Science.gov

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. Statistical Complexity of Neural Spike Trains

    DTIC Science & Technology

    2014-08-28

    SECURITY CLASSIFICATION OF: We present closed-form expressions for the entropy rate, statistical complexity, and predictive information for the spike...Triangle Park, NC 27709-2211 information, entropy rate, statistical complexity, excess entropy , integrate and fire neuron REPORT DOCUMENTATION PAGE 11...for the entropy rate, statistical complexity, and predictive information for the spike train of a single neuron in terms of the first passage time

  3. Statistical technique for analysing functional connectivity of multiple spike trains.

    PubMed

    Masud, Mohammad Shahed; Borisyuk, Roman

    2011-03-15

    A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains.

  4. 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.

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

    PubMed

    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.

  6. 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

  7. Kinetics of fast short-term depression are matched to spike train statistics to reduce noise.

    PubMed

    Khanbabaie, Reza; Nesse, William H; Longtin, Andre; Maler, Leonard

    2010-06-01

    Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (approximately 5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.

  8. Wavelet transform of neural spike trains

    NASA Astrophysics Data System (ADS)

    Kim, Youngtae; Jung, Min Whan; Kim, Yunbok

    2000-02-01

    Wavelet transform of neural spike trains recorded with a tetrode in the rat primary somatosensory cortex is described. Continuous wavelet transform (CWT) of the spike train clearly shows singularities hidden in the noisy or chaotic spike trains. A multiresolution analysis of the spike train is also carried out using discrete wavelet transform (DWT) for denoising and approximating at different time scales. Results suggest that this multiscale shape analysis can be a useful tool for classifying the spike trains.

  9. Training Deep Spiking Neural Networks Using Backpropagation

    PubMed Central

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

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations. PMID:27877107

  10. Training Deep Spiking Neural Networks Using Backpropagation.

    PubMed

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

    2016-01-01

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

  11. A simple algorithm for averaging spike trains.

    PubMed

    Julienne, Hannah; Houghton, Conor

    2013-02-25

    Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

  12. 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

  13. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    NASA Astrophysics Data System (ADS)

    Urdapilleta, Eugenio

    2016-09-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  14. Multiscale analysis of neural spike trains.

    PubMed

    Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

    2014-01-30

    This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code.

  15. Finding the event structure of neuronal spike trains.

    PubMed

    Toups, J Vincent; Fellous, Jean-Marc; Thomas, Peter J; Sejnowski, Terrence J; Tiesinga, Paul H

    2011-09-01

    Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.

  16. 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.

  17. 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.

  18. 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.

  19. Unbiased estimation of precise temporal correlations between spike trains.

    PubMed

    Stark, Eran; Abeles, Moshe

    2009-04-30

    A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.

  20. Stochasticity, spikes and decoding: sufficiency and utility of order statistics.

    PubMed

    Richmond, Barry J

    2009-06-01

    For over 75 years it has been clear that the number of spikes in a neural response is an important part of the neuronal code. Starting as early as the 1950's with MacKay and McCullough, there has been speculation over whether each spike and its exact time of occurrence carry information. Although it is obvious that the firing rate carries information it has been less clear as to whether there is information in exactly timed patterns, when they arise from the dynamics of the neurons and networks, as opposed to when they represent some strong external drive that entrains them. One strong null hypothesis that can be applied is that spike trains arise from stochastic sampling of an underlying deterministic temporally modulated rate function, that is, there is a time-varying rate function. In this view, order statistics seem to provide a sufficient theoretical construct to both generate simulated spike trains that are indistinguishable from those observed experimentally, and to evaluate (decode) the data recovered from experiments. It remains to learn whether there are physiologically important signals that are not described by such a null hypothesis.

  1. 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

  2. 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.

  3. Detection of bursts and pauses in spike trains.

    PubMed

    Ko, D; Wilson, C J; Lobb, C J; Paladini, C A

    2012-10-15

    Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons.

  4. Advantage of support vector machine for neural spike train decoding under spike sorting errors.

    PubMed

    Hwan Kim, Kyung; Shin Kim, Sung; June Kim, Sung

    2005-01-01

    Decoding of kinematic variables from neuronal spike trains is important for neuroprosthetic devices. The spike trains from single units must be extracted from extracellular neural signals and thus spike detection and sorting procedure is essential. Since the spike detection and sorting procedure may yield considerable errors, decoding algorithm should be robust against spike train errors. Here we showed that the spike train decoding algorithms employing a nonlinear mapping, especially support vector machine (SVM), may be more advantageous contrary to conventional belief that linear filter is sufficient. The advantage became more conspicuous with erroneous spike trains. Using the SVM, satisfactory performance could be obtained much more easily, compared to the case of using multilayer perceptron, which was employed for previous studies. The results suggests the possibility of neuroprosthetic device with a low-quality spike sorting preprocessor.

  5. The stochastic properties of input spike trains control neuronal arithmetic.

    PubMed

    Bures, Zbynek

    2012-02-01

    In the nervous system, the representation of signals is based predominantly on the rate and timing of neuronal discharges. In most everyday tasks, the brain has to carry out a variety of mathematical operations on the discharge patterns. Recent findings show that even single neurons are capable of performing basic arithmetic on the sequences of spikes. However, the interaction of the two spike trains, and thus the resulting arithmetic operation may be influenced by the stochastic properties of the interacting spike trains. If we represent the individual discharges as events of a random point process, then an arithmetical operation is given by the interaction of two point processes. Employing a probabilistic model based on detection of coincidence of random events and complementary computer simulations, we show that the point process statistics control the arithmetical operation being performed and, particularly, that it is possible to switch from subtraction to division solely by changing the distribution of the inter-event intervals of the processes. Consequences of the model for evaluation of binaural information in the auditory brainstem are demonstrated. The results accentuate the importance of the stochastic properties of neuronal discharge patterns for information processing in the brain; further studies related to neuronal arithmetic should therefore consider the statistics of the interacting spike trains.

  6. PySpike-A Python library for analyzing spike train synchrony

    NASA Astrophysics Data System (ADS)

    Mulansky, Mario; Kreuz, Thomas

    Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

  7. Analyzing multiple spike trains with nonparametric Granger causality.

    PubMed

    Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou

    2009-08-01

    Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

  8. On the Mathematical Consequences of Binning Spike Trains.

    PubMed

    Cessac, Bruno; Le Ny, Arnaud; Löcherbach, Eva

    2017-01-01

    We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process that is no longer Markov but is instead a variable-length Markov chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, that is, to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future, and we discuss how binning may affect our conclusions on this ability. We finally comment on the possible consequences of binning in the detection of spurious phase transitions or in the detection of incorrect evidence of criticality.

  9. 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.

  10. 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.

  11. Asymptotic Linear Spectral Statistics for Spiked Hermitian Random Matrices

    NASA Astrophysics Data System (ADS)

    Passemier, Damien; McKay, Matthew R.; Chen, Yang

    2015-07-01

    Using the Coulomb Fluid method, this paper derives central limit theorems (CLTs) for linear spectral statistics of three "spiked" Hermitian random matrix ensembles. These include Johnstone's spiked model (i.e., central Wishart with spiked correlation), non-central Wishart with rank-one non-centrality, and a related class of non-central matrices. For a generic linear statistic, we derive simple and explicit CLT expressions as the matrix dimensions grow large. For all three ensembles under consideration, we find that the primary effect of the spike is to introduce an correction term to the asymptotic mean of the linear spectral statistic, which we characterize with simple formulas. The utility of our proposed framework is demonstrated through application to three different linear statistics problems: the classical likelihood ratio test for a population covariance, the capacity analysis of multi-antenna wireless communication systems with a line-of-sight transmission path, and a classical multiple sample significance testing problem.

  12. 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.

  13. A new class of metrics for spike trains.

    PubMed

    Rusu, Cătălin V; Florian, Răzvan V

    2014-02-01

    The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.

  14. Wavelet-based processing of neuronal spike trains prior to discriminant analysis.

    PubMed

    Laubach, Mark

    2004-04-30

    Investigations of neural coding in many brain systems have focused on the role of spike rate and timing as two means of encoding information within a spike train. Recently, statistical pattern recognition methods, such as linear discriminant analysis (LDA), have emerged as a standard approach for examining neural codes. These methods work well when data sets are over-determined (i.e., there are more observations than predictor variables). But this is not always the case in many experimental data sets. One way to reduce the number of predictor variables is to preprocess data prior to classification. Here, a wavelet-based method is described for preprocessing spike trains. The method is based on the discriminant pursuit (DP) algorithm of Buckheit and Donoho [Proc. SPIE 2569 (1995) 540-51]. DP extracts a reduced set of features that are well localized in the time and frequency domains and that can be subsequently analyzed with statistical classifiers. DP is illustrated using neuronal spike trains recorded in the motor cortex of an awake, behaving rat [Laubach et al. Nature 405 (2000) 567-71]. In addition, simulated spike trains that differed only in the timing of spikes are used to show that DP outperforms another method for preprocessing spike trains, principal component analysis (PCA) [Richmond and Optican J. Neurophysiol. 57 (1987) 147-61].

  15. 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).

  16. 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.

  17. The time-rescaling theorem and its application to neural spike train data analysis.

    PubMed

    Brown, Emery N; Barbieri, Riccardo; Ventura, Valérie; Kass, Robert E; Frank, Loren M

    2002-02-01

    Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time histograms (PSTH) and rate functions estimated by spike train smoothing. The time-rescaling theorem is a well-known result in probability theory, which states that any point process with an integrable conditional intensity function may be transformed into a Poisson process with unit rate. We describe how the theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. We apply these tests in two examples: a comparison of PSTH, inhomogeneous Poisson, and inhomogeneous Markov interval models of neural spike trains from the supplementary eye field of a macque monkey and a comparison of temporal and spatial smoothers, inhomogeneous Poisson, inhomogeneous gamma, and inhomogeneous inverse gaussian models of rat hippocampal place cell spiking activity. To help make the logic behind the time-rescaling theorem more accessible to researchers in neuroscience, we present a proof using only elementary probability theory arguments. We also show how the theorem may be used to simulate a general point process model of a spike train. Our paradigm makes it possible to compare parametric and histogram-based neural spike train models directly. These results suggest that the time-rescaling theorem can be a valuable tool for neural spike train data analysis.

  18. Multi-scale detection of rate changes in spike trains with weak dependencies.

    PubMed

    Messer, Michael; Costa, Kauê M; Roeper, Jochen; Schneider, Gaby

    2017-04-01

    The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains.

  19. Improved similarity measures for small sets of spike trains.

    PubMed

    Naud, Richard; Gerhard, Felipe; Mensi, Skander; Gerstner, Wulfram

    2011-12-01

    Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in neuroprosthetic devices and electrophysiological experiments. Frequently only a few spike trains are available in each class. We derive analytical expressions for the small-sample bias present when comparing estimators of the time-dependent firing intensity. We then exploit analogies between the comparison of firing intensities and previously used spike train metrics and show that improved spike train measures can be successfully used for fitting neuron models to experimental data, for comparisons of spike trains, and classification of spike train data. In classification tasks, the improved similarity measures can increase the recovered information. We demonstrate that when similarity measures are used for fitting mathematical models, all previous methods systematically underestimate the noise. Finally, we show a striking implication of this deterministic bias by reevaluating the results of the single-neuron prediction challenge.

  20. 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.

  1. Regular Patterns in Cerebellar Purkinje Cell Simple Spike Trains

    PubMed Central

    Shin, Soon-Lim; Hoebeek, Freek E.; Schonewille, Martijn; De Zeeuw, Chris I.; Aertsen, Ad; De Schutter, Erik

    2007-01-01

    Background Cerebellar Purkinje cells (PC) in vivo are commonly reported to generate irregular spike trains, documented by high coefficients of variation of interspike-intervals (ISI). In strong contrast, they fire very regularly in the in vitro slice preparation. We studied the nature of this difference in firing properties by focusing on short-term variability and its dependence on behavioral state. Methodology/Principal Findings Using an analysis based on CV2 values, we could isolate precise regular spiking patterns, lasting up to hundreds of milliseconds, in PC simple spike trains recorded in both anesthetized and awake rodents. Regular spike patterns, defined by low variability of successive ISIs, comprised over half of the spikes, showed a wide range of mean ISIs, and were affected by behavioral state and tactile stimulation. Interestingly, regular patterns often coincided in nearby Purkinje cells without precise synchronization of individual spikes. Regular patterns exclusively appeared during the up state of the PC membrane potential, while single ISIs occurred both during up and down states. Possible functional consequences of regular spike patterns were investigated by modeling the synaptic conductance in neurons of the deep cerebellar nuclei (DCN). Simulations showed that these regular patterns caused epochs of relatively constant synaptic conductance in DCN neurons. Conclusions/Significance Our findings indicate that the apparent irregularity in cerebellar PC simple spike trains in vivo is most likely caused by mixing of different regular spike patterns, separated by single long intervals, over time. We propose that PCs may signal information, at least in part, in regular spike patterns to downstream DCN neurons. PMID:17534435

  2. Stationary transmission distribution of random spike trains by dynamical synapses

    NASA Astrophysics Data System (ADS)

    Hahnloser, Richard H.

    2003-02-01

    Many nonlinearities in neural media are strongly dependent on spike timing jitter and intrinsic dynamics of synaptic transmission. Here we are interested in the stationary density of evoked postsynaptic potentials transmitted by depressing synapses for Poisson spike trains of fixed mean rates. We present a nonperturbative iterative method for computing the stationary density over increasing intervals. We conclude by showing how this method generalizes to other types of synapses, such as facilitating and hybrid synapses.

  3. Improved spike-sorting by modeling firing statistics and burst-dependent spike amplitude attenuation: a Markov chain Monte Carlo approach.

    PubMed

    Pouzat, Christophe; Delescluse, Matthieu; Viot, Pascal; Diebolt, Jean

    2004-06-01

    Spike-sorting techniques attempt to classify a series of noisy electrical waveforms according to the identity of the neurons that generated them. Existing techniques perform this classification ignoring several properties of actual neurons that can ultimately improve classification performance. In this study, we propose a more realistic spike train generation model. It incorporates both a description of "nontrivial" (i.e., non-Poisson) neuronal discharge statistics and a description of spike waveform dynamics (e.g., the events amplitude decays for short interspike intervals). We show that this spike train generation model is analogous to a one-dimensional Potts spin-glass model. We can therefore tailor to our particular case the computational methods that have been developed in fields where Potts models are extensively used, including statistical physics and image restoration. These methods are based on the construction of a Markov chain in the space of model parameters and spike train configurations, where a configuration is defined by specifying a neuron of origin for each spike. This Markov chain is built such that its unique stationary density is the posterior density of model parameters and configurations given the observed data. A Monte Carlo simulation of the Markov chain is then used to estimate the posterior density. We illustrate the way to build the transition matrix of the Markov chain with a simple, but realistic, model for data generation. We use simulated data to illustrate the performance of the method and to show that this approach can easily cope with neurons firing doublets of spikes and/or generating spikes with highly dynamic waveforms. The method cannot automatically find the "correct" number of neurons in the data. User input is required for this important problem and we illustrate how this can be done. We finally discuss further developments of the method.

  4. 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

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

    PubMed

    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.

  6. Balanced synaptic input shapes the correlation between neural spike trains.

    PubMed

    Litwin-Kumar, Ashok; Oswald, Anne-Marie M; Urban, Nathaniel N; Doiron, Brent

    2011-12-01

    Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

  7. Information content in cortical spike trains during brain state transitions.

    PubMed

    Arnold, Maria M; Szczepanski, Janusz; Montejo, Noelia; Amigó, José M; Wajnryb, Eligiusz; Sanchez-Vives, Maria V

    2013-02-01

    Even in the absence of external stimuli there is ongoing activity in the cerebral cortex as a result of recurrent connectivity. This paper attempts to characterize one aspect of this ongoing activity by examining how the information content carried by specific neurons varies as a function of brain state. We recorded from rats chronically implanted with tetrodes in the primary visual cortex during awake and sleep periods. Electro-encephalogram and spike trains were recorded during 30-min periods, and 2-4 neuronal spikes were isolated per tetrode off-line. All the activity included in the analysis was spontaneous, being recorded from the visual cortex in the absence of visual stimuli. The brain state was determined through a combination of behavior evaluation, electroencephalogram and electromyogram analysis. Information in the spike trains was determined by using Lempel-Ziv Complexity. Complexity was used to estimate the entropy of neural discharges and thus the information content (Amigóet al. Neural Comput., 2004, 16: 717-736). The information content in spike trains (range 4-70 bits s(-1) ) was evaluated during different brain states and particularly during the transition periods. Transitions toward states of deeper sleep coincided with a decrease of information, while transitions to the awake state resulted in an increase in information. Changes in both directions were of the same magnitude, about 30%. Information in spike trains showed a high temporal correlation between neurons, reinforcing the idea of the impact of the brain state in the information content of spike trains.

