funding in Australia's government-funded vocational education and training (VET) system (broadly defined as all activity delivered by government providers and government-funded activity delivered by community education and other registered…

  2. Government-Funded Students and Courses, 2015. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2016

    2016-01-01

    This publication provides a summary of 2015 and time-series data relating to students, programs, subjects, training providers and funding in Australia's government-funded vocational education and training (VET) system (broadly defined as all activity delivered by government providers and government-funded activity delivered by community education…

  3. High-Performance Vision Training Improves Batting Statistics for University of Cincinnati Baseball Players

    PubMed Central

    Clark, Joseph F.; Ellis, James K.; Bench, Johnny; Khoury, Jane; Graman, Pat

    2012-01-01

    Purpose Baseball requires an incredible amount of visual acuity and eye-hand coordination, especially for the batters. The learning objective of this work is to observe that traditional vision training as part of injury prevention or conditioning can be added to a team's training schedule to improve some performance parameters such as batting and hitting. Methods All players for the 2010 to 2011 season underwent normal preseason physicals and baseline testing that is standard for the University of Cincinnati Athletics Department. Standard vision training exercises were implemented 6 weeks before the start of the season. Results are reported as compared to the 2009 to 2010 season. Pre season conditioning was followed by a maintenance program during the season of vision training. Results The University of Cincinnati team batting average increased from 0.251 in 2010 to 0.285 in 2011 and the slugging percentage increased by 0.033. The rest of the Big East's slugging percentage fell over that same time frame 0.082. This produces a difference of 0.115 with 95% confidence interval (0.024, 0.206). As with the batting average, the change for University of Cincinnati is significantly different from the rest of the Big East (p = 0.02). Essentially all batting parameters improved by 10% or more. Similar differences were seen when restricting the analysis to games within the Big East conference. Conclusion Vision training can combine traditional and technological methodologies to train the athletes' eyes and improve batting. Vision training as part of conditioning or injury prevention can be applied and may improve batting performance in college baseball players. High performance vision training can be instituted in the pre-season and maintained throughout the season to improve batting parameters. PMID:22276103

  4. Statistical Methods for Tissue Array Images – Algorithmic Scoring and Co-training

    PubMed Central

    Yan, Donghui; Wang, Pei; Knudsen, Beatrice S.; Linden, Michael; Randolph, Timothy W.

    2012-01-01

    Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm—Tissue Array Co-Occurrence Matrix Analysis (TACOMA)—for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists’ input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high dimensional setting when there is “sufficient” redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists’ performance in terms of accuracy and repeatability. PMID:22984376

  5. Statistics of Nurse Training Schools, 1919-1920. Bulletin, 1921, No. 51

    ERIC Educational Resources Information Center

    Bureau of Education, Department of the Interior, 1922

    1922-01-01

    This report contains summaries by States, but no detailed statistics of individual schools. Such statistics were published in Bureau of Education Bulletin, 1918, No. 73, and only slight changes have occurred since that document was printed. Graphic and interpretative treatment of the data in this bulletin will appear in a bulletin to be published…

  6. Spatiotemporal spike encoding of a continuous external signal.

    PubMed

    Masuda, Naoki; Aihara, Kazuyuki

    2002-07-01

    Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode stimulus information passed from the sensory model neurons in the form of interspike intervals. Each sensory neuron connected to the cortical neuron contributes equally to the information collection by the cortical neuron. Although the incident spike train to the cortical neuron is a superimposition of spike trains from many sensory neurons, it need not be decomposed into spike trains according to the input neurons. These results are also preserved for generalizations of sensory neurons such as a small amount of leak, noise, inhomogeneity in firing rates, or biases introduced in the phase distributions. PMID:12079548

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

    PubMed

    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. PMID:19695289

  8. Spike firing pattern of output neurons of the Limulus circadian clock.

    PubMed

    Liu, Jiahui S; Passaglia, Christopher L

    2011-08-01

    The lateral eyes of the horseshoe crab (Limulus polyphemus) show a daily rhythm in visual sensitivity that is mediated by efferent nerve signals from a circadian clock in the crab's brain. How these signals communicate circadian messages is not known for this or other animals. Here the authors describe in quantitative detail the spike firing pattern of clock output neurons in living horseshoe crabs and discuss its possible significance to clock organization and function. Efferent fiber spike trains were recorded extracellularly for several hours to days, and in some cases, the electroretinogram was simultaneously acquired to monitor eye sensitivity. Statistical features of single- and multifiber recordings were characterized via interval distribution, serial correlation, and power spectral analysis. The authors report that efferent feedback to the eyes has several scales of temporal structure, consisting of multicellular bursts of spikes that group into clusters and packets of clusters that repeat throughout the night and disappear during the day. Except near dusk and dawn, the bursts occur every 1 to 2 sec in clusters of 10 to 30 bursts separated by a minute or two of silence. Within a burst, each output neuron typically fires a single spike with a preferred order, and intervals between bursts and clusters are positively correlated in length. The authors also report that efferent activity is strongly modulated by light at night and that just a brief flash has lasting impact on clock output. The multilayered firing pattern is likely important for driving circadian rhythms in the eye and other target organs. PMID:21775292

  9. Nonsmooth dynamics in spiking neuron models

    NASA Astrophysics Data System (ADS)

    Coombes, S.; Thul, R.; Wedgwood, K. C. A.

    2012-11-01

    Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reduction in the dimensionality of both parameter and variable space that facilitates more effective simulation studies. In this latter case the single neuron model of choice is often a variant of the classic integrate-and-fire model, which is described by a nonsmooth dynamical system. In this paper we review some of the more popular spiking models of this class and describe the types of spiking pattern that they can generate (ranging from tonic to burst firing). We show that a number of techniques originally developed for the study of impact oscillators are directly relevant to their analysis, particularly those for treating grazing bifurcations. Importantly we highlight one particular single neuron model, capable of generating realistic spike trains, that is both computationally cheap and analytically tractable. This is a planar nonlinear integrate-and-fire model with a piecewise linear vector field and a state dependent reset upon spiking. We call this the PWL-IF model and analyse it at both the single neuron and network level. The techniques and terminology of nonsmooth dynamical systems are used to flesh out the bifurcation structure of the single neuron model, as well as to develop the notion of Lyapunov exponents. We also show how to construct the phase response curve for this system, emphasising that techniques in mathematical neuroscience may also translate back to the field of nonsmooth dynamical systems. The stability of periodic spiking orbits is assessed using a linear stability analysis of spiking times. At the network level we consider linear coupling between voltage

  10. Spike sorting of synchronous spikes from local neuron ensembles.

    PubMed

    Franke, Felix; Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R P; Obermayer, Klaus; Munk, Matthias H J

    2015-10-01

    Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473

  11. Spike sorting of synchronous spikes from local neuron ensembles

    PubMed Central

    Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R. P.; Obermayer, Klaus; Munk, Matthias H. J.

    2015-01-01

    Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473

  12. Spiking neural network for recognizing spatiotemporal sequences of spikes

    NASA Astrophysics Data System (ADS)

    Jin, Dezhe Z.

    2004-02-01

    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.

  13. Spiking Neurons for Analysis of Patterns

    NASA Technical Reports Server (NTRS)

    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

  14. Designing optimal stimuli to control neuronal spike timing.

    PubMed

    Ahmadian, Yashar; Packer, Adam M; Yuste, Rafael; Paninski, Liam

    2011-08-01

    Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

  15. Participation in Formal Technical and Vocational Education and Training Programmes Worldwide: An Initial Statistical Study

    ERIC Educational Resources Information Center

    UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training, 2006

    2006-01-01

    There is a common perception that technical and vocational education and training (TVET) is diversifying and expanding in terms of its supply and demand. Practitioners and policymakers often believe that educational systems are offering a wider array of programmes at different levels and in various fields of study. They also assume that these…

  16. Australian Vocational Education and Training Statistics: Apprentices & Trainees, September Quarter 2014

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2014

    2014-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the September quarter 2014. The figures in this publication are derived from the National Apprentice and Trainee Collection no. 82 (December 2014 estimates). There were 341,300 apprentices and trainees in-training as at 30 September 2014, a decrease of 18.4%…

  17. Women in VET, 2000. Australian Vocational Education & Training Statistics, 2000. At a Glance.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    This report is a summary update of a more extensive report on the participation of women in the Australian public VET (vocational and technical education) sector in 2000. Data were obtained from publicly-funded training providers, including TAFE (Technical and Further Education) institutions and some schools and universities, registered community…

  18. Australian Vocational Education and Training Statistics: Employers' Use and Views of the VET System, 2009

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2009

    2009-01-01

    This survey collects information about employers' use and views of the vocational education and training (VET) system and the various ways employers use the VET system to meet their skill needs. Information collected is designed to measure the awareness, engagement and satisfaction of employers with the VET system. (Contains 12 tables.)…

  19. Australian Vocational Education and Training Statistics: Apprentices and Trainees. September Quarter, 2010

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the September quarter 2010. The figures in this publication are derived from the National Apprentice and Trainee Collection no.66 (December 2010 estimates). The most recent figures in this publication are estimated (those for training activity from the March…

  20. Australian Vocational Education and Training Statistics: Apprentices and Trainees. December Quarter, 2011

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the December quarter 2011. The figures in this publication are derived from the National Apprentice and Trainee Collection no.71 (March 2012 estimates). The most recent figures in this publication are estimated (those for training activity from the June quarter…

  1. Australian Vocational Education and Training Statistics: Apprentices and Trainees. December Quarter, 2010

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the December quarter 2010. The figures in this publication are derived from the National Apprentice and Trainee Collection no.67 (March 2011 estimates). The most recent figures in this publication are estimated (those for training activity from the June quarter…

  2. TAFE Graduates: Do They Get What They Want from Training? Statistics 2001.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    The question of whether graduates of Australia's technical and further education (TAFE) programs are getting what they want from training was examined. A market segmentation approach was used to analyze data from the 2001 Student Outcomes Survey (SOS). The market segments analyzed covered 93% of TAFE graduates surveyed in the 2001 SOS. The…

  3. Australian Vocational Education and Training Statistics: Completion and Attrition Rates for Apprentices and Trainees, 2011

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    By tracking the outcome of a contract of training over time, contract completion and attrition rates can be measured. This method requires enough time to pass to accurately report on outcomes for the majority of contracts. This publication presents completion and attrition rates for apprentices and trainees using three different methodologies: (1)…

  4. Australian Vocational Education and Training Statistics: Completion and Attrition Rates for Apprentices and Trainees, 2010

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    By tracking the outcome of a contract of training over time, individuals can measure contract completion and attrition rates. This method requires enough time to pass to accurately report on outcomes for the majority of contracts. This publication presents completion and attrition rates for apprentices and trainees using three different…

  5. Total VET Students and Courses 2014: Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    In November 2012, the Council of Australian Governments (COAG) Standing Council on Tertiary Education, Skills and Employment (SCOTESE) agreed to the introduction of mandatory reporting of nationally recognised training activity from 2014 onward. Under the mandatory reporting requirements, all Australian providers (excluding those exempted by…

  6. Australian Vocational Education and Training Statistics: Apprentices and Trainees. June Quarter, 2012

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the June quarter 2012. The figures in this publication are derived from the National Apprentice and Trainee Collection no.73 (September 2012 estimates). The most recent figures in this publication are estimated (those for training activity from the December…

  7. Equity Groups in Total VET Students and Courses 2014: Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    This publication provides summary information on equity groups in vocational education and training (VET) delivered by 4601 Australian providers in 2014, under the first collection of "total VET activity" data. In 2014, there were: (1) 146,500 Indigenous students (3.7% of all students); (2) 201,000 students with a disability (5.1% of all…

  8. Government-Funded Program Completions 2014. Preliminary. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    This publication provides data on Australian Qualifications Framework (AQF) programs completed from 2010 to 2014 in Australia's government-funded vocational education and training (VET) system (broadly defined as all activity delivered by government providers and government-funded activity delivered by community education and other registered…

  9. Apprentices and Trainees 2014. December Quarter. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2015

    2015-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the December quarter 2014. The figures in this publication are derived from the National Apprentice and Trainee Collection no. 83 (March 2015 estimates). The most recent figures in this publication are estimated (those for training activity from the June…

  10. Australian Vocational Education and Training Statistics: Apprentices and Trainees. 2015 September Quarter

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2016

    2016-01-01

    This publication presents estimates of apprentice and trainee activity in Australia for the September quarter 2015. The figures in this publication are derived from the National Apprentice and Trainee Collection no.86 (December 2015 estimates). The most recent figures in this publication are estimated (that is, for training activity from the March…

  11. Collaborative Training in Statistical and Data Library Services: Lessons from the Canadian Data Liberation Initiative

    ERIC Educational Resources Information Center

    Humphrey, Charles

    2005-01-01

    New technology and knowledge push organizations to upgrade and improve the skills of their staff. Paying for professional development programming is a common way of providing continuing education. This article describes a collaborative training program introduced to develop baseline competencies in Canadian academic libraries to support data…

  12. Spiking neuron network Helmholtz machine

    PubMed Central

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    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. PMID:25954191

  13. Effects of nicotine stimulation on spikes, theta frequency oscillations, and spike-theta oscillation relationship in rat medial septum diagonal band Broca slices

    PubMed Central

    Wen, Dong; Peng, Ce; Ou-yang, Gao-xiang; Henderson, Zainab; Li, Xiao-li; Lu, Cheng-biao

    2013-01-01

    Aim: Spiking activities and neuronal network oscillations in the theta frequency range have been found in many cortical areas during information processing. The aim of this study is to determine whether nicotinic acetylcholine receptors (nAChRs) mediate neuronal network activity in rat medial septum diagonal band Broca (MSDB) slices. Methods: Extracellular field potentials were recorded in the slices using an Axoprobe 1A amplifier. Data analysis was performed off-line. Spike sorting and local field potential (LFP) analyses were performed using Spike2 software. The role of spiking activity in the generation of LFP oscillations in the slices was determined by analyzing the phase-time relationship between the spikes and LFP oscillations. Circular statistic analysis based on the Rayleigh test was used to determine the significance of phase relationships between the spikes and LFP oscillations. The timing relationship was examined by quantifying the spike-field coherence (SFC). Results: Application of nicotine (250 nmol/L) induced prominent LFP oscillations in the theta frequency band and both small- and large-amplitude population spiking activity in the slices. These spikes were phase-locked to theta oscillations at specific phases. The Rayleigh test showed a statistically significant relationship in phase-locking between the spikes and theta oscillations. Larger changes in the SFC were observed for large-amplitude spikes, indicating an accurate timing relationship between this type of spike and LFP oscillations. The nicotine-induced spiking activity (large-amplitude population spikes) was suppressed by the nAChR antagonist dihydro-β-erythroidine (0.3 μmol/L). Conclusion: The results demonstrate that large-amplitude spikes are phase-locked to theta oscillations and have a high spike-timing accuracy, which are likely a main contributor to the theta oscillations generated in MSDB during nicotine receptor activation. PMID:23474704

  14. Spike initiation by transmembrane current: a white-noise analysis.

    PubMed Central

    Bryant, H L; Segundo, J P

    1976-01-01

    1. Those features of a transmembrane current correlated with spike initiation were examined in Aplysia neurones using a Gaussian white-noise stimulus. This stimulus has the advantages that it presents numerous wave forms in random order without prejudgement as to their efficacies, and that it allows straightforward statistical calculations. 2. Stimulation with a repeating segment of Gaussian white-noise current revealed remarkable invariance in the firing times of the tested neurones and indicated a high degree of reliability of their response. 3. Frequencies (less than 5 Hz) involved in spike triggering propagated faithfully for up to several millimetres, justifying intrasomatic current injection to examine spike initiation at the trigger locus. 4. Examination of current wave forms preceding spikes indicated that a wide variety could be effective. Hence, a statistical analysis was performed, including computation of probability densities, averages, standard deviations and correlation coefficients of pairs of current values. Each statistic was displayed as a function of time before the spike. 5. The average current trajectory preceding a spike was multiphasic and depended on the presence and polarity of a d.c. bias. An early relatively small inward- or outward-going phase was followed by a large outward phase before the spike. The early phase tended to oppose the polarity of the d.c. bias. 6. The late outward phase of the average current trajectory reached a maximum 40--75 msec before triggering the action potential (AP) and returned to near zero values at the moment of triggering. The fact that the current peak occurs in advance of the AP may be partially explained by a phase delay between the transmembrane current and potential. The failure of the average current trajectory to return to control values immediately following the peak argues for a positive role of the declining phase in spike triggering. 7. Probability densities preceding spikes were Gaussian

  15. The neuronal response at extended timescales: a linearized spiking input–output relation

    PubMed Central

    Soudry, Daniel; Meir, Ron

    2014-01-01

    Many biological systems are modulated by unknown slow processes. This can severely hinder analysis – especially in excitable neurons, which are highly non-linear and stochastic systems. We show the analysis simplifies considerably if the input matches the sparse “spiky” nature of the output. In this case, a linearized spiking Input–Output (I/O) relation can be derived semi-analytically, relating input spike trains to output spikes based on known biophysical properties. Using this I/O relation we obtain closed-form expressions for all second order statistics (input – internal state – output correlations and spectra), construct optimal linear estimators for the neuronal response and internal state and perform parameter identification. These results are guaranteed to hold, for a general stochastic biophysical neuron model, with only a few assumptions (mainly, timescale separation). We numerically test the resulting expressions for various models, and show that they hold well, even in cases where our assumptions fail to hold. In a companion paper we demonstrate how this approach enables us to fit a biophysical neuron model so it reproduces experimentally observed temporal firing statistics on days-long experiments. PMID:24765073

  16. The neuronal response at extended timescales: a linearized spiking input-output relation.

    PubMed

    Soudry, Daniel; Meir, Ron

    2014-01-01

    Many biological systems are modulated by unknown slow processes. This can severely hinder analysis - especially in excitable neurons, which are highly non-linear and stochastic systems. We show the analysis simplifies considerably if the input matches the sparse "spiky" nature of the output. In this case, a linearized spiking Input-Output (I/O) relation can be derived semi-analytically, relating input spike trains to output spikes based on known biophysical properties. Using this I/O relation we obtain closed-form expressions for all second order statistics (input - internal state - output correlations and spectra), construct optimal linear estimators for the neuronal response and internal state and perform parameter identification. These results are guaranteed to hold, for a general stochastic biophysical neuron model, with only a few assumptions (mainly, timescale separation). We numerically test the resulting expressions for various models, and show that they hold well, even in cases where our assumptions fail to hold. In a companion paper we demonstrate how this approach enables us to fit a biophysical neuron model so it reproduces experimentally observed temporal firing statistics on days-long experiments.

  17. Graph structure modeling for multi-neuronal spike data

    NASA Astrophysics Data System (ADS)

    Akaho, Shotaro; Higuchi, Sho; Iwasaki, Taishi; Hino, Hideitsu; Tatsuno, Masami; Murata, Noboru

    2016-03-01

    We propose a method to extract connectivity between neurons for extracellularly recorded multiple spike trains. The method removes pseudo-correlation caused by propagation of information along an indirect pathway, and is also robust against the influence from unobserved neurons. The estimation algorithm consists of iterations of a simple matrix inversion, which is scalable to large data sets. The performance is examined by synthetic spike data.

  18. Causal Inference and Explaining Away in a Spiking Network

    PubMed Central

    Moreno-Bote, Rubén; Drugowitsch, Jan

    2015-01-01

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

  19. Australian Apprentice & Trainee Statistics: Mechanical Engineering and Fabrication Trades, 1995-1999. Australian Vocational Education & Training.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    Statistics regarding Australians participating in apprenticeships and traineeships in the mechanical engineering and fabrication trades in 1995-1999 were reviewed to provide an indication of where skill shortages may be occurring or will likely occur in relation to the following occupations: mechanical engineering trades; fabrication engineering…

  20. Australian Apprentice & Trainee Statistics: Electrical and Electronics Trades, 1995 to 1999. Australian Vocational Education & Training.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    Statistics regarding Australians participating in apprenticeships and traineeships in the electrical and electronics trades in 1995-1999 were reviewed to provide an indication of where skill shortages may be occurring or will likely occur in relation to the following occupations: electrical engineering associate professional; electronics…

  1. The Impact of Training and Demographics in WIA Program Performance: A Statistical Analysis

    ERIC Educational Resources Information Center

    Moore, Richard W.; Gorman, Philip C.

    2009-01-01

    The Workforce Investment Act (WIA) measures participant labor market outcomes to drive program performance. This article uses statistical analysis to examine the relationship between participant characteristics and key outcome measures in one large California local WIA program. This study also measures the impact of different training…

  2. Australian Apprentice & Trainee Statistics: Automotive Repairs and Service Trades, 1995 to 1999. Australian Vocational Education & Training.

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research, Leabrook (Australia).

    Statistics regarding Australians participating in apprenticeships and traineeships in the automotive repairs and service trades in 1995-1999 were reviewed to provide an indication of where skill shortages may be occurring or will likely occur in relation to the following occupations: motor mechanic, automotive electrician, and panel beater. The…

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

    NASA Astrophysics Data System (ADS)

    Salmasi, Mehrdad; Büttner, Ulrich; Glasauer, Stefan

    2016-06-01

    Objective. 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. Approach. 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. Main results. 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. Significance. 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.

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

    PubMed Central

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

    2016-01-01

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

  5. Radioxenon spiked air

    DOE PAGES

    Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; Houghton, Tracy P.; Jenson, Douglas D.; Mann, Nick R.

    2015-08-27

    Four of the radioactive xenon isotopes (131mXe, 133mXe, 133Xe and 135Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The Internationalmore » Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This study focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.« less

  6. Radioxenon spiked air.

    PubMed

    Watrous, Matthew G; Delmore, James E; Hague, Robert K; Houghton, Tracy P; Jenson, Douglas D; Mann, Nick R

    2015-12-01

    Four of the radioactive xenon isotopes ((131m)Xe, (133m)Xe, (133)Xe and (135)Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This paper focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities. PMID:26318775

  7. Radioxenon spiked air.

    PubMed

    Watrous, Matthew G; Delmore, James E; Hague, Robert K; Houghton, Tracy P; Jenson, Douglas D; Mann, Nick R

    2015-12-01

    Four of the radioactive xenon isotopes ((131m)Xe, (133m)Xe, (133)Xe and (135)Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This paper focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.

  8. Influence of spiking activity on cortical local field potentials

    PubMed Central

    Waldert, Stephan; Lemon, Roger N; Kraskov, Alexander

    2013-01-01

    The intra-cortical local field potential (LFP) reflects a variety of electrophysiological processes including synaptic inputs to neurons and their spiking activity. It is still a common assumption that removing high frequencies, often above 300 Hz, is sufficient to exclude spiking activity from LFP activity prior to analysis. Conclusions based on such supposedly spike-free LFPs can result in false interpretations of neurophysiological processes and erroneous correlations between LFPs and behaviour or spiking activity. Such findings might simply arise from spike contamination rather than from genuine changes in synaptic input activity. Although the subject of recent studies, the extent of LFP contamination by spikes is unclear, and the fundamental problem remains. Using spikes recorded in the motor cortex of the awake monkey, we investigated how different factors, including spike amplitude, duration and firing rate, together with the noise statistic, can determine the extent to which spikes contaminate intra-cortical LFPs. We demonstrate that such contamination is realistic for LFPs with a frequency down to ∼10 Hz. For LFP activity below ∼10 Hz, such as movement-related potential, contamination is theoretically possible but unlikely in real situations. Importantly, LFP frequencies up to the (high-) gamma band can remain unaffected. This study shows that spike–LFP crosstalk in intra-cortical recordings should be assessed for each individual dataset to ensure that conclusions based on LFP analysis are valid. To this end, we introduce a method to detect and to visualise spike contamination, and provide a systematic guide to assess spike contamination of intra-cortical LFPs. PMID:23981719

  9. Rayleigh--Taylor spike evaporation

    SciTech Connect

    Schappert, G. T.; Batha, S. H.; Klare, K. A.; Hollowell, D. E.; Mason, R. J.