  8. Studying spike trains using a van Rossum metric with a synapse-like filter.

    PubMed

    Houghton, Conor

    2009-02-01

    Spike trains are unreliable. For example, in the primary sensory areas, spike patterns and precise spike times will vary between responses to the same stimulus. Nonetheless, information about sensory inputs is communicated in the form of spike trains. A challenge in understanding spike trains is to assess the significance of individual spikes in encoding information. One approach is to define a spike train metric, allowing a distance to be calculated between pairs of spike trains. In a good metric, this distance will depend on the information the spike trains encode. This method has been used previously to calculate the timescale over which the precision of spike times is significant. Here, a new metric is constructed based on a simple model of synaptic conductances which includes binding site depletion. Including binding site depletion in the metric means that a given individual spike has a smaller effect on the distance if it occurs soon after other spikes. The metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the primary auditory area of the zebra finch fore-brain. This shows that this is an effective metric for these spike trains suggesting that in these spike trains the significance of a spike is modulated by its proximity to previous spikes. This modulation is a putative information-coding property of spike trains.

  9. Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data.

    PubMed

    Chen, Zhe; Putrino, David F; Ghosh, Soumya; Barbieri, Riccardo; Brown, Emery N

    2011-04-01

    The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l(2) or l(1) regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.

  10. State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.

    PubMed

    Shimazaki, Hideaki; Amari, Shun-Ichi; Brown, Emery N; Grün, Sonja

    2012-01-01

    Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods

  11. Kernel Methods on Spike Train Space for Neuroscience: A Tutorial

    NASA Astrophysics Data System (ADS)

    Park, Il Memming; Seth, Sohan; Paiva, Antonio R. C.; Li, Lin; Principe, Jose C.

    2013-07-01

    Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal processing experts. This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed. The presentation incorporates simple mathematical analogies and convincing practical examples in an attempt to show the yet unexplored potential of positive definite functions to quantify point processes. It also provides a detailed overview of the current state of the art and future challenges with the hope of engaging the readers in active participation.

  12. Retention of lung distension information in pump cell spike trains.

    PubMed

    Marchenko, Vitaliy; Rogers, Robert F

    2007-07-01

    Respiratory control requires feedback signals from the viscera, including mechanoreceptors and chemoreceptors. We previously showed that typical pulmonary stretch receptor (PSR) spike trains provide the central nervous system with approximately 31% of the theoretical maximum information regarding the amplitude of lung distension. However, it is unknown whether the spatiotemporal convergence of many PSR inputs onto second-order neurons (e.g., pump cells) results in more, or less, information about the stimulus carried by second-order cell spike trains. We recorded pump cell activity in adult, anesthetized, paralyzed, artificially ventilated rabbits during continuous manipulation of ventilator rate and volume to test the hypothesis that less information is carried by spike trains of individual pump cells than PSRs. Using previously developed analytic methods, we quantified the information carried by the pump cell spike trains and compared it with the same values derived from PSR data. Our results provide evidence that rejects our hypothesis: pump cells as a group did not carry significantly less information about the lung distension stimulus than PSRs, although that trend was implied by the data. By comparing the response variances with the theoretical minimum, we discovered that the trend toward information loss depends on response strength, with higher mean responses associated with larger response variances in pump cells than in PSRs. Thus spatiotemporal integration may result in information loss within certain analytic/stimulus parameters, but this is counterbalanced by the consistency of pump cell responses during brief integration times and/or low stimulus amplitudes, resulting in retention of total information.

  13. An interactive tool for visualization of spike train synchronization.

    PubMed

    Terry, Kevin

    2010-08-15

    A number of studies have examined the synchronization of central and peripheral spike trains by applying signal analysis techniques in the time and frequency domains. These analyses can reveal the presence of one or more common neural inputs that produce synchronization. However, synchronization measurements can fluctuate significantly due to the inherent variability of neural discharges and a finite data record length. Moreover, the effect of these natural variations is further compounded by the number of parameters available for calculating coherence in the frequency domain and the number of indices used to quantify short-term synchronization (STS) in the time domain. The computational tool presented here provides the user with an interactive environment that dynamically calculates and displays spike train properties along with STS and coherence indices to show how these factors interact. It is intended for a broad range of users, from those who are new to synchronization to experienced researchers who want to develop more meaningful and effective computational and experimental studies. To ensure this freely available tool meets the needs of all users, there are two versions. The first is a stand-alone version for educational use that can run on any computer. The second version can be modified and expanded by researchers who want to explore more in-depth questions about synchronization. Therefore, the distribution and use of this tool should both improve the understanding of fundamental spike train synchronization dynamics and produce more efficient and meaningful synchronization studies.

  14. 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.

  15. Predictive information in spike trains from the blowfly and monkey visual systems

    NASA Astrophysics Data System (ADS)

    Bruder, Seth Daniel

    1998-12-01

    One of the principal goals of the study of neural computation is to understand how the phenomenology of the brain arises from an assemblage of computational subunits called neurons. An aspect of this problem is that of relating, where possible, signals recorded from individual neurons, called spike trains, to concurrently recorded stimuli or behavioral responses. In this dissertation, we introduce time-domain analogs of real-space renormalization procedures for this purpose. For these procedures, block variable transformations are selected to preserve the information that blocks have about their neighbors and, for comparison, to preserve information that blocks have about stimuli or responses. We propose that, as a spike train is iteratively coarse-grained, information about stimuli or responses, that is available within the spike train on successively longer time scales, may be extracted. To test this idea, we apply it to the analysis of spike trains recorded from a motion-sensitive neuron in the visual system of a blowfly (Calliphora erythrocephela ) and to spike trains recorded from a pattern-selective neuron in the inferior temporal cortex of a monkey (Macaca mulatto) trained to report the exclusive perception of any one of several images. We find that the temporal correlations in the activity of these neurons can be used to identify features of the spike train that provide real-time information about stimuli or reports. Additionally, in the case of the monkey, we find that for periods when the monkey views static ambiguous stimuli, we are able to extract a statistically significant amount of information about the monkey's report from the spike train, supporting the claim that the activity of this neuron reflects internal perceptual state as opposed to strictly retinal stimulation. Finally, we generalize our renormalization procedure for application to three-dimensional Ising spin systems. We find that, for a ferromagnet and antiferromagnet this yields a

  16. Single-trial Spike Trains in Parietal Cortex Reveal Discrete Steps During Decision-making

    PubMed Central

    Latimer, Kenneth W.; Yates, Jacob L.; Meister, Miriam L. R.; Huk, Alexander C.; Pillow, Jonathan W.

    2016-01-01

    Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upwards or downwards during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence towards a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete “stepping” dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal’s choice than spike counts. PMID:26160947

  17. State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

    PubMed Central

    Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grün, Sonja

    2012-01-01

    Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods

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

    PubMed Central

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

    2012-01-01

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

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. Estimating the correlation between bursty spike trains and local field potentials.

    PubMed

    Li, Zhaohui; Ouyang, Gaoxiang; Yao, Li; Li, Xiaoli

    2014-09-01

    To further understand rhythmic neuronal synchronization, an increasingly useful method is to determine the relationship between the spiking activity of individual neurons and the local field potentials (LFPs) of neural ensembles. Spike field coherence (SFC) is a widely used method for measuring the synchronization between spike trains and LFPs. However, due to the strong dependency of SFC on the burst index, it is not suitable for analyzing the relationship between bursty spike trains and LFPs, particularly in high frequency bands. To address this issue, we developed a method called weighted spike field correlation (WSFC), which uses the first spike in each burst multiple times to estimate the relationship. In the calculation, the number of times that the first spike is used is equal to the spike count per burst. The performance of this method was demonstrated using simulated bursty spike trains and LFPs, which comprised sinusoids with different frequencies, amplitudes, and phases. This method was also used to estimate the correlation between pyramidal cells in the hippocampus and gamma oscillations in rats performing behaviors. Analyses using simulated and real data demonstrated that the WSFC method is a promising measure for estimating the correlation between bursty spike trains and high frequency LFPs.

  4. Long-term correlations in the spike trains of medullary sympathetic neurons.

    PubMed

    Lewis, C D; Gebber, G L; Larsen, P D; Barman, S M

    2001-04-01

    Fano factor analysis was used to characterize the spike trains of single medullary neurons with sympathetic nerve-related activity in cats that were decerebrate or anesthetized with Dial-urethan or urethan. For this purpose, values (Fano factor) of the variance of the number of extracellularly recorded spikes divided by the mean number of spikes were calculated for window sizes of systematically varied length. For window sizes < or =10 ms, the Fano factor was close to one, as expected for a Bernoulli process with a low probability of success. The Fano factor dipped below one as the window size approached the shortest interspike interval (ISI) and reached its nadir at window sizes near the modal ISI. The extent of the dip reflected the shape (skewness) of the ISI histogram with the dip being smallest for the most asymmetric distributions. Most importantly, for a wide range of window sizes exceeding the modal ISI, the Fano factor curve took the form of a power law function. This was the case independent of the component (cardiac related, 10 Hz, or 2--6 Hz) of inferior cardiac sympathetic nerve discharge to which unit activity was correlated or the medullary region (lateral tegmental field, raphe, caudal and rostral ventrolateral medulla) in which the neuron was located. The power law relationship in the Fano factor curves was eliminated by randomly shuffling the ISIs even though the distribution of the intervals was unchanged. Thus the power law relationship arose from long-term correlations among ISIs that were disrupted by shuffling the data. The presence of long-term correlations across different time scales reflects the property of statistical self-similarity that is characteristic of fractal processes. In most cases, we found that mean ISI and variance for individual spike trains increased as a function of the number of intervals counted. This can be attributed to the clustering of long and short ISIs, which also is an inherent property of fractal time series

  5. Variational Latent Gaussian Process for Recovering Single-Trial Dynamics from Population Spike Trains.

    PubMed

    Zhao, Yuan; Park, Il Memming

    2017-05-01

    When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded populations of neurons can be explained by a small number of shared factors, or a low-dimensional trajectory. Recovering these latent trajectories, particularly from single-trial population recordings, may help us understand the dynamics that drive neural computation. However, due to the biophysical constraints and noise in the spike trains, inferring trajectories from data is a challenging statistical problem in general. Here, we propose a practical and efficient inference method, the variational latent gaussian process (vLGP). The vLGP combines a generative model with a history-dependent point process observation, together with a smoothness prior on the latent trajectories. The vLGP improves on earlier methods for recovering latent trajectories, which assume either observation models inappropriate for point processes or linear dynamics. We compare and validate vLGP on both simulated data sets and population recordings from the primary visual cortex. In the V1 data set, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space and the noise correlation. These results show that vLGP is a robust method with the potential to reveal hidden neural dynamics from large-scale neural recordings.

  6. Quantification of clustering in joint interspike interval scattergrams of spike trains.

    PubMed

    Dodla, Ramana; Wilson, Charles J

    2010-06-02

    Joint interval scattergrams are usually employed in determining serial correlations between events of spike trains. However, any inherent structures in such scattergrams that are often seen in experimental records are not quantifiable by serial correlation coefficients. Here, we develop a method to quantify clustered structures in any two-dimensional scattergram of pairs of interspike intervals. The method gives a cluster coefficient as well as clustering density function that could be used to quantify clustering in scattergrams obtained from first- or higher-order interval return maps of single spike trains, or interspike interval pairs drawn from simultaneously recorded spike trains. The method is illustrated using numerical spike trains as well as in vitro pairwise recordings of rat striatal tonically active neurons.

  7. 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.

  8. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-11-01

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

  11. Multisensory Interactions Influence Neuronal Spike Train Dynamics in the Posterior Parietal Cortex

    PubMed Central

    VanGilder, Paul; Shi, Ying; Apker, Gregory; Dyson, Keith; Buneo, Christopher A.

    2016-01-01

    Although significant progress has been made in understanding multisensory interactions at the behavioral level, their underlying neural mechanisms remain relatively poorly understood in cortical areas, particularly during the control of action. In recent experiments where animals reached to and actively maintained their arm position at multiple spatial locations while receiving either proprioceptive or visual-proprioceptive position feedback, multisensory interactions were shown to be associated with reduced spiking (i.e. subadditivity) as well as reduced intra-trial and across-trial spiking variability in the superior parietal lobule (SPL). To further explore the nature of such interaction-induced changes in spiking variability we quantified the spike train dynamics of 231 of these neurons. Neurons were classified as Poisson, bursty, refractory, or oscillatory (in the 13–30 Hz “beta-band”) based on their spike train power spectra and autocorrelograms. No neurons were classified as Poisson-like in either the proprioceptive or visual-proprioceptive conditions. Instead, oscillatory spiking was most commonly observed with many neurons exhibiting these oscillations under only one set of feedback conditions. The results suggest that the SPL may belong to a putative beta-synchronized network for arm position maintenance and that position estimation may be subserved by different subsets of neurons within this network depending on available sensory information. In addition, the nature of the observed spiking variability suggests that models of multisensory interactions in the SPL should account for both Poisson-like and non-Poisson variability. PMID:28033334

  12. Subthreshold Membrane-Potential Resonances Shape Spike-Train Patterns in the Entorhinal Cortex

    PubMed Central

    Engel, T. A.; Schimansky-Geier, L.; Herz, A.V.M.; Schreiber, S.; Erchova, I.

    2008-01-01

    Many neurons exhibit subthreshold membrane-potential resonances, such that the largest voltage responses occur at preferred stimulation frequencies. Because subthreshold resonances are known to influence the rhythmic activity at the network level, it is vital to understand how they affect spike generation on the single-cell level. We therefore investigated both resonant and nonresonant neurons of rat entorhinal cortex. A minimal resonate-and-fire type model based on measured physiological parameters captures fundamental properties of neuronal firing statistics surprisingly well and helps to shed light on the mechanisms that shape spike patterns: 1) subthreshold resonance together with a spike-induced reset of subthreshold oscillations leads to spike clustering and 2) spike-induced dynamics influence the fine structure of interspike interval (ISI) distributions and are responsible for ISI correlations appearing at higher firing rates (≥3 Hz). Both mechanisms are likely to account for the specific discharge characteristics of various cell types. PMID:18450582

  13. Measures of complexity in neural spike-trains of the slowly adapting stretch receptor organs.

    PubMed

    Jiménez-Montaño, M A; Penagos, H; Hernández Torres, A; Diez-Martínez, O

    2000-01-01

    Discrete sequence analysis methods were applied to study spike-trains generated by the isolated neuron of the slowly adapting stretch receptor organ. Calculation of the algorithmic complexity and block entropies of digitized individual spike-train forms allowed us to distinguish different classes of neural behavior. While some spike-trains exhibited significant structure, others displayed diverse degrees of randomness. The sequences recorded during the stimulated portions of the intermittent and walk-through forms, differed considerably from their randomly shuffled surrogates. Informational and grammar complexity measures (in two, four and eight-letter alphabets), tell us things about the structure of spike-trains that are not obtained with conventional spike analysis. Comparison of the conditional entropies for the digitized signals showed that the method distinguishes between different stimulated conditions. Additionally, comparison of the different stimulated conditions with their corresponding surrogates showed that, both, conditional entropies and complexities were significantly different for the two groups. Although the original and the randomly shuffled sequences had the same distribution and average firing rate, their complexity values were different. The results obtained with both measures of sequence structure were quite consistent.