    2001-09-01

    Laser-based experiments have shown that Rayleigh--Taylor (RT) growth in thin, perturbed copper foils leads to a phase dominated by narrow spikes between thin bubbles. These experiments were well modeled and diagnosed until this '' spike'' phase, but not into this spike phase. Experiments were designed, modeled, and performed on the OMEGA laser [T. R. Boehly, D. L. Brown, R. S. Craxton , Opt. Commun. 133, 495 (1997)] to study the late-time spike phase. To simulate the conditions and evolution of late time RT, a copper target was fabricated consisting of a series of thin ridges (spikes in cross section) 150 {mu}m apart on a thin flat copper backing. The target was placed on the side of a scale-1.2 hohlraum with the ridges pointing into the hohlraum, which was heated to 190 eV. Side-on radiography imaged the evolution of the ridges and flat copper backing into the typical RT bubble and spike structure including the '' mushroom-like feet'' on the tips of the spikes. RAGE computer models [R. M. Baltrusaitis, M. L. Gittings, R. P. Weaver, R. F. Benjamin, and J. M. Budzinski, Phys. Fluids 8, 2471 (1996)] show the formation of the '' mushrooms,'' as well as how the backing material converges to lengthen the spike. The computer predictions of evolving spike and bubble lengths match measurements fairly well for the thicker backing targets but not for the thinner backings.

  10. Radioxenon spiked air

    SciTech Connect

    Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; Houghton, Tracy P.; Jenson, Douglas D.; Mann, Nick R.

    2015-08-27

    Four of the radioactive xenon isotopes (131mXe, 133mXe, 133Xe and 135Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This study focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.

  11. Automatic fitting of spiking neuron models to electrophysiological recordings.

    PubMed

    Rossant, Cyrille; Goodman, Dan F M; Platkiewicz, Jonathan; Brette, Romain

    2010-01-01

    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. PMID:20224819

  12. Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays

    PubMed Central

    Khosravi, Farhad; Trainor, Patrick; Rai, Shesh N; Kloecker, Goetz; Wickstrom, Eric; Panchapakesan, Balaji

    2016-01-01

    We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the ‘liquid biopsy’ was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ~100% sensitivity, ~91% specificity and ~96% accuracy. In the blinded test, the signals were classified with ~91% sensitivity, ~82% specificity and ~86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ~1—17 cells per 5 µl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays. PMID:26901310

  13. Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays.

    PubMed

    Khosravi, Farhad; Trainor, Patrick; Rai, Shesh N; Kloecker, Goetz; Wickstrom, Eric; Panchapakesan, Balaji

    2016-04-01

    We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the 'liquid biopsy' was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ∼100% sensitivity, ∼91% specificity and ∼96% accuracy. In the blinded test, the signals were classified with ∼91% sensitivity, ∼82% specificity and ∼86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ∼1-17 cells per 5 μl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays.

  14. Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays.

    PubMed

    Khosravi, Farhad; Trainor, Patrick; Rai, Shesh N; Kloecker, Goetz; Wickstrom, Eric; Panchapakesan, Balaji

    2016-04-01

    We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the 'liquid biopsy' was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ∼100% sensitivity, ∼91% specificity and ∼96% accuracy. In the blinded test, the signals were classified with ∼91% sensitivity, ∼82% specificity and ∼86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ∼1-17 cells per 5 μl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays. PMID:26901310

  15. Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays

    NASA Astrophysics Data System (ADS)

    Khosravi, Farhad; Trainor, Patrick; Rai, Shesh N.; Kloecker, Goetz; Wickstrom, Eric; Panchapakesan, Balaji

    2016-04-01

    We demonstrate the rapid and label-free capture of breast cancer cells spiked in buffy coats using nanotube-antibody micro-arrays. Single wall carbon nanotube arrays were manufactured using photo-lithography, metal deposition, and etching techniques. Anti-epithelial cell adhesion molecule (EpCAM) antibodies were functionalized to the surface of the nanotube devices using 1-pyrene-butanoic acid succinimidyl ester functionalization method. Following functionalization, plain buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the nanotube device and electrical signatures were recorded for differences in interaction between samples. A statistical classifier for the ‘liquid biopsy’ was developed to create a predictive model based on dynamic time warping to classify device electrical signals that corresponded to plain (control) or spiked buffy coats (case). In training test, the device electrical signals originating from buffy versus spiked buffy samples were classified with ˜100% sensitivity, ˜91% specificity and ˜96% accuracy. In the blinded test, the signals were classified with ˜91% sensitivity, ˜82% specificity and ˜86% accuracy. A heatmap was generated to visually capture the relationship between electrical signatures and the sample condition. Confocal microscopic analysis of devices that were classified as spiked buffy coats based on their electrical signatures confirmed the presence of cancer cells, their attachment to the device and overexpression of EpCAM receptors. The cell numbers were counted to be ˜1-17 cells per 5 μl per device suggesting single cell sensitivity in spiked buffy coats that is scalable to higher volumes using the micro-arrays.

  16. Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data

    PubMed Central

    Mountrakis, Giorgos; Zhuang, Wei

    2012-01-01

    Background This study discusses the theoretical underpinnings of a novel multi-scale radial basis function (MSRBF) neural network along with its application to classification and regression tasks in remote sensing. The novelty of the proposed MSRBF network relies on the integration of both local and global error statistics in the node selection process. Methodology and Principal Findings The method was tested on a binary classification task, detection of impervious surfaces using a Landsat satellite image, and a regression problem, simulation of waveform LiDAR data. In the classification scenario, results indicate that the MSRBF is superior to existing radial basis function and back propagation neural networks in terms of obtained classification accuracy and training-testing consistency, especially for smaller datasets. The latter is especially important as reference data acquisition is always an issue in remote sensing applications. In the regression case, MSRBF provided improved accuracy and consistency when contrasted with a multi kernel RBF network. Conclusion and Significance Results highlight the potential of a novel training methodology that is not restricted to a specific algorithmic type, therefore significantly advancing machine learning algorithms for classification and regression tasks. The MSRBF is expected to find numerous applications within and outside the remote sensing field. PMID:22876278

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

    PubMed

    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.

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

    PubMed Central

    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

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

    PubMed

    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

  20. Computing complex visual features with retinal spike times.

    PubMed

    Gütig, Robert; Gollisch, Tim; Sompolinsky, Haim; Meister, Markus

    2013-01-01

    Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.

  1. The Likelihood of Completing a Government-Funded VET Program, 2010-14. Australian Vocational Education and Training Statistics

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2016

    2016-01-01

    The Australian vocational education and training (VET) system provides training across a wide range of subject areas and is delivered through a variety of training institutions and enterprises (including to apprentices and trainees). The system provides training for students of all ages and backgrounds. Students may study individual subjects or…

  2. Spike-timing-dependent construction.

    PubMed

    Lightheart, Toby; Grainger, Steven; Lu, Tien-Fu

    2013-10-01

    Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.

  3. Geophone with depth sensitive spikes

    SciTech Connect

    Rice, J.A.; Houston, L.M.; Arevalo, R.

    1992-06-23

    This patent describes a geophone. It comprises a seismic sensitive element for sensing elastic motion and converting the motion to an electrical signal, a housing for enclosing the seismic element, and an elongated spike attachable to the housing.

  4. Wavelet analysis of epileptic spikes

    NASA Astrophysics Data System (ADS)

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

    2003-05-01

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

  5. Reduced Spiking in Entorhinal Cortex during the Delay Period of a Cued Spatial Response Task

    ERIC Educational Resources Information Center

    Gupta, Kishan; Keller, Lauren A.; Hasselmo, Michael E.

    2012-01-01

    Intrinsic persistent spiking mechanisms in medial entorhinal cortex (mEC) neurons may play a role in active maintenance of working memory. However, electrophysiological studies of rat mEC units have primarily focused on spatial modulation. We sought evidence of differential spike rates in the mEC in rats trained on a T-maze, cued spatial delayed…

  6. Computing spike directivity with tetrodes.

    PubMed

    Aur, Dorian; Connolly, Christoper I; Jog, Mandar S

    2005-11-30

    The ability of neurons to generate electrical signals is strongly dependent on the evolution of ion-specific pumps and channels that allow the transfer of charges under the influence of electric fields and concentration gradients. This paper presents a novel method by which flow of these charge fluxes may be computed to provide directivity of charge movement. Simulations of charge flow as well as actual electrophysiological data recorded by tetrodes are used to demonstrate the method. The propagation of charge fluxes in space in data from simulation and actual recordings during action potential can be analyzed using signals recorded by tetrodes. Variation in spike directivity can be estimated by computing singular value decomposition of the estimated 3D trajectory data. The analysis of the spike model can be accomplished by performing simulations of presumed equivalent moving charges recorded by the tetrode tips. For in vivo spike recordings, the variation of spike directivity could be obtained using several spikes of selected neurons considering the charge movement model (CMM). The relationship between computer simulation results and tetrode data recordings is examined. The paper concludes by showing that the method for calculating directivity in actual spike recordings is robust. The method allows for improved filtering of data and more importantly may shed light on furthering the study of spatio-temporal encoding in neurons. PMID:15978667

  7. Changes in complex spike activity during classical conditioning

    PubMed Central

    Rasmussen, Anders; Jirenhed, Dan-Anders; Wetmore, Daniel Z.; Hesslow, Germund

    2014-01-01

    The cerebellar cortex is necessary for adaptively timed conditioned responses (CRs) in eyeblink conditioning. During conditioning, Purkinje cells acquire pause responses or “Purkinje cell CRs” to the conditioned stimuli (CS), resulting in disinhibition of the cerebellar nuclei (CN), allowing them to activate motor nuclei that control eyeblinks. This disinhibition also causes inhibition of the inferior olive (IO), via the nucleo-olivary pathway (N-O). Activation of the IO, which relays the unconditional stimulus (US) to the cortex, elicits characteristic complex spikes in Purkinje cells. Although Purkinje cell activity, as well as stimulation of the CN, is known to influence IO activity, much remains to be learned about the way that learned changes in simple spike firing affects the IO. In the present study, we analyzed changes in simple and complex spike firing, in extracellular Purkinje cell records, from the C3 zone, in decerebrate ferrets undergoing training in a conditioning paradigm. In agreement with the N-O feedback hypothesis, acquisition resulted in a gradual decrease in complex spike activity during the conditioned stimulus, with a delay that is consistent with the long N-O latency. Also supporting the feedback hypothesis, training with a short interstimulus interval (ISI), which does not lead to acquisition of a Purkinje cell CR, did not cause a suppression of complex spike activity. In contrast, observations that extinction did not lead to a recovery in complex spike activity and the irregular patterns of simple and complex spike activity after the conditioned stimulus are less conclusive. PMID:25140129

  8. Doctoral Training in Statistics, Measurement, and Methodology in Psychology: Replication and Extension of Aiken, West, Sechrest, and Reno's (1990) Survey of PhD Programs in North America

    ERIC Educational Resources Information Center

    Aiken, Leona S.; West, Stephen G.; Millsap, Roger E.

    2008-01-01

    In a survey of all PhD programs in psychology in the United States and Canada, the authors documented the quantitative methodology curriculum (statistics, measurement, and research design) to examine the extent to which innovations in quantitative methodology have diffused into the training of PhDs in psychology. In all, 201 psychology PhD…

  9. Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons.

    PubMed

    Aldworth, Zane N; Miller, John P; Gedeon, Tomás; Cummins, Graham I; Dimitrov, Alexander G

    2005-06-01

    What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was approximately 5 ms.

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

    PubMed Central

    Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

    2015-01-01

    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. PMID:26082710

  11. Australian Vocational Education and Training Statistics: Government-Funded Students and Courses--January to March 2016

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2016

    2016-01-01

    This publication provides a summary of data relating to students, programs, subjects, and training providers in Australia's government-funded vocational education and training (VET) system (defined as Commonwealth and state/territory government funded training). This is the first time that government-funded data from one quarter is compared with…

  12. Event-driven contrastive divergence for spiking neuromorphic systems

    PubMed Central

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

    2014-01-01

    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. PMID:24574952

  13. Why Is Statistics Perceived as Difficult and Can Practice during Training Change Perceptions? Insights from a Prospective Mathematics Teacher

    ERIC Educational Resources Information Center

    Fitzmaurice, Olivia; Leavy, Aisling; Hannigan, Ailish

    2014-01-01

    An investigation into prospective mathematics/statistics teachers' (n = 134) conceptual understanding of statistics and attitudes to statistics carried out at the University of Limerick revealed an overall positive attitude to statistics but a perception that it can be a difficult subject, in particular that it requires a great deal of discipline…

  14. A novel rotate-and-fire digital spiking neuron and its neuron-like bifurcations and responses.

    PubMed

    Hishiki, Tetsuya; Torikai, Hiroyuki

    2011-05-01

    A novel rotate-and-fire digital spiking neuron is presented. The digital neuron is a wired system of shift registers and thus it is suited to on-chip learning unlike many other analog spiking neuron models. By adjusting the wiring pattern among the registers, the digital neuron can generate spike trains with various spike patterns and can exhibit related bifurcations. A discrete-continuous hybrid map, which describes the neuron dynamics without any approximation, is derived analytically. Using the hybrid map, it is shown that the digital spiking neuron can mimic typical bifurcation phenomena and various nonlinear responses of biological neurons.

  15. Implementation and evaluation of an interictal spike detector

    NASA Astrophysics Data System (ADS)

    Horak, Peter C.; Meisenhelter, Stephen; Testorf, Markus E.; Connolly, Andrew C.; Davis, Kathryn A.; Jobst, Barbara C.

    2015-09-01

    The detection of epileptiform activity, such as interictal spikes, in electrical brain recordings has important clinical and research applications. Identification of interictal spikes is often carried out manually by trained neurologists. It is a time-consuming process and can exhibit variability between experts. In this work, we develop and evaluate an automated spike detector. We implement a template-matching approach and improve its accuracy on one set of recordings using a supervised machine-learning algorithm. Evaluation with two independent datasets shows the template-matching detector to perform comparably with experts and the version augmented with a classifier. In one test dataset, variations of the detection threshold partially explain discrepancies between experts. In the other, the detector demonstrates similar behavior to an existing algorithm developed with this dataset.

  16. Statistical generation of training sets for measuring NO3(-), NH4(+) and major ions in natural waters using an ion selective electrode array.

    PubMed

    Mueller, Amy V; Hemond, Harold F

    2016-05-18

    Knowledge of ionic concentrations in natural waters is essential to understand watershed processes. Inorganic nitrogen, in the form of nitrate and ammonium ions, is a key nutrient as well as a participant in redox, acid-base, and photochemical processes of natural waters, leading to spatiotemporal patterns of ion concentrations at scales as small as meters or hours. Current options for measurement in situ are costly, relying primarily on instruments adapted from laboratory methods (e.g., colorimetric, UV absorption); free-standing and inexpensive ISE sensors for NO3(-) and NH4(+) could be attractive alternatives if interferences from other constituents were overcome. Multi-sensor arrays, coupled with appropriate non-linear signal processing, offer promise in this capacity but have not yet successfully achieved signal separation for NO3(-) and NH4(+)in situ at naturally occurring levels in unprocessed water samples. A novel signal processor, underpinned by an appropriate sensor array, is proposed that overcomes previous limitations by explicitly integrating basic chemical constraints (e.g., charge balance). This work further presents a rationalized process for the development of such in situ instrumentation for NO3(-) and NH4(+), including a statistical-modeling strategy for instrument design, training/calibration, and validation. Statistical analysis reveals that historical concentrations of major ionic constituents in natural waters across New England strongly covary and are multi-modal. This informs the design of a statistically appropriate training set, suggesting that the strong covariance of constituents across environmental samples can be exploited through appropriate signal processing mechanisms to further improve estimates of minor constituents. Two artificial neural network architectures, one expanded to incorporate knowledge of basic chemical constraints, were tested to process outputs of a multi-sensor array, trained using datasets of varying degrees of

  17. Learning Universal Computations with Spikes

    PubMed Central

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

    2016-01-01

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

  18. Learning Universal Computations with Spikes.

    PubMed

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

    2016-06-01

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

  19. Automatic spike sorting using tuning information.

    PubMed

    Ventura, Valérie

    2009-09-01

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

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

    PubMed

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

    2015-08-01

    The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537

  1. Neural coding properties based on spike timing and pattern correlation of retinal ganglion cells

    PubMed Central

    Gong, Han-Yan; Zhang, Ying-Ying; Liang, Pei-Ji

    2010-01-01

    Correlation between spike trains or neurons sometimes indicates certain neural coding rules in the visual system. In this paper, the relationship between spike timing correlation and pattern correlation is discussed, and their ability to represent stimulus features is compared to examine their coding strategies not only in individual neurons but also in population. Two kinds of stimuli, natural movies and checkerboard, are used to arouse firing activities in chicken retinal ganglion cells. The spike timing correlation and pattern correlation are calculated by cross-correlation function and Lempel–Ziv distance respectively. According to the correlation values, it is demonstrated that spike trains with similar spike patterns are not necessarily concerted in firing time. Moreover, spike pattern correlation values between individual neurons’ responses reflect the difference of natural movies and checkerboard; neurons cooperate with each other with higher pattern correlation values which represent spatiotemporal correlations during response to natural movies. Spike timing does not reflect stimulus features as obvious as spike patterns, caused by their particular coding properties or physiological foundation. As a result, separating the pattern correlation out of traditional timing correlation concept uncover additional insight in neural coding. PMID:22132042

  2. Upper Limb Biomechanics During the Volleyball Serve and Spike

    PubMed Central

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

    2010-01-01

    Background: The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Hypothesis: Significant kinetic differences exist among specific types of volleyball serves and spikes. Study Design: Controlled laboratory study. Methods: 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. Results: 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. Conclusion: 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. Clinical Relevance: Volleyball-specific overhead skills, such as the spike and serve, produce considerable upper extremity force and torque, which may contribute to the risk of

  3. Copula regression analysis of simultaneously recorded frontal eye field and inferotemporal spiking activity during object-based working memory.

    PubMed

    Hu, Meng; Clark, Kelsey L; Gong, Xiajing; Noudoost, Behrad; Li, Mingyao; Moore, Tirin; Liang, Hualou

    2015-06-10

    Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain. PMID:26063909

  4. Prospective Coding by Spiking Neurons.

    PubMed

    Brea, Johanni; Gaál, Alexisz Tamás; Urbanczik, Robert; Senn, Walter

    2016-06-01

    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). PMID:27341100

  5. Prospective Coding by Spiking Neurons

    PubMed Central

    Brea, Johanni; Gaál, Alexisz Tamás; Senn, Walter

    2016-01-01

    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). PMID:27341100

  6. Detection of interictal spikes and artifactual data through orthogonal transformations.

    PubMed

    Adjouadi, Malek; Cabrerizo, Mercedes; Ayala, Melvin; Sanchez, Danmary; Yaylali, Ilker; Jayakar, Prasanna; Barreto, Armando

    2005-01-01

    This study introduces an integrated algorithm based on the Walsh transform to detect interictal spikes and artifactual data in epileptic patients using recorded EEG data. The algorithm proposes a unique mathematical use of Walsh-transformed EEG signals to identify those criteria that best define the morphologic characteristics of interictal spikes. EEG recordings were accomplished using the 10-20 system interfaced with the Electrical Source Imaging System with 256 channels (ESI-256) for enhanced preprocessing and on-line monitoring and visualization. The merits of the algorithm are: (1) its computational simplicity; (2) its integrated design that identifies and localizes interictal spikes while automatically removing or discarding the presence of different artifacts such as electromyography, electrocardiography, and eye blinks; and (3) its potential implication to other types of EEG analysis, given the mathematical basis of this algorithm, which can be patterned or generalized to other brain dysfunctions. The mathematics that were applied here assumed a dual role, that of transforming EEG signals into mutually independent bases and in ascertaining quantitative measures for those morphologic characteristics deemed important in the identification process of interictal spikes. Clinical experiments involved 31 patients with focal epilepsy. EEG data collected from 10 of these patients were used initially in a training phase to ascertain the reliability of the observable and formulated features that were used in the spike detection process. Three EEG experts annotated spikes independently. On evaluation of the algorithm using the 21 remaining patients in the testing phase revealed a precision (positive predictive value) of 92% and a sensitivity of 82%. Based on the 20- to 30-minute epochs of continuous EEG recording per subject, the false detection rate is estimated at 1.8 per hour of continuous EEG. These are positive results that support further development of this

  7. Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.

    PubMed

    Mitra, S; Fusi, S; Indiveri, G

    2009-02-01

    Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated. PMID:23853161

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

    PubMed

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

    2012-06-01

    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. PMID:22617339

  9. Spike voltage topography in temporal lobe epilepsy.

    PubMed

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

    2016-07-15

    We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology. PMID:27288809

  10. Spike voltage topography in temporal lobe epilepsy.

    PubMed

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

    2016-07-15

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

  11. Simple and complex spike firing patterns in Purkinje cells during classical conditioning.

    PubMed

    Rasmussen, Anders; Jirenhed, Dan-Anders; Hesslow, Germund

    2008-01-01

    Classical blink conditioning is known to depend critically on the cerebellum and the relevant circuitry is gradually being unravelled. Several lines of evidence support the theory that the conditioned stimulus is transmitted by mossy fibers to the cerebellar cortex whereas the unconditioned stimulus is transmitted by climbing fibers. This view has been dramatically confirmed by recent Purkinje cell recordings during training with a classical conditioning paradigm. We have tracked the activity of single Purkinje cells with microelectrodes for several hours in decerebrate ferrets during learning, extinction, and relearning. Paired peripheral forelimb and periocular stimulation, as well as paired direct stimulation of cerebellar afferent pathways (mossy and climbing fibers) causes acquisition of a pause response in Purkinje cell simple spike firing. This conditioned Purkinje cell response has temporal properties that match those of the behavioral response. Its latency varies with the interstimulus interval and it responds to manipulations of the conditioned stimulus in the same way that the blink does. Complex spike firing largely mirrors the simple spike behavior. We have previously suggested that cerebellar learning is subject to a negative feedback control via the inhibitory nucleo-olivary pathway. As the Purkinje cell learns to respond to the conditioned stimulus with a suppression of simple spikes, disinhibition of anterior interpositus neurons would be expected to cause inhibition of the inferior olive. Observations of complex spike firing in the Purkinje cells during conditioning and extinction confirm this prediction. Before training, complex spikes are unaffected or facilitated by the conditioned stimulus, but as the simple spike pause response develops, spontaneous and stimulus-evoked complex spikes are also strongly suppressed by the conditioned stimulus. After extinction of the simple spike pause response, the complex spikes reappear.

  12. Simple and complex spike firing patterns in Purkinje cells during classical conditioning.

    PubMed

    Rasmussen, Anders; Jirenhed, Dan-Anders; Hesslow, Germund

    2008-01-01

    Classical blink conditioning is known to depend critically on the cerebellum and the relevant circuitry is gradually being unravelled. Several lines of evidence support the theory that the conditioned stimulus is transmitted by mossy fibers to the cerebellar cortex whereas the unconditioned stimulus is transmitted by climbing fibers. This view has been dramatically confirmed by recent Purkinje cell recordings during training with a classical conditioning paradigm. We have tracked the activity of single Purkinje cells with microelectrodes for several hours in decerebrate ferrets during learning, extinction, and relearning. Paired peripheral forelimb and periocular stimulation, as well as paired direct stimulation of cerebellar afferent pathways (mossy and climbing fibers) causes acquisition of a pause response in Purkinje cell simple spike firing. This conditioned Purkinje cell response has temporal properties that match those of the behavioral response. Its latency varies with the interstimulus interval and it responds to manipulations of the conditioned stimulus in the same way that the blink does. Complex spike firing largely mirrors the simple spike behavior. We have previously suggested that cerebellar learning is subject to a negative feedback control via the inhibitory nucleo-olivary pathway. As the Purkinje cell learns to respond to the conditioned stimulus with a suppression of simple spikes, disinhibition of anterior interpositus neurons would be expected to cause inhibition of the inferior olive. Observations of complex spike firing in the Purkinje cells during conditioning and extinction confirm this prediction. Before training, complex spikes are unaffected or facilitated by the conditioned stimulus, but as the simple spike pause response develops, spontaneous and stimulus-evoked complex spikes are also strongly suppressed by the conditioned stimulus. After extinction of the simple spike pause response, the complex spikes reappear. PMID:18931885

  13. Combined Statistical Analysis Method Assessing Fast Versus Slow Movement Training in a Patient With Cerebellar Stroke: A Single-Case Study

    PubMed Central

    Kimberley, Teresa J.; Durfee, William K.; Dressler, Brittany L.; Steil, Carie; Carey, James R.