  14. Decoding of retinal ganglion cell spike trains evoked by temporally patterned electrical stimulation.

    PubMed

    Ryu, Sang Baek; Ye, Jang Hee; Goo, Yong Sook; Kim, Chi Hyun; Kim, Kyung Hwan

    2010-08-12

    For successful restoration of vision by retinal prostheses, the neural activity of retinal ganglion cells (RGCs) evoked by electrical stimulation should represent the information of spatiotemporal patterns of visual input. We propose a method to evaluate the effectiveness of stimulation pulse trains so that the crucial temporal information of a visual input is accurately represented in the RGC responses as the amplitudes of pulse trains are modulated according to the light intensity. This was enabled by spike train decoding. The effectiveness of the stimulation was evaluated by the accuracy of decoding pulse amplitude from the RGC spike train, i.e., by the similarity between the original and the decoded pulse amplitude time series. When the parameters of stimulation were suitably determined, the RGC responses were reliably modulated by varying the amplitude of electrical pulses. Accordingly, the temporal pattern of pulse amplitudes could be successfully decoded from multiunit RGC spike trains. The range of pulse amplitude and the pulse rate were critical for accurate representation of input information in RGC responses. These results suggest that pulse amplitude modulation is a feasible means to encode temporal visual information by RGC spike trains and thus to implement stimulus encoding strategies for retinal prostheses.

  15. Dynamic statistical parametric mapping for analyzing ictal magnetoencephalographic spikes in patients with intractable frontal lobe epilepsy

    PubMed Central

    Tanaka, Naoaki; Cole, Andrew J.; von Pechmann, Deidre; Wakeman, Daniel G.; Hämäläinen, Matti S.; Liu, Hesheng; Madsen, Joseph R.; Bourgeois, Blaise F.; Stufflebeam, Steven M.

    2009-01-01

    The purpose of this study is to assess the clinical value of spatiotemporal source analysis for analyzing ictal magnetoencephalography (MEG). Ictal MEG and simultaneous scalp EEG was recorded in five patients with medically intractable frontal lobe epilepsy. Dynamic statistical parametric maps (dSPMs) were calculated at the peak of early ictal spikes for the purpose of estimating the spatiotemporal cortical source distribution. DSPM solutions were mapped onto a cortical surface, which was derived from each patient's MRI. Equivalent current dipoles (ECDs) were calculated using a single-dipole model for comparison with dSPMs. In all patients, dSPMs tended to have a localized activation, consistent with the clinically-determined ictal onset zone, whereas most ECDs were considered to be inappropriate sources according to their goodness-of-fit values. Analyzing ictal MEG spikes by using dSPMs may provide useful information in presurgical evaluation of epilepsy. PMID:19394198

  16. Dynamic statistical parametric mapping for analyzing ictal magnetoencephalographic spikes in patients with intractable frontal lobe epilepsy.

    PubMed

    Tanaka, Naoaki; Cole, Andrew J; von Pechmann, Deidre; Wakeman, Daniel G; Hämäläinen, Matti S; Liu, Hesheng; Madsen, Joseph R; Bourgeois, Blaise F; Stufflebeam, Steven M

    2009-08-01

    The purpose of this study is to assess the clinical value of spatiotemporal source analysis for analyzing ictal magnetoencephalography (MEG). Ictal MEG and simultaneous scalp EEG was recorded in five patients with medically intractable frontal lobe epilepsy. Dynamic statistical parametric maps (dSPMs) were calculated at the peak of early ictal spikes for the purpose of estimating the spatiotemporal cortical source distribution. DSPM solutions were mapped onto a cortical surface, which was derived from each patient's MRI. Equivalent current dipoles (ECDs) were calculated using a single-dipole model for comparison with dSPMs. In all patients, dSPMs tended to have a localized activation, consistent with the clinically determined ictal onset zone, whereas most ECDs were considered to be inappropriate sources according to their goodness-of-fit values. Analyzing ictal MEG spikes by using dSPMs may provide useful information in presurgical evaluation of epilepsy.

  17. A continuous entropy rate estimator for spike trains using a K-means-based context tree.

    PubMed

    Lin, Tiger W; Reeke, George N

    2010-04-01

    Entropy rate quantifies the change of information of a stochastic process (Cover & Thomas, 2006). For decades, the temporal dynamics of spike trains generated by neurons has been studied as a stochastic process (Barbieri, Quirk, Frank, Wilson, & Brown, 2001; Brown, Frank, Tang, Quirk, & Wilson, 1998; Kass & Ventura, 2001; Metzner, Koch, Wessel, & Gabbiani, 1998; Zhang, Ginzburg, McNaughton, & Sejnowski, 1998). We propose here to estimate the entropy rate of a spike train from an inhomogeneous hidden Markov model of the spike intervals. The model is constructed by building a context tree structure to lay out the conditional probabilities of various subsequences of the spike train. For each state in the Markov chain, we assume a gamma distribution over the spike intervals, although any appropriate distribution may be employed as circumstances dictate. The entropy and confidence intervals for the entropy are calculated from bootstrapping samples taken from a large raw data sequence. The estimator was first tested on synthetic data generated by multiple-order Markov chains, and it always converged to the theoretical Shannon entropy rate (except in the case of a sixth-order model, where the calculations were terminated before convergence was reached). We also applied the method to experimental data and compare its performance with that of several other methods of entropy estimation.

  18. Synchronization phenomena in pulse-coupled networks driven by spike-train inputs.

    PubMed

    Torikai, Hiroyuki; Saito, Toshimichi

    2004-03-01

    We present a pulse-coupled network (PCN) of spiking oscillators (SOCs) which can be implemented as a simple electrical circuit. The SOC has a periodic reset level that can realize rich dynamics represented by chaotic spike-trains. Applying a spike-train input, the PCN can exhibit the following interesting phenomena. 1) Each SOC synchronizes with a part of the input without overlapping, i.e., the input is decomposed. 2) Some SOCs synchronize with a part of the input with overlapping, i.e., the input is decomposed and the SOCs are clustered. The PCN has multiple synchronization phenomena and exhibits one of them depending on the initial state. We clarify the numbers of the synchronization phenomena and the parameter regions in which these phenomena can be observed. Also stability of the synchronization phenomena is clarified. Presenting a simple test circuit, typical phenomena are confirmed experimentally.

  19. Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation.

    PubMed

    Quiroga-Lombard, Claudio S; Hass, Joachim; Durstewitz, Daniel

    2013-07-01

    Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then "slicing" spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.

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

    PubMed

    Houghton, Conor

    2015-05-01

    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.

  1. 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.

  2. 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

  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. 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.

  5. Population activity statistics dissect subthreshold and spiking variability in V1.

    PubMed

    Bányai, Mihály; Koman, Zsombor; Orbán, Gergő

    2017-03-15

    Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the Doubly Stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the Rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. In order to test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modelling of stochasticity provides an efficient strategy to model correlations.

  6. A generative spike train model with time-structured higher order correlations.

    PubMed

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

    2013-01-01

    Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics.

  7. A generative spike train model with time-structured higher order correlations

    PubMed Central

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

    2013-01-01

    Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics. PMID:23908626

  8. 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.

  9. 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.

  10. On The Use of Dynamic Bayesian Networks in Reconstructing Functional Neuronal Networks from Spike Train Ensembles

    PubMed Central

    Eldawlatly, Seif; Zhou, Yang; Jin, Rong; Oweiss, Karim G.

    2009-01-01

    Coordination among cortical neurons is believed to be key element in mediating many high level cortical processes such as perception, attention, learning and memory formation. Inferring the topology of the neural circuitry underlying this coordination is important to characterize the highly non-linear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of Dynamic Bayesian Networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying non-linear and time-varying causal interactions between these neurons and can discriminate between mono and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally-Poisson spike train data mimicking spiking activity of cortical networks of small and moderately-large sizes. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks. PMID:19852619

  11. 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.

  12. Functional connectivity among spike trains in neural assemblies during rat working memory task.

    PubMed

    Xie, Jiacun; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2014-11-01

    Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity.

  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. A hidden Markov model for decoding and the analysis of replay in spike trains.

    PubMed

    Box, Marc; Jones, Matt W; Whiteley, Nick

    2016-12-01

    We present a hidden Markov model that describes variation in an animal's position associated with varying levels of activity in action potential spike trains of individual place cell neurons. The model incorporates a coarse-graining of position, which we find to be a more parsimonious description of the system than other models. We use a sequential Monte Carlo algorithm for Bayesian inference of model parameters, including the state space dimension, and we explain how to estimate position from spike train observations (decoding). We obtain greater accuracy over other methods in the conditions of high temporal resolution and small neuronal sample size. We also present a novel, model-based approach to the study of replay: the expression of spike train activity related to behaviour during times of motionlessness or sleep, thought to be integral to the consolidation of long-term memories. We demonstrate how we can detect the time, information content and compression rate of replay events in simulated and real hippocampal data recorded from rats in two different environments, and verify the correlation between the times of detected replay events and of sharp wave/ripples in the local field potential.

  15. 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

  16. 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.

  17. The periodogram and Allan variance reveal fractal exponents greater than unity in auditory-nerve spike trains.

    PubMed

    Lowen, S B; Teich, M C

    1996-06-01

    Auditory-nerve spike trains exhibit fractal behavior, and therefore traditional renewal-point-process models fail to describe them adequately. Previous measures of the fractal exponent of these spike trains are based on the Fano factor and consequently cannot exceed unity. Two estimates of the fractal exponent are considered which do not suffer from this limit: one derived from the Allan variance, which was developed by the authors, and one based on the periodogram. These measures indicate that fractal exponents do indeed exceed unity for some nerve-spike recordings from stimulated primary afferent cat auditory-nerve fibers.

  18. 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

  19. 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

  20. Dynamic programming algorithms for comparing multineuronal spike trains via cost-based metrics and alignments.

    PubMed

    Victor, Jonathan D; Goldberg, David H; Gardner, Daniel

    2007-04-15

    Cost-based metrics formalize notions of distance, or dissimilarity, between two spike trains, and are applicable to single- and multineuronal responses. As such, these metrics have been used to characterize neural variability and neural coding. By examining the structure of an efficient algorithm [Aronov D, 2003. Fast algorithm for the metric-space analysis of simultaneous responses of multiple single neurons. J Neurosci Methods 124(2), 175-79] implementing a metric for multineuronal responses, we determine criteria for its generalization, and identify additional efficiencies that are applicable when related dissimilarity measures are computed in parallel. The generalized algorithm provides the means to test a wide range of coding hypotheses.

  1. Correlation-based analysis and generation of multiple spike trains using hawkes models with an exogenous input.

    PubMed

    Krumin, Michael; Reutsky, Inna; Shoham, Shy

    2010-01-01

    The correlation structure of neural activity is believed to play a major role in the encoding and possibly the decoding of information in neural populations. Recently, several methods were developed for exactly controlling the correlation structure of multi-channel synthetic spike trains (Brette, 2009; Krumin and Shoham, 2009; Macke et al., 2009; Gutnisky and Josic, 2010; Tchumatchenko et al., 2010) and, in a related work, correlation-based analysis of spike trains was used for blind identification of single-neuron models (Krumin et al., 2010), for identifying compact auto-regressive models for multi-channel spike trains, and for facilitating their causal network analysis (Krumin and Shoham, 2010). However, the diversity of correlation structures that can be explained by the feed-forward, non-recurrent, generative models used in these studies is limited. Hence, methods based on such models occasionally fail when analyzing correlation structures that are observed in neural activity. Here, we extend this framework by deriving closed-form expressions for the correlation structure of a more powerful multivariate self- and mutually exciting Hawkes model class that is driven by exogenous non-negative inputs. We demonstrate that the resulting Linear-Non-linear-Hawkes (LNH) framework is capable of capturing the dynamics of spike trains with a generally richer and more biologically relevant multi-correlation structure, and can be used to accurately estimate the Hawkes kernels or the correlation structure of external inputs in both simulated and real spike trains (recorded from visually stimulated mouse retinal ganglion cells). We conclude by discussing the method's limitations and the broader significance of strengthening the links between neural spike train analysis and classical system identification.

  2. 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

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

    PubMed

    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.

  4. 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.

  5. 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.

  6. 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.

  7. Segmental Bayesian estimation of gap-junctional and inhibitory conductance of inferior olive neurons from spike trains with complicated dynamics.

    PubMed

    Hoang, Huu; Yamashita, Okito; Tokuda, Isao T; Sato, Masa-Aki; Kawato, Mitsuo; Toyama, Keisuke

    2015-01-01

    The inverse problem for estimating model parameters from brain spike data is an ill-posed problem because of a huge mismatch in the system complexity between the model and the brain as well as its non-stationary dynamics, and needs a stochastic approach that finds the most likely solution among many possible solutions. In the present study, we developed a segmental Bayesian method to estimate the two parameters of interest, the gap-junctional (gc ) and inhibitory conductance (gi ) from inferior olive spike data. Feature vectors were estimated for the spike data in a segment-wise fashion to compensate for the non-stationary firing dynamics. Hierarchical Bayesian estimation was conducted to estimate the gc and gi for every spike segment using a forward model constructed in the principal component analysis (PCA) space of the feature vectors, and to merge the segmental estimates into single estimates for every neuron. The segmental Bayesian estimation gave smaller fitting errors than the conventional Bayesian inference, which finds the estimates once across the entire spike data, or the minimum error method, which directly finds the closest match in the PCA space. The segmental Bayesian inference has the potential to overcome the problem of non-stationary dynamics and resolve the ill-posedness of the inverse problem because of the mismatch between the model and the brain under the constraints based, and it is a useful tool to evaluate parameters of interest for neuroscience from experimental spike train data.

  8. 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.

  9. Input-output relationship in social communications characterized by spike train analysis

    NASA Astrophysics Data System (ADS)

    Aoki, Takaaki; Takaguchi, Taro; Kobayashi, Ryota; Lambiotte, Renaud

    2016-10-01

    We study the dynamical properties of human communication through different channels, i.e., short messages, phone calls, and emails, adopting techniques from neuronal spike train analysis in order to characterize the temporal fluctuations of successive interevent times. We first measure the so-called local variation (LV) of incoming and outgoing event sequences of users and find that these in- and out-LV values are positively correlated for short messages and uncorrelated for phone calls and emails. Second, we analyze the response-time distribution after receiving a message to focus on the input-output relationship in each of these channels. We find that the time scales and amplitudes of response differ between the three channels. To understand the effects of the response-time distribution on the correlations between the LV values, we develop a point process model whose activity rate is modulated by incoming and outgoing events. Numerical simulations of the model indicate that a quick response to incoming events and a refractory effect after outgoing events are key factors to reproduce the positive LV correlations.

  10. CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains

    PubMed Central

    Rotter, Stefan; Grün, Sonja

    2009-01-01

    Recent developments in electrophysiological and optical recording techniques enable the simultaneous observation of large numbers of neurons. A meaningful interpretation of the resulting multivariate data, however, presents a serious challenge. In particular, the estimation of higher-order correlations that characterize the cooperative dynamics of groups of neurons is impeded by the combinatorial explosion of the parameter space. The resulting requirements with respect to sample size and recording time has rendered the detection of coordinated neuronal groups exceedingly difficult. Here we describe a novel approach to infer higher-order correlations in massively parallel spike trains that is less susceptible to these problems. Based on the superimposed activity of all recorded neurons, the cumulant-based inference of higher-order correlations (CuBIC) presented here exploits the fact that the absence of higher-order correlations imposes also strong constraints on correlations of lower order. Thus, estimates of only few lower-order cumulants suffice to infer higher-order correlations in the population. As a consequence, CuBIC is much better compatible with the constraints of in vivo recordings than previous approaches, which is shown by a systematic analysis of its parameter dependence. PMID:19862611

  11. 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.

  12. 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

  13. Sinusoidal stimulation trains suppress epileptiform spikes induced by 4-AP in the rat hippocampal CA1 region in-vivo.

    PubMed

    Zheshan Guo; Zhouyan Feng; Ying Yu; Wenjie Zhou; Zhaoxiang Wang; Xuefeng Wei

    2016-08-01

    Deep brain stimulation (DBS) shows promises in the treatment of refractory epilepsy. Due to the complex causes of epilepsy, the mechanisms of DBS are still unclear. Depolarization block caused by the persistent excitation of neurons may be one of the possible mechanisms. To test the hypothesis, 4-aminopyridine (4-AP) was injected in rat hippocampal CA1 region in-vivo to induce epileptiform activity. Sinusoidal stimulation trains were applied to the afferent pathway (Schaffer collaterals) of CA1 region to suppress the epileptiform spikes. Results show that 2-min long trains of sinusoidal stimulation (50 Hz) decreased the firing rate of population spikes (PS) and decreased the PS amplitudes significantly. In addition, small positive sharp waves replaced PS activity during the periods of stimulation. A lower frequency sinusoidal stimulation (10 Hz) failed to decrease the firing rate of PS, but decreased the PS amplitudes significantly. These results suggest that stimulation trains of sinusoidal waves could suppress epileptiform spikes. Presumably, the stimulation with a high enough frequency might excite the downstream neurons persistently and elevate the membrane potentials continuously, thereby cause depolarization blocks in the neurons. The findings of the study provide insights in revealing the mechanisms of DBS, and have important implications to the clinical treatment of epilepsy.

  14. 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

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

    PubMed

    Reynaud-Bouret, Patricia; Rivoirard, Vincent; Grammont, Franck; Tuleau-Malot, Christine

    2014-04-17

    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 MaterialThe online version of this article (doi:10.1186/2190-8567-4-3) contains supplementary material.

  16. 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.

  17. Quality Assurance and Statistics. High-Technology Training Module.

    ERIC Educational Resources Information Center

    Wirsbinski, William

    This high technology quality assurance and statistics training module is a part of the statistics unit for an algebra I or algebra II course. This module fits into high school math classes in which students compute and display measures of central tendency and variability. The module contains a description, objectives, and content outline--phase I…

  18. Time scales of spike-train correlation for neural oscillators with common drive.

    PubMed

    Barreiro, Andrea K; Shea-Brown, Eric; Thilo, Evan L

    2010-01-01

    We examine the effect of the phase-resetting curve on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy superthreshold stimulation. We use linear-response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem and contrast the results for type I vs type II models and across the different time scales over which spike correlations can be assessed. We find that, on long time scales, type I oscillators transfer correlations much more efficiently than type II oscillators. On short time scales this trend reverses, with the relative efficiency switching at a time scale that depends on the mean and standard deviation of input currents. This switch occurs over time scales that could be exploited by downstream circuits.

  19. 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

  20. A point-process response model for spike trains from single neurons in neural circuits under optogenetic stimulation.

    PubMed

    Luo, X; Gee, S; Sohal, V; Small, D

    2016-02-10

    Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high-frequency point process (neuronal spikes) while the input is another high-frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point-process response functions. The resulting model, point-process responses for optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area-under-the-curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters.

  1. Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements

    PubMed Central

    Ma, Xuan; Ma, Chaolin; Huang, Jian; Zhang, Peng; Xu, Jiang; He, Jiping

    2017-01-01

    Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed

  2. Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements.

    PubMed

    Ma, Xuan; Ma, Chaolin; Huang, Jian; Zhang, Peng; Xu, Jiang; He, Jiping

    2017-01-01

    Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed

  3. Statistical models suggest presence of two distinct subpopulations of miniature EPSCs in fast-spiking interneurons of rat prefrontal cortex.

    PubMed

    Malkin, S L; Kim, K K; Tikhonov, D B; Magazanik, L G; Zaitsev, A V

    2015-08-20

    Properties of excitatory synaptic responses in fast-spiking interneurons (FSIs) and pyramidal neurons (PNs) are different; however, the mechanisms and determinants of this diversity have not been fully investigated. In the present study, voltage-clamp recording of miniature excitatory post-synaptic currents (mEPSCs) was performed of layer 2-3 FSIs and PNs in the medial prefrontal cortex of rats aged 19-22days. The average mEPSCs in the FSIs exhibited amplitudes that were two times larger than those of the PNs and with much faster rise and decay. The mEPSC amplitude distributions in both cell types were asymmetric and in FSIs, the distributions were more skewed and had two-times larger coefficients of variation than in the PNs. In PNs but not in FSIs, the amplitude distributions were fitted well by different skewed unimodal functions that have been used previously for this purpose. In the FSIs, the distributions were well approximated only by a sum of two such functions, suggesting the presence of at least two subpopulations of events with different modal amplitudes. According to our estimates, two-thirds of the mEPSCs in FSIs belong to the high-amplitude subpopulation, and the modal amplitude in this subpopulation is approximately two times larger than that in the low-amplitude subpopulation. Using different statistical models, varying binning size, and data subsets, we confirmed the robustness and consistency of these findings.

  4. 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.

  5. Kinetic theory for neuronal networks with fast and slow excitatory conductances driven by the same spike train.

    PubMed

    Rangan, Aaditya V; Kovacic, Gregor; Cai, David

    2008-04-01

    We present a kinetic theory for all-to-all coupled networks of identical, linear, integrate-and-fire, excitatory point neurons in which a fast and a slow excitatory conductance are driven by the same spike train in the presence of synaptic failure. The maximal-entropy principle guides us in deriving a set of three (1+1) -dimensional kinetic moment equations from a Boltzmann-like equation describing the evolution of the one-neuron probability density function. We explain the emergence of correlation terms in the kinetic moment and Boltzmann-like equations as a consequence of simultaneous activation of both the fast and slow excitatory conductances and furnish numerical evidence for their importance in correctly describing the coarse-grained dynamics of the underlying neuronal network.

  6. Spike-train acquisition, analysis and real-time experimental control using a graphical programming language (LabView).

    PubMed

    Nordstrom, M A; Mapletoft, E A; Miles, T S

    1995-11-01

    A solution is described for the acquisition on a personal computer of standard pulses derived from neuronal discharge, measurement of neuronal discharge times, real-time control of stimulus delivery based on specified inter-pulse interval conditions in the neuronal spike train, and on-line display and analysis of the experimental data. The hardware consisted of an Apple Macintosh IIci computer and a plug-in card (National Instruments NB-MIO16) that supports A/D, D/A, digital I/O and timer functions. The software was written in the object-oriented graphical programming language LabView. Essential elements of the source code of the LabView program are presented and explained. The use of the system is demonstrated in an experiment in which the reflex responses to muscle stretch are assessed for a single motor unit in the human masseter muscle.

  7. 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.

  8. Neuronal spike-train responses in the presence of threshold noise.

    PubMed

    Coombes, S; Thul, R; Laudanski, J; Palmer, A R; Sumner, C J

    2011-03-01

    The variability of neuronal firing has been an intense topic of study for many years. From a modelling perspective it has often been studied in conductance based spiking models with the use of additive or multiplicative noise terms to represent channel fluctuations or the stochastic nature of neurotransmitter release. Here we propose an alternative approach using a simple leaky integrate-and-fire model with a noisy threshold. Initially, we develop a mathematical treatment of the neuronal response to periodic forcing using tools from linear response theory and use this to highlight how a noisy threshold can enhance downstream signal reconstruction. We further develop a more general framework for understanding the responses to large amplitude forcing based on a calculation of first passage times. This is ideally suited to understanding stochastic mode-locking, for which we numerically determine the Arnol'd tongue structure. An examination of data from regularly firing stellate neurons within the ventral cochlear nucleus, responding to sinusoidally amplitude modulated pure tones, shows tongue structures consistent with these predictions and highlights that stochastic, as opposed to deterministic, mode-locking is utilised at the level of the single stellate cell to faithfully encode periodic stimuli.

  9. 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.

  10. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

    PubMed Central

    Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

    2009-01-01

    Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast and data-robust computations

  11. [Comparative analysis of forms of statistical relationship between the background spike activity of cortical neurons and the EEG in different functional states of the brain].

    PubMed

    Karnup, S V

    1980-01-01

    The types and values of statistic dependence between extracellularly recorded background spike activity of cortical neurones and EEG oscillations were studied in chronic experiments on intact alert rabbits. It was shown that the relative number of neurones with discharges significantly connected with EEG slow components, remains practically the same in different functional states of the brain and amounts to about 80%. The mean level of the studied dependence changes following transitions from one state to another:it is lowered during the extinction of orienting reaction and continues to go down during the elaboration of the conditioned reflex.

  12. 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

  13. 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

  14. Statistical mentoring at early training and career stages

    DOE PAGES

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

    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

  15. Statistical mentoring at early training and career stages

    SciTech Connect

    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 activities 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.

  16. Predicting spike timing in highly synchronous auditory neurons at different sound levels

    PubMed Central

    Fontaine, Bertrand; Benichoux, Victor; Joris, Philip X.

    2013-01-01

    A challenge for sensory systems is to encode natural signals that vary in amplitude by orders of magnitude. The spike trains of neurons in the auditory system must represent the fine temporal structure of sounds despite a tremendous variation in sound level in natural environments. It has been shown in vitro that the transformation from dynamic signals into precise spike trains can be accurately captured by simple integrate-and-fire models. In this work, we show that the in vivo responses of cochlear nucleus bushy cells to sounds across a wide range of levels can be precisely predicted by deterministic integrate-and-fire models with adaptive spike threshold. Our model can predict both the spike timings and the firing rate in response to novel sounds, across a large input level range. A noisy version of the model accounts for the statistical structure of spike trains, including the reliability and temporal precision of responses. Spike threshold adaptation was critical to ensure that predictions remain accurate at different levels. These results confirm that simple integrate-and-fire models provide an accurate phenomenological account of spike train statistics and emphasize the functional relevance of spike threshold adaptation. PMID:23864375

  17. Australian Vocational Education and Training Statistics: Students and Courses, 2010

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2011

    2011-01-01

    This publication provides a summary of 2010 data relating to students, courses, qualifications, training providers and funding in Australia's publicly funded vocational education and training (VET) system. The Australian VET system provides training across a wide range of subject areas and is delivered through a variety of training institutions…

  18. Australian Vocational Education and Training Statistics: Students & Courses. 2013

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2014

    2014-01-01

    This publication provides a summary of 2013 data relating to students, courses, qualifications, training providers and funding in Australia's publicly funded vocational education and training (VET) system. The Australian VET system provides training across a wide range of subject areas and is delivered through a variety of training institutions…

  19. Australian Vocational Education and Training Statistics: Students and Courses, 2009

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2010

    2010-01-01

    This publication provides a summary of 2009 data relating to students, courses, qualifications, training providers and funding in Australia's publicly funded vocational education and training (VET) system. The Australian VET system provides training across a wide range of subject areas and is delivered through a variety of training institutions…

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

    PubMed

    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.

  1. Comparing effects in spike-triggered averages of rectified EMG across different behaviors

    PubMed Central

    Davidson, Adam G.; O’Dell, Ryan; Chan, Vanessa; Schieber, Marc H.

    2007-01-01

    Effects in spike-triggered averages (SpikeTAs) of rectified electromyographic activity (EMG) compiled for the same neuron-muscle pair during various behaviors often appear different. Do these differences represent significant changes in the effect of the neuron on the muscle activity? Quantitative comparison of such differences has been limited by two methodological problems, which we address here. First, although the linear baseline trend of many SpikeTAs can be adjusted with ramp subtraction, the curvilinear baseline trend of other SpikeTAs can not. To address this problem, we estimated baseline trends using a form of moving average. Artificial triggers were created in 1 ms increments from 40 ms before to 40 ms after each spike used to compile the SpikeTA. These 81 triggers were used to compile another average of rectified EMG, which we call a single-spike increment shifted average (single-spike ISA). Single-spike ISAs were averaged to produce an overall ISA, which captured slow trends in the baseline EMG while distributing any spike-locked features evenly throughout the 80 ms analysis window. The overall ISA then was subtracted from the initial SpikeTA, removing any slow baseline trends for more accurate measurement of SpikeTA effects. Second, the measured amplitude and temporal characteristics of SpikeTA effects produced by the same neuron-muscle pair may vary during different behaviors. But whether or not such variation is significant has been difficult to ascertain. We therefore applied a multiple fragment approach to permit statistical comparison of the measured features of SpikeTA effects for the same neuron-muscle pair during different behavioral epochs. Spike trains recorded in each task were divided into non-overlapping fragments of 100 spikes each, and a separate, ISA-corrected, SpikeTA was compiled for each fragment. Measurements made on these fragment SpikeTAs then were used as test statistics for comparison of peak percent increase, mean percent

  2. 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

  3. A Statistically Based Training Diagnostic Tool for Marine Aviation

    DTIC Science & Technology

    2014-06-01

    to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT...trends in visual form. Such a system could allow for early training interventions for those who struggle and better training management for those who...system could allow for early training interventions for those who struggle and better training management for those who are excelling. Although

  4. 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…

  5. Australian Vocational Education and Training Statistics, 1998. An Overview.

    ERIC Educational Resources Information Center

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

    Analysis of the vocational education and training (VET) sector in Australia shows that almost 1.54 million students undertook training in the publicly-funded VET sector; 51.5 percent were males and 48.5 percent were females; since 1997, total annual hours of training increased by 10.6 million to 312.8 million hours; and 4 fields of study accounted…

  6. Australian Vocational Education and Training Statistics: Student Outcomes, 2006. Summary

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2006

    2006-01-01

    This publication provides information regarding the training outcomes for students who completed their vocational education and training (VET) during 2005. The findings presented relate to students who are awarded a qualification (graduates), or who successfully complete part of a course and then leave the VET system (module completers). This…

  7. Australian Vocational Education and Training Statistics, 2001: An Overview.

    ERIC Educational Resources Information Center

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

    In 2001, 1.76 million students undertook training in Australia's public vocational education and training (VET) system. Nearly one-third of all Australians between the ages of 15 and 19 years participated in VET. Over the four years from 1998 to 2001, the number of students reported as participating in the public VET system increased by 221,500…

  8. 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…

  9. Australian Vocational Education and Training Statistics: Student Outcomes, 2012

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2012

    2012-01-01

    This publication presents information about the outcomes of students who completed their vocational education and training (VET) during 2011. The figures are derived from the Student Outcomes Survey, which is an annual survey that covers students who have an Australian address as their usual address and are awarded a qualification (graduates), or…

  10. Financial Information 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 information on how government-funded vocational education and training (VET) in Australia is financed and where the money is spent. Government-funded VET in the 2015 reporting year is broadly defined as all activity delivered by government providers and government-funded activity delivered by community education providers…

  11. 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…

  12. 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…

  13. 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…

  14. Internationally Comparable Statistics on Education, Training and Skills: Current State and Proposals

    ERIC Educational Resources Information Center

    Descy, Pascaline; Nestler, Katja; Tessaring, Manfred

    2005-01-01

    Comparable statistics on education, training and skills are not only used by research and analysis to provide explanation and evidence of the functioning of European labour markets and of education and training systems, but also to construct indicators comparing EU Member States, comparing the EU with competitors and assessing the achievement of…

  15. 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…

  16. 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.

  17. The Undergraduate Statistics Major--A Prelude to Actuarial Science Training.

    ERIC Educational Resources Information Center

    Ratliff, Michael I.; Williams, Raymond E.

    Recently there has been increased interest related to the Actuarial Science field. An actuary is a business professional who uses mathematical skills to define, analyze, and solve financial and social problems. This paper examines: (1) the interface between Statistical and Actuarial Science training; (2) statistical courses corresponding to…

  18. Reliability of Spike Timing in Neocortical Neurons

    NASA Astrophysics Data System (ADS)

    Mainen, Zachary F.; Sejnowski, Terrence J.

    1995-06-01

    It is not known whether the variability of neural activity in the cerebral cortex carries information or reflects noisy underlying mechanisms. In an examination of the reliability of spike generation using recordings from neurons in rat neocortical slices, the precision of spike timing was found to depend on stimulus transients. Constant stimuli led to imprecise spike trains, whereas stimuli with fluctuations resembling synaptic activity produced spike trains with timing reproducible to less than 1 millisecond. These data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.

  19. 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.

  20. 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.

  1. Vocational Education and Training: Programs and Outcomes. An Overview. Australian Vocational Education & Training Statistics, 2000.