    2013-01-01

    Background Gold standards of data analysis for single-case research do not currently exist. Objective The purpose of this study was to determine whether a combined statistical analysis method is more effective in assessing movement training effects in a patient with cerebellar stroke. Design A crossover single-case research design was conducted. Methods The patient was a 69-year-old man with a chronic cerebellar infarct who received two 5-week phases of finger tracking training at different movement rates. Changes were measured with the Box and Block Test, the Jebsen-Taylor test, the finger extension force test, and the corticospinal excitability test. Both visual analysis and statistical tests (including split-middle line method, t test, confidence interval, and effect size) were used to assess potential intervention effects. Results The results of the t tests were highly consistent with the confidence interval tests, but less consistent with the split-middle line method. Most results produced medium to large effect sizes. Limitations The possibility of an incomplete washout effect was a confounding factor in the current analyses. Conclusions The combined statistical analysis method may assist researchers in assessing intervention effects in single-case stroke rehabilitation studies. PMID:23329559

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

    NASA Astrophysics Data System (ADS)

    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.

  15. Online Equivalence-Based Instruction about Statistical Inference Using Written Explanation Instead of Match-to-Sample Training

    ERIC Educational Resources Information Center

    Critchfield, Thomas S.

    2014-01-01

    Equivalence-based instruction of college students was adapted for use in a commercial online course-delivery system, with written explanation replacing match-to-sample training. Outcomes rivaled those of previous studies in which students were taught in low-distraction settings through match-to-sample procedures that were controlled by custom…

  16. A memristive spiking neuron with firing rate coding

    PubMed Central

    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. PMID:26539074

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

    PubMed

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

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

  18. A memristive spiking neuron with firing rate coding.

    PubMed

    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. PMID:26539074

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

    PubMed Central

    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

  20. Emergent Oscillations in Networks of Stochastic Spiking Neurons

    PubMed Central

    van Drongelen, Wim; Cowan, Jack D.

    2011-01-01

    Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

  1. Solar microwave millisecond spike at 2.84 GHz

    NASA Technical Reports Server (NTRS)

    Fu, Qi-Jun; Jin, Sheng-Zhen; Zhao, Ren-Yang; Zheng, Le-Ping; Liu, Yu-Ying; Li, Xiao-Cong; Wang, Shu-Lan; Chen, Zhi-Jun; Hu, Chu-Min

    1986-01-01

    Using the high time resolution of 1 ms, the data of solar microwave millisecond spike (MMS) event was recorded more than two hundred times at the frequency of 2.84 GHz at Beijing (Peking) Observatory since May 1981. A preliminary analysis was made. It can be seen from the data that the MMS-events have a variety of the fast activities such as the dispersed and isolated spikes, the clusters of the crowded spikes, the weak spikes superimposed on the noise background, and the phenomena of absorption. The marked differences from that observed with lower time resolution are presented. Using the data, a valuable statistical analysis was made. There are close correlations between MMS-events and hard X-ray bursts, and fast drifting bursts. The MMS events are highly dependent on the type of active regions and the magnetic field configuration. It seems to be crucial to find out the accurate positions on the active region where the MMS-events happen and to make co-operative observations at different bands during the special period when specific active regions appear on the solar disk.

  2. A Spiking Network Model of Decision Making Employing Rewarded STDP

    PubMed Central

    Bazhenov, Maxim

    2014-01-01

    Reward-modulated spike timing dependent plasticity (STDP) combines unsupervised STDP with a reinforcement signal that modulates synaptic changes. It was proposed as a learning rule capable of solving the distal reward problem in reinforcement learning. Nonetheless, performance and limitations of this learning mechanism have yet to be tested for its ability to solve biological problems. In our work, rewarded STDP was implemented to model foraging behavior in a simulated environment. Over the course of training the network of spiking neurons developed the capability of producing highly successful decision-making. The network performance remained stable even after significant perturbations of synaptic structure. Rewarded STDP alone was insufficient to learn effective decision making due to the difficulty maintaining homeostatic equilibrium of synaptic weights and the development of local performance maxima. Our study predicts that successful learning requires stabilizing mechanisms that allow neurons to balance their input and output synapses as well as synaptic noise. PMID:24632858

  3. Control of a brain-computer interface without spike sorting

    NASA Astrophysics Data System (ADS)

    Fraser, George W.; Chase, Steven M.; Whitford, Andrew; Schwartz, Andrew B.

    2009-10-01

    Two rhesus monkeys were trained to move a cursor using neural activity recorded with silicon arrays of 96 microelectrodes implanted in the primary motor cortex. We have developed a method to extract movement information from the recorded single and multi-unit activity in the absence of spike sorting. By setting a single threshold across all channels and fitting the resultant events with a spline tuning function, a control signal was extracted from this population using a Bayesian particle-filter extraction algorithm. The animals achieved high-quality control comparable to the performance of decoding schemes based on sorted spikes. Our results suggest that even the simplest signal processing is sufficient for high-quality neuroprosthetic control.

  4. Contribution of spike timing to the information transmitted by HVC neurons.

    PubMed

    Huetz, Chloé; Del Negro, Catherine; Lebas, Nicolas; Tarroux, Philippe; Edeline, Jean-Marc

    2006-08-01

    In many species, neurons with highly selective stimulus-response properties characterize higher order sensory areas and/or sensory motor areas of the CNS. In the songbird nuclei, the responses of HVC (used as a proper name) neurons during playback of the bird's own song (BOS) are probably one of the most striking examples of selectivity for natural stimuli. We examined here to what extent spike-timing carries information about natural and time-reversed versions of the BOS. From a heterogenous population of 107 HVC neurons recorded in long-day or short-day conditions, a standard indicator of stimulus preference based on spike-count (the d' index) indicates that a limited proportion of cells can be classified as selective for the BOS (20% with a |d'| > 1). In contrast, quantifying the information conveyed by spike trains with the metric-space of J.D. Victor & K.P Purpura [(1996) J. Neurophysiol., 76, 1310-1326] indicates that 62% of the cells display significant amounts of transmitted information, among which 77% are 'temporal cells'. 'Temporal cells' correspond to cells transmitting significant amounts of information when spike-timing is considered, whereas no information, or lower amounts of transmitted information, is obtained when only spike-count is considered. Computing a correlation index between spike trains [S. Schreiber et al. (2003) Neurocomputing, 52-54,925-931] revealed that spike-timing reliability is higher for the forward than for the reverse BOS, whatever the day length and the cell type are. Cells classified as selective in terms of spike-counts (d' index) had greater amounts of transmitted information, but cells classified as non-selective (d' < 0.5) can also transmit significant amounts of information. Thus, information theory methods demonstrate that a much larger proportion of neurons than expected based on spike-count only participate in the discrimination between stimuli.

  5. Multiagent reinforcement learning: spiking and nonspiking agents in the iterated Prisoner's Dilemma.

    PubMed

    Vassiliades, Vassilis; Cleanthous, Aristodemos; Christodoulou, Chris

    2011-04-01

    This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking and nonspiking agents in the Iterated Prisoner's Dilemma by exploring the conditions required to enhance its cooperative outcome. The spiking agents are neural networks with leaky integrate-and-fire neurons trained with two different learning algorithms: 1) reinforcement of stochastic synaptic transmission, or 2) reward-modulated spike-timing-dependent plasticity with eligibility trace. The nonspiking agents use a tabular representation and are trained with Q- and SARSA learning algorithms, with a novel reward transformation process also being applied to the Q-learning agents. According to the results, the cooperative outcome is enhanced by: 1) transformed internal reinforcement signals and a combination of a high learning rate and a low discount factor with an appropriate exploration schedule in the case of non-spiking agents, and 2) having longer eligibility trace time constant in the case of spiking agents. Moreover, it is shown that spiking and nonspiking agents have similar behavior and therefore they can equally well be used in a multiagent interaction setting. For training the spiking agents in the case where more than one output neuron competes for reinforcement, a novel and necessary modification that enhances competition is applied to the two learning algorithms utilized, in order to avoid a possible synaptic saturation. This is done by administering to the networks additional global reinforcement signals for every spike of the output neurons that were not "responsible" for the preceding decision. PMID:21421435

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

    PubMed Central

    Andersen, Richard A.

    2012-01-01

    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. PMID:22072511

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

    PubMed

    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. PMID:22072511

  8. A "Last Word" on Ice Spikes.

    ERIC Educational Resources Information Center

    Perry, Helene F.

    1995-01-01

    Attempts an explanation of how "ice spikes" are formed. The spikes are upward protrusions of ice that occur when water expands as it cools in a rigid container of low thermal conductivity. Describes the results of an investigation and includes color photos. (LZ)

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

    NASA Astrophysics Data System (ADS)

    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

  10. An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

    PubMed Central

    Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M.; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

    2013-01-01

    We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes. PMID:23408739

  11. An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation.

    PubMed

    Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

    2013-01-01

    We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

  12. Supervised Learning with Complex Spikes and Spike-Timing-Dependent Plasticity

    PubMed Central

    Houghton, Conor

    2014-01-01

    One distinctive feature of Purkinje cells is that they have two types of discharge: in addition to simple spikes they fire complex spikes in response to input from the climbing fibers. These complex spikes have an initial rapid burst of spikes and spikelets followed by a sustained depolarization; in some models of cerebellar function this climbing fiber input supervises learning in Purkinje cells. On the other hand, synaptic plasticity is often thought to rely on the timing of pre-synaptic and post-synaptic spikes. It is suggested here that the period of depolarization following a complex spike, combined with a simple spike-timing-dependent plasticity rule, gives a mechanism for the climbing fiber to supervise learning in the Purkinje cell. This proposal is illustrated using a simple simulation in which it is seen that the climbing fiber succeeds in supervising the learning. PMID:24945786

  13. Bursts and Isolated Spikes Code for Opposite Movement Directions in Midbrain Electrosensory Neurons

    PubMed Central

    Khosravi-Hashemi, Navid; Chacron, Maurice J.

    2012-01-01

    Directional selectivity, in which neurons respond strongly to an object moving in a given direction but weakly or not at all to the same object moving in the opposite direction, is a crucial computation that is thought to provide a neural correlate of motion perception. However, directional selectivity has been traditionally quantified by using the full spike train, which does not take into account particular action potential patterns. We investigated how different action potential patterns, namely bursts (i.e. packets of action potentials followed by quiescence) and isolated spikes, contribute to movement direction coding in a mathematical model of midbrain electrosensory neurons. We found that bursts and isolated spikes could be selectively elicited when the same object moved in opposite directions. In particular, it was possible to find parameter values for which our model neuron did not display directional selectivity when the full spike train was considered but displayed strong directional selectivity when bursts or isolated spikes were instead considered. Further analysis of our model revealed that an intrinsic burst mechanism based on subthreshold T-type calcium channels was not required to observe parameter regimes for which bursts and isolated spikes code for opposite movement directions. However, this burst mechanism enhanced the range of parameter values for which such regimes were observed. Experimental recordings from midbrain neurons confirmed our modeling prediction that bursts and isolated spikes can indeed code for opposite movement directions. Finally, we quantified the performance of a plausible neural circuit and found that it could respond more or less selectively to isolated spikes for a wide range of parameter values when compared with an interspike interval threshold. Our results thus show for the first time that different action potential patterns can differentially encode movement and that traditional measures of directional selectivity

  14. Emergent properties of interacting populations of spiking neurons.

    PubMed

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

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

  15. Stimulus-response curves of a neuronal model for noisy subthreshold oscillations and related spike generation.

    PubMed

    Huber, Martin Tobias; Braun, Hans Albert

    2006-04-01

    We investigate the stimulus-dependent tuning properties of a noisy ionic conductance model for intrinsic subthreshold oscillations in membrane potential and associated spike generation. Upon depolarization by an applied current, the model exhibits subthreshold oscillatory activity with an occasional spike generation when oscillations reach the spike threshold. We consider how the amount of applied current, the noise intensity, variation of maximum conductance values, and scaling to different temperature ranges alter the responses of the model with respect to voltage traces, interspike intervals and their statistics, and the mean spike frequency curves. We demonstrate that subthreshold oscillatory neurons in the presence of noise can sensitively and also selectively be tuned by the stimulus-dependent variation of model parameters. PMID:16711858

  16. Spike detection using the continuous wavelet transform.

    PubMed

    Nenadic, Zoran; Burdick, Joel W

    2005-01-01

    This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution. PMID:15651566

  17. Vibration (?) spikes during natural rain events

    NASA Technical Reports Server (NTRS)

    Short, David A.

    1994-01-01

    Limited analysis of optical rain gauge (ORG) data from shipboard and ground based sensors has shown the existence of spikes, possibly attributable to sensor vibration, while rain is occurring. An extreme example of this behavior was noted aboard the PRC#5 on the evening of December 24, 1992 as the ship began repositioning during a rain event in the TOGA/COARE IFA. The spikes are readily evident in the one-second resolution data, but may be indistinguishable from natural rain rate fluctuations in subsampled or averaged data. Such spikes result in increased rainfall totals.

  18. Vectorized algorithms for spiking neural network simulation.

    PubMed

    Brette, Romain; Goodman, Dan F M

    2011-06-01

    High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages. PMID:21395437

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

    PubMed

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

    2016-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  1. Sparse Data Analysis Strategy for Neural Spike Classification

    PubMed Central

    Vigneron, Vincent; Chen, Hsin

    2014-01-01

    Many of the multichannel extracellular recordings of neural activity consist of attempting to sort spikes on the basis of shared characteristics with some feature detection techniques. Then spikes can be sorted into distinct clusters. There are in general two main statistical issues: firstly, spike sorting can result in well-sorted units, but by with no means one can be sure that one is dealing with single units due to the number of neurons adjacent to the recording electrode. Secondly, the waveform dimensionality is reduced in a small subset of discriminating features. This shortening dimension effort was introduced as an aid to visualization and manual clustering, but also to reduce the computational complexity in automatic classification. We introduce a metric based on common neighbourhood to introduce sparsity in the dataset and separate data into more homogeneous subgroups. The approach is particularly well suited for clustering when the individual clusters are elongated (that is nonspherical). In addition it does need not to select the number of clusters, it is very efficient to visualize clusters in a dataset, it is robust to noise, it can handle imbalanced data, and it is fully automatic and deterministic. PMID:25101122

  2. Separating Spike Count Correlation from Firing Rate Correlation.

    PubMed

    Vinci, Giuseppe; Ventura, Valérie; Smith, Matthew A; Kass, Robert E

    2016-05-01

    Populations of cortical neurons exhibit shared fluctuations in spiking activity over time. When measured for a pair of neurons over multiple repetitions of an identical stimulus, this phenomenon emerges as correlated trial-to-trial response variability via spike count correlation (SCC). However, spike counts can be viewed as noisy versions of firing rates, which can vary from trial to trial. From this perspective, the SCC for a pair of neurons becomes a noisy version of the corresponding firing rate correlation (FRC). Furthermore, the magnitude of the SCC is generally smaller than that of the FRC and is likely to be less sensitive to experimental manipulation. We provide statistical methods for disambiguating time-averaged drive from within-trial noise, thereby separating FRC from SCC. We study these methods to document their reliability, and we apply them to neurons recorded in vivo from area V4 in an alert animal. We show how the various effects we describe are reflected in the data: within-trial effects are largely negligible, while attenuation due to trial-to-trial variation dominates and frequently produces comparisons in SCC that, because of noise, do not accurately reflect those based on the underlying FRC.

  3. Repertoires of Spike Avalanches Are Modulated by Behavior and Novelty.

    PubMed

    Ribeiro, Tiago L; Ribeiro, Sidarta; Copelli, Mauro

    2016-01-01

    Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here, we show that spike avalanches, recorded from hippocampus (HP) and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation.

  4. Repertoires of Spike Avalanches Are Modulated by Behavior and Novelty

    PubMed Central

    Ribeiro, Tiago L.; Ribeiro, Sidarta; Copelli, Mauro

    2016-01-01

    Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here, we show that spike avalanches, recorded from hippocampus (HP) and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation. PMID:27047341

  5. Repertoires of Spike Avalanches Are Modulated by Behavior and Novelty.

    PubMed

    Ribeiro, Tiago L; Ribeiro, Sidarta; Copelli, Mauro

    2016-01-01

    Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here, we show that spike avalanches, recorded from hippocampus (HP) and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation. PMID:27047341

  6. STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron.

    PubMed

    Luthman, Johannes; Hoebeek, Freek E; Maex, Reinoud; Davey, Neil; Adams, Rod; De Zeeuw, Chris I; Steuber, Volker

    2011-12-01

    Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.

  7. Modeling spiking behavior of neurons with time-dependent Poisson processes.

    PubMed

    Shinomoto, S; Tsubo, Y

    2001-10-01

    Three kinds of interval statistics, as represented by the coefficient of variation, the skewness coefficient, and the correlation coefficient of consecutive intervals, are evaluated for three kinds of time-dependent Poisson processes: pulse regulated, sinusoidally regulated, and doubly stochastic. Among these three processes, the sinusoidally regulated and doubly stochastic Poisson processes, in the case when the spike rate varies slowly compared with the mean interval between spikes, are found to be consistent with the three statistical coefficients exhibited by data recorded from neurons in the prefrontal cortex of monkeys.

  8. Modeling spiking behavior of neurons with time-dependent Poisson processes

    NASA Astrophysics Data System (ADS)

    Shinomoto, Shigeru; Tsubo, Yasuhiro

    2001-10-01

    Three kinds of interval statistics, as represented by the coefficient of variation, the skewness coefficient, and the correlation coefficient of consecutive intervals, are evaluated for three kinds of time-dependent Poisson processes: pulse regulated, sinusoidally regulated, and doubly stochastic. Among these three processes, the sinusoidally regulated and doubly stochastic Poisson processes, in the case when the spike rate varies slowly compared with the mean interval between spikes, are found to be consistent with the three statistical coefficients exhibited by data recorded from neurons in the prefrontal cortex of monkeys.

  9. Noise-induced burst and spike synchronizations in an inhibitory small-world network of subthreshold bursting neurons.

    PubMed

    Kim, Sang-Yoon; Lim, Woochang

    2015-04-01

    We are interested in noise-induced firings of subthreshold neurons which may be used for encoding environmental stimuli. Noise-induced population synchronization was previously studied only for the case of global coupling, unlike the case of subthreshold spiking neurons. Hence, we investigate the effect of complex network architecture on noise-induced synchronization in an inhibitory population of subthreshold bursting Hindmarsh-Rose neurons. For modeling complex synaptic connectivity, we consider the Watts-Strogatz small-world network which interpolates between regular lattice and random network via rewiring, and investigate the effect of small-world connectivity on emergence of noise-induced population synchronization. Thus, noise-induced burst synchronization (synchrony on the slow bursting time scale) and spike synchronization (synchrony on the fast spike time scale) are found to appear in a synchronized region of the [Formula: see text]-[Formula: see text] plane ([Formula: see text]: synaptic inhibition strength and [Formula: see text]: noise intensity). As the rewiring probability [Formula: see text] is decreased from 1 (random network) to 0 (regular lattice), the region of spike synchronization shrinks rapidly in the [Formula: see text]-[Formula: see text] plane, while the region of the burst synchronization decreases slowly. We separate the slow bursting and the fast spiking time scales via frequency filtering, and characterize the noise-induced burst and spike synchronizations by employing realistic order parameters and statistical-mechanical measures introduced in our recent work. Thus, the bursting and spiking thresholds for the burst and spike synchronization transitions are determined in terms of the bursting and spiking order parameters, respectively. Furthermore, we also measure the degrees of burst and spike synchronizations in terms of the statistical-mechanical bursting and spiking measures, respectively.

  10. Spike-like Bursts as Fine Structure of Zebras

    NASA Astrophysics Data System (ADS)

    Zlobec, P.; Karlický, M.

    2007-12-01

    We studied the characteristics of the zebra-associated spike-like bursts that were recorded with high time resolution at 1420 MHz in four intervals (from 12:45 to 12:48 UT) during 5 August 2003. Our detailed analysis is based on the selection of more than 500 such spike-like bursts and it is, at least to our knowledge, the first study devoted to such short-lived bursts. Their characteristics are different from those pertinent to “normal” spike bursts, as presented in the paper by Güdel and Benz ( Astron. Astrophys. 231, 202, 1990); in particular, their duration (about 7.4 ms at half power) is shorter, so they should be members of the SSS (super short structures) family (Magdalenić et al., Astrophys. J. 642, L77, 2006). The bursts were generally strongly R-polarized; however, during the decaying part of interval I a low R-polarized and L-polarized bursts were also present. This change of polarization shows a trend that resembles the peculiar form of the zebra lines in the spectral dominion (“V” like). A global statistical analysis on the bursts observed in the two polarimetric channels shows that the highest cross-correlation coefficient (about 0.5) was pertinent to interval I. The zebras and the bursts can be interpreted by the same double plasma resonance process as proposed by Bárta and Karlický ( Astron. Astrophys. 379, 1045, 2001) and Karlický et al. ( Astron. Astrophys. 375, 638, 2001); in particular, the spikes are generated by the interruption of this process by assumed turbulence (density or magnetic field variations). This process should be present in the region close to the reconnection site ( e.g., in the plasma reconnection outflows) where the density and the magnetic field vary strongly.

  11. Axon terminal hyperexcitability associated with epileptogenesis in vitro. I. Origin of ectopic spikes.

    PubMed

    Stasheff, S F; Hines, M; Wilson, W A

    1993-09-01

    1. Intracellular and extracellular recording techniques were used to study the increase in ectopic (i.e., nonsomatic) action-potential generation occurring among CA3 pyramidal cells during the kindling-like induction of electrographic seizures (EGSs) in this subpopulation of the hippocampal slice. Kindling-like stimulus trains (60 Hz, 2 s) were delivered to s. radiatum of CA3 at 10-min intervals. As EGSs developed, the frequency of ectopic firing increased markedly (by 10.33 +/- 3.29 spikes/min, mean +/- SE, P < 0.01). Several methods were applied to determine the initiation site for these action potentials within the cell (axons vs. dendrites). 2. Collision tests were conducted between known antidromic and orthodromic action potentials in CA3 cells to determine the critical period, c, for collision. Attempts were then made to collide ectopic spikes with known antidromic action potentials. At intervals less than c, ectopic spikes failed to collide with antidromic ones, in 5 of 10 cases. In these cells, this clearly indicates that the ectopic spikes were themselves of axonal origin. In the remaining five cases, ectopic spikes collided with antidromic action potentials at intervals approximately equal to c, most likely because of interactions within the complex system of recurrent axon collaterals in CA3. 3. Action potentials of CA3 pyramidal cells were simulated with the use of a compartmental computer model, NEURON. These simulations were based on prior models of CA3 pyramidal neurons and of the motoneuron action potential. Simulated action potentials generated in axonal compartments possessed a prominent inflection on their rising phase (IS-SD break), which was difficult to appreciate in those spikes generated in somatic or dendritic compartments. 4. An analysis of action potentials recorded experimentally from CA3 pyramidal cells also showed that antidromic spikes possess a prominent IS-SD break that is not present in orthodromic spikes. In addition to identified

  12. Building blocks for electronic spiking neural networks.

    PubMed

    van Schaik, A

    2001-01-01

    We present an electronic circuit modelling the spike generation process in the biological neuron. This simple circuit is capable of simulating the spiking behaviour of several different types of biological neurons. At the same time, the circuit is small so that many neurons can be implemented on a single silicon chip. This is important, as neural computation obtains its power not from a single neuron, but from the interaction between a large number of neurons. Circuits that model these interactions are also presented in this paper. They include the circuits for excitatory, inhibitory and shunting inhibitory synapses, a circuit which models the regeneration of spikes on the axon, and a circuit which models the reduction of input strength with the distance of the synapse to the cell body on the dendrite of the cell. Together these building blocks allow the implementation of electronic spiking neural networks.

  13. Retractable spiked barrier strip for law enforcement

    SciTech Connect

    Marts, D.J.; Barker, S.G.

    1995-03-01

    The Idaho National Engineering Laboratory has designed an laboratory tested a prototype retractable spiked barrier strip for law enforcement. The proposed system, which is ready for controlled field testing, expands the functionality of existing spiked barrier strips. A retractable barrier strip, one that can place the spikes in either the active (vertical) or passive (horizontal) position, would allow law enforcement personnel to lay the unobtrusive strip across a road far in advance of a fleeing vehicle. No damage occurs to passing vehicles until the spikes are activated, and that can be done from a safe distance and at a strategic location when the offending vehicle is close to the strip. The concept also allows the strips to be place safely across several roadways that are potential paths of a fleeing vehicle. Since they are not activated until needed, they are harmless to nonoffending vehicles. The laboratory tests conducted on the system indicate that it will puncture tires only when the spikes are rotated to the active position and is safe to travel over when the spikes are in the down position. The strip itself will not cause instability to a vehicle driving over it, nor is the strip disturbed or adversely affected by vehicles driving over it. The spikes can be quickly rotated between the active (vertical) and passive (horizontal) position. However, the laboratory tests have only demonstrated that the retractable spiked barrier strip can perform its intended function in a laboratory environment. Field tests are needed to finalize the design and develop the system into a functional law enforcement tool.

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

    PubMed Central

    Martinelli, Eugenio; Polese, Davide; Dini, Francesca; Paolesse, Roberto; Filippini, Daniel; Lundström, Ingemar; Di Natale, Corrado

    2011-01-01

    Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time. PMID:22194721

  15. Ca2+ spikes in the flagellum control chemotactic behavior of sperm

    PubMed Central

    Böhmer, Martin; Van, Qui; Weyand, Ingo; Hagen, Volker; Beyermann, Michael; Matsumoto, Midori; Hoshi, Motonori; Hildebrand, Eilo; Kaupp, Ulrich Benjamin

    2005-01-01

    The events that occur during chemotaxis of sperm are only partly known. As an essential step toward determining the underlying mechanism, we have recorded Ca2+ dynamics in swimming sperm of marine invertebrates. Stimulation of the sea urchin Arbacia punctulata by the chemoattractant or by intracellular cGMP evokes Ca2+ spikes in the flagellum. A Ca2+ spike elicits a turn in the trajectory followed by a period of straight swimming (‘turn-and-run'). The train of Ca2+ spikes gives rise to repetitive loop-like movements. When sperm swim in a concentration gradient of the attractant, the Ca2+ spikes and the stimulus function are synchronized, suggesting that precise timing of Ca2+ spikes controls navigation. We identified the peptide asterosap as a chemotactic factor of the starfish Asterias amurensis. The Ca2+ spikes and swimming behavior of sperm from starfish and sea urchin are similar, implying that the signaling pathway of chemotaxis has been conserved for almost 500 million years. PMID:16001082

  16. Network Structures Arising from Spike-Timing Dependent Plasticity

    NASA Astrophysics Data System (ADS)

    Babadi, Baktash

    Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains, and organizes neural circuits in functionally useful ways. In this dissertation, I study the structures arising from STDP in a population of synapses with an emphasis on the interplay between synaptic stability and Hebbian competition, explained in Chapter 1. Starting from the simplest description of STDP which relates synaptic modification to the intervals between pairs of pre- and postsynaptic spikes, I show in Chapter 2 that stability and Hebbian competition are incompatible in this class of "pair-based" STDP models, either when hard bounds or soft bounds are imposed to the synapses. In chapter 3, I propose an alternative biophysically inspired method for imposing bounds to synapses, i.e. introducing a small temporal shift in the STDP window. Shifted STDP overcomes the incompatibility of synaptic stability and competition and can implement both Hebbian and anti-Hebbian forms of competitive plasticity. In light of experiments the explored a variety of spike patterns, STDP models have been augmented to account for interactions between multiple pre- and postsynaptic action potentials. In chapter 4, I study the stability/competition interplay in three different proposed multi-spike models of STDP. I show that the "triplet model" leads to a partially steady-state distribution of synaptic weights and induces Hebbian competition. The "suppression model" develops a stable distribution of weights when the average weight is high and shows predominantly anti-Hebbian competition. The "NMDAR-based" model can lead to either stable or partially stable synaptic weight distribution and exhibits both Hebbian and anti-Hebbian competition, depending on the parameters. I conclude that multi-spike STDP models can produce radically different effects at the population level depending on how they implement multi-spike interactions

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

    PubMed

    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

  18. Propagation of Spiking and Burst-Spiking Synchronous States in a Feed-Forward Neuronal Network

    NASA Astrophysics Data System (ADS)

    Zhang, Xi; Huang, Hong-Bin; Li, Pei-Jun; Wu, Fang-Ping; Wu, Wang-Jie; Jiang, Min

    2012-12-01

    Neuronal firing that carries information can propagate stably in neuronal networks. One important feature of the stable states is their spatiotemporal correlation (STC) developed in the propagation. The propagation of synchronous states of spiking and burst-spiking neuronal activities in a feed-forward neuronal network with high STC is studied. Different dynamic regions and synchronous regions of the second layer are clarified for spiking and burst-spiking neuronal activities. By calculating correlation, it is found that five layers are needed for stable propagation. Synchronous regions of the 4th layer and the 10th layer are compared.

  19. Reconstructing Stimuli from the Spike Times of Leaky Integrate and Fire Neurons

    PubMed Central

    Gerwinn, Sebastian; Macke, Jakob H.; Bethge, Matthias

    2010-01-01

    Reconstructing stimuli from the spike trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case. PMID:21390287

  20. Information Carried by Population Spike Times in the Whisker Sensory Cortex can be Decoded Without Knowledge of Stimulus Time

    PubMed Central

    Panzeri, Stefano; Diamond, Mathew E.

    2010-01-01

    Computational analyses have revealed that precisely timed spikes emitted by somatosensory cortical neuronal populations encode basic stimulus features in the rat's whisker sensory system. Efficient spike time based decoding schemes both for the spatial location of a stimulus and for the kinetic features of complex whisker movements have been defined. To date, these decoding schemes have been based upon spike times referenced to an external temporal frame – the time of the stimulus itself. Such schemes are limited by the requirement of precise knowledge of the stimulus time signal, and it is not clear whether stimulus times are known to rats making sensory judgments. Here, we first review studies of the information obtained from spike timing referenced to the stimulus time. Then we explore new methods for extracting spike train information independently of any external temporal reference frame. These proposed methods are based on the detection of stimulus-dependent differences in the firing time within a neuronal population. We apply them to a data set using single-whisker stimulation in anesthetized rats and find that stimulus site can be decoded based on the millisecond-range relative differences in spike times even without knowledge of stimulus time. If spike counts alone are measured over tens or hundreds of milliseconds rather than milliseconds, such decoders are much less effective. These results suggest that decoding schemes based on millisecond-precise spike times are likely to subserve robust and information-rich transmission of information in the somatosensory system. PMID:21423503

  1. Spike timing precision changes with spike rate adaptation in the owl's auditory space map

    PubMed Central

    Takahashi, Terry T.

    2015-01-01

    Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113–136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking. PMID:26269555

  2. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    PubMed Central

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

    2016-01-01

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

  3. Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

    PubMed

    Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

    2015-01-01

    Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

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

    PubMed Central

    2012-01-01

    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 competitive with respect to

  5. Gradient Learning in Spiking Neural Networks by Dynamic Perturbation of Conductances

    SciTech Connect

    Fiete, Ila R.; Seung, H. Sebastian

    2006-07-28

    We present a method of estimating the gradient of an objective function with respect to the synaptic weights of a spiking neural network. The method works by measuring the fluctuations in the objective function in response to dynamic perturbation of the membrane conductances of the neurons. It is compatible with recurrent networks of conductance-based model neurons with dynamic synapses. The method can be interpreted as a biologically plausible synaptic learning rule, if the dynamic perturbations are generated by a special class of 'empiric' synapses driven by random spike trains from an external source.

  6. Mikkelson sweep/spike chisel plow shovel

    SciTech Connect

    Not Available

    1992-01-01

    Profitability comparisons are reported between the Mikkelson Sweep/Spike Chisel Plow Shovel standard sweeps. This evaluation covers the first year of testing of the new Sweep/Spike design. The data are not averaged over treatments due to significant interaction between treatments and environmental factors. The cost of fuel, fall and spring, to perform the various treatments ranged from $1.27 to $3.36 per acre. Use of the sweep/spike shovel always reduced total fuel cost. Savings varied from $0.11 to $0.71 per acre depending on prior treatment. This means there will be money saved, to off-set expenses, when converting present chisel plows or for special options on new chisel plows, needed for use of the sweep/spike shovel. A summary of 1991--1992 energy measurements. They indicate that more power will be required to pull a chisel plow equipped with the sweep/spike shovel. A larger tractor, narrower chisel plow and/or slower speed will be required to avoid the wheel slippage problems encountered on soft or wet field surfaces.

  7. Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity

    PubMed Central

    Babadi, Baktash; Abbott, L. F.

    2016-01-01

    Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different “multi-spike” STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses. PMID:26939080

  8. Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields.

    PubMed

    Yamamoto, Kenji; Kawato, Mitsuo; Kotosaka, Shinya; Kitazawa, Shigeru

    2007-02-01

    It is controversial whether simple-spike activity of cerebellar Purkinje cells during arm movements encodes movement kinematics like velocity or dynamics like muscle activities. To examine this issue, we trained monkeys to flex or extend the elbow by 45 degrees in 400 ms under resistive and assistive force fields but without altering kinematics. During the task movements after training, simple-spike discharges were recorded in the intermediate part of the cerebellum in lobules V-VI, and electromyographic activity was recorded from arm muscles. Velocity profiles (kinematics) in the two force fields were almost identical to each other, whereas not only the electromyographic activities (dynamics) but also simple-spike activities in many Purkinje cells differed distinctly depending on the type of force field. Simple-spike activities encoded much larger mutual information with the type of force field than that with the residual small difference in the height of peak velocity. The difference in simple-spike activities averaged over the recorded Purkinje-cells increased approximately 40 ms before the appearance of the difference in electromyographic activities between the two force fields, suggesting that the difference of simple-spike activities could be the origin of the difference of muscle activities. Simple-spike activity of many Purkinje cells correlated with electromyographic activity with a lead of approximately 80 ms, and these neurons had little overlap with another group of neurons the simple-spike activity of which correlated with velocity profiles. These results show that simple-spike activity of at least a group of Purkinje cells in the intermediate part of cerebellar lobules V-VI encodes movement dynamics.

  9. Spiking Models for Level-Invariant Encoding

    PubMed Central

    Brette, Romain

    2012-01-01

    Levels of ecological sounds vary over several orders of magnitude, but the firing rate and membrane potential of a neuron are much more limited in range. In binaural neurons of the barn owl, tuning to interaural delays is independent of level differences. Yet a monaural neuron with a fixed threshold should fire earlier in response to louder sounds, which would disrupt the tuning of these neurons. How could spike timing be independent of input level? Here I derive theoretical conditions for a spiking model to be insensitive to input level. The key property is a dynamic change in spike threshold. I then show how level invariance can be physiologically implemented, with specific ionic channel properties. It appears that these ingredients are indeed present in monaural neurons of the sound localization pathway of birds and mammals. PMID:22291634

  10. Using Sun Spikes to Measure Mesospheric Conductivity

    NASA Astrophysics Data System (ADS)

    Shimogawa, M. R.; Holzworth, R. H.

    2005-12-01

    Our payload was designed to study the electrodynamics of noctilucent clouds (NLCs) using double Langmuir probes. Sun spikes in the probe voltage, which occur naturally when a probe is shadowed by the rocket body, were two to three times larger when the rocket was above the NLC than when below it, on both the upleg and downleg portions of the flight. In the low conductivity found below the NLC, the sun spikes did not saturate, so a rough conductivity measurement could be made using these sun spike data. We found the conductivity to be about 8×10-10>S/m at 80 km altitude, which is in agreement with measurements made of the positive ion conductivity during the flight. This is effectively the same as the relaxation method for measuring conductivity in the lower atmosphere, shown here to work in the mesosphere.

  11. Spiking models for level-invariant encoding.

    PubMed

    Brette, Romain

    2011-01-01

    Levels of ecological sounds vary over several orders of magnitude, but the firing rate and membrane potential of a neuron are much more limited in range. In binaural neurons of the barn owl, tuning to interaural delays is independent of level differences. Yet a monaural neuron with a fixed threshold should fire earlier in response to louder sounds, which would disrupt the tuning of these neurons. How could spike timing be independent of input level? Here I derive theoretical conditions for a spiking model to be insensitive to input level. The key property is a dynamic change in spike threshold. I then show how level invariance can be physiologically implemented, with specific ionic channel properties. It appears that these ingredients are indeed present in monaural neurons of the sound localization pathway of birds and mammals. PMID:22291634

  12. Spike potentials recorded from the insect photoreceptor.

    PubMed

    NAKA, K I; EGUCHI, E

    1962-03-01

    Slow and spike potentials were recorded from single cells in the receptor layer of the compound eye of the drone of the honeybee. From electron microscopic observation of the drone ommatidium, it was concluded that the response had been recorded from the retinula cell. The following hypothesis is suggested for the initiation of spike potentials in the drone compound eye: Photic stimulation results in a decrease in the resistance of all or part of the retinula cell membrane, giving rise to the retinal action potential. The retinal action potential causes outflow of the current through the proximal process of the cell. This depolarizing current initiates spike potentials in the proximal process or axon of the retinula cell which are recorded across the soma membrane of the retinula cell.

  13. Toward a definition of MEG spike: parametric description of spikes recorded simultaneously by MEG and depth electrodes.

    PubMed

    Nowak, Rafal; Santiuste, Marta; Russi, Antonio

    2009-11-01

    There is not yet a formal definition of magnetoencephalography (MEG) spike. This study provides a parametric description and definition of clear-cut MEG spikes recorded simultaneously by MEG and depth electrodes (iEEG). A total number of 367 simultaneous MEG/iEEG spikes were selected for analysis. Distribution of morphologic spike parameters and detailed quantitative analysis of the basic morphologic characteristics of MEG spikes is provided.

  14. Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen

    NASA Technical Reports Server (NTRS)

    Hinton, Yolanda L.

    1999-01-01

    Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

  15. Slow and spike potentials recorded from retinula cells of the honeybee drone in response to light.

    PubMed

    Baumann, F

    1968-12-01

    Responses to light recorded by means of intracellular microelectrodes in isolated heads kept in oxygenated Ringer solution consist of a slow depolarization. Light adaptation increases the rates of depolarization and repolarization and decreases the amplitude of the response. Qualitatively these changes are similar to those observed in Limulus by Fuortes and Hodgkin. They are rapidly reversible during dark adaptation. In retinula cells of the drone eye a large single spike is recorded superimposed on the rising phase of the slow potential. The spike is a regenerative phenomenon; it can be triggered with electric current and is markedly reduced, sometimes abolished by tetrodotoxin. In rare cases cells were found which responded to light with a train of spikes. This behavior was only found under "unusual" experimental conditions; i.e., towards the end of a long experiment, during impalement, or at the beginning of responses to steps of strongly light-adapted preparations.

  16. Failure tolerance of spike phase synchronization in coupled neural networks

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi

    2011-09-01

    Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdős-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose model was considered as the mathematical model for the individual neurons, and the phase synchronization of the spike trains was monitored as a function of the percentage/number of removed nodes. The numerical simulations were supplemented by considering coupled non-identical Kuramoto oscillators. Failures based on the clustering coefficient, i.e., removing the nodes with high values of the clustering coefficient, had the least effect on the spike synchrony in all of the networks. This was followed by errors where the nodes were removed randomly. However, the behavior of the other three attack strategies was not uniform across the networks, and different strategies were the most influential in different network structure.

  17. A Three-dimensional Statistical Reconstruction Model of Grapevine (Vitis vinifera) Simulating Canopy Structure Variability within and between Cultivar/Training System Pairs

    PubMed Central

    Louarn, Gaëtan; Lecoeur, Jérémie; Lebon, Eric

    2008-01-01

    Background and Aims In grapevine, canopy-structure-related variations in light interception and distribution affect productivity, yield and the quality of the harvested product. A simple statistical model for reconstructing three-dimensional (3D) canopy structures for various cultivar–training system (C × T) pairs has been implemented with special attention paid to balance the time required for model parameterization and accuracy of the representations from organ to stand scales. Such an approach particularly aims at overcoming the weak integration of interplant variability using the usual direct 3D measurement methods. Model This model is original in combining a turbid-medium-like envelope enclosing the volume occupied by vine shoots with the use of discrete geometric polygons representing leaves randomly located within this volume to represent plant structure. Reconstruction rules were adapted to capture the main determinants of grapevine shoot architecture and their variability. Using a simplified set of parameters, it was possible to describe (1) the 3D path of the main shoot, (2) the volume occupied by the foliage around this path and (3) the orientation of individual leaf surfaces. Model parameterization (estimation of the probability distribution for each parameter) was carried out for eight contrasting C × T pairs. Key Results and Conclusions The parameter values obtained in each situation were consistent with our knowledge of grapevine architecture. Quantitative assessments for the generated virtual scenes were carried out at the canopy and plant scales. Light interception efficiency and local variations of light transmittance within and between experimental plots were correctly simulated for all canopies studied. The approach predicted these key ecophysiological variables significantly more accurately than the classical complete digitization method with a limited number of plants. In addition, this model accurately reproduced the characteristics of a

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

    PubMed

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

    2006-09-01

    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/cm(2) 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 that the removal rate was higher in kaolinite than in both spiked soil and industrial polluted soil. The duration of spiking was found to be an important factor too, when attempting to relate remediation of spiked soil or kaolinite to remediation of industrially polluted soils. Spiking for 2 days was too short. However, spiking for 30 days resulted in a pattern that was more similar to that of industrially polluted soils with similar compositions both regarding sequential extraction and electrodialytic remediation result, though the remediation still progressed slightly faster in the spiked soil. Generalisation of remediation results to a variety of soil types must on the other hand be done with caution since the remediation results of different industrially polluted soils were very different. In one soil a total of 76% Cu was removed and in another soil no Cu was removed only redistributed within the soil. The factor with the highest influence on removal success was soil pH, which must be low in order to mobilize Cu, and thus the buffering capacity against acidification was

  19. Effects of the action of microwave-frequency electromagnetic radiation on the spike activity of neurons in the supraoptic nucleus of the hypothalamus in rats.

    PubMed

    Minasyan, S M; Grigoryan, G Yu; Saakyan, S G; Akhumyan, A A; Kalantaryan, V P

    2007-02-01

    Acute experiments on white rats anesthetized with Nembutal (40 mg/kg, i.p.) were performed with extracellular recording and analysis of background spike activity from neurons in the supraoptic nucleus of the hypothalamus after exposure to electromagnetic radiation in the millimeter range. The distribution of neurons was determined in terms of the degree of regularity, the nature of the dynamics of neural streams, and the modalities of histograms of interspike intervals; the mean neuron spike frequency was calculated, along with the coefficient of variation of interspike intervals. These studies demonstrated changes in the background spike activity, predominantly affecting the internal structure of the spike streams recorded. The major changes were in the duration of interspike intervals and the degree of regularity of spike activity. Statistically significant changes in the mean spike frequencies of neuron populations in individual frequency ranges were also seen.

  20. Fast sigmoidal networks via spiking neurons.

    PubMed

    Maass, W

    1997-02-15

    We show that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedforward sigmoidal neural nets in a way that has previously not been considered. This new approach is based on temporal coding by single spikes (respectively by the timing of synchronous firing in pools of neurons) rather than on the traditional interpretation of analog variables in terms of firing rates. The resulting new simulation is substantially faster and hence more consistent with experimental results about the maximal speed of information processing in cortical neural systems. As a consequence we can show that networks of noisy spiking neurons are "universal approximators" in the sense that they can approximate with regard to temporal coding any given continuous function of several variables. This result holds for a fairly large class of schemes for coding analog variables by firing times of spiking neurons. This new proposal for the possible organization of computations in networks of spiking neurons systems has some interesting consequences for the type of learning rules that would be needed to explain the self-organization of such networks. Finally, the fast and noise-robust implementation of sigmoidal neural nets by temporal coding points to possible new ways of implementing feedforward and recurrent sigmoidal neural nets with pulse stream VLSI.

  1. An Unsupervised Online Spike-Sorting Framework.

    PubMed

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

    2016-08-01

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

  2. JFK in Blackface: Spike Lee's "Malcolm X."

    ERIC Educational Resources Information Center

    Walker, Clarence E.

    1993-01-01

    Discusses the failure of filmmaker Spike Lee to grapple with the real politics of Malcolm X before and after he left the Nation of Islam. Acknowledging the complexity of the man and his context would avoid creating a mythical figure similar to Oliver Stone's movie "JFK." (SLD)

  3. Spiking neuron computation with the time machine.

    PubMed

    Garg, Vaibhav; Shekhar, Ravi; Harris, John G

    2012-04-01

    The Time Machine (TM) is a spike-based computation architecture that represents synaptic weights in time. This choice of weight representation allows the use of virtual synapses, providing an excellent tradeoff in terms of flexibility, arbitrary weight connections and hardware usage compared to dedicated synapse architectures. The TM supports an arbitrary number of synapses and is limited only by the number of simultaneously active synapses to each neuron. SpikeSim, a behavioral hardware simulator for the architecture, is described along with example algorithms for edge detection and objection recognition. The TM can implement traditional spike-based processing as well as recently developed time mode operations where step functions serve as the input and output of each neuron block. A custom hybrid digital/analog implementation and a fully digital realization of the TM are discussed. An analog chip with 32 neurons, 1024 synapses and an address event representation (AER) block has been fabricated in 0.5 μm technology. A fully digital field-programmable gate array (FPGA)-based implementation of the architecture has 6,144 neurons and 100,352 simultaneously active synapses. Both implementations utilize a digital controller for routing spikes that can process up to 34 million synapses per second. PMID:23852979

  4. Physics of volleyball: Spiking with a purpose

    NASA Astrophysics Data System (ADS)

    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?

  5. Time-free spiking neural P systems.

    PubMed

    Pan, Linqiang; Zeng, Xiangxiang; Zhang, Xingyi

    2011-05-01

    Different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, a realistic definition of nonsynchronized spiking neural P systems (SN P systems, for short) is considered: during the work of an SN P system, the execution times of spiking rules cannot be known exactly (i.e., they are arbitrary). In order to establish robust systems against the environmental factors, a special class of SN P systems, called time-free SN P systems, is introduced, which always produce the same computation result independent of the execution times of the rules. The universality of time-free SN P systems is investigated. It is proved that these P systems with extended rules (several spikes can be produced by a rule) are equivalent to register machines. However, if the number of spikes present in the system is bounded, then the power of time-free SN P systems falls, and in this case, a characterization of semilinear sets of natural numbers is obtained.

  6. Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution.