    ERIC Educational Resources Information Center

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

    In the year 2000, approximately 1.75 million Australians (13.2% of the country's population) undertook some form of vocational education and training (VET). Of all VET students, 75.5% undertook training with Technical and Further Education (TAFE) and other government providers versus 13.0% with community providers and 11.5% with other registered…

  2. 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

  3. 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.

  4. Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

    PubMed

    Capecci, Elisa; Kasabov, Nikola; Wang, Grace Y

    2015-08-01

    The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging.

  5. Periodically-modulated inhibition of living pacemaker neurons--III. The heterogeneity of the postsynaptic spike trains, and how control parameters affect it.

    PubMed

    Segundo, J P; Vibert, J F; Stiber, M

    1998-11-01

    Codings involving spike trains at synapses with inhibitory postsynaptic potentials on pacemakers were examined in crayfish stretch receptor organs by modulating presynaptic instantaneous rates periodically (triangles or sines; frequencies, slopes and depths under, respectively, 5.0 Hz, 40.0/s/s and 25.0/s). Timings were described by interspike and cross-intervals ("phases"); patterns (dispersions, sequences) and forms (timing classes) were identified using pooled graphs (instant along the cycle when a spike occurs vs preceding interval) and return maps (plots of successive intervals). A remarkable heterogeneity of postsynaptic intervals and phases characterizes each modulation. All cycles separate into the same portions: each contains a particular form and switches abruptly to the next. Forms differ in irregularity and predictability: they are (see text) "p:q alternations", "intermittent", "phase walk-throughs", "messy erratic" and "messy stammering". Postsynaptic cycles are asymmetric (hysteresis). This contrasts with the presynaptic homogeneity, smoothness and symmetry. All control parameters are, individually and jointly, strongly influential. Presynaptic slopes, say, act through a postsynaptic sensitivity to their magnitude and sign; when increasing, hysteresis augments and forms change or disappear. Appropriate noise attenuates between-train contrasts, providing modulations are under 0.5 Hz. Postsynaptic natural intervals impose critical time bases, separating presynaptic intervals (around, above or below them) with dissimilar consequences. Coding rules are numerous and have restricted domains; generalizations are misleading. Modulation-driven forms are trendy pacemaker-driven forms. However, dissimilarities, slight when patterns are almost pacemaker, increase as inhibition departs from pacemaker and incorporate unpredictable features. Physiological significance-(1) Pacemaker-driven forms, simple and ubiquitous, appear to be elementary building blocks of

  6. Students in Vocational Education and Training: An Overview. Australian Vocational Education & Training Statistics, 2000.

    ERIC Educational Resources Information Center

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

    In the year 2000, approximately 13.2% of Australia's working age population (ages 15-64) was involved in some form of vocational education and training (VET) within the country's publicly-funded VET system, which represents an increase of nearly 2% compared with enrollment levels in 1977. Although female participation in VET has improved, males…

  7. A model of synaptic vesicle-pool depletion and replenishment can account for the interspike interval distributions and nonrenewal properties of spontaneous spike trains of auditory-nerve fibers.

    PubMed

    Peterson, Adam J; Irvine, Dexter R F; Heil, Peter

    2014-11-05

    In mammalian auditory systems, the spiking characteristics of each primary afferent (type I auditory-nerve fiber; ANF) are mainly determined by a single ribbon synapse in a single receptor cell (inner hair cell; IHC). ANF spike trains therefore provide a window into the operation of these synapses and cells. It was demonstrated previously (Heil et al., 2007) that the distribution of interspike intervals (ISIs) of cat ANFs during spontaneous activity can be modeled as resulting from refractoriness operating on a non-Poisson stochastic point process of excitation (transmitter release events from the IHC). Here, we investigate nonrenewal properties of these cat-ANF spontaneous spike trains, manifest as negative serial ISI correlations and reduced spike-count variability over short timescales. A previously discussed excitatory process, the constrained failure of events from a homogeneous Poisson point process, can account for these properties, but does not offer a parsimonious explanation for certain trends in the data. We then investigate a three-parameter model of vesicle-pool depletion and replenishment and find that it accounts for all experimental observations, including the ISI distributions, with only the release probability varying between spike trains. The maximum number of units (single vesicles or groups of simultaneously released vesicles) in the readily releasable pool and their replenishment time constant can be assumed to be constant (∼4 and 13.5 ms, respectively). We suggest that the organization of the IHC ribbon synapses not only enables sustained release of neurotransmitter but also imposes temporal regularity on the release process, particularly when operating at high rates.

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

    PubMed Central

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

    2016-01-01

    Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate. PMID:28066225

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

    PubMed

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

    2016-01-01

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

  10. Regulation of spike timing in visual cortical circuits

    PubMed Central

    Tiesinga, Paul; Fellous, Jean-Marc; Sejnowski, Terrence J.

    2010-01-01

    A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role. PMID:18200026

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

    PubMed

    Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

    2013-01-01

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

  12. 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

    2014-02-05

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning.

  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. Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from spike trains by network model simulation.

    PubMed

    Onizuka, Miho; Hoang, Huu; Kawato, Mitsuo; Tokuda, Isao T; Schweighofer, Nicolas; Katori, Yuichi; Aihara, Kazuyuki; Lang, Eric J; Toyama, Keisuke

    2013-11-01

    The inferior olive (IO) possesses synaptic glomeruli, which contain dendritic spines from neighboring neurons and presynaptic terminals, many of which are inhibitory and GABAergic. Gap junctions between the spines electrically couple neighboring neurons whereas the GABAergic synaptic terminals are thought to act to decrease the effectiveness of this coupling. Thus, the glomeruli are thought to be important for determining the oscillatory and synchronized activity displayed by IO neurons. Indeed, the tendency to display such activity patterns is enhanced or reduced by the local administration of the GABA-A receptor blocker picrotoxin (PIX) or the gap junction blocker carbenoxolone (CBX), respectively. We studied the functional roles of the glomeruli by solving the inverse problem of estimating the inhibitory (gi) and gap-junctional conductance (gc) using an IO network model. This model was built upon a prior IO network model, in which the individual neurons consisted of soma and dendritic compartments, by adding a glomerular compartment comprising electrically coupled spines that received inhibitory synapses. The model was used in the forward mode to simulate spike data under PIX and CBX conditions for comparison with experimental data consisting of multi-electrode recordings of complex spikes from arrays of Purkinje cells (complex spikes are generated in a one-to-one manner by IO spikes and thus can substitute for directly measuring IO spike activity). The spatiotemporal firing dynamics of the experimental and simulation spike data were evaluated as feature vectors, including firing rates, local variation, auto-correlogram, cross-correlogram, and minimal distance, and were contracted onto two-dimensional principal component analysis (PCA) space. gc and gi were determined as the solution to the inverse problem such that the simulation and experimental spike data were closely matched in the PCA space. The goodness of the match was confirmed by an analysis of variance

  15. 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.

  16. 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.

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

    PubMed Central

    Lütcke, Henry; Gerhard, Felipe; Zenke, Friedemann; Gerstner, Wulfram; Helmchen, Fritjof

    2013-01-01

    Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence (“spike trains”) from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties. PMID:24399936

  18. Statistical analysis and decoding of neural activity in the rodent geniculate ganglion using a metric-based inference system.

    PubMed

    Wu, Wei; Mast, Thomas G; Ziembko, Christopher; Breza, Joseph M; Contreras, Robert J

    2013-01-01

    We analyzed the spike discharge patterns of two types of neurons in the rodent peripheral gustatory system, Na specialists (NS) and acid generalists (AG) to lingual stimulation with NaCl, acetic acid, and mixtures of the two stimuli. Previous computational investigations found that both spike rate and spike timing contribute to taste quality coding. These studies used commonly accepted computational methods, but they do not provide a consistent statistical evaluation of spike trains. In this paper, we adopted a new computational framework that treated each spike train as an individual data point for computing summary statistics such as mean and variance in the spike train space. We found that these statistical summaries properly characterized the firing patterns (e. g. template and variability) and quantified the differences between NS and AG neurons. The same framework was also used to assess the discrimination performance of NS and AG neurons and to remove spontaneous background activity or "noise" from the spike train responses. The results indicated that the new metric system provided the desired decoding performance and noise-removal improved stimulus classification accuracy, especially of neurons with high spontaneous rates. In summary, this new method naturally conducts statistical analysis and neural decoding under one consistent framework, and the results demonstrated that individual peripheral-gustatory neurons generate a unique and reliable firing pattern during sensory stimulation and that this pattern can be reliably decoded.

  19. 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…

  20. 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

  1. 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.

  2. 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.

  3. Neuronal communication: firing spikes with spikes.

    PubMed

    Brecht, Michael

    2012-08-21

    Spikes of single cortical neurons can exert powerful effects even though most cortical synapses are too weak to fire postsynaptic neurons. A recent study combining single-cell stimulation with population imaging has visualized in vivo postsynaptic firing in genetically identified target cells. The results confirm predictions from in vitro work and might help to understand how the brain reads single-neuron activity.

  4. Statistical analysis of North Atlantic right whale (Eubalaena glacialis) signal trains in Cape Cod Bay, spring 2012.

    PubMed

    Urazghildiiev, Ildar R

    2014-11-01

    Statistical characteristics of signal trains produced by North Atlantic right whales (NARW) during the winter and early spring in Cape Cod Bay, MA are described. Data analysis was based on four days of acoustic recordings that were obtained with synchronized hydrophones. Based on temporal and geographical clustering of detected signals, 7264 NARW sounds were identified and associated with 559 signal trains. The detected signals were assigned to four classes of narrowband tonal calls--upcalls, downcalls, complex, and high frequency, and two classes of wideband sounds--gunshots and complex. Empirical distributions of the number of signals in trains, total duration of trains, the positions of NARW, and signal classes are presented. Results indicate that 68.9% of all signal trains consisted of 10 or fewer signals. Low and high frequency tonals that lacked wideband sounds formed 69.1% of trains; 5.0% of trains lacked tonals. Trains consisting of only upcalls comprised 44.2% of all detected trains. Because 18.3% of trains contained no upcalls, using detectors that identify all signal classes would improve right whale detection.

  5. 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.

  6. Spike-Based Population Coding and Working Memory

    PubMed Central

    Boerlin, Martin; Denève, Sophie

    2011-01-01

    Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons. PMID:21379319

  7. 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…

  8. Australian Vocational Education and Training Statistics: Apprentices and Trainees. Annual, 2008

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2009

    2009-01-01

    This annual publication provides a summary of training activity in apprenticeships and traineeships in Australia, from the period 1998 to 2008, including information on training rates, attrition rates, completion rates, training within the trades and duration of training. The figures in this publication are derived from the National Apprentice and…

  9. 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…

  10. A Study of Teacher Training at Sixth-Cycle Teacher Corps Projects. Volume 3, Supplementary Statistical Tables.

    ERIC Educational Resources Information Center

    Marsh, David D.; And Others

    This document is Volume 3 of the report on the first phase of a two-phase longitudinal study of the Teacher Corps program being conducted by Pacific Training and Technical Assistance Corporation. It contains supplementary material, usually statistical tables or technical material, which supports Volume 1, the main volume of the report (see ED 098…

  11. More than Money Can Say. The Impact of ESL and Literacy Training in the Australian Workplace. Volume II. Statistical Overview.

    ERIC Educational Resources Information Center

    Australian Dept. of Employment, Education, Training and Youth Affairs, Canberra.

    This volume contains all statistical data from a research project that determined the impact of workplace language and literacy inclusive training (LLIT) on key aspects of the workplace in regard to the whole process of workplace change. The document presents the principal data on which the findings and recommendations are based. A general…

  12. 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)

  13. Solar Decameter Spikes

    NASA Astrophysics Data System (ADS)

    Melnik, V. N.; Shevchuk, N. V.; Konovalenko, A. A.; Rucker, H. O.; Dorovskyy, V. V.; Poedts, S.; Lecacheux, A.

    2014-05-01

    We analyze and discuss the properties of decameter spikes observed in July - August 2002 by the UTR-2 radio telescope. These bursts have a short duration (about one second) and occur in a narrow frequency bandwidth (50 - 70 kHz). They are chaotically located in the dynamic spectrum. Decameter spikes are weak bursts: their fluxes do not exceed 200 - 300 s.f.u. An interesting feature of these spikes is the observed linear increase of the frequency bandwidth with frequency. This dependence can be explained in the framework of the plasma mechanism that causes the radio emission, taking into account that Langmuir waves are generated by fast electrons within a narrow angle θ≈13∘ - 18∘ along the direction of the electron propagation. In the present article we consider the problem of the short lifetime of decameter spikes and discuss why electrons generate plasma waves in limited regions.

  14. Australian Vocational Education and Training Statistics: Government-Funded Students and Courses-- January to September 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). The data in this publication cover the period of 1 January to 30 September 2016. For…

  15. 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…

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

    PubMed

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

    2010-11-17

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

  17. 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.

  18. Students with a Disability in Vocational Education and Training. Australian Vocational Education and Training Statistics, 2000. At a Glance.

    ERIC Educational Resources Information Center

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

    The overall participation and performance of disabled students in vocational education and training (VET) programs in Australia in 2000 were analyzed. The analysis was based on data from National Centre for Vocational Education Research collections and surveys. The following were among the key findings: (1) the number of VET students reporting a…

  19. Australian Vocational Education and Training Statistics, 1999. Survey of Employer Views on Vocational Education and Training. At a Glance.

    ERIC Educational Resources Information Center

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

    The 1999 Australian Survey of Employer Views on vocational education and training (VET) followed previous surveys in 1995 and 1997. The number of organizations employing recent VET graduates increased steadily over the last 5 years, from 63,000 in 1995 to 104,000 in 1997 to 117,000 in 1999. On the whole, employer views on VET were more positive in…

  20. A spiking neural network architecture for nonlinear function approximation.

    PubMed

    Iannella, N; Back, A D

    2001-01-01

    Multilayer perceptrons have received much attention in recent years due to their universal approximation capabilities. Normally, such models use real valued continuous signals, although they are loosely based on biological neuronal networks that encode signals using spike trains. Spiking neural networks are of interest both from a biological point of view and in terms of a method of robust signaling in particularly noisy or difficult environments. It is important to consider networks based on spike trains. A basic question that needs to be considered however, is what type of architecture can be used to provide universal function approximation capabilities in spiking networks? In this paper, we propose a spiking neural network architecture using both integrate-and-fire units as well as delays, that is capable of approximating a real valued function mapping to within a specified degree of accuracy.

  1. How Can Students' Academic Performance in Statistics Be Improved? Testing the Influence of Social and Temporal-Self Comparison Feedback in a Web-Based Training Environment

    ERIC Educational Resources Information Center

    Delaval, Marine; Michinov, Nicolas; Le Bohec, Olivier; Le Hénaff, Benjamin

    2017-01-01

    The aim of this study was to examine how social or temporal-self comparison feedback, delivered in real-time in a web-based training environment, could influence the academic performance of students in a statistics examination. First-year psychology students were given the opportunity to train for a statistics examination during a semester by…

  2. Designing a Course in Statistics for a Learning Health Systems Training Program

    ERIC Educational Resources Information Center

    Samsa, Gregory P.; LeBlanc, Thomas W.; Zaas, Aimee; Howie, Lynn; Abernethy, Amy P.

    2014-01-01

    The core pedagogic problem considered here is how to effectively teach statistics to physicians who are engaged in a "learning health system" (LHS). This is a special case of a broader issue--namely, how to effectively teach statistics to academic physicians for whom research--and thus statistics--is a requirement for professional…

  3. Total VET Graduate Outcomes, 2016: 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 the outcomes of graduates who completed their vocational education and training (VET) in Australia during 2015 and were awarded a qualification. For the first time, the outcomes of all graduates are reported; that is, those in receipt of Commonwealth or state funding and those who paid for their training. The…

  4. 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…

  5. 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…

  6. 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…

  7. Training of Educational Administrators in China. Occasional Paper in Educational Planning, Management and Statistics No. 12.