    PubMed

    Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

    2016-01-01

    This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented. PMID:27516737

  7. Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution.

    PubMed

    Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

    2016-01-01

    This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.

  8. Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution

    PubMed Central

    Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I.; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

    2016-01-01

    This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented. PMID:27516737

  9. Differential involvement of excitatory and inhibitory neurons of cat motor cortex in coincident spike activity related to behavioral context.

    PubMed

    Putrino, David; Brown, Emery N; Mastaglia, Frank L; Ghosh, Soumya

    2010-06-01

    To assess temporal associations in spike activity between pairs of neurons in the primary motor cortex (MI) related to different behaviors, we compared the incidence of coincident spiking activity of task-related (TR) and non-task-related (NTR) neurons during a skilled motor task and sitting quietly in adult cats (Felis domestica). Chronically implanted microwires were used to record spike activity of MI neurons in four animals (two male and two female) trained to perform a skilled reaching task or sit quietly. Neurons were identified as TR if spike activity was modulated during the task (and NTR if not). Based on spike characteristics, they were also classified as either regular-spiking (RS, putatively excitatory) or fast-spiking (FS, putatively inhibitory) neurons. Temporal associations in the activities of simultaneously recorded neurons were evaluated using shuffle-corrected cross-correlograms. Pairs of NTR and TR neurons showed associations in their firing patterns over wide areas of MI (representing forelimb and hindlimb movements) during quiet sitting, more commonly involving RS neurons. During skilled task performance, however, significantly coincident firing was seen almost exclusively between TR neurons in a smaller part of MI (representing forelimb movements), involving mainly FS neurons. The findings of this study show evidence for widespread interactions in MI when the animal sits quietly, which changes to a more specific and restricted pattern of interactions during task performance. Different populations of excitatory and inhibitory neurons appear to be synchronized during skilled movement and quiet sitting.

  10. Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric

    PubMed Central

    Schwarz, Douglas M.; Zilany, Muhammad S. A.; Skevington, Melissa; Huang, Nicholas J.; Flynn, Brian C.; Carney, Laurel H.

    2012-01-01

    Sorting action potentials (spikes) from tetrode recordings can be time consuming, labor intensive, and inconsistent, depending on the methods used and the experience of the operator. The techniques presented here were designed to address these issues. A feature related to the slope of the spike during repolarization is computed. A small subsample of the features obtained from the tetrode (ca. 10,000–20,000 events) is clustered using a modified version of k-means that uses Mahalanobis distance and a scaling factor related to the cluster size. The cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size. The full data set is then classified from the statistics of the clusters. The technique yields consistent results for a chosen number of clusters. A MATLAB implementation is able to classify more than 5000 spikes per second on a modern workstation. PMID:22387262

  11. A Frank mixture copula family for modeling higher-order correlations of neural spike counts

    NASA Astrophysics Data System (ADS)

    Onken, Arno; Obermayer, Klaus

    2009-12-01

    In order to evaluate the importance of higher-order correlations in neural spike count codes, flexible statistical models of dependent multivariate spike counts are required. Copula families, parametric multivariate distributions that represent dependencies, can be applied to construct such models. We introduce the Frank mixture family as a new copula family that has separate parameters for all pairwise and higher-order correlations. In contrast to the Farlie-Gumbel-Morgenstern copula family that shares this property, the Frank mixture copula can model strong correlations. We apply spike count models based on the Frank mixture copula to data generated by a network of leaky integrate-and-fire neurons and compare the goodness of fit to distributions based on the Farlie-Gumbel-Morgenstern family. Finally, we evaluate the importance of using proper single neuron spike count distributions on the Shannon information. We find notable deviations in the entropy that increase with decreasing firing rates. Moreover, we find that the Frank mixture family increases the log likelihood of the fit significantly compared to the Farlie-Gumbel-Morgenstern family. This shows that the Frank mixture copula is a useful tool to assess the importance of higher-order correlations in spike count codes.

  12. Millimeter-scale epileptiform spike patterns and their relationship to seizures.

    PubMed

    Chamberlain, Ann C; Viventi, Jonathan; Blanco, Justin A; Kim, Dae-Hyeong; Rogers, John A; Litt, Brian

    2011-01-01

    Advances in neural electrode technology are enabling brain recordings with increasingly fine spatial and temporal resolution. We explore spatio-temporal (ST) patterns of local field potential spikes using a new high-density active electrode array with 500 μm resolution. We record subdural micro-electrocorticographic (μECoG) signals in vivo from a feline model of acute neocortical epileptiform spikes and seizures induced with local administration of the GABA antagonist, picrotoxin. We employ a clustering algorithm to separate 2-dimensional (2-D) spike patterns to isolate distinct classes of spikes unique to the interictal and ictal states. Our findings indicate that the 2-D patterns can be used to distinguish seizures from non-seizure state. We find two statistically significant ST patterns that uniquely characterize ictal epochs. We conclude that millimeter-scale ST spike dynamics contain useful information about ictal state. This finding may be important to understanding mechanisms underlying local circuit activity during seizure generation. Further work will investigate whether patterns we identify can increase our understanding of seizure dynamics and their underlying mechanisms and inform new electrical stimulation protocols for seizure termination.

  13. Generalized Volterra kernel model identification of spike-timing-dependent plasticity from simulated spiking activity.

    PubMed

    Robinson, Brian S; Song, Dong; Berger, Theodore W

    2014-01-01

    This paper presents a methodology to estimate a learning rule that governs activity-dependent plasticity from behaviorally recorded spiking events. To demonstrate this framework, we simulate a probabilistic spiking neuron with spike-timing-dependent plasticity (STDP) and estimate all model parameters from the simulated spiking data. In the neuron model, output spiking activity is generated by the combination of noise, feedback from the output, and an input-feedforward component whose magnitude is modulated by synaptic weight. The synaptic weight is calculated with STDP with the following features: (1) weight change based on the relative timing of input-output spike pairs, (2) prolonged plasticity induction, and (3) considerations for system stability. Estimation of all model parameters is achieved iteratively by formulating the model as a generalized linear model with Volterra kernels and basis function expansion. Successful estimation of all model parameters in this study demonstrates the feasibility of this approach for in-vivo experimental studies. Furthermore, the consideration of system stability and prolonged plasticity induction enhances the ability to capture how STDP affects a neural population's signal transformation properties over a realistic time course. Plasticity characterization with this estimation method could yield insights into functional implications of STDP and be incorporated into a cortical prosthesis.

  14. 16 CFR 1507.7 - Handles and spikes.

    Code of Federal Regulations, 2014 CFR

    2014-01-01

    ... FIREWORKS DEVICES § 1507.7 Handles and spikes. (a) Fireworks devices which are intended to be hand-held and...) Spikes provided with fireworks devices shall protrude at least 2 inches from the base of the device...

  15. 16 CFR 1507.7 - Handles and spikes.

    Code of Federal Regulations, 2012 CFR

    2012-01-01

    ... FIREWORKS DEVICES § 1507.7 Handles and spikes. (a) Fireworks devices which are intended to be hand-held and...) Spikes provided with fireworks devices shall protrude at least 2 inches from the base of the device...

  16. 16 CFR 1507.7 - Handles and spikes.

    Code of Federal Regulations, 2013 CFR

    2013-01-01

    ... FIREWORKS DEVICES § 1507.7 Handles and spikes. (a) Fireworks devices which are intended to be hand-held and...) Spikes provided with fireworks devices shall protrude at least 2 inches from the base of the device...

  17. 16 CFR 1507.7 - Handles and spikes.

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... FIREWORKS DEVICES § 1507.7 Handles and spikes. (a) Fireworks devices which are intended to be hand-held and...) Spikes provided with fireworks devices shall protrude at least 2 inches from the base of the device...

  18. 16 CFR 1507.7 - Handles and spikes.

    Code of Federal Regulations, 2011 CFR

    2011-01-01

    ... FIREWORKS DEVICES § 1507.7 Handles and spikes. (a) Fireworks devices which are intended to be hand-held and...) Spikes provided with fireworks devices shall protrude at least 2 inches from the base of the device...

  19. Saturation current spikes eliminated in saturable core transformers

    NASA Technical Reports Server (NTRS)

    Schwarz, F. C.

    1971-01-01

    Unsaturating composite magnetic core transformer, consisting of two separate parallel cores designed so impending core saturation causes signal generation, terminates high current spike in converter primary circuit. Simplified waveform, demonstrates transformer effectiveness in eliminating current spikes.

  20. Europa's Opposition Spike: Preliminary Results from Galileo E14 Observations

    NASA Technical Reports Server (NTRS)

    Kreslavsky, M. A.; Helfenstein, P.; Shkuratov, Yu. G.

    2000-01-01

    The phase function at 0-0.3deg phase angle is studied using high-resolution SSI images. The opposition spike is very sharp, especially for dark material. Some stratigraphically young terrains show anomalously weak opposition spike.

  1. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

    PubMed

    Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

    2013-05-01

    On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

  2. Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity.

    PubMed

    Karoly, Philippa J; Freestone, Dean R; Boston, Ray; Grayden, David B; Himes, David; Leyde, Kent; Seneviratne, Udaya; Berkovic, Samuel; O'Brien, Terence; Cook, Mark J

    2016-04-01

    We report on a quantitative analysis of electrocorticography data from a study that acquired continuous ambulatory recordings in humans over extended periods of time. The objectives were to examine patterns of seizures and spontaneous interictal spikes, their relationship to each other, and the nature of periodic variation. The recorded data were originally acquired for the purpose of seizure prediction, and were subsequently analysed in further detail. A detection algorithm identified potential seizure activity and a template matched filter was used to locate spikes. Seizure events were confirmed manually and classified as either clinically correlated, electroencephalographically identical but not clinically correlated, or subclinical. We found that spike rate was significantly altered prior to seizure in 9 out of 15 subjects. Increased pre-ictal spike rate was linked to improved predictability; however, spike rate was also shown to decrease before seizure (in 6 out of the 9 subjects). The probability distribution of spikes and seizures were notably similar, i.e. at times of high seizure likelihood the probability of epileptic spiking also increased. Both spikes and seizures showed clear evidence of circadian regulation and, for some subjects, there were also longer term patterns visible over weeks to months. Patterns of spike and seizure occurrence were highly subject-specific. The pre-ictal decrease in spike rate is not consistent with spikes promoting seizures. However, the fact that spikes and seizures demonstrate similar probability distributions suggests they are not wholly independent processes. It is possible spikes actively inhibit seizures, or that a decreased spike rate is a secondary symptom of the brain approaching seizure. If spike rate is modulated by common regulatory factors as seizures then spikes may be useful biomarkers of cortical excitability. PMID:26912639

  3. Evolving spiking networks with variable resistive memories.

    PubMed

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

    2014-01-01

    Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types. PMID:23614774

  4. Collision-spike sputtering of Au nanoparticles

    SciTech Connect

    Sandoval, Luis; Urbassek, Herbert M.

    2015-08-06

    Ion irradiation of nanoparticles leads to enhanced sputter yields if the nanoparticle size is of the order of the ion penetration depth. While this feature is reasonably well understood for collision-cascade sputtering, we explore it in the regime of collision-spike sputtering using molecular-dynamics simulation. For this specific case of 200-keV Xe bombardment of Au particles, we show that collision spikes lead to abundant sputtering with an average yield of 397 ± 121 atoms compared to only 116 ± 48 atoms for a bulk Au target. Only around 31% of the impact energy remains in the nanoparticles after impact; the remainder is transported away by the transmitted projectile and the ejecta. The sputter yield of supported nanoparticles is estimated to be around 80% of that of free nanoparticles due to the suppression of forward sputtering.

  5. Collision-spike sputtering of Au nanoparticles

    DOE PAGES

    Sandoval, Luis; Urbassek, Herbert M.

    2015-08-06

    Ion irradiation of nanoparticles leads to enhanced sputter yields if the nanoparticle size is of the order of the ion penetration depth. While this feature is reasonably well understood for collision-cascade sputtering, we explore it in the regime of collision-spike sputtering using molecular-dynamics simulation. For this specific case of 200-keV Xe bombardment of Au particles, we show that collision spikes lead to abundant sputtering with an average yield of 397 ± 121 atoms compared to only 116 ± 48 atoms for a bulk Au target. Only around 31% of the impact energy remains in the nanoparticles after impact; the remaindermore » is transported away by the transmitted projectile and the ejecta. The sputter yield of supported nanoparticles is estimated to be around 80% of that of free nanoparticles due to the suppression of forward sputtering.« less

  6. Collision-spike Sputtering of Au Nanoparticles.

    PubMed

    Sandoval, Luis; Urbassek, Herbert M

    2015-12-01

    Ion irradiation of nanoparticles leads to enhanced sputter yields if the nanoparticle size is of the order of the ion penetration depth. While this feature is reasonably well understood for collision-cascade sputtering, we explore it in the regime of collision-spike sputtering using molecular-dynamics simulation. For the particular case of 200-keV Xe bombardment of Au particles, we show that collision spikes lead to abundant sputtering with an average yield of 397 ± 121 atoms compared to only 116 ± 48 atoms for a bulk Au target. Only around 31 % of the impact energy remains in the nanoparticles after impact; the remainder is transported away by the transmitted projectile and the ejecta. The sputter yield of supported nanoparticles is estimated to be around 80 % of that of free nanoparticles due to the suppression of forward sputtering.

  7. Spikes and ribbon synapses in early vision.

    PubMed

    Baden, Tom; Euler, Thomas; Weckström, Matti; Lagnado, Leon

    2013-08-01

    Image processing begins in the retina, where neurons respond with graded voltage changes that must be converted into spikes. This conversion from 'analog' to 'digital' coding is a fundamental transformation carried out by the visual system, but the mechanisms are still not well understood. Recent work demonstrates that, in vertebrates, graded-to-spiking conversion of the visual signal begins in the axonal system of bipolar cells (BCs), which transmit visual information through ribbon-type synapses specialized for responding to graded voltage signals. Here, we explore the evidence for and against the idea that ribbon synapses also transmit digital information. We then discuss the potential costs and benefits of digitization at different stages of visual pathways in vertebrates and invertebrates. PMID:23706152

  8. Intra-spike crosslinking overcomes antibody evasion by HIV-1.

    PubMed

    Galimidi, Rachel P; Klein, Joshua S; Politzer, Maria S; Bai, Shiyu; Seaman, Michael S; Nussenzweig, Michel C; West, Anthony P; Bjorkman, Pamela J

    2015-01-29

    Antibodies developed during HIV-1 infection lose efficacy as the viral spike mutates. We postulated that anti-HIV-1 antibodies primarily bind monovalently because HIV's low spike density impedes bivalent binding through inter-spike crosslinking, and the spike structure prohibits bivalent binding through intra-spike crosslinking. Monovalent binding reduces avidity and potency, thus expanding the range of mutations permitting antibody evasion. To test this idea, we engineered antibody-based molecules capable of bivalent binding through intra-spike crosslinking. We used DNA as a "molecular ruler" to measure intra-epitope distances on virion-bound spikes and construct intra-spike crosslinking molecules. Optimal bivalent reagents exhibited up to 2.5 orders of magnitude increased potency (>100-fold average increases across virus panels) and identified conformational states of virion-bound spikes. The demonstration that intra-spike crosslinking lowers the concentration of antibodies required for neutralization supports the hypothesis that low spike densities facilitate antibody evasion and the use of molecules capable of intra-spike crosslinking for therapy or passive protection. PMID:25635457

  9. Spike-dip transformation of Setaria viridis.

    PubMed

    Saha, Prasenjit; Blumwald, Eduardo

    2016-04-01

    Traditional method of Agrobacterium-mediated transformation through the generation of tissue culture had limited success for Setaria viridis, an emerging C4 monocot model. Here we present an efficient in planta method for Agrobacterium-mediated genetic transformation of S. viridis using spike dip. Pre-anthesis developing spikes were dipped into a solution of Agrobacterium tumefaciens strain AGL1 harboring the β-glucuronidase (GUS) reporter gene driven by the cauliflower mosaic virus 35S (CaMV35S) promoter to standardize and optimize conditions for transient as well as stable transformations. A transformation efficiency of 0.8 ± 0.1% was obtained after dipping of 5-day-old S3 spikes for 20 min in Agrobacterium cultures containing S. viridis spike-dip medium supplemented with 0.025% Silwet L-77 and 200 μm acetosyringone. Reproducibility of this method was demonstrated by generating stable transgenic lines expressing β-glucuronidase plus (GUSplus), green fluorescent protein (GFP) and Discosoma sp. red fluorescent protein (DsRed) reporter genes driven by either CaMV35S or intron-interrupted maize ubiquitin (Ubi) promoters from three S. viridis genotypes. Expression of these reporter genes in transient assays as well as in T1 stable transformed plants was monitored using histochemical, fluorometric GUS activity and fluorescence microscopy. Molecular analysis of transgenic lines revealed stable integration of transgenes into the genome, and inherited transgenes expressed in the subsequent generations. This approach provides opportunities for the high-throughput transformation and potentially facilitates translational research in a monocot model plant. PMID:26932666

  10. Spike processing with a graphene excitable laser

    NASA Astrophysics Data System (ADS)

    Shastri, Bhavin J.; Nahmias, Mitchell A.; Tait, Alexander N.; Rodriguez, Alejandro W.; Wu, Ben; Prucnal, Paul R.

    2016-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.

  11. Spike processing with a graphene excitable laser

    PubMed Central

    Shastri, Bhavin J.; Nahmias, Mitchell A.; Tait, Alexander N.; Rodriguez, Alejandro W.; Wu, Ben; Prucnal, Paul R.

    2016-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms. PMID:26753897

  12. Pulvinar thalamic nucleus allows for asynchronous spike propagation through the cortex

    PubMed Central

    Cortes, Nelson; van Vreeswijk, Carl

    2015-01-01

    We create two multilayered feedforward networks composed of excitatory and inhibitory integrate-and-fire neurons in the balanced state to investigate the role of cortico-pulvino-cortical connections. The first network consists of ten feedforward levels where a Poisson spike train with varying firing rate is applied as an input in layer one. Although the balanced state partially avoids spike synchronization during the transmission, the average firing-rate in the last layer either decays or saturates depending on the feedforward pathway gain. The last layer activity is almost independent of the input even for a carefully chosen intermediate gain. Adding connections to the feedforward pathway by a nine areas Pulvinar structure improves the firing-rate propagation to become almost linear among layers. Incoming strong pulvinar spikes balance the low feedforward gain to have a unit input-output relation in the last layer. Pulvinar neurons evoke a bimodal activity depending on the magnitude input: synchronized spike bursts between 20 and 80 Hz and an asynchronous activity for very both low and high frequency inputs. In the first regime, spikes of last feedforward layer neurons are asynchronous with weak, low frequency, oscillations in the rate. Here, the uncorrelated incoming feedforward pathway washes out the synchronized thalamic bursts. In the second regime, spikes in the whole network are asynchronous. As the number of cortical layers increases, long-range pulvinar connections can link directly two or more cortical stages avoiding their either saturation or gradual activity falling. The Pulvinar acts as a shortcut that supplies the input-output firing-rate relationship of two separated cortical areas without changing the strength of connections in the feedforward pathway. PMID:26042026

  13. Biennial Survey of Education in the United States, 1930-1932. Bulletin, 1933, No. 2. Chapter IV: Statistics of Nurse-Training Schools, 1930-31

    ERIC Educational Resources Information Center

    Foster, Emery M.

    1933-01-01

    There are nearly twice as many nurses in training now in the United States as there were 11 years ago, according to reports made to the Office of Education by the nurse-training schools for the year 1930-31. More than 100,000 student nurses were reported enrolled by a total of 1,844 schools. The Office of Education sent inquiry blanks to a total…

  14. Explorations in statistics: statistical facets of reproducibility.

    PubMed

    Curran-Everett, Douglas

    2016-06-01

    Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eleventh installment of Explorations in Statistics explores statistical facets of reproducibility. If we obtain an experimental result that is scientifically meaningful and statistically unusual, we would like to know that our result reflects a general biological phenomenon that another researcher could reproduce if (s)he repeated our experiment. But more often than not, we may learn this researcher cannot replicate our result. The National Institutes of Health and the Federation of American Societies for Experimental Biology have created training modules and outlined strategies to help improve the reproducibility of research. These particular approaches are necessary, but they are not sufficient. The principles of hypothesis testing and estimation are inherent to the notion of reproducibility in science. If we want to improve the reproducibility of our research, then we need to rethink how we apply fundamental concepts of statistics to our science.

  15. Ideas for Effective Communication of Statistical Results

    DOE PAGES

    Anderson-Cook, Christine M.

    2015-03-01

    Effective presentation of statistical results to those with less statistical training, including managers and decision-makers requires planning, anticipation and thoughtful delivery. Here are several recommendations for effectively presenting statistical results.

  16. Branching Shoots and Spikes from Lateral Meristems in Bread Wheat

    PubMed Central

    Wang, Ying; Miao, Fang; Yan, Liuling

    2016-01-01

    Wheat grain yield consists of three components: spikes per plant, grains per spike (i.e. head or ear), and grain weight; and the grains per spike can be dissected into two subcomponents: spikelets per spike and grains per spikelet. An increase in any of these components will directly contribute to grain yield. Wheat morphology biology tells that a wheat plant has no lateral meristem that forms any branching shoot or spike. In this study, we report two novel shoot and spike traits that were produced from lateral meristems in bread wheat. One is supernumerary shoot that was developed from an axillary bud at the axil of leaves on the elongated internodes of the main stem. The other is supernumerary spike that was generated from a spikelet meristem on a spike. In addition, supernumerary spikelets were generated on the same rachis node of the spike in the plant that had supernumerary shoot and spikes. All of these supernumerary shoots/spikes/spikelets found in the super wheat plants produced normal fertility and seeds, displaying huge yield potential in bread wheat. PMID:26986738

  17. Statistics of a neuron model driven by asymmetric colored noise.

    PubMed

    Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

    2015-02-01

    Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

  18. Statistics of a neuron model driven by asymmetric colored noise

    NASA Astrophysics Data System (ADS)

    Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

    2015-02-01

    Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

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

    PubMed

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

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

  20. Millisecond solar radio spikes observed at 1420 MHz

    NASA Astrophysics Data System (ADS)

    Dabrowski, B. P.; Kus, A. J.

    We present results from observations of narrowband solar millisecond radio spikes at 1420 MHz. Observing data were collected between February 2000 and December 2001 with the 15-m radio telescope at the Centre for Astronomy Nicolaus Copernicus University in Torun, Poland, equipped with a radio spectrograph that covered the 1352-1490 MHz frequency band. The radio spectrograph has 3 MHz frequency resolution and 80 microsecond time resolution. We analyzed the individual radio spike duration, bandwidth and rate of frequency drift. A part of the observed spikes showed well-outlined subtle structures. On dynamic radio spectrograms of the investigated events we notice complex structures formed by numerous individual spikes known as chains of spikes and distinctly different structure of columns. Positions of active regions connected with radio spikes emission were investigated. It turns out that most of them are located near the center of the solar disk, suggesting strong beaming of the spikes emission.

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

    NASA Astrophysics Data System (ADS)

    Schaffer, J. David

    2015-06-01

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

  2. Spike-triggered dendritic calcium transients depend on synaptic activity in the cricket giant interneurons.

    PubMed

    Ogawa, Hiroto; Baba, Yoshichika; Oka, Kotaro

    2002-02-15

    The relationship between electrical activity and spike-induced Ca2+ increases in dendrites was investigated in the identified wind-sensitive giant interneurons in the cricket. We applied a high-speed Ca2+ imaging technique to the giant interneurons, and succeeded in recording the transient Ca2+ increases (Ca2+ transients) induced by a single action potential, which was evoked by presynaptic stimulus to the sensory neurons. The dendritic Ca2+ transients evoked by a pair of action potentials accumulated when spike intervals were shorter than 100 ms. The amplitude of the Ca2+ transients induced by a train of spikes depended on the number of action potentials. When stimulation pulses evoking the same numbers of action potentials were separately applied to the ipsi- or contra-lateral cercal sensory nerves, the dendritic Ca2+ transients induced by these presynaptic stimuli were different in their amplitude. Furthermore, the side of presynaptic stimulation that evoked larger Ca2+ transients depended on the location of the recorded dendritic regions. This result means that the spike-triggered Ca2+ transients in dendrites depend on postsynaptic activity. It is proposed that Ca2+ entry through voltage-dependent Ca2+ channels activated by the action potentials will be enhanced by excitatory synaptic inputs at the dendrites in the cricket giant interneurons.

  3. Spike-timing-dependent plasticity in spiking neuron networks for robot navigation control

    NASA Astrophysics Data System (ADS)

    Arena, Paolo; Danieli, Fabio; Fortuna, Luigi; Frasca, Mattia; Patane, Luca

    2005-06-01

    In this paper a biologically-inspired network of spiking neurons is used for robot navigation control. The implemented scheme is able to process information coming from the robot contact sensors in order to avoid obstacles and on the basis of these actions to learn how to respond to stimuli coming from range finder sensors. The implemented network is therefore able of reinforcement learning through a mechanism based on operant conditioning. This learning takes place according to a plasticity law in the synapses, based on spike timing. Simulation results discussed in the paper show the suitability of the approach and interesting adaptive properties of the network.

  4. Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

    PubMed Central

    Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

    2015-01-01

    Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

  5. Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

    PubMed

    Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

    2015-01-01

    Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

  6. Employers' Views on Hiring and Training. A Non-Statistical Approach to Data-Gathering. Third Annual Report, 1980-81.

    ERIC Educational Resources Information Center

    Lynton, Edith F.; Seldin, Joel R.

    This report consists primarily of employer views on hiring practices and training needs in 17 occupations in five occupational areas (office work, distribution, service, communications, and crafts). Described first are the activities of the Labor Market Information Network that resulted in the compilation of data presented in the report. The…

  7. Aspartame exacerbates EEG spike-wave discharge in children with generalized absence epilepsy: a double-blind controlled study.

    PubMed

    Camfield, P R; Camfield, C S; Dooley, J M; Gordon, K; Jollymore, S; Weaver, D F

    1992-05-01

    There are anecdotal reports of increased seizures in humans after ingestion of aspartame. We studied 10 children with newly diagnosed but untreated generalized absence seizures. Ambulatory cassette recording of EEG allowed quantification of numbers and length of spike-wave discharges in a double-blind study on two consecutive days. On one day the children received 40 mg/kg aspartame and on the other day, a sucrose-sweetened drink. Baseline EEG was the same before aspartame and sucrose. Following aspartame compared with sucrose, the number of spike-wave discharges per hour and mean length of spike-wave discharges increased but not to a statistically significant degree. However, the total duration of spike-wave discharge per hour was significantly increased after aspartame (p = 0.028), with a 40% +/- 17% (SEM) increase in the number of seconds per hour of EEG recording that the children spent in spike-wave discharge. Aspartame appears to exacerbate the amount of EEG spike wave in children with absence seizures. Further studies are needed to establish if this effect occurs at lower doses and in other seizure types.

  8. Cosmic statistics of statistics

    NASA Astrophysics Data System (ADS)

    Szapudi, István; Colombi, Stéphane; Bernardeau, Francis

    1999-12-01

    The errors on statistics measured in finite galaxy catalogues are exhaustively investigated. The theory of errors on factorial moments by Szapudi & Colombi is applied to cumulants via a series expansion method. All results are subsequently extended to the weakly non-linear regime. Together with previous investigations this yields an analytic theory of the errors for moments and connected moments of counts in cells from highly non-linear to weakly non-linear scales. For non-linear functions of unbiased estimators, such as the cumulants, the phenomenon of cosmic bias is identified and computed. Since it is subdued by the cosmic errors in the range of applicability of the theory, correction for it is inconsequential. In addition, the method of Colombi, Szapudi & Szalay concerning sampling effects is generalized, adapting the theory for inhomogeneous galaxy catalogues. While previous work focused on the variance only, the present article calculates the cross-correlations between moments and connected moments as well for a statistically complete description. The final analytic formulae representing the full theory are explicit but somewhat complicated. Therefore we have made available a fortran program capable of calculating the described quantities numerically (for further details e-mail SC at colombi@iap.fr). An important special case is the evaluation of the errors on the two-point correlation function, for which this should be more accurate than any method put forward previously. This tool will be immensely useful in the future for assessing the precision of measurements from existing catalogues, as well as aiding the design of new galaxy surveys. To illustrate the applicability of the results and to explore the numerical aspects of the theory qualitatively and quantitatively, the errors and cross-correlations are predicted under a wide range of assumptions for the future Sloan Digital Sky Survey. The principal results concerning the cumulants ξ, Q3 and Q4 is that

  9. Dynamic Finite Size Effects in Spiking Neural Networks

    PubMed Central

    Buice, Michael A.; Chow, Carson C.

    2013-01-01

    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. PMID:23359258

  10. Spatio-temporal filtering properties of a dendritic cable with active spines: a modeling study in the spike-diffuse-spike framework.

    PubMed

    Timofeeva, Yulia; Lord, Gabriel J; Coombes, Stephen

    2006-12-01

    The spike-diffuse-spike (SDS) model describes a passive dendritic tree with active dendritic spines. Spine-head dynamics is modeled with a simple integrate-and-fire process, whilst communication between spines is mediated by the cable equation. In this paper we develop a computational framework that allows the study of multiple spiking events in a network of such spines embedded on a simple one-dimensional cable. In the first instance this system is shown to support saltatory waves with the same qualitative features as those observed in a model with Hodgkin-Huxley kinetics in the spine-head. Moreover, there is excellent agreement with the analytically calculated speed for a solitary saltatory pulse. Upon driving the system with time-varying external input we find that the distribution of spines can play a crucial role in determining spatio-temporal filtering properties. In particular, the SDS model in response to periodic pulse train shows a positive correlation between spine density and low-pass temporal filtering that is consistent with the experimental results of Rose and Fortune [1999, 'Mechanisms for generating temporal filters in the electrosensory system,' The Journal of Experimental Biology 202: 1281-1289]. Further, we demonstrate the robustness of observed wave properties to natural sources of noise that arise both in the cable and the spine-head, and highlight the possibility of purely noise induced waves and coherent oscillations.

  11. Eliminating thermal violin spikes from LIGO noise

    SciTech Connect

    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.

  12. Communication through resonance in spiking neuronal networks.

    PubMed

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

    2014-08-01

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

  13. EMG spike time difference based feedback control.

    PubMed

    Butala, Jaydrath; Arkles, Anthony; Gray, John R

    2007-01-01

    Flight control in insects has been studied extensively; however the underlying neural mechanisms are not fully understood. Output from the central nervous system (CNS) must drive wing phase shifts and flight muscle depressor asymmetries associated with adaptive flight maneuvers. These maneuvers will, in turn, influence the insect's sensory environment, thus closing the feedback loop. We present a novel method that utilizes asymmetrical timing of bilateral depressor muscles, the forewing first basalars (m97), of the locust to close a visual feedback loop in a computer-generated flight simulator. The method converts the time difference between left and right m97s to analog voltage values. These voltage values can be obtained using open-loop experiments (visual motion controlled by the experimenter), or can be used to control closed-loop experiments (muscle activity controls the visual stimuli) experiments. Electromyographic (EMG) signals were obtained from right and left m97 muscles; spike time difference between them was calculated and converted to voltage values. Testing this circuit with real animals, we were able to detect the spike time difference and convert that to voltage that controlled the presentation of a stimulus in a closed-loop environment. This method may be used in conjunction with the flight simulator to understand the manner in which sensory information is integrated with the activity of the flight circuitry to study the neural control of this complex behaviour. PMID:18003414

  14. Conversion of phase information into a spike-count code by bursting neurons.

    PubMed

    Samengo, Inés; Montemurro, Marcelo A

    2010-03-12

    Single neurons in the cerebral cortex are immersed in a fluctuating electric field, the local field potential (LFP), which mainly originates from synchronous synaptic input into the local neural neighborhood. As shown by recent studies in visual and auditory cortices, the angular phase of the LFP at the time of spike generation adds significant extra information about the external world, beyond the one contained in the firing rate alone. However, no biologically plausible mechanism has yet been suggested that allows downstream neurons to infer the phase of the LFP at the soma of their pre-synaptic afferents. Therefore, so far there is no evidence that the nervous system can process phase information. Here we study a model of a bursting pyramidal neuron, driven by a time-dependent stimulus. We show that the number of spikes per burst varies systematically with the phase of the fluctuating input at the time of burst onset. The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics. Our results suggest that cortical bursting neurons could play a crucial role in translating LFP phase information into an easily decodable spike count code.

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

    PubMed

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

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons. PMID:26648863

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

    PubMed Central

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

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh–Rose neurons. PMID:26648863

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

    PubMed

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

    2012-07-11

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

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

    PubMed

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

    2012-07-11

    A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a 'soma' circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions. PMID:22710137

  20. Spiking synchronization of ion channel clusters on an axon

    NASA Astrophysics Data System (ADS)

    Zeng, Shangyou; Tang, Yi; Jung, Peter

    2007-07-01

    Ion channels are distributed in clusters in squid giant axons, rat retinal nerve fiber layers, pyramidal cell dendrites of Apteronotus, etc. Ion channel clusters along the unmyelinated axon generate spontaneous spiking due to ion channel noise. Ion channel clusters are coupled by the axonal cable, and spontaneous spiking of each ion channel cluster can be synchronized. This paper considers the spiking synchronization of two ion channel clusters coupled by an axon. It is shown that axonal parameters affect the spiking synchronization exponentially and ion channel clusters have maximal spiking synchronization when they have the same size. It is further shown that there is an optimal length of the ion channel clusters with maximal spiking synchronization and the optimal length accords with the length of the node of Ranvier in the myelinated axon.

  1. Propagating Neural Source Revealed by Doppler Shift of Population Spiking Frequency

    PubMed Central

    Zhang, Mingming; Shivacharan, Rajat S.; Chiang, Chia-Chu; Gonzalez-Reyes, Luis E.

    2016-01-01

    Electrical activity in the brain during normal and abnormal function is associated with propagating waves of various speeds and directions. It is unclear how both fast and slow traveling waves with sometime opposite directions can coexist in the same neural tissue. By recording population spikes simultaneously throughout the unfolded rodent hippocampus with a penetrating microelectrode array, we have shown that fast and slow waves are causally related, so a slowly moving neural source generates fast-propagating waves at ∼0.12 m/s. The source of the fast population spikes is limited in space and moving at ∼0.016 m/s based on both direct and Doppler measurements among 36 different spiking trains among eight different hippocampi. The fact that the source is itself moving can account for the surprising direction reversal of the wave. Therefore, these results indicate that a small neural focus can move and that this phenomenon could explain the apparent wave reflection at tissue edges or multiple foci observed at different locations in neural tissue. SIGNIFICANCE STATEMENT The use of novel techniques with an unfolded hippocampus and penetrating microelectrode array to record and analyze neural activity has revealed the existence of a source of neural signals that propagates throughout the hippocampus. The source itself is electrically silent, but its location can be inferred by building isochrone maps of population spikes that the source generates. The movement of the source can also be tracked by observing the Doppler frequency shift of these spikes. These results have general implications for how neural signals are generated and propagated in the hippocampus; moreover, they have important implications for the understanding of seizure generation and foci localization. PMID:27013678

  2. The Role of Spike Temporal Latencies in Artificial Olfaction

    NASA Astrophysics Data System (ADS)

    Polese, D.; Martinelli, E.; Dini, F.; Paolesse, R.; Filippini, D.; Lundström, I.; Di Natale, C.

    2011-09-01

    In this paper we investigate the recognition power of spike time latencies in an artificial olfactory system. For the scope we used a recently introduced platform for artificial olfaction implementing an artificial olfactory epithelium, formed by thousands sensors, and an abstract olfactory bulb1. Results show that correct volatile compounds classification can be achieved considering only the first two spikes of the neural network output evidencing that the latency of the first spikes contains actually enough information for odor identification.

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

    PubMed

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

  4. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

    PubMed Central

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

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

    PubMed

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

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

  6. Adaptive time-frequency parametrization of epileptic spikes

    NASA Astrophysics Data System (ADS)

    Durka, Piotr J.

    2004-05-01

    Adaptive time-frequency approximations of signals have proven to be a valuable tool in electroencephalogram (EEG) analysis and research, where it is believed that oscillatory phenomena play a crucial role in the brain’s information processing. This paper extends this paradigm to the nonoscillating structures such as the epileptic EEG spikes, and presents the advantages of their parametrization in general terms such as amplitude and half-width. A simple detector of epileptic spikes in the space of these parameters, tested on a limited data set, gives very promising results. It also provides a direct distinction between randomly occurring spikes or spike/wave complexes and rhythmic discharges.

  7. An 8-channel neural spike processing IC with unsupervised closed-loop control based on spiking probability estimation.

    PubMed

    Wu, Tong; Yang, Zhi

    2014-01-01

    This paper presents a neural spike processing IC for simultaneous spike detection, alignment, and transmission on 8 recording channels with unsupervised closed-loop control. In this work, spikes are detected according to online estimated spiking probability maps, which reliably predict the possibility of spike occurrence. The closed-loop control has been made possible by estimating firing rates based on alignment results and turning on/off channels individually and automatically. The 8-channel neural spike processing IC, implemented in a 0.13 μm CMOS process, has a varied power dissipation from 36 μW to 54.4 μW per channel at a voltage supply of 1.2 V. The chip also achieves a 380× data rate reduction for the testing in vivo data, allowing easy integration with wireless data transmission modules. PMID:25571180

  8. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.

    PubMed

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A; Przekwas, Andrzej; Francis, Joseph T; Lytton, William W

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  9. See globally, spike locally: oscillations in a retinal model encode large visual features.

    PubMed

    Stephens, Greg J; Neuenschwander, Sergio; George, John S; Singer, Wolf; Kenyon, Garrett T

    2006-10-01

    We show that coherent oscillations among neighboring ganglion cells in a retinal model encode global topological properties, such as size, that cannot be deduced unambiguously from their local, time-averaged firing rates. Whereas ganglion cells may fire similar numbers of spikes in response to both small and large spots, only large spots evoke coherent high frequency oscillations, potentially allowing downstream neurons to infer global stimulus properties from their local afferents. To determine whether such information might be extracted over physiologically realistic spatial and temporal scales, we analyzed artificial spike trains whose oscillatory correlations were similar to those measured experimentally. Oscillatory power in the upper gamma band, extracted on single-trials from multi-unit spike trains, supported good to excellent size discrimination between small and large spots, with performance improving as the number of cells and/or duration of the analysis window was increased. By using Poisson distributed spikes to normalize the firing rate across stimulus conditions, we further found that coincidence detection, or synchrony, yielded substantially poorer performance on identical size discrimination tasks. To determine whether size encoding depended on contiguity independent of object shape, we examined the total oscillatory activity across the entire model retina in response to random binary images. As the ON-pixel probability crossed the percolation threshold, which marks the sudden emergence of large connected clusters, the total gamma-band activity exhibited a sharp transition, a phenomena that may be experimentally observable. Finally, a reanalysis of previously published oscillatory responses from cat ganglion cells revealed size encoding consistent with that predicted by the retinal model. PMID:16897092

  10. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

    PubMed Central

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A.; Przekwas, Andrzej; Francis, Joseph T.; Lytton, William W.

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  11. High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor.

    PubMed

    Gong, Yiyang; Huang, Cheng; Li, Jin Zhong; Grewe, Benjamin F; Zhang, Yanping; Eismann, Stephan; Schnitzer, Mark J

    2015-12-11

    Genetically encoded voltage indicators (GEVIs) are a promising technology for fluorescence readout of millisecond-scale neuronal dynamics. Previous GEVIs had insufficient signaling speed and dynamic range to resolve action potentials in live animals. We coupled fast voltage-sensing domains from a rhodopsin protein to bright fluorophores through resonance energy transfer. The resulting GEVIs are sufficiently bright and fast to report neuronal action potentials and membrane voltage dynamics in awake mice and flies, resolving fast spike trains with 0.2-millisecond timing precision at spike detection error rates orders of magnitude better than previous GEVIs. In vivo imaging revealed sensory-evoked responses, including somatic spiking, dendritic dynamics, and intracellular voltage propagation. These results empower in vivo optical studies of neuronal electrophysiology and coding and motivate further advancements in high-speed microscopy. PMID:26586188

  12. High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor

    PubMed Central

    Gong, Yiyang; Huang, Cheng; Li, Jin Zhong; Grewe, Benjamin F.; Zhang, Yanping; Eismann, Stephan; Schnitzer, Mark J.

    2016-01-01

    Genetically encoded voltage indicators (GEVIs) are a promising technology for fluorescence readout of millisecond-scale neuronal dynamics. Previous GEVIs had insufficient signaling speed and dynamic range to resolve action potentials in live animals. We coupled fast voltage-sensing domains from a rhodopsin protein to bright fluorophores through resonance energy transfer. The resulting GEVIs are sufficiently bright and fast to report neuronal action potentials and membrane voltage dynamics in awake mice and flies, resolving fast spike trains with 0.2-millisecond timing precision at spike detection error rates orders of magnitude better than previous GEVIs. In vivo imaging revealed sensory-evoked responses, including somatic spiking, dendritic dynamics, and intracellular voltage propagation. These results empower in vivo optical studies of neuronal electrophysiology and coding and motivate further advancements in high-speed microscopy. PMID:26586188

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

    PubMed

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

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

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

    PubMed

    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. PMID:23768570

  15. Evolving unipolar memristor spiking neural networks

    NASA Astrophysics Data System (ADS)

    Howard, David; Bull, Larry; De Lacy Costello, Ben

    2015-10-01

    Neuromorphic computing - brain-like computing in hardware - typically requires myriad complimentary metal oxide semiconductor spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently cited as strong synapse candidates due to their statefulness and potential for low-power implementations. To date, plentiful research has focused on the bipolar memristor synapse, which is capable of incremental weight alterations and can provide adaptive self-organisation under a Hebbian learning scheme. In this paper, we consider the unipolar memristor synapse - a device capable of non-Hebbian switching between only two states (conductive and resistive) through application of a suitable input voltage - and discuss its suitability for neuromorphic systems. A self-adaptive evolutionary process is used to autonomously find highly fit network configurations. Experimentation on two robotics tasks shows that unipolar memristor networks evolve task-solving controllers faster than both bipolar memristor networks and networks containing constant non-plastic connections whilst performing at least comparably.

  16. Spike sorting for large, dense electrode arrays.

    PubMed

    Rossant, Cyrille; Kadir, Shabnam N; Goodman, Dan F M; Schulman, John; Hunter, Maximilian L D; Saleem, Aman B; Grosmark, Andres; Belluscio, Mariano; Denfield, George H; Ecker, Alexander S; Tolias, Andreas S; Solomon, Samuel; Buzsáki, György; Carandini, Matteo; Harris, Kenneth D

    2016-04-01

    Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%. PMID:26974951

  17. Spike sorting for large, dense electrode arrays

    PubMed Central

    Goodman, Dan F. M.; Schulman, John; Hunter, Maximilian L.D.; Saleem, Aman B.; Grosmark, Andres; Belluscio, Mariano; Denfield, George H.; Ecker, Alexander S.; Tolias, Andreas S.; Solomon, Samuel; Buzsaki, Gyorgy; Carandini, Matteo; Harris, Kenneth D.

    2016-01-01

    Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely-spaced recording sites, and electrodes with thousands of sites are currently under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons, from the raw data captured from the probes. Here, we present a set of novel tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus, and thalamus of rat, mouse, macaque, and marmoset, demonstrating error rates as low as 5%. PMID:26974951

  18. Characterizing spiking in noisy type II neurons.

    PubMed

    Boďová, Katarína; Paydarfar, David; Forger, Daniel B

    2015-01-21

    Understanding the dynamics of noisy neurons remains an important challenge in neuroscience. Here, we describe a simple probabilistic model that accurately describes the firing behavior in a large class (type II) of neurons. To demonstrate the usefulness of this model, we show how it accurately predicts the interspike interval (ISI) distributions, bursting patterns and mean firing rates found by: (1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental data from squid giant axon with a noisy input current and (3) experimental data on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple model has 6 parameters, however, in some cases, two of these parameters are coupled and only 5 parameters account for much of the known behavior. From these parameters, many properties of spiking can be found through simple calculation. Thus, we show how the complex effects of noise can be understood through a simple and general probabilistic model.

  19. High-resolution adaptive spiking sonar.

    PubMed

    Alvarez, Fernando J; Kuc, Roman

    2009-05-01

    A new sonar system based on the conventional 6500 ranging module is presented that generates a sequence of spikes whose temporal density is related to the strength of the received echo. This system notably improves the resolution of a previous system by shortening the discharge cycle of the integrator included in the module. The operation is controlled by a PIC18F452 device, which can adapt the duration of the discharge to changing features of the echo, providing the system with a novel adaptive behavior. The performance of the new sensor is characterized and compared with that of the previous system by performing rotational scans of simple objects with different reflecting strengths. Some applications are suggested that exploit the high resolution and adaptability of this sensor.

  20. Acoustic emission during quench training of superconducting accelerator magnets

    NASA Astrophysics Data System (ADS)

    Marchevsky, M.; Sabbi, G.; Bajas, H.; Gourlay, S.

    2015-07-01

    Acoustic emission (AE) sensing is a viable tool for superconducting magnet diagnostics. Using in-house developed cryogenic amplified piezoelectric sensors, we conducted AE studies during quench training of the US LARP's high-field quadrupole HQ02 and the LBNL's high-field dipole HD3. For both magnets, AE bursts were observed, with spike amplitude and frequency increasing toward the quench current during current up-ramps. In the HQ02, the AE onset upon current ramping is distinct and exhibits a clear memory of the previously-reached quench current (Kaiser effect). On the other hand, in the HD3 magnet the AE amplitude begins to increase well before the previously-reached quench current (felicity effect), suggesting an ongoing progressive mechanical motion in the coils. A clear difference in the AE signature exists between the untrained and trained mechanical states in HD3. Time intervals between the AE signals detected at the opposite ends of HD3 coils were processed using a combination of narrow-band pass filtering; threshold crossing and correlation algorithms, and the spatial distributions of AE sources and the mechanical energy release were calculated. Both distributions appear to be consistent with the quench location distribution. Energy statistics of the AE spikes exhibits a power-law scaling typical for the self-organized critical state.

  1. 20 CFR 634.4 - Statistical standards.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... Employees' Benefits EMPLOYMENT AND TRAINING ADMINISTRATION, DEPARTMENT OF LABOR LABOR MARKET INFORMATION PROGRAMS UNDER TITLE IV, PART E OF THE JOB TRAINING PARTNERSHIP ACT Comprehensive Labor Market Information... statistical standards prescribed by the Bureau of Labor Statistics for cooperative statistical programs....

  2. 20 CFR 634.4 - Statistical standards.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... Employees' Benefits EMPLOYMENT AND TRAINING ADMINISTRATION, DEPARTMENT OF LABOR LABOR MARKET INFORMATION PROGRAMS UNDER TITLE IV, PART E OF THE JOB TRAINING PARTNERSHIP ACT Comprehensive Labor Market Information... statistical standards prescribed by the Bureau of Labor Statistics for cooperative statistical programs....