    ERIC Educational Resources Information Center

    Fuquan, Zhang

    China has recently developed a national infrastructure of institutional units, from the central to county level, for the training of educational administrators. The range, scope, and dimensions of educational administrative training as conducted in China in 1985 are described in this document. Contents include an introduction to the Chinese system…

  8. A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

    PubMed

    Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

    2013-01-01

    We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

  9. A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

    PubMed Central

    Chichilnisky, E. J.; Simoncelli, Eero P.

    2013-01-01

    We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth. PMID:23671583

  10. Temporal pairwise spike correlations fully capture single-neuron information

    PubMed Central

    Dettner, Amadeus; Münzberg, Sabrina; Tchumatchenko, Tatjana

    2016-01-01

    To crack the neural code and read out the information neural spikes convey, it is essential to understand how the information is coded and how much of it is available for decoding. To this end, it is indispensable to derive from first principles a minimal set of spike features containing the complete information content of a neuron. Here we present such a complete set of coding features. We show that temporal pairwise spike correlations fully determine the information conveyed by a single spiking neuron with finite temporal memory and stationary spike statistics. We reveal that interspike interval temporal correlations, which are often neglected, can significantly change the total information. Our findings provide a conceptual link between numerous disparate observations and recommend shifting the focus of future studies from addressing firing rates to addressing pairwise spike correlation functions as the primary determinants of neural information. PMID:27976717

  11. 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

  12. Gamma oscillations of spiking neural populations enhance signal discrimination.

    PubMed

    Masuda, Naoki; Doiron, Brent

    2007-11-01

    Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive.

  13. 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

  14. Reliability of spike and burst firing in thalamocortical relay cells.

    PubMed

    Zeldenrust, Fleur; Chameau, Pascal J P; Wadman, Wytse J

    2013-12-01

    The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell soma. The neuron responded with spike trains that showed trial-to-trial variability, due to amongst others slow changes in its internal state and the experimental setup. The DC current allowed to bring the neuron in different states, characterized by a well defined membrane voltage (between -80 and -50 mV) and by a specific firing regime that on depolarization gradually shifted from a predominantly bursting regime to a tonic spiking regime. The filtered frozen white noise generated a spike pattern output with a broad spike interval distribution. The coincidence factor and the Hunter and Milton measure were used as reliability measures of the output spike train. In the experimental TCR cell as well as the Morris-Lecar model cell the reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. The reliability of the output of the TCR cell increases with depolarization. In hyperpolarized states bursts are more reliable than single spikes. The analytically derived relations were capable to predict several of the experimentally recorded spike features.

  15. 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.

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

    PubMed

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

    2013-01-01

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

  17. 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

  18. Training's Benchmarks. A Statistical Sketch of Employer-Provided Training and Education: 1969-1981. Task I Report: The Impact of Public Policy on Education and Training in the Private Sector.

    ERIC Educational Resources Information Center

    Zemsky, Robert; And Others

    This report, comprised of five separate reports, presents a set of statistical benchmarks for gauging the growth and development of corporate training and education over the last decade. "Summary Findings" (Robert Zemsky) presents in capsule form the technical analyses of the findings of the other four reports. It considers these…

  19. A Statistical Analysis of the Determinants of Naval Flight Officer Training Attrition

    DTIC Science & Technology

    1998-03-01

    variables utilized in the model include commissioning source, race, and undergraduate major. The statistical analysis sought to determine the effect of each...of these demographic factors on the probability of attrition by reason. The results show that commissioning source has a significant effect on...in the model include commissioning source, race, and undergraduate major. The statistical analysis sought to determine the effect of each of these

  20. 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

  1. Filter based phase distortions in extracellular spikes

    PubMed Central

    Yael, Dorin

    2017-01-01

    Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results. PMID:28358895

  2. 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.

  3. 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

  4. 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…

  5. 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…

  6. Government-Funded Student Outcomes, 2016: 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 the outcomes of students who completed government-funded vocational education and training (VET) during 2015, with the data collected in mid-2016. Government-funded VET is broadly defined as all activity delivered by government providers and government-funded activity delivered by community education and…

  7. 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…

  8. 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…

  9. Australian Vocational Education and Training Statistics: Apprentices and Trainees. March Quarter, 2011

    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 March quarter 2011. The figures in this publication are derived from the National Apprentice and Trainee Collection no. 68 (June 2011 estimates). The most recent figures in this publication are estimated (those for training activity from the September…

  10. 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…

  11. 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…

  12. 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)…

  13. 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…

  14. Completion and Attrition Rates for Apprentices and Trainees 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 presents completion and attrition rates for apprentices and trainees using three different methodologies: (1) contract completion and attrition rates: based on the outcomes of contracts of training; (2) individual completion rates: based on contract completion rates and adjusted for factors representing average recommencements by…

  15. 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…

  16. Australian Vocational Education and Training Statistics: Outcomes from the Productivity Places Program, 2009

    ERIC Educational Resources Information Center

    National Centre for Vocational Education Research (NCVER), 2009

    2009-01-01

    Information is presented in this publication about the outcomes for students who completed their vocational education and training (VET) under the Productivity Places Program (PPP) during 2008. The Productivity Places Program Survey covers students who were awarded a qualification in 2008 with funding from the PPP. The survey focuses on students'…

  17. 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.)…

  18. 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…

  19. 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…

  20. 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

  1. Spiking neuron network Helmholtz machine.

    PubMed

    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.

  2. 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

  3. 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…

  4. 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…

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

    NASA Astrophysics Data System (ADS)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

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

  6. 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

  7. Lyondell outage spikes prices

    SciTech Connect

    1996-08-07

    Methanol spot markets in the US Gulf Coast cooled a bit late last week from their Monday spike in the wake of a pipeline rupture and fire that shut down Lyondell Petrochemical`s Channelview, TX complex and its 248-million gal/year methanol plant. The unit resumed production last week and was expected to return to full service by August 3. Offering prices shot up at least 10% over the pre-accident level of about 50 cts/gal fob. No actual business could be confirmed at a price of more than 52 cts-53 cts/gal, however.

  8. 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.

  9. 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

  10. Use of Computer Statistical Packages to Generate Quality Control Reports on Training

    DTIC Science & Technology

    1980-01-01

    dI NI 40MMY 6V 6locknIumibff) Training Data Processing Management Attitudes (Psychology) Computers Computer Applications Automation Computer Programs... processing and produces graphic displays very similar to quality control charts. [The output allows a ai4eeto fdvAn alp advmmnI. anaw. 4u OP W 3 on"" of...David W. Bessemer Acession Forand -- kF Brian L. Kottas Submitted by: ByDonald F. Haggard, Chief ByFORT KNOX FIELD UNIT r o:, A&1 1l.d/Qr sp a C i ELI

  11. Radioxenon spiked air

    DOE PAGES

    Watrous, Matthew G.; Delmore, James E.; Hague, Robert K.; ...

    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

  12. Voltammogram spikes interpreted as envelopes of spikes resulting from electrode crystals of various sizes: Application to the UPD of Cu on Au(111)

    NASA Astrophysics Data System (ADS)

    Medved', Igor; Huckaby, Dale A.

    2003-06-01

    We study and explain shapes of voltammogram spikes, observed during underpotential deposition (UPD) on electrode surfaces, as averaged envelopes of mutually shifted spikes associated with first-order phase transitions that occur in crystalline domains of various sizes that are formed on the electrode surface. This concept, already used in our previous work for two-phase systems and symmetric voltammogram spike shapes, is here substantially generalized to systems with multiple-phase coexistence and asymmetric spike shapes, using the rigorous statistical mechanical techniques of Borgs and Kotecký. Rather than mere numerical plots, we extract explicit functions that accurately describe the spike shapes. For the sake of clarity, we present our analysis and apply our results to fit the voltammogram of the UPD of Cu on Au(111) in sulfuric acid medium. This voltammogram shows two distinct spikes with a broad foot region near the spike at higher potentials. As was done in earlier treatments, we explain each of the two spikes as a result of a first-order transition. Here, though, the spikes are obtained as envelopes of closely spaced spikes resulting from crystals of various sizes. In contrast to earlier studies, however, we also explain the foot region in the same way. The foot's shape, despite its large width and small height, can be equally well obtained as an envelope of shifted crystal spikes that are broader and smaller than those giving rise to the two distinct spikes. We achieve very good agreement with experiment.

  13. 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.

  14. 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.

  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. 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.

  17. 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

  18. 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

  19. Boosted leave-many-out cross-validation: the effect of training and test set diversity on PLS statistics.

    PubMed

    Clark, Robert D

    2003-01-01

    It is becoming increasingly common in quantitative structure/activity relationship (QSAR) analyses to use external test sets to evaluate the likely stability and predictivity of the models obtained. In some cases, such as those involving variable selection, an internal test set--i.e., a cross-validation set--is also used. Care is sometimes taken to ensure that the subsets used exhibit response and/or property distributions similar to those of the data set as a whole, but more often the individual observations are simply assigned 'at random.' In the special case of MLR without variable selection, it can be analytically demonstrated that this strategy is inferior to others. Most particularly, D-optimal design performs better if the form of the regression equation is known and the variables involved are well behaved. This report introduces an alternative, non-parametric approach termed 'boosted leave-many-out' (boosted LMO) cross-validation. In this method, relatively small training sets are chosen by applying optimizable k-dissimilarity selection (OptiSim) using a small subsample size (k = 4, in this case), with the unselected observations being reserved as a test set for the corresponding reduced model. Predictive errors for the full model are then estimated by aggregating results over several such analyses. The countervailing effects of training and test set size, diversity, and representativeness on PLS model statistics are described for CoMFA analysis of a large data set of COX2 inhibitors.

  20. Predictability of EEG interictal spikes.

    PubMed Central

    Scott, D A; Schiff, S J

    1995-01-01

    To determine whether EEG spikes are predictable, time series of EEG spike intervals were generated from subdural and depth electrode recordings from four patients. The intervals between EEG spikes were hand edited to ensure high accuracy and eliminate false positive and negative spikes. Spike rates (per minute) were generated from longer time series, but for these data hand editing was usually not feasible. Linear and nonlinear models were fit to both types of data. One patient had no linear or nonlinear predictability, two had predictability that could be well accounted for with a linear stochastic model, and one had a degree of nonlinear predictability for both interval and rate data that no linear model could adequately account for. PMID:8580318

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

    PubMed Central

    Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E.

    2014-01-01

    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. PMID:25392163

  2. 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.

  3. Neural cache: a low-power online digital spike-sorting architecture.

    PubMed

    Peng, Chung-Ching; Sabharwal, Pawan; Bashirullah, Rizwan

    2008-01-01

    Transmitting large amounts of data sensed by multi-electrode array devices is widely considered to be a challenging problem in designing implantable neural recording systems. Spike sorting is an important step to reducing the data bandwidth before wireless data transmission. The feasibility of spike sorting algorithms in scaled CMOS technologies, which typically operate on low frequency neural spikes, is determined largely by its power consumption, a dominant portion of which is leakage power. Our goal is to explore energy saving architectures for online spike sorting without sacrificing performance. In the face of non-stationary neural data, training is not always a feasible option. We present a low-power architecture for real-time online spike sorting that does not require any training period and has the capability to quickly respond to the changing spike shapes.

  4. Point-process analysis of neural spiking activity of muscle spindles recorded from thin-film longitudinal intrafascicular electrodes.

    PubMed

    Citi, Luca; Djilas, Milan; Azevedo-Coste, Christine; Yoshida, Ken; Brown, Emery N; Barbieri, Riccardo

    2011-01-01

    Recordings from thin-film Longitudinal Intra-Fascicular Electrodes (tfLIFE) together with a wavelet-based de-noising and a correlation-based spike sorting algorithm, give access to firing patterns of muscle spindle afferents. In this study we use a point process probability structure to assess mechanical stimulus-response characteristics of muscle spindle spike trains. We assume that the stimulus intensity is primarily a linear combination of the spontaneous firing rate, the muscle extension, and the stretch velocity. By using the ability of the point process framework to provide an objective goodness of fit analysis, we were able to distinguish two classes of spike clusters with different statistical structure. We found that spike clusters with higher SNR have a temporal structure that can be fitted by an inverse Gaussian distribution while lower SNR clusters follow a Poisson-like distribution. The point process algorithm is further able to provide the instantaneous intensity function associated with the stimulus-response model with the best goodness of fit. This important result is a first step towards a point process decoding algorithm to estimate the muscle length and possibly provide closed loop Functional Electrical Stimulation (FES) systems with natural sensory feedback information.

  5. 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

  6. Dendritic Spikes in Sensory Perception

    PubMed Central

    Manita, Satoshi; Miyakawa, Hiroyoshi; Kitamura, Kazuo; Murayama, Masanori

    2017-01-01

    What is the function of dendritic spikes? One might argue that they provide conditions for neuronal plasticity or that they are essential for neural computation. However, despite a long history of dendritic research, the physiological relevance of dendritic spikes in brain function remains unknown. This could stem from the fact that most studies on dendrites have been performed in vitro. Fortunately, the emergence of novel techniques such as improved two-photon microscopy, genetically encoded calcium indicators (GECIs), and optogenetic tools has provided the means for vital breakthroughs in in vivo dendritic research. These technologies enable the investigation of the functions of dendritic spikes in behaving animals, and thus, help uncover the causal relationship between dendritic spikes, and sensory information processing and synaptic plasticity. Understanding the roles of dendritic spikes in brain function would provide mechanistic insight into the relationship between the brain and the mind. In this review article, we summarize the results of studies on dendritic spikes from a historical perspective and discuss the recent advances in our understanding of the role of dendritic spikes in sensory perception. PMID:28261060

  7. 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.

    PubMed

    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 programs (86%) participated. This survey replicated and extended a previous survey (L. S. Aiken, S. G. West, L. B. Sechrest, & R. R. Reno, 1990), permitting examination of curriculum development. Most training supported laboratory and not field research. The median of 1.6 years of training in statistics and measurement was mainly devoted to the modally 1-year introductory statistics course, leaving little room for advanced study. Curricular enhancements were noted in statistics and to a minor degree in measurement. Additional coverage of both fundamental and innovative quantitative methodology is needed. The research design curriculum has largely stagnated, a cause for great concern. Elite programs showed no overall advantage in quantitative training. Forces that support curricular innovation are characterized. Human capital challenges to quantitative training, including recruiting and supporting young quantitative faculty, are discussed. Steps must be taken to bring innovations in quantitative methodology into the curriculum of PhD programs in psychology.

  8. Spikes removal in surface measurement

    NASA Astrophysics Data System (ADS)

    Podulka, P.; Pawlus, P.; Dobrzański, P.; Lenart, A.

    2014-03-01

    Several cylinder surface topographies made from grey cast iron were measured by Talysurf CCI white light interferometer with and without use of spikes filter. They were plateau honed by abrasive stones. Measured area was 3.3 mm × 3.3 mm, height resolution was 0.01 nm. The forms were eliminated using polynomial of the 3rd degree. After it, spikes were removed using four methods. These approaches were compared. Parameters of the smaller and highest sensitivity on spikes presence were selected.

  9. 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…

  10. The microwave spectrum of solar millisecond spikes

    NASA Technical Reports Server (NTRS)

    Staehli, M.; Magun, A.

    1986-01-01

    The microwave radiation from solar flares sometimes shows short and intensive spikes which are superimposed on the burst continuum. In order to determine the upper frequency limit of their occurrence and the circular polarization, a statistical analysis was performed on digital microwave observations from 3.2 to 92.5 GHz. Additionally, fine structures were investigated with a fast 32-channel spectrometer at 3.47 GHz. It was found that about 10 percent of the bursts show fine structures at 3.2 and 5.2 GHz, whereas none occurred above 8.4 GHz. Most of the observed spikes were very short and their bandwidth varied from below 0.5 MHz to more than 200 MHz. Simultaneous observations at two further frequencies showed no coincident spikes at the second and third harmonic. The observations can be explained by the theory of electron cyclotron masering if the observed bandwidths are determined by magnetic field inhomogeneities or if the rise times are independent of the source diameters. The latter would imply source sizes between 50 and 100 km.

  11. 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…

  12. Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation.