  3. Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

    PubMed

    Zhou, Pengcheng; Burton, Shawn D; Snyder, Adam C; Smith, Matthew A; Urban, Nathaniel N; Kass, Robert E

    2015-10-01

    Pairs of active neurons frequently fire action potentials or "spikes" nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony. PMID:26465621

  4. Spike Avalanches Exhibit Universal Dynamics across the Sleep-Wake Cycle

    PubMed Central

    Ribeiro, Tiago L.; Copelli, Mauro; Caixeta, Fábio; Belchior, Hindiael; Chialvo, Dante R.; Nicolelis, Miguel A. L.; Ribeiro, Sidarta

    2010-01-01

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

  5. Ambiguity and nonidentifiability in the statistical analysis of neural codes

    PubMed Central

    Amarasingham, Asohan; Geman, Stuart; Harrison, Matthew T.

    2015-01-01

    Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature. PMID:25934918

  6. Reliability of directional information in unsorted spikes and local field potentials recorded in human motor cortex

    PubMed Central

    Perge, János A.; Zhang, Shaomin; Malik, Wasim Q.; Homer, Mark L.; Cash, Sydney; Friehs, Gerhard; Eskandar, Emad N.; Donoghue, John P.; Hochberg, Leigh R.

    2014-01-01

    Objective Action potentials and local field potentials (LFPs) recorded in primary motor cortex contain information about the direction of movement. LFPs are assumed to be more robust to signal instabilities than action potentials, which makes LFPs along with action potentials a promising signal source for brain-computer interface applications. Still, relatively little research has directly compared the utility of LFPs to action potentials in decoding movement direction in human motor cortex. Approach We conducted intracortical multielectrode recordings in motor cortex of two persons (T2 and [S3]) as they performed a motor imagery task. We then compared the offline decoding performance of LFPs and spiking extracted from the same data recorded across a one-year period in each participant. Main results We obtained offline prediction accuracy of movement direction and endpoint velocity in multiple LFP bands, with the best performance in the highest (200–400Hz) LFP frequency band, presumably also containing low-pass filtered action potentials. Cross-frequency correlations of preferred directions and directional modulation index showed high similarity of directional information between action potential firing rates (spiking) and high frequency LFPs (70–400Hz), and increasing disparity with lower frequency bands (0–7, 10–40 and 50–65Hz). Spikes predicted the direction of intended movement more accurately than any individual LFP band, however combined decoding of all LFPs was statistically indistinguishable from spike based performance. As the quality of spiking signals (i.e. signal amplitude) and the number of significantly modulated spiking units decreased, the offline decoding performance decreased 3.6[5.65]%/month (for T2 and [S3] respectively). The decrease in the number of significantly modulated LFP signals and their decoding accuracy followed a similar trend (2.4[2.85]%/month, ANCOVA, p=0.27[0.03]). Significance Field potentials provided comparable

  7. Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo

    PubMed Central

    Deneux, Thomas; Kaszas, Attila; Szalay, Gergely; Katona, Gergely; Lakner, Tamás; Grinvald, Amiram; Rózsa, Balázs; Vanzetta, Ivo

    2016-01-01

    Extracting neuronal spiking activity from large-scale two-photon recordings remains challenging, especially in mammals in vivo, where large noises often contaminate the signals. We propose a method, MLspike, which returns the most likely spike train underlying the measured calcium fluorescence. It relies on a physiological model including baseline fluctuations and distinct nonlinearities for synthetic and genetically encoded indicators. Model parameters can be either provided by the user or estimated from the data themselves. MLspike is computationally efficient thanks to its original discretization of probability representations; moreover, it can also return spike probabilities or samples. Benchmarked on extensive simulations and real data from seven different preparations, it outperformed state-of-the-art algorithms. Combined with the finding obtained from systematic data investigation (noise level, spiking rate and so on) that photonic noise is not necessarily the main limiting factor, our method allows spike extraction from large-scale recordings, as demonstrated on acousto-optical three-dimensional recordings of over 1,000 neurons in vivo. PMID:27432255

  8. An investigation of laboratory-grown ice spikes

    NASA Astrophysics Data System (ADS)

    Libbrecht, Kenneth G.; Lui, Kevin

    We have investigated the formation of 10-50 mm long ice spikes that sometimes appear on the free surface of water when it solidifies. By freezing water under different conditions, we measured the probability of ice-spike formation as a function of: (1) the air temperature in the freezing chamber, (2) air motion in the freezing chamber (which promotes evaporative cooling), (3) the quantity of dissolved salts in the water, and (4) the size, shape and composing material of the freezing vessel. We found that the probability of ice-spike formation is greatest when the air temperature is near -7°C, the water is pure and the air in the freezing chamber is moving. Even small quantities of dissolved solids greatly reduce the probability of ice-spike formation. Under optimal conditions, approximately half the ice cubes in an ordinary ice-cube tray will form ice spikes. Guided by these observations, we have examined the Bally-Dorsey model for the formation of ice spikes. In this model, the density change during solidification forces super-cooled water up through a hollow ice tube, where it freezes around the rim to lengthen the tube. We propose that any dissolved solids in the water will tend to concentrate at the tip of a growing ice spike and inhibit its growth. This can qualitatively explain the observation that ice spikes do not readily form using water containing even small quantities of dissolved solids.

  9. 27 CFR 21.130 - Spike lavender oil, natural.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Spike lavender oil, natural. 21.130 Section 21.130 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE... Denaturants § 21.130 Spike lavender oil, natural. (a) Alcohol content (as borneol). Not less than 30...

  10. A neural network model of reliably optimized spike transmission.

    PubMed

    Samura, Toshikazu; Ikegaya, Yuji; Sato, Yasuomi D

    2015-06-01

    We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.

  11. 27 CFR 21.130 - Spike lavender oil, natural.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2013-04-01 2013-04-01 false Spike lavender oil, natural. 21.130 Section 21.130 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE... Denaturants § 21.130 Spike lavender oil, natural. (a) Alcohol content (as borneol). Not less than 30...

  12. 27 CFR 21.130 - Spike lavender oil, natural.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2011-04-01 2011-04-01 false Spike lavender oil, natural. 21.130 Section 21.130 Alcohol, Tobacco Products and Firearms ALCOHOL AND TOBACCO TAX AND TRADE... Denaturants § 21.130 Spike lavender oil, natural. (a) Alcohol content (as borneol). Not less than 30...

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

    PubMed Central

    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

  14. Sharpness of Spike Initiation in Neurons Explained by Compartmentalization

    PubMed Central

    Brette, Romain

    2013-01-01

    In cortical neurons, spikes are initiated in the axon initial segment. Seen at the soma, they appear surprisingly sharp. A standard explanation is that the current coming from the axon becomes sharp as the spike is actively backpropagated to the soma. However, sharp initiation of spikes is also seen in the input–output properties of neurons, and not only in the somatic shape of spikes; for example, cortical neurons can transmit high frequency signals. An alternative hypothesis is that Na channels cooperate, but it is not currently supported by direct experimental evidence. I propose a simple explanation based on the compartmentalization of spike initiation. When Na channels are placed in the axon, the soma acts as a current sink for the Na current. I show that there is a critical distance to the soma above which an instability occurs, so that Na channels open abruptly rather than gradually as a function of somatic voltage. PMID:24339755

  15. Measurement of Training Outcomes.

    ERIC Educational Resources Information Center

    Bond, Nicholas A., Jr.; Rigney, Joseph W.

    Measurement of training outcomes is a requirement for evaluating new training techniques, but is one that is different to meet. Managers of education and training may have different concepts of what they want, as favorable outcomes, than do the investigators doing the research. Classical statistical and experimental designs assume laboratory rigor…

  16. SPIKE PENETRATION IN BLAST-WAVE-DRIVEN INSTABILITIES

    SciTech Connect

    Drake, R. P.

    2012-01-10

    The problem of interest is the unstable growth of structure at density transitions affected by blast waves, which arise in natural environments such as core-collapse supernovae and in laboratory experiments. The resulting spikes of dense material, which penetrate the less dense material, develop broadened tips, but the degree of broadening varies substantially across both experiments and simulations. The variable broadening presumably produces variations in the drag experienced by the spike tips as they penetrate the less dense material. The present work has used semianalytic theory to address the question of how the variation in drag might affect the spike penetration, for cases in which the post-shock interface deceleration can be described by a power law in a normalized time variable. It did so by following the evolution of structure on the interface through the initial shock passage, the subsequent small-amplitude phase of Rayleigh-Taylor instability growth, and the later phase in which the spike growth involves the competition of buoyancy and drag. In all phases, the expansion of the system during its evolution was accounted for and was important. The calculated spike length is strongly affected by the drag attributed to spike tip broadening. One finds from such a calculation that it is not unreasonable for narrow spikes to keep up with the shock front of the blast wave. The implication is that the accuracy of prediction of spike penetration and consequent structure by simulations very likely depends on how accurately they treat the broadening of the spike tips and the associated drag. Experimental validation of spike morphology in simulations would be useful.

  17. Optical recording of neuronal spiking activity from unbiased populations of neurons with high spike detection efficiency and high temporal precision.

    PubMed

    Ranganathan, Gayathri N; Koester, Helmut J

    2010-09-01

    Activity in populations of neurons is essential for cortical function including signaling of information and signal transport. Previous methods have made advances in recording activity from many neurons but have both technical and analytical limitations. Here we present an optical method, dithered random-access functional calcium imaging, to record somatic calcium signals from up to 100 neurons, in vitro and in vivo. We further developed a maximum-likelihood deconvolution algorithm to detect spikes and precise spike timings from the recorded calcium fluorescence signals. Spike detection efficiency and spike timing detection was determined in acute slices of juvenile mice. The results indicate that the combination of the two methods detected precise spiking activity from unbiased and spatially distributed populations of neurons in acute slices with high efficiency of spike detection (>97%), low rate of false positives (0.0023 spikes/s), and high temporal precision. The results further indicate that there is only a small window of excitation intensities where high spike detection can be achieved consistently.

  18. Ground Reaction Force Differences Between Running Shoes, Racing Flats, and Distance Spikes in Runners.

    PubMed Central

    Logan, Suzanna; Hunter, Ian; J. Ty Hopkins, J.T.; Feland, J. Brent; Parcell, Allen C.

    2010-01-01

    Various shoes are worn by distance runners throughout a training season. This study measured the differences in ground reaction forces between running shoes, racing flats, and distance spikes in order to provide information about the potential effects of footwear on injury risk in highly competitive runners. Ten male and ten female intercollegiate distance runners ran across a force plate at 6.7 m·s-1 (for males) and 5.7 m·s-1 (for females) in each of the three types of shoes. To control for differences in foot strike, only subjects who exhibited a heel strike were included in the data analysis. Two repeated-measures ANOVAs with Tukey’s post-hoc tests (p < 0.05) were used to detect differences in shoe types among males and females. For the males, loading rate, peak vertical impact force and peak braking forces were significantly greater in flats and spikes compared to running shoes. Vertical stiffness in spikes was also significantly greater than in running shoes. Females had significantly shorter stance times and greater maximum propulsion forces in racing flats compared to running shoes. Changing footwear between the shoes used in this study alters the loads placed on the body. Care should be taken as athletes enter different phases of training where different footwear is required. Injury risk may be increased since the body may not be accustomed to the differences in force, stance time, and vertical stiffness. Key points To determine the differences in ground reaction forces between regular running shoes and competitive footwear, force plate data was obtained from 10 males (6.7 m·s-1) and 10 females (5.7 m·s-1) for each of three shoe types. Data from men and women were analyzed in two separate groups, and significant differences were found for various GRF components between the three types of shoes. The significant increases in GRF components in competitive footwear suggest that the body must deal with greater impact forces in these shoes than in running

  19. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

    NASA Astrophysics Data System (ADS)

    Prezioso, M.; Merrikh Bayat, F.; Hoskins, B.; Likharev, K.; Strukov, D.

    2016-02-01

    Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses (“spikes”) in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor’s conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2-x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors.

  20. Spontaneous EEG spikes in the normal hippocampus. III. Relations to evoked potentials.

    PubMed

    Suzuki, S S; Smith, G K

    1988-06-01

    Spontaneous EEG spikes (SPKs) were recorded from the CA1 region of the dorsal hippocampus in normal rats during behavioral states not accompanied by rhythmical slow activity (RSA). SPKs were positive in stratum oriens, negative in stratum radiatum and accompanied by population bursts (PBs) in stratum pyramidale. In order to examine the origin of SPKs and PBs single pulse or brief high frequency electrical stimuli were applied to the Schaffer collateral/commissural pathway. Evoked potentials were recorded and compared with spontaneous SPKs and PBs. The results indicate the following: (1) the laminar amplitude profile of spontaneous SPKs was similar to that of population EPSPs evoked by stimulation of the Schaffer collateral/commissural pathway; (2) the population EPSP most similar to the spontaneous SPK was evoked by a brief (20-60 msec) train of high frequency (125-500 Hz) pulses; (3) the same pattern of stimulation was also found to be most efficient in evoking a series of multiple population spikes resembling a type of spontaneous PB (ripple). These observations suggest that SPKs and PBs in CA1 represent population EPSPs and multiple population spikes, respectively and that these CA1 events are triggered by brief, high frequency burst discharges of CA3 pyramidal cells via the Schaffer collateral and commissural pathway. PMID:2453331

  1. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

    PubMed Central

    Prezioso, M.; Merrikh Bayat, F.; Hoskins, B.; Likharev, K.; Strukov, D.

    2016-01-01

    Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses – the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses (“spikes”) in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor’s conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2−x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors. PMID:26893175

  2. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    PubMed

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

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

    PubMed

    Emadi-Koochak, Hamid; Yazdi, Farhad; Haji Abdolbaghi, Mahboubeh; Salehi, Mohammad Reza; Shadloo, Behrang; Rahimi-Movaghar, Afarin

    2016-05-01

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

  4. Recurrent Spiking Networks Solve Planning Tasks.

    PubMed

    Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

    2016-01-01

    A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high-reward state trajectories. A general class of reward-modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co-worker scenarios. PMID:26888174

  5. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    PubMed

    Harish, Omri; Hansel, David

    2015-07-01

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

  6. Memory recall and spike-frequency adaptation

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  7. Recurrent Spiking Networks Solve Planning Tasks

    NASA Astrophysics Data System (ADS)

    Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

    2016-02-01

    A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high-reward state trajectories. A general class of reward-modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co-worker scenarios.

  8. Primordial spikes from wrapped brane inflation

    SciTech Connect

    Kobayashi, Takeshi; Yokoyama, Jun'ichi E-mail: yokoyama@resceu.s.u-tokyo.ac.jp

    2013-02-01

    Cosmic inflation driven by branes wrapping the extra dimensions involves Kaluza-Klein (KK) degrees of freedom in addition to the zero-mode position of the brane which plays the role of the inflaton. As the wrapped brane passes by localized sources or features along its inflationary trajectory in the extra dimensional space, the KK modes along the wrapped direction are excited and start to oscillate during inflation. We show that the oscillating KK modes induce parametric resonance for the curvature perturbations, generating sharp signals in the perturbation spectrum. The effective four dimensional picture is a theory where the inflaton couples to the heavy KK modes. The Nambu-Goto action of the brane sources couplings between the inflaton kinetic terms and the KK modes, which trigger significant resonant amplification of the curvature perturbations. We find that the strong resonant effects are localized to narrow wave number ranges, producing spikes in the perturbation spectrum. Investigation of such resonant signals opens up the possibility of probing the extra dimensional space through cosmological observations.

  9. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    PubMed Central

    Harish, Omri; Hansel, David

    2015-01-01

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

  10. Recurrent Spiking Networks Solve Planning Tasks

    PubMed Central

    Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

    2016-01-01

    A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high-reward state trajectories. A general class of reward-modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co-worker scenarios. PMID:26888174

  11. Phase diagram of spiking neural networks

    PubMed Central

    Seyed-allaei, Hamed

    2015-01-01

    In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters – excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli. PMID:25788885

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

    SciTech Connect

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

    1990-12-31

    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.

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

    SciTech Connect

    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.

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

    PubMed

    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. PMID:25904712

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

    PubMed Central

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

    2015-01-01

    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. PMID:25904712

  16. Perineuronal Nets Enhance the Excitability of Fast-Spiking Neurons.

    PubMed

    Balmer, Timothy S

    2016-01-01

    Perineuronal nets (PNNs) are specialized complexes of extracellular matrix molecules that surround the somata of fast-spiking neurons throughout the vertebrate brain. PNNs are particularly prevalent throughout the auditory brainstem, which transmits signals with high speed and precision. It is unknown whether PNNs contribute to the fast-spiking ability of the neurons they surround. Whole-cell recordings were made from medial nucleus of the trapezoid body (MNTB) principal neurons in acute brain slices from postnatal day 21 (P21) to P27 mice. PNNs were degraded by incubating slices in chondroitinase ABC (ChABC) and were compared to slices that were treated with a control enzyme (penicillinase). ChABC treatment did not affect the ability of MNTB neurons to fire at up to 1000 Hz when driven by current pulses. However, f-I (frequency-intensity) curves constructed by injecting Gaussian white noise currents superimposed on DC current steps showed that ChABC treatment reduced the gain of spike output. An increase in spike threshold may have contributed to this effect, which is consistent with the observation that spikes in ChABC-treated cells were delayed relative to control-treated cells. In addition, parvalbumin-expressing fast-spiking cortical neurons in >P70 slices that were treated with ChABC also had reduced excitability and gain. The development of PNNs around somata of fast-spiking neurons may be essential for fast and precise sensory transmission and synaptic inhibition in the brain. PMID:27570824

  17. Perineuronal Nets Enhance the Excitability of Fast-Spiking Neurons

    PubMed Central

    2016-01-01

    Perineuronal nets (PNNs) are specialized complexes of extracellular matrix molecules that surround the somata of fast-spiking neurons throughout the vertebrate brain. PNNs are particularly prevalent throughout the auditory brainstem, which transmits signals with high speed and precision. It is unknown whether PNNs contribute to the fast-spiking ability of the neurons they surround. Whole-cell recordings were made from medial nucleus of the trapezoid body (MNTB) principal neurons in acute brain slices from postnatal day 21 (P21) to P27 mice. PNNs were degraded by incubating slices in chondroitinase ABC (ChABC) and were compared to slices that were treated with a control enzyme (penicillinase). ChABC treatment did not affect the ability of MNTB neurons to fire at up to 1000 Hz when driven by current pulses. However, f–I (frequency–intensity) curves constructed by injecting Gaussian white noise currents superimposed on DC current steps showed that ChABC treatment reduced the gain of spike output. An increase in spike threshold may have contributed to this effect, which is consistent with the observation that spikes in ChABC-treated cells were delayed relative to control-treated cells. In addition, parvalbumin-expressing fast-spiking cortical neurons in >P70 slices that were treated with ChABC also had reduced excitability and gain. The development of PNNs around somata of fast-spiking neurons may be essential for fast and precise sensory transmission and synaptic inhibition in the brain. PMID:27570824

  18. Spike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study

    PubMed Central

    Sundt, Danielle; Gamper, Nikita

    2015-01-01

    Unmyelinated C-fibers are a major type of sensory neurons conveying pain information. Action potential conduction is regulated by the bifurcation (T-junction) of sensory neuron axons within the dorsal root ganglia (DRG). Understanding how C-fiber signaling is influenced by the morphology of the T-junction and the local expression of ion channels is important for understanding pain signaling. In this study we used biophysical computer modeling to investigate the influence of axon morphology within the DRG and various membrane conductances on the reliability of spike propagation. As expected, calculated input impedance and the amplitude of propagating action potentials were both lowest at the T-junction. Propagation reliability for single spikes was highly sensitive to the diameter of the stem axon and the density of voltage-gated Na+ channels. A model containing only fast voltage-gated Na+ and delayed-rectifier K+ channels conducted trains of spikes up to frequencies of 110 Hz. The addition of slowly activating KCNQ channels (i.e., KV7 or M-channels) to the model reduced the following frequency to 30 Hz. Hyperpolarization produced by addition of a much slower conductance, such as a Ca2+-dependent K+ current, was needed to reduce the following frequency to 6 Hz. Attenuation of driving force due to ion accumulation or hyperpolarization produced by a Na+-K+ pump had no effect on following frequency but could influence the reliability of spike propagation mutually with the voltage shift generated by a Ca2+-dependent K+ current. These simulations suggest how specific ion channels within the DRG may contribute toward therapeutic treatments for chronic pain. PMID:26334005

  19. Circuit mechanisms revealed by spike-timing correlations in macaque area MT.

    PubMed

    Huang, Xin; Lisberger, Stephen G

    2013-02-01

    We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative flanks were most prevalent and deepest when the neurons were driven to high rates by visual stimuli that moved in the neurons' preferred directions. The temporal development of the negative flanks in the CCG coincided with a parallel, modest reduction of the noise correlation between the spike counts of the neurons. Computational analysis of a model cortical circuit suggested that negative flanks in the CCG arise from the excitation-triggered mutual cross-inhibition between pairs of excitatory neurons. Intracortical recurrent inhibition and afterhyperpolarization caused by intrinsic outward currents, such as the calcium-activated potassium current of small conductance, can both contribute to the negative flanks in the ACG. In the model circuit, stronger intracortical inhibition helped to maintain the temporal precision between the spike trains of pairs of neurons and led to weaker noise correlations. Our results suggest a neural circuit architecture that can leverage activity-dependent intracortical inhibition to adaptively modulate both the synchrony of spike timing and the correlations in response variability.

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

    PubMed

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

    2015-01-01

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

  1. Does arousal interfere with operant conditioning of spike-wave discharges in genetic epileptic rats?

    PubMed

    Osterhagen, Lasse; Breteler, Marinus; van Luijtelaar, Gilles

    2010-06-01

    One of the ways in which brain computer interfaces can be used is neurofeedback (NF). Subjects use their brain activation to control an external device, and with this technique it is also possible to learn to control aspects of the brain activity by operant conditioning. Beneficial effects of NF training on seizure occurrence have been described in epileptic patients. Little research has been done about differentiating NF effectiveness by type of epilepsy, particularly, whether idiopathic generalized seizures are susceptible to NF. In this experiment, seizures that manifest themselves as spike-wave discharges (SWDs) in the EEG were reinforced during 10 sessions in 6 rats of the WAG/Rij strain, an animal model for absence epilepsy. EEG's were recorded before and after the training sessions. Reinforcing SWDs let to decreased SWD occurrences during training; however, the changes during training were not persistent in the post-training sessions. Because behavioural states are known to have an influence on the occurrence of SWDs, it is proposed that the reinforcement situation increased arousal which resulted in fewer SWDs. Additional tests supported this hypothesis. The outcomes have implications for the possibility to train SWDs with operant learning techniques.

  2. Morbidity statistics

    PubMed Central

    Smith, Alwyn

    1969-01-01

    This paper is based on an analysis of questionnaires sent to the health ministries of Member States of WHO asking for information about the extent, nature, and scope of morbidity statistical information. It is clear that most countries collect some statistics of morbidity and many countries collect extensive data. However, few countries relate their collection to the needs of health administrators for information, and many countries collect statistics principally for publication in annual volumes which may appear anything up to 3 years after the year to which they refer. The desiderata of morbidity statistics may be summarized as reliability, representativeness, and relevance to current health problems. PMID:5306722

  3. Establishing a Statistical Link between Network Oscillations and Neural Synchrony

    PubMed Central

    Zhou, Pengcheng; Burton, Shawn D.; Snyder, Adam C.; Smith, Matthew A.; Urban, Nathaniel N.; Kass, Robert E.

    2015-01-01

    Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony. PMID:26465621

  4. Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain

    PubMed Central

    Brette, Romain

    2015-01-01

    Does the brain use a firing rate code or a spike timing code? Considering this controversial question from an epistemological perspective, I argue that progress has been hampered by its problematic phrasing. It takes the perspective of an external observer looking at whether those two observables vary with stimuli, and thereby misses the relevant question: which one has a causal role in neural activity? When rephrased in a more meaningful way, the rate-based view appears as an ad hoc methodological postulate, one that is practical but with virtually no empirical or theoretical support. PMID:26617496

  5. Asynchronous spiking photonic neuron for lightwave neuromorphic signal processing.

    PubMed

    Fok, Mable P; Tian, Yue; Rosenbluth, David; Prucnal, Paul R

    2012-08-15

    We developed an asynchronous spiking photonic neuron that forms the basic building block for hybrid analog/digital lightwave neuromorphic processing. Our approach enables completely asynchronous spiking in response to input signals while maximizing the throughput relative to synchronous approaches. Asynchronous operation is achieved by generating the spike source for the photonic neuron through four-wave mixing. This hybrid analog/digital photonic neuron has an electro-absorption modulator as the temporal integration unit for analog processing, while the digital processing portion employs optical thresholding in a highly Ge-doped nonlinear loop mirror.