    PubMed

    Riehle, A; Grammont, F; Diesmann, M; Grün, S

    2000-01-01

    Movement preparation is considered to be based on central processes which are responsible for improving motor performance. For instance, it has been shown that motor cortical neurones change their activity selectively in relation to prior information about movement parameters. However, it is not clear how groups of neurones dynamically organize their activity to cope with computational demands. The aim of the study was to compare the firing rate of multiple simultaneously recorded neurones with the interaction between them by describing not only the frequency of occurrence of epochs of significant synchronization, but also its modulation in time and its changes in temporal precision during an instructed delay. Multiple single-neurone activity was thus recorded in monkey motor cortex during the performance of two different delayed multi-directional pointing tasks. In order to detect conspicuous spike coincidences in simultaneously recorded spike trains by tolerating temporal jitter ranging from 0 to 20 ms and to calculate their statistical significance, a modified method of the 'Unitary Events' analysis was used. Two main results were obtained. First, simultaneously recorded neurones synchronize their spiking activity in a highly dynamic way. Synchronization becomes significant only during short periods (about 100 to 200 ms). Several such periods occurred during a behavioural trial more or less regularly. Second, in many pairs of neurones, the temporal precision of synchronous activity was highest at the end of the preparatory period. As a matter of fact, at the beginning of this period, after the presentation of the preparatory signal, neurones significantly synchronize their spiking activity, but with low temporal precision. As time advances, significant synchronization becomes more precise. Data indicate that not only the discharge rate is involved in preparatory processes, but also temporal aspects of neuronal activity as expressed in the precise synchronization

  13. Spike-timing-dependent synaptic plasticity depends on dendritic location

    NASA Astrophysics Data System (ADS)

    Froemke, Robert C.; Poo, Mu-ming; Dan, Yang

    2005-03-01

    In the neocortex, each neuron receives thousands of synaptic inputs distributed across an extensive dendritic tree. Although postsynaptic processing of each input is known to depend on its dendritic location, it is unclear whether activity-dependent synaptic modification is also location-dependent. Here we report that both the magnitude and the temporal specificity of spike-timing-dependent synaptic modification vary along the apical dendrite of rat cortical layer 2/3 pyramidal neurons. At the distal dendrite, the magnitude of long-term potentiation is smaller, and the window of pre-/postsynaptic spike interval for long-term depression (LTD) is broader. The spike-timing window for LTD correlates with the window of action potential-induced suppression of NMDA (N-methyl-D-aspartate) receptors; this correlation applies to both their dendritic location-dependence and pharmacological properties. Presynaptic stimulation with partial blockade of NMDA receptors induced LTD and occluded further induction of spike-timing-dependent LTD, suggesting that NMDA receptor suppression underlies LTD induction. Computer simulation studies showed that the dendritic inhomogeneity of spike-timing-dependent synaptic modification leads to differential input selection at distal and proximal dendrites according to the temporal characteristics of presynaptic spike trains. Such location-dependent tuning of inputs, together with the dendritic heterogeneity of postsynaptic processing, could enhance the computational capacity of cortical pyramidal neurons.

  14. 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…

  15. 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…

  16. 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

  17. 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

  18. Event-driven contrastive divergence for spiking neuromorphic systems.

    PubMed

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

    2013-01-01

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

  19. 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…

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

    PubMed

    Graupner, Michael; Wallisch, Pascal; Ostojic, Srdjan

    2016-11-02

    Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity.

  1. Sediment spiking for toxicity testing

    SciTech Connect

    Murdoch, M.H.; Norman, D.M.; Chapman, P.M.; Norman, D.M.; Quintino, V.M.

    1994-12-31

    Sediment toxicity testing integrates responses to sediment variables and hence does not directly indicate cause-and-effect. One tool for determining cause-and-effect is sediment spiking in which relatively uncontaminated sediment is amended with known amounts of contaminants, then tested for toxicity. Based on the concentration-response relationship(s), the relative toxicity of the spiked contaminants and their significance in sediment mixtures can be assessed. However, sediment spiking methods vary considerably. The present study details an appropriate methodology for amending sediments with a range of organic contaminant concentrations including different solvent schemes and an equilibration period. This methodology is described as appropriate because predicted and actual concentrations were similar, and responses in an acute 10-d amphipod test matched predictions and other data.

  2. Consequences and mechanisms of spike broadening of R20 cells in Aplysia californica.

    PubMed

    Ma, M; Koester, J

    1995-10-01

    We studied frequency-dependent spike broadening in the two electrically coupled R20 neurons in the abdominal ganglion of Aplysia. The peptidergic R20 cells excite the R25/L25 interneurons (which trigger respiratory pumping) and inhibit the RB cells. When fired at 1-10 Hz, the duration of the falling phase of the action potential in R20 neurons increases 2-10 fold during a spike train. Spike broadening recorded from the somata of the R20 cells affected synaptic transmission to nearby follower cells. Chemically mediated synaptic output was reduced by approximately 50% when recorded trains of nonbroadened action potentials were used as command signals for a voltage-clamped R20 cell. Electrotonic EPSPs between the R20 cells, which normally facilitated by two- to fourfold during a high frequency spike train, showed no facilitation when spike broadening was prevented under voltage-clamp control. To examine the mechanism of frequency-dependent spike broadening, we applied two-electrode voltage-clamp and pharmacological techniques to the somata of R20 cells. Several voltage-gated ionic currents were isolated, including INa, a multicomponent ICa, and three K+ currents--a high threshold, fast transient A-type K+ current (IAdepol), a delayed rectifier K+ current (IK-V), and a Ca(2+)-sensitive K+ current (IK-Ca), made up of two components. The influences of different currents on spike broadening were determined by using the recorded train of gradually broadening action potentials as the command for the voltage clamp. We found the following. (1) IAdepol is the major outward current that contributes to repolarization of nonbroadened spikes. It undergoes pronounced cumulative inactivation that is a critical determinant of spike broadening. (2) Activity-dependent changes in IK-V, IK-Ca, and ICa have complex effects on the kinetics and extent of broadening. (3) The time integral of ICa during individual action potentials increases approximately threefold during spike broadening.

  3. 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

  4. Sparse and powerful cortical spikes.

    PubMed

    Wolfe, Jason; Houweling, Arthur R; Brecht, Michael

    2010-06-01

    Activity in cortical networks is heterogeneous, sparse and often precisely timed. The functional significance of sparseness and precise spike timing is debated, but our understanding of the developmental and synaptic mechanisms that shape neuronal discharge patterns has improved. Evidence for highly specialized, selective and abstract cortical response properties is accumulating. Singe-cell stimulation experiments demonstrate a high sensitivity of cortical networks to the action potentials of some, but not all, single neurons. It is unclear how this sensitivity of cortical networks to small perturbations comes about and whether it is a generic property of cortex. The unforeseen sensitivity to cortical spikes puts serious constraints on the nature of neural coding schemes.

  5. Optimization Methods for Spiking Neurons and Networks

    PubMed Central

    Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

    2011-01-01

    Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

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

    PubMed Central

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

    2009-01-01

    The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically-constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient’s MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes. PMID:20006721

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

    PubMed

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

    2010-03-01

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

  8. Government-Funded Students and Courses--January to June 2016. 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 data relating to students, programs, subjects, and training providers in Australia's government-funded vocational education and training (VET) system. This is broadly defined as all activity delivered by government providers and government-funded activity delivered by community education and other registered…

  9. Training

    EPA Pesticide Factsheets

    The Drinking Water Academy provides online training and information to ensure that water professionals, public officials, and involved citizens have the knowledge and skills necessary to protect our drinking water supply.

  10. 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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  13. 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

  14. Electrophysiology of connection current spikes.

    PubMed

    Fish, Raymond M; Geddes, Leslie A

    2008-12-01

    Connection to a 60-Hz or other voltage source can result in cardiac dysrhythmias, a startle reaction, muscle contractions, and a variety of other physiological responses. Such responses can lead to injury, especially if significant ventricular cardiac dysrhythmias occur, or if a person is working at some height above ground and falls as a result of a musculoskeletal response. Physiological reactions are known to relate to intensity and duration of current exposure. The connection current that flows is a function of the applied voltage at the instant of connection, and the electrical impedance encountered by the voltage source in contact with the skin or other body tissues. In this article we describe a rarely investigated phenomenon, namely a contact, or connection, current spike that is many times higher than the steady-state current. This current spike occurs when an electrical connection is made at a non-zero voltage time in a sine wave or other waveform. Such current spikes may occur when electronic or manual switching or connecting of conductors occurs in electronic instrumentation connected to a patient. These findings are relevant to medical devices and instrumentation and to electrical safety in general.

  15. On the Analytical Solution of Firing Time for SpikeProp.

    PubMed

    de Montigny, Simon; Mâsse, Benoît R

    2016-08-24

    Error backpropagation in networks of spiking neurons (SpikeProp) shows promise for the supervised learning of temporal patterns. However, its widespread use is hindered by its computational load and occasional convergence failures. In this letter, we show that the neuronal firing time equation at the core of SpikeProp can be solved analytically using the Lambert W function, offering a marked reduction in execution time over the step-based method used in the literature. Applying this analytical method to SpikeProp, we find that training time per epoch can be reduced by 12% to 56% under different experimental conditions. Finally, this work opens the way for further investigations of SpikeProp's convergence behavior.

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

    PubMed Central

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

    2015-01-01

    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; expertsorting 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 spikesorting performs as well as computationally or manually more intensive methods, which

  17. 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.

  18. 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…

  19. 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

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

    PubMed

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

    2017-03-01

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

  1. 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.

  2. On the applicability of STDP-based learning mechanisms to spiking neuron network models

    NASA Astrophysics Data System (ADS)

    Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

    2016-11-01

    The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

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

    PubMed Central

    Takekawa, Takashi; Isomura, Yoshikazu; Fukai, Tomoki

    2012-01-01

    This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term “multimodality-weighted principal component analysis” (mPCA), and a clustering method by variational Bayes for Student's t mixture model (SVB). The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP) inference for the “degree of freedom” parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these “difficult” neurons. A parallelized implementation of the proposed algorithm (EToS version 3) is available as open-source code at http://etos.sourceforge.net/. PMID:22448159

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

    PubMed

    Takekawa, Takashi; Isomura, Yoshikazu; Fukai, Tomoki

    2012-01-01

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

  5. 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.

  6. The dynamic relationship between cerebellar Purkinje cell simple spikes and the spikelet number of complex spikes

    PubMed Central

    Burroughs, Amelia; Wise, Andrew K.; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y.; Lang, Eric J.

    2016-01-01

    Key points Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes.Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets.The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear.We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number.This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range. Abstract Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake

  7. Statistical and time domain signal analysis of the thermal behaviour of wind turbine drive train components under dynamic operation conditions

    NASA Astrophysics Data System (ADS)

    Nienhaus, K.; Hilbert, M.; Baltes, R.; Bernet, C.

    2012-05-01

    Gearboxes and generators are fundamental components of all electrical machines and the backbone of all electricity generation. Since the wind energy represents one of the key energy sources of the future, the number of wind turbines installed worldwide is rapidly increasing. Unlike in the past wind turbines are more often positioned in arctic as well as in desert like regions, and thereby exposed to harsh environmental conditions. Especially the temperature in those regions is a key factor that defines the design and choice of components and materials of the drive train. To optimize the design and health monitoring under varying temperatures it is important to understand the thermal behaviour dependent on environmental and machine parameters. This paper investigates the behaviour of the stator temperature of the double fed induction generator of a wind turbine. Therefore, different scenarios such as start of the turbine after a long period of no load, stop of the turbine after a long period of full load and others are isolated and analysed. For each scenario the dependences of the temperature on multiple wind turbine parameters such as power, speed and torque are studied. With the help of the regression analysis for multiple variables, it is pointed out which parameters have high impact on the thermal behaviour. Furthermore, an analysis was done to study the dependences in the time domain. The research conducted is based on 10 months of data of a 2 MW wind turbine using an adapted data acquisition system for high sampled data. The results appear promising, and lead to a better understanding of the thermal behaviour of a wind turbine drive train. Furthermore, the results represent the base of future research of drive trains under harsh environmental conditions, and it can be used to improve the fault diagnosis and design of electrical machines.

  8. Independent component analysis in spiking neurons.

    PubMed

    Savin, Cristina; Joshi, Prashant; Triesch, Jochen

    2010-04-22

    Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.

  9. A spiking network model of decision making employing rewarded STDP.

    PubMed

    Skorheim, Steven; Lonjers, Peter; 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.

  10. 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.

  11. Investigating spike backpropagation induced Ca2+ influx in models of hippocampal and cortical pyramidal neurons.

    PubMed

    Marsálek, P; Santamaría, F

    1998-01-01

    We modeled the influx of calcium ions into dendrites following active backpropagation of spike trains in a dendritic tree, using compartmental models of anatomically reconstructed pyramidal cells in a GENESIS program. Basic facts of ion channel densities in pyramidal cells were taken into account. The time scale of the backpropagating spike train development was longer than in previous models. We also studied the relationship between intracellular calcium dynamics and membrane voltage. Comparisons were made between two pyramidal cell prototypes and in simplified model. Our results show that: (1) sodium and potassium channels are enough to explain regenerative backpropagating spike trains; (2) intracellular calcium concentration changes are consistent in the range of milliseconds to seconds; (3) the simulations support several experimental observations in both hippocampal and neocortical cells. No additional parameter search optimization was necessary. Compartmental models can be used for investigating the biology of neurons, and then simplified for constructing neural networks.

  12. 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.

  13. 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.

  14. Adaptive inverse control of neural spatiotemporal spike patterns with a reproducing kernel Hilbert space (RKHS) framework.

    PubMed

    Li, Lin; Park, Il Memming; Brockmeier, Austin; Chen, Badong; Seth, Sohan; Francis, Joseph T; Sanchez, Justin C; Príncipe, José C

    2013-07-01

    The precise control of spiking in a population of neurons via applied electrical stimulation is a challenge due to the sparseness of spiking responses and neural system plasticity. We pose neural stimulation as a system control problem where the system input is a multidimensional time-varying signal representing the stimulation, and the output is a set of spike trains; the goal is to drive the output such that the elicited population spiking activity is as close as possible to some desired activity, where closeness is defined by a cost function. If the neural system can be described by a time-invariant (homogeneous) model, then offline procedures can be used to derive the control procedure; however, for arbitrary neural systems this is not tractable. Furthermore, standard control methodologies are not suited to directly operate on spike trains that represent both the target and elicited system response. In this paper, we propose a multiple-input multiple-output (MIMO) adaptive inverse control scheme that operates on spike trains in a reproducing kernel Hilbert space (RKHS). The control scheme uses an inverse controller to approximate the inverse of the neural circuit. The proposed control system takes advantage of the precise timing of the neural events by using a Schoenberg kernel defined directly in the space of spike trains. The Schoenberg kernel maps the spike train to an RKHS and allows linear algorithm to control the nonlinear neural system without the danger of converging to local minima. During operation, the adaptation of the controller minimizes a difference defined in the spike train RKHS between the system and the target response and keeps the inverse controller close to the inverse of the current neural circuit, which enables adapting to neural perturbations. The results on a realistic synthetic neural circuit show that the inverse controller based on the Schoenberg kernel outperforms the decoding accuracy of other models based on the conventional rate

  15. Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

    PubMed

    Schafer, Phillip B; Jin, Dezhe Z

    2014-03-01

    Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

  16. Studies with spike initiators - Linearization by noise allows continuous signal modulation in neural networks

    NASA Technical Reports Server (NTRS)

    Yu, Xiaolong; Lewis, Edwin R.

    1989-01-01

    It is shown that noise can be an important element in the translation of neuronal generator potentials (summed inputs) to neuronal spike trains (outputs), creating or expanding a range of amplitudes over which the spike rate is proportional to the generator potential amplitude. Noise converts the basically nonlinear operation of a spike initiator into a nearly linear modulation process. This linearization effect of noise is examined in a simple intuitive model of a static threshold and in a more realistic computer simulation of spike initiator based on the Hodgkin-Huxley (HH) model. The results are qualitatively similar; in each case larger noise amplitude results in a larger range of nearly linear modulation. The computer simulation of the HH model with noise shows linear and nonlinear features that were earlier observed in spike data obtained from the VIIIth nerve of the bullfrog. This suggests that these features can be explained in terms of spike initiator properties, and it also suggests that the HH model may be useful for representing basic spike initiator properties in vertebrates.