  6. Integrated workflows for spiking neuronal network simulations

    PubMed Central

    Antolík, Ján; Davison, Andrew P.

    2013-01-01

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

  7. Integrated workflows for spiking neuronal network simulations.

    PubMed

    Antolík, Ján; Davison, Andrew P

    2013-01-01

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

  8. A new EC-PC threshold estimation method for in vivo neural spike detection

    NASA Astrophysics Data System (ADS)

    Yang, Zhi; Liu, Wentai; Keshtkaran, Mohammad Reza; Zhou, Yin; Xu, Jian; Pikov, Victor; Guan, Cuntai; Lian, Yong

    2012-08-01

    This paper models in vivo neural signals and noise for extracellular spike detection. Although the recorded data approximately follow Gaussian distribution, they clearly deviate from white Gaussian noise due to neuronal synchronization and sparse distribution of spike energy. Our study predicts the coexistence of two components embedded in neural data dynamics, one in the exponential form (noise) and the other in the power form (neural spikes). The prediction of the two components has been confirmed in experiments of in vivo sequences recorded from the hippocampus, cortex surface, and spinal cord; both acute and long-term recordings; and sleep and awake states. These two components are further used as references for threshold estimation. Different from the conventional wisdom of setting a threshold at 3×RMS, the estimated threshold exhibits a significant variation. When our algorithm was tested on synthesized sequences with a different signal to noise ratio and on/off firing dynamics, inferred threshold statistics track the benchmarks well. We envision that this work may be applied to a wide range of experiments as a front-end data analysis tool.

  9. Determinants of spikes in ultrafine particle concentration whilst commuting by bus

    NASA Astrophysics Data System (ADS)

    Lim, Shanon; Dirks, Kim N.; Salmond, Jennifer A.; Xie, Shanju

    2015-07-01

    This paper examines concentration of ultrafine particles (UFPs) based on data collected using high-resolution UFP monitors whilst travelling by bus during rush hour along three different urban routes in Auckland, New Zealand. The factors influencing in-bus UFP concentration were assessed using a combination of spatial, statistical and GIS analysis techniques to determine both spatial and temporal variability. Results from 68 bus trips showed that concentrations varied more within a route than between on a given day, despite differences in urban morphology, land use and traffic densities between routes. A number of trips were characterised by periods of very rapid increases in UFPs (concentration 'spikes'), followed by slow declines. Trips which recorded at least one spike (an increase of greater than 10,000 pt/cm3) resulted in significantly higher mean concentrations. Spikes in UFPs were significantly more likely to occur when travelling at low speeds and when passengers were alighting and boarding at bus stops close to traffic light intersections.

  10. Statistical Diversions

    ERIC Educational Resources Information Center

    Petocz, Peter; Sowey, Eric

    2012-01-01

    The term "data snooping" refers to the practice of choosing which statistical analyses to apply to a set of data after having first looked at those data. Data snooping contradicts a fundamental precept of applied statistics, that the scheme of analysis is to be planned in advance. In this column, the authors shall elucidate the statistical…

  11. Fitting FFT-derived spectra: Theory, tool, and application to solar radio spike decomposition

    SciTech Connect

    Nita, Gelu M.; Fleishman, Gregory D.; Gary, Dale E.; Marin, William; Boone, Kristine

    2014-07-10

    Spectra derived from fast Fourier transform (FFT) analysis of time-domain data intrinsically contain statistical fluctuations whose distribution depends on the number of accumulated spectra contributing to a measurement. The tail of this distribution, which is essential for separating the true signal from the statistical fluctuations, deviates noticeably from the normal distribution for a finite number of accumulations. In this paper, we develop a theory to properly account for the statistical fluctuations when fitting a model to a given accumulated spectrum. The method is implemented in software for the purpose of automatically fitting a large body of such FFT-derived spectra. We apply this tool to analyze a portion of a dense cluster of spikes recorded by our FASR Subsystem Testbed instrument during a record-breaking event that occurred on 2006 December 6. The outcome of this analysis is briefly discussed.

  12. Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Liao, Yuxi; She, Xiwei; Wang, Yiwen; Zhang, Shaomin; Zhang, Qiaosheng; Zheng, Xiaoxiang; Principe, Jose C.

    2015-12-01

    Objective. Representation of movement in the motor cortex (M1) has been widely studied in brain-machine interfaces (BMIs). The electromyogram (EMG) has greater bandwidth than the conventional kinematic variables (such as position, velocity), and is functionally related to the discharge of cortical neurons. As the stochastic information of EMG is derived from the explicit spike time structure, point process (PP) methods will be a good solution for decoding EMG directly from neural spike trains. Previous studies usually assume linear or exponential tuning curves between neural firing and EMG, which may not be true. Approach. In our analysis, we estimate the tuning curves in a data-driven way and find both the traditional functional-excitatory and functional-inhibitory neurons, which are widely found across a rat’s motor cortex. To accurately decode EMG envelopes from M1 neural spike trains, the Monte Carlo point process (MCPP) method is implemented based on such nonlinear tuning properties. Main results. Better reconstruction of EMG signals is shown on baseline and extreme high peaks, as our method can better preserve the nonlinearity of the neural tuning during decoding. The MCPP improves the prediction accuracy (the normalized mean squared error) 57% and 66% on average compared with the adaptive point process filter using linear and exponential tuning curves respectively, for all 112 data segments across six rats. Compared to a Wiener filter using spike rates with an optimal window size of 50 ms, MCPP decoding EMG from a point process improves the normalized mean square error (NMSE) by 59% on average. Significance. These results suggest that neural tuning is constantly changing during task execution and therefore, the use of spike timing methodologies and estimation of appropriate tuning curves needs to be undertaken for better EMG decoding in motor BMIs.

  13. Viterbi sparse spike detection and a compositional origin to ultralow-velocity zones

    NASA Astrophysics Data System (ADS)

    Brown, Samuel Paul

    Accurate interpretation of seismic travel times and amplitudes in both the exploration and global scales is complicated by the band-limited nature of seismic data. We present a stochastic method, Viterbi sparse spike detection (VSSD), to reduce a seismic waveform into a most probable constituent spike train. Model waveforms are constructed from a set of candidate spike trains convolved with a source wavelet estimate. For each model waveform, a profile hidden Markov model (HMM) is constructed to represent the waveform as a stochastic generative model with a linear topology corresponding to a sequence of samples. The Viterbi algorithm is employed to simultaneously find the optimal nonlinear alignment between a model waveform and the seismic data, and to assign a score to each candidate spike train. The most probable travel times and amplitudes are inferred from the alignments of the highest scoring models. Our analyses show that the method can resolve closely spaced arrivals below traditional resolution limits and that travel time estimates are robust in the presence of random noise and source wavelet errors. We applied the VSSD method to constrain the elastic properties of a ultralow- velocity zone (ULVZ) at the core-mantle boundary beneath the Coral Sea. We analyzed vertical component short period ScP waveforms for 16 earthquakes occurring in the Tonga-Fiji trench recorded at the Alice Springs Array (ASAR) in central Australia. These waveforms show strong pre and postcursory seismic arrivals consistent with ULVZ layering. We used the VSSD method to measure differential travel-times and amplitudes of the post-cursor arrival ScSP and the precursor arrival SPcP relative to ScP. We compare our measurements to a database of approximately 340,000 synthetic seismograms finding that these data are best fit by a ULVZ model with an S-wave velocity reduction of 24%, a P-wave velocity reduction of 23%, a thickness of 8.5 km, and a density increase of 6%. We simultaneously

  14. Spectrotemporal processing differences between auditory cortical fast-spiking and regular-spiking neurons

    PubMed Central

    Atencio, Craig A.; Schreiner, Christoph E.

    2008-01-01

    Excitatory pyramidal neurons and inhibitory interneurons constitute the main elements of cortical circuitry and have distinctive morphologic and electrophysiological properties. Here, we differentiate them by analyzing the time course of their action potentials (APs) and characterizing their receptive field properties in auditory cortex. Pyramidal neurons have longer APs and discharge as Regular-Spiking Units (RSUs), while basket and chandelier cells, which are inhibitory interneurons, have shorter APs and are Fast-Spiking Units (FSUs). To compare these neuronal classes we stimulated cat primary auditory cortex neurons with a dynamic moving ripple stimulus and constructed single-unit spectrotemporal receptive fields (STRFs) and their associated nonlinearities. FSUs had shorter latencies, broader spectral tuning, greater stimulus specificity, and higher temporal precision than RSUs. The STRF structure of FSUs was more separable, suggesting more independence between spectral and temporal processing regimes. The nonlinearities associated with the two cell classes was indicative of higher feature selectivity for FSUs. These global functional differences between RSUs and FSUs suggest fundamental distinctions between putative excitatory and inhibitory neurons that shape auditory cortical processing. PMID:18400888

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

    PubMed

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

    2006-07-01

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

  16. Adaptive gain control for spike-based map communication in a neuromorphic vision system.

    PubMed

    Meng, Yicong; Shi, Bertram E

    2008-06-01

    To support large numbers of model neurons, neuromorphic vision systems are increasingly adopting a distributed architecture, where different arrays of neurons are located on different chips or processors. Spike-based protocols are used to communicate activity between processors. The spike activity in the arrays depends on the input statistics as well as internal parameters such as time constants and gains. In this paper, we investigate strategies for automatically adapting these parameters to maintain a constant firing rate in response to changes in the input statistics. We find that under the constraint of maintaining a fixed firing rate, a strategy based upon updating the gain alone performs as well as an optimal strategy where both the gain and the time constant are allowed to vary. We discuss how to choose the time constant and propose an adaptive gain control mechanism whose operation is robust to changes in the input statistics. Our experimental results on a mobile robotic platform validate the analysis and efficacy of the proposed strategy.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed

    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.

  19. Learning real-world stimuli in a neural network with spike-driven synaptic dynamics.

    PubMed

    Brader, Joseph M; Senn, Walter; Fusi, Stefano

    2007-11-01

    We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons). PMID:17883345

  20. A new method to infer higher-order spike correlations from membrane potentials.

    PubMed

    Reimer, Imke C G; Staude, Benjamin; Boucsein, Clemens; Rotter, Stefan

    2013-10-01

    What is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete. Moreover, this new approach truly emphasizes the functional aspects of higher-order statistics, since we infer exactly those correlations which are seen by a neuron. Our approach is to represent the subthreshold membrane potential fluctuations as presynaptic activity filtered with a fixed kernel, as it would be the case for a leaky integrator neuron model. This allows us to adapt the recently proposed method CuBIC (cumulant based inference of higher-order correlations from the population spike count; Staude et al., J Comput Neurosci 29(1-2):327-350, 2010c) with which the maximal order of correlation can be inferred. By numerical simulation we show that our new method is reasonably sensitive to weak higher-order correlations, and that only short stretches of membrane potential are required for their reliable inference. Finally, we demonstrate its remarkable robustness against violations of the simplifying assumptions made for its construction, and discuss how it can be employed to analyze in vivo intracellular recordings of membrane potentials.

  1. Benchmarking Spike Rate Inference in Population Calcium Imaging.

    PubMed

    Theis, Lucas; Berens, Philipp; Froudarakis, Emmanouil; Reimer, Jacob; Román Rosón, Miroslav; Baden, Tom; Euler, Thomas; Tolias, Andreas S; Bethge, Matthias

    2016-05-01

    A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience. PMID:27151639

  2. Benchmarking Spike Rate Inference in Population Calcium Imaging.

    PubMed

    Theis, Lucas; Berens, Philipp; Froudarakis, Emmanouil; Reimer, Jacob; Román Rosón, Miroslav; Baden, Tom; Euler, Thomas; Tolias, Andreas S; Bethge, Matthias

    2016-05-01

    A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.

  3. Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity

    PubMed Central

    Osogami, Takayuki; Otsuka, Makoto

    2015-01-01

    An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image “SCIENCE” and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule. PMID:26374672

  4. Noise-assisted spike propagation in myelinated neurons

    NASA Astrophysics Data System (ADS)

    Ochab-Marcinek, Anna; Schmid, Gerhard; Goychuk, Igor; Hänggi, Peter

    2009-01-01

    We consider noise-assisted spike propagation in myelinated axons within a multicompartment stochastic Hodgkin-Huxley model. The noise originates from a finite number of ion channels in each node of Ranvier. For the subthreshold internodal electric coupling, we show that (i) intrinsic noise removes the sharply defined threshold for spike propagation from node to node and (ii) there exists an optimum number of ion channels which allows for the most efficient signal propagation and it corresponds to the actual physiological values.

  5. Statistics Clinic

    NASA Technical Reports Server (NTRS)

    Feiveson, Alan H.; Foy, Millennia; Ploutz-Snyder, Robert; Fiedler, James

    2014-01-01

    Do you have elevated p-values? Is the data analysis process getting you down? Do you experience anxiety when you need to respond to criticism of statistical methods in your manuscript? You may be suffering from Insufficient Statistical Support Syndrome (ISSS). For symptomatic relief of ISSS, come for a free consultation with JSC biostatisticians at our help desk during the poster sessions at the HRP Investigators Workshop. Get answers to common questions about sample size, missing data, multiple testing, when to trust the results of your analyses and more. Side effects may include sudden loss of statistics anxiety, improved interpretation of your data, and increased confidence in your results.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

  8. Reduction of spike frequency adaptation and blockade of M-current in rat CA1 pyramidal neurones by linopirdine (DuP 996), a neurotransmitter release enhancer.

    PubMed Central

    Aiken, S. P.; Lampe, B. J.; Murphy, P. A.; Brown, B. S.

    1995-01-01

    1. Linopirdine (DuP 996) has been shown to enhance depolarization-induced release of several neurotransmitters in the CNS through a mechanism which may involve K+ channel blockade. The electrophysiological effects of linopirdine were therefore investigated directly, by use of conventional voltage recording and single electrode voltage-clamp. 2. Linopirdine (10 microM) reduced spike frequency adaptation (SFA) in rat hippocampal CA1 pyramidal neurones in vitro. The reduction of SFA comprised an increase in number of spikes and a reduction in inter-spike intervals after the first, but with no effect on time to first spike. Linopirdine also caused a voltage-dependent depolarization of resting membrane potential (RMP). 3. M-current (IM), a current known to underlie SFA and to set RMP, was blocked by linopirdine in a reversible, concentration-dependent manner (IC50 = 8.5 microM). This block was not reversed by atropine (10 microM). 4. Linopirdine did not affect IQ, the slow after-hyperpolarization following a spike train, or spike duration. 5. Linopirdine may represent a novel class of K+ blocker with relative selectivity for the M-current. This block of IM is consistent with the suggestion from a previous study that linopirdine may affect a tetraethylammonium-sensitive channel, and it could be speculated that IM blockade may be involved with the enhancement of neurotransmitter release by linopirdine. PMID:7582539

  9. SEER Statistics

    Cancer.gov

    The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute works to provide information on cancer statistics in an effort to reduce the burden of cancer among the U.S. population.

  10. Cancer Statistics

    MedlinePlus

    ... cancer statistics across the world. U.S. Cancer Mortality Trends The best indicator of progress against cancer is ... the number of cancer survivors has increased. These trends show that progress is being made against the ...

  11. Statistical Physics

    NASA Astrophysics Data System (ADS)

    Hermann, Claudine

    Statistical Physics bridges the properties of a macroscopic system and the microscopic behavior of its constituting particles, otherwise impossible due to the giant magnitude of Avogadro's number. Numerous systems of today's key technologies - such as semiconductors or lasers - are macroscopic quantum objects; only statistical physics allows for understanding their fundamentals. Therefore, this graduate text also focuses on particular applications such as the properties of electrons in solids with applications, and radiation thermodynamics and the greenhouse effect.

  12. Enhanced high-frequency membrane potential fluctuations control spike output in striatal fast-spiking interneurones in vivo

    PubMed Central

    Schulz, Jan M; Pitcher, Toni L; Savanthrapadian, Shakuntala; Wickens, Jeffery R; Oswald, Manfred J; Reynolds, John N J

    2011-01-01

    Abstract Fast-spiking interneurones (FSIs) constitute a prominent part of the inhibitory microcircuitry of the striatum; however, little is known about their recruitment by synaptic inputs in vivo. Here, we report that, in contrast to cholinergic interneurones (CINs), FSIs (n = 9) recorded in urethane-anaesthetized rats exhibit Down-to-Up state transitions very similar to spiny projection neurones (SPNs). Compared to SPNs, the FSI Up state membrane potential was noisier and power spectra exhibited significantly larger power at frequencies in the gamma range (55–95 Hz). The membrane potential exhibited short and steep trajectories preceding spontaneous spike discharge, suggesting that fast input components controlled spike output in FSIs. Spontaneous spike data contained a high proportion (43.6 ± 32.8%) of small inter-spike intervals (ISIs) of <30 ms, setting FSIs clearly apart from SPNs and CINs. Cortical-evoked inputs had slower dynamics in SPNs than FSIs, and repetitive stimulation entrained SPN spike output only if the stimulation was delivered at an intermediate frequency (20 Hz), but not at a high frequency (100 Hz). Pharmacological induction of an activated ECoG state, known to promote rapid FSI spiking, mildly increased the power (by 43 ± 55%, n = 13) at gamma frequencies in the membrane potential of SPNs, but resulted in few small ISIs (<30 ms; 4.3 ± 6.4%, n = 8). The gamma frequency content did not change in CINs (n = 8). These results indicate that FSIs are uniquely responsive to high-frequency input sequences. By controlling the spike output of SPNs, FSIs could serve gating of top-down signals and long-range synchronisation of gamma-oscillations during behaviour. PMID:21746788

  13. Cell Type-Specific Differences in Spike Timing and Spike Shape in the Rat Parasubiculum and Superficial Medial Entorhinal Cortex.

    PubMed

    Ebbesen, Christian Laut; Reifenstein, Eric Torsten; Tang, Qiusong; Burgalossi, Andrea; Ray, Saikat; Schreiber, Susanne; Kempter, Richard; Brecht, Michael

    2016-07-26

    The medial entorhinal cortex (MEC) and the adjacent parasubiculum are known for their elaborate spatial discharges (grid cells, border cells, etc.) and the precessing of spikes relative to the local field potential. We know little, however, about how spatio-temporal firing patterns map onto cell types. We find that cell type is a major determinant of spatio-temporal discharge properties. Parasubicular neurons and MEC layer 2 (L2) pyramids have shorter spikes, discharge spikes in bursts, and are theta-modulated (rhythmic, locking, skipping), but spikes phase-precess only weakly. MEC L2 stellates and layer 3 (L3) neurons have longer spikes, do not discharge in bursts, and are weakly theta-modulated (non-rhythmic, weakly locking, rarely skipping), but spikes steeply phase-precess. The similarities between MEC L3 neurons and MEC L2 stellates on one hand and parasubicular neurons and MEC L2 pyramids on the other hand suggest two distinct streams of temporal coding in the parahippocampal cortex. PMID:27425616

  14. Spike-interval triggered averaging reveals a quasi-periodic spiking alternative for stochastic resonance in catfish electroreceptors.

    PubMed

    Lankheet, Martin J M; Klink, P Christiaan; Borghuis, Bart G; Noest, André J

    2012-01-01

    Catfish detect and identify invisible prey by sensing their ultra-weak electric fields with electroreceptors. Any neuron that deals with small-amplitude input has to overcome sensitivity limitations arising from inherent threshold non-linearities in spike-generation mechanisms. Many sensory cells solve this issue with stochastic resonance, in which a moderate amount of intrinsic noise causes irregular spontaneous spiking activity with a probability that is modulated by the input signal. Here we show that catfish electroreceptors have adopted a fundamentally different strategy. Using a reverse correlation technique in which we take spike interval durations into account, we show that the electroreceptors generate a supra-threshold bias current that results in quasi-periodically produced spikes. In this regime stimuli modulate the interval between successive spikes rather than the instantaneous probability for a spike. This alternative for stochastic resonance combines threshold-free sensitivity for weak stimuli with similar sensitivity for excitations and inhibitions based on single interspike intervals. PMID:22403709

  15. Energy-efficient Encoding by Shifting Spikes in Neocortical Neurons

    PubMed Central

    Malyshev, Aleksey; Tchumatchenko, Tatjana; Volgushev, Stanislav; Volgushev, Maxim

    2013-01-01

    The speed of computations in neocortical networks critically depends on the ability of populations of spiking neurons to rapidly detect subtle changes of the input and translate them into firing rate changes. However, high sensitivity to perturbations may lead to explosion of noise and increased energy consumption. Can neuronal networks reconcile the requirements for high sensitivity, operation in low-noise regime and constrained energy consumption? Using intracellular recordings in slices from rat visual cortex we show that layer 2/3 pyramidal neurons are highly sensitive to minor input perturbations. They can change their population firing rate in response to small artificial excitatory postsynaptic currents (EPSCs) immersed in fluctuating noise very quickly, within 2–2.5 ms. These quick responses were mediated by generation of new, additional action potentials, but also by shifting spikes into the response peak. In that latter case, the spike count increase during the peak and the decrease after the peak cancelled each other, thus producing quick responses without increases of total spike count and associated energy costs. The contribution of spikes from one or the other source depended on the EPSC timing relative to the waves of depolarization produced by on-going activity. Neurons responded by shifting spikes to EPSCs arriving at the beginning of a depolarization wave, but generated additional spikes in response to EPSCs arriving towards the end of a wave. We conclude that neuronal networks can combine high sensitivity to perturbations and operation in low-noise regime. Moreover, certain patterns of on-going activity favor this combination and energy-efficient computations. PMID:23941643

  16. Basic statistics in cell biology.

    PubMed

    Vaux, David L

    2014-01-01

    The physicist Ernest Rutherford said, "If your experiment needs statistics, you ought to have done a better experiment." Although this aphorism remains true for much of today's research in cell biology, a basic understanding of statistics can be useful to cell biologists to help in monitoring the conduct of their experiments, in interpreting the results, in presenting them in publications, and when critically evaluating research by others. However, training in statistics is often focused on the sophisticated needs of clinical researchers, psychologists, and epidemiologists, whose conclusions depend wholly on statistics, rather than the practical needs of cell biologists, whose experiments often provide evidence that is not statistical in nature. This review describes some of the basic statistical principles that may be of use to experimental biologists, but it does not cover the sophisticated statistics needed for papers that contain evidence of no other kind.

  17. Spikes in the mixmaster regime of G{sub 2} cosmologies

    SciTech Connect

    Lim, Woei Chet; Andersson, Lars; Garfinkle, David; Pretorius, Frans

    2009-06-15

    We produce numerical evidence that spikes in the mixmaster regime of G{sub 2} cosmologies are transient and recurring, supporting the conjecture that the generalized mixmaster behavior is asymptotically nonlocal where spikes occur. Higher-order spike transitions are observed to split into separate first-order spike transitions.

  18. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

    PubMed

    Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R

    2015-02-01

    Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline. PMID:25689273

  19. Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

    PubMed Central

    Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R.

    2015-01-01

    Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline. PMID:25689273

  20. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines.

    PubMed

    Spindel, Jennifer; Begum, Hasina; Akdemir, Deniz; Virk, Parminder; Collard, Bertrand; Redoña, Edilberto; Atlin, Gary; Jannink, Jean-Luc; McCouch, Susan R

    2015-02-01

    Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.