  17. Auto- and Crosscorrelograms for the Spike Response of Leaky Integrate-and-Fire Neurons with Slow Synapses

    SciTech Connect

    Moreno-Bote, Ruben; Parga, Nestor

    2006-01-20

    An analytical description of the response properties of simple but realistic neuron models in the presence of noise is still lacking. We determine completely up to the second order the firing statistics of a single and a pair of leaky integrate-and-fire neurons receiving some common slowly filtered white noise. In particular, the auto- and cross-correlation functions of the output spike trains of pairs of cells are obtained from an improvement of the adiabatic approximation introduced previously by Moreno-Bote and Parga [Phys. Rev. Lett. 92, 028102 (2004)]. These two functions define the firing variability and firing synchronization between neurons, and are of much importance for understanding neuron communication.

  18. 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

  19. 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)

  20. 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

  1. 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.

  2. Millisecond Radio Spikes in the Decimetric Band

    NASA Astrophysics Data System (ADS)

    Dąbrowski, B. P.; Rudawy, P.; Karlický, M.

    We present the results of the analysis of thirteen events consisting of dm-spikes observed in Toruń between 15 March 2000 and 30 October 2001. The events were obtained with a very high time resolution (80 microseconds) radio spectrograph in the 1352 - 1490 MHz range. These data were complemented with observations from the radio spectrograph at Ondřejov in the 0.8 - 2.0 GHz band. We evaluated the basic characteristics of the individual spikes (duration, spectral width, and frequency drifts), as well as their groups and chains, the location of their emission sources, and the temporal correlations of the emissions with various phases of the associated solar flares. We found that the mean duration and spectral width of the radio spikes are equal to 0.036 s and 9.96 MHz, respectively. Distributions of the duration and spectral widths of the spikes have positive skewness for all investigated events. Each spike shows positive or negative frequency drift. The mean negative and positive drifts of the investigated spikes are equal to -776 MHz s-1 and 1608 MHz s-1, respectively. The emission sources of the dm-spikes are located mainly at disk center. We have noticed two kinds of chains, with and without frequency drifts. The mean durations of the chains vary between 0.067 s and 0.509 s, while their spectral widths vary between 7.2 MHz and 17.25 MHz. The mean duration of an individual spike observed in a chain was equal to 0.03 s. While we found some agreement between the global characteristics of the groups of spikes recorded with the two instruments located in Toruń and Ondřejov, we did not find any one-to-one relation between individual spikes.

  3. Millisecond Radio Spikes in the Decimetric Band

    NASA Astrophysics Data System (ADS)

    dąbrowski, B. P.; Rudawy, P.; Karlický, M.

    2011-11-01

    We present the results of the analysis of thirteen events consisting of dm-spikes observed in Toruń between 15 March 2000 and 30 October 2001. The events were obtained with a very high time resolution (80 microseconds) radio spectrograph in the 1352 - 1490 MHz range. These data were complemented with observations from the radio spectrograph at Ondřejov in the 0.8 - 2.0 GHz band. We evaluated the basic characteristics of the individual spikes (duration, spectral width, and frequency drifts), as well as their groups and chains, the location of their emission sources, and the temporal correlations of the emissions with various phases of the associated solar flares. We found that the mean duration and spectral width of the radio spikes are equal to 0.036 s and 9.96 MHz, respectively. Distributions of the duration and spectral widths of the spikes have positive skewness for all investigated events. Each spike shows positive or negative frequency drift. The mean negative and positive drifts of the investigated spikes are equal to -776 MHz s-1 and 1608 MHz s-1, respectively. The emission sources of the dm-spikes are located mainly at disk center. We have noticed two kinds of chains, with and without frequency drifts. The mean durations of the chains vary between 0.067 s and 0.509 s, while their spectral widths vary between 7.2 MHz and 17.25 MHz. The mean duration of an individual spike observed in a chain was equal to 0.03 s. While we found some agreement between the global characteristics of the groups of spikes recorded with the two instruments located in Toruń and Ondřejov, we did not find any one-to-one relation between individual spikes.

  4. The Global Spike: Conserved Dendritic Properties Enable Unique Ca2+ Spike Generation in Low-Threshold Spiking Neurons

    PubMed Central

    Connelly, William M.; Crunelli, Vincenzo

    2015-01-01

    Low-threshold Ca2+ spikes (LTS) are an indispensible signaling mechanism for neurons in areas including the cortex, cerebellum, basal ganglia, and thalamus. They have critical physiological roles and have been strongly associated with disorders including epilepsy, Parkinson's disease, and schizophrenia. However, although dendritic T-type Ca2+ channels have been implicated in LTS generation, because the properties of low-threshold spiking neuron dendrites are unknown, the precise mechanism has remained elusive. Here, combining data from fluorescence-targeted dendritic recordings and Ca2+ imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS generation is established. Our data demonstrate that key somatodendritic electrical conduction properties are highly conserved between glutamatergic thalamocortical neurons and GABAergic thalamic reticular nucleus neurons and that these properties are critical for LTS generation. In particular, the efficiency of soma to dendrite voltage transfer is highly asymmetric in low-threshold spiking cells, and in the somatofugal direction, these neurons are particularly electrotonically compact. Our data demonstrate that LTS have remarkably similar amplitudes and occur synchronously throughout the dendritic tree. In fact, these Ca2+ spikes cannot occur locally in any part of the cell, and hence we reveal that LTS are generated by a unique whole-cell mechanism that means they always occur as spatially global spikes. This all-or-none, global electrical and biochemical signaling mechanism clearly distinguishes LTS from other signals, including backpropagating action potentials and dendritic Ca2+/NMDA spikes, and has important consequences for dendritic function in low-threshold spiking neurons. SIGNIFICANCE STATEMENT Low-threshold Ca2+ spikes (LTS) are critical for important physiological processes, including generation of sleep-related oscillations, and are

  5. Motor control by precisely timed spike patterns.

    PubMed

    Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel; Pack, Andrea R; Elemans, Coen P H; Nemenman, Ilya; Sober, Samuel J

    2017-01-31

    A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior-namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.

  6. Fuzzy logic-based spike sorting system.

    PubMed

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2011-05-15

    We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a 'spikiness index' from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

  7. 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.

  8. Spiking sediment with organochlorines for toxicity testing

    SciTech Connect

    Murdoch, M.H.; Chapman, P.M.; Norman, D.M.; Quintino, V.M.

    1997-07-01

    Sediment toxicity testing integrates responses to sediment variables and hence does not directly indicate cause and effect. One tool for determining cause and effect is sediment spiking, in which relatively uncontaminated sediment is amended with known amounts of contaminants, then tested for toxicity. However, sediment spiking methods vary considerably. The present study details appropriate methodologies (dry and wet spiking) for amending sediments with a range of organic contaminant concentrations, i.e., polychlorinated biphenyl (PCB). Target and actual concentrations were similar. A dose-response was determined, but PCB was not toxic in an acute sediment toxicity test. Chronic testing of these same sediments is reported in a companion article in this issue.

  9. Capacity of a single spiking neuron

    NASA Astrophysics Data System (ADS)

    Ikeda, Shiro; Manton, Jonathan H.

    2009-12-01

    It is widely believed the neurons transmit information in the form of spikes. Since the spike patterns are known to be noisy, the neuron information channel is noisy. We have investigated the channel capacity of this "Spiking neuron channel" for both of the "temporal coding" and the "rate coding," which are two main coding considered in the neuroscience [1, 2]. As the result, we've proved that the distribution of inputs, which achieves the channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. In this draft, we show the details of the proof.

  10. 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

  11. 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

  12. 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

  13. Spike library based simulator for extracellular single unit neuronal signals.

    PubMed

    Thorbergsson, P T; Jorntell, H; Bengtsson, F; Garwicz, M; Schouenborg, J; Johansson, A

    2009-01-01

    A well defined set of design criteria is of great importance in the process of designing brain machine interfaces (BMI) based on extracellular recordings with chronically implanted micro-electrode arrays in the central nervous system (CNS). In order to compare algorithms and evaluate their performance under various circumstances, ground truth about their input needs to be present. Obtaining ground truth from real data would require optimal algorithms to be used, given that those exist. This is not possible since it relies on the very algorithms that are to be evaluated. Using realistic models of the recording situation facilitates the simulation of extracellular recordings. The simulation gives access to a priori known signal characteristics such as spike times and identities. In this paper, we describe a simulator based on a library of spikes obtained from recordings in the cat cerebellum and observed statistics of neuronal behavior during spontaneous activity. The simulator has proved to be useful in the task of generating extracellular recordings with realistic background noise and known ground truth to use in the evaluation of algorithms for spike detection and sorting.

  14. 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.

  15. Separating Spike Count Correlation from Firing Rate Correlation

    PubMed Central

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

    2016-01-01

    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. PMID:26942746

  16. Advances in applications of spiking neuron networks

    NASA Astrophysics Data System (ADS)

    Cios, Krzysztof J.; Sala, Dorel M.

    2000-03-01

    In this paper, we present new findings in constructing and applications of artificial neural networks that use a biologically inspired spiking neuron model. The used model is a point neuron with the interaction between neurons described by postsynaptic potentials. The synaptic plasticity is achieved by using a temporal correlation learning rule, specified as a function of time difference between the firings of pre- and post-synaptic neurons. Using this rule we show how certain associations between neurons in a network of spiking neurons can be implemented. As an example we analyze the dynamic properties of networks of laterally connected spiking neurons and we show their capability to self-organize into topological maps in response to external stimulation. In another application we explore the capability networks of spiking neurons to solve graph algorithms by using temporal coding of distances in a given spatial configuration. The paper underlines the importance of temporal dimension in artificial neural network information processing.

  17. Integration of asynchronously released quanta prolongs the postsynaptic spike window.

    PubMed

    Iremonger, Karl J; Bains, Jaideep S

    2007-06-20

    Classically, the release of glutamate in response to a presynaptic action potential causes a brief increase in postsynaptic excitability. Previous reports indicate that at some central synapses, a single action potential can elicit multiple, asynchronous release events. This raises the possibility that the temporal dynamics of neurotransmitter release may determine the duration of altered postsynaptic excitability. In response to physiological challenges, the magnocellular neurosecretory cells (MNCs) in the paraventricular nucleus of the hypothalamus (PVN) exhibit robust and prolonged increases in neuronal activity. Although the postsynaptic conductances that may facilitate this form of activity have been investigated thoroughly, the role of presynaptic release has been largely overlooked. Because the specific patterns of activity generated by MNCs require the activation of excitatory synaptic inputs, we sought to characterize the release dynamics at these synapses and determine whether they contribute to prolonged excitability in these cells. We obtained whole-cell recordings from MNCs in brain slices of postnatal day 21-44 rats. Stimulation of glutamatergic inputs elicited large and prolonged postsynaptic events that resulted from the summation of multiple, asynchronously released quanta. Asynchronous release was selectively inhibited by the slow calcium buffer EGTA-AM and potentiated by brief high-frequency stimulus trains. These trains caused a prolonged increase in postsynaptic spike activity that could also be eliminated by EGTA-AM. Our results demonstrate that glutamatergic terminals in PVN exhibit asynchronous release, which is important in generating large postsynaptic depolarizations and prolonged spiking in response to brief, high-frequency bursts of presynaptic activity.

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

    PubMed

    Lajoie, Guillaume; Thivierge, Jean-Philippe; Shea-Brown, Eric

    2014-01-01

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

  19. 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

  20. 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

  1. Graded Synaptic Transmission between Spiking Neurons

    NASA Astrophysics Data System (ADS)

    Graubard, Katherine; Raper, Jonathan A.; Hartline, Daniel K.

    1980-06-01

    Graded synaptic transmission occurs between spiking neurons of the lobster stomatogastric ganglion. In addition to eliciting spike-evoked inhibitory potentials in postsynaptic cells, these neurons also release functionally significant amounts of transmitter below the threshold for action potentials. The spikeless postsynaptic potentials grade in amplitude with presynaptic voltage and can be maintained for long periods. Graded synaptic transmission can be modulated by synaptic input to the presynaptic neuron.

  2. Automated stationary source dynamic spiking. Final report

    SciTech Connect

    McGaughey, J.F.

    1998-06-17

    Methods of collection and analysis for monitoring stationary sources must demonstrate conclusively that the methodology is functioning properly and according to specified EPA criteria. The appropriate procedure for demonstrating proper operation of the method is to perform dynamic spiking of the analyte in the field, at the specified source being monitored. Gaseous dynamic spiking, using certified gas mixtures as the spiking medium has been used in previous EPA stationary source sampling methods and documented in EPA reports. Liquid dynamic spiking, using mixtures of liquid and solid analytes in an organic or aqueous solvent has also been used in previous EPA field tests. To remove, as much as possible, the potential for human error, the EPA has developed a prototype liquid dynamic spiking system employing computerized operation of the analyte spiking procedure with video monitoring and control of the liquid droplet frequency and size. This report describes development of the system applicability to stationary source sampling, the individual parts incorporated into the system, and the standard operating procedures.

  3. Modulation of spike timing by sensory deprivation during induction of cortical map plasticity.

    PubMed

    Celikel, Tansu; Szostak, Vanessa A; Feldman, Daniel E

    2004-05-01

    Deprivation-induced plasticity of sensory cortical maps involves long-term potentiation (LTP) and depression (LTD) of cortical synapses, but how sensory deprivation triggers LTP and LTD in vivo is unknown. Here we tested whether spike timing-dependent forms of LTP and LTD are involved in this process. We measured spike trains from neurons in layer 4 (L4) and layers 2 and 3 (L2/3) of rat somatosensory cortex before and after acute whisker deprivation, a manipulation that induces whisker map plasticity involving LTD at L4-to-L2/3 (L4-L2/3) synapses. Whisker deprivation caused an immediate reversal of firing order for most L4 and L2/3 neurons and a substantial decorrelation of spike trains, changes known to drive timing-dependent LTD at L4-L2/3 synapses in vitro. In contrast, spike rate changed only modestly. Thus, whisker deprivation is likely to drive map plasticity by spike timing-dependent mechanisms.

  4. A-current and type I/type II transition determine collective spiking from common input.

    PubMed

    Barreiro, Andrea K; Thilo, Evan L; Shea-Brown, Eric

    2012-09-01

    The mechanisms and impact of correlated, or synchronous, firing among pairs and groups of neurons are under intense investigation throughout the nervous system. A ubiquitous circuit feature that can give rise to such correlations consists of overlapping, or common, inputs to pairs and populations of cells, leading to common spike train responses. Here, we use computational tools to study how the transfer of common input currents into common spike outputs is modulated by the physiology of the recipient cells. We focus on a key conductance, g(A), for the A-type potassium current, which drives neurons between "type II" excitability (low g(A)), and "type I" excitability (high g(A)). Regardless of g(A), cells transform common input fluctuations into a tendency to spike nearly simultaneously. However, this process is more pronounced at low g(A) values. Thus, for a given level of common input, type II neurons produce spikes that are relatively more correlated over short time scales. Over long time scales, the trend reverses, with type II neurons producing relatively less correlated spike trains. This is because these cells' increased tendency for simultaneous spiking is balanced by an anticorrelation of spikes at larger time lags. These findings extend and interpret prior findings for phase oscillators to conductance-based neuron models that cover both oscillatory (superthreshold) and subthreshold firing regimes. We demonstrate a novel implication for neural signal processing: downstream cells with long time constants are selectively driven by type I cell populations upstream and those with short time constants by type II cell populations. Our results are established via high-throughput numerical simulations and explained via the cells' filtering properties and nonlinear dynamics.

  5. Kv7 channels regulate pairwise spiking covariability in health and disease.

    PubMed

    Ocker, Gabriel Koch; Doiron, Brent

    2014-07-15

    Low-threshold M currents are mediated by the Kv7 family of potassium channels. Kv7 channels are important regulators of spiking activity, having a direct influence on the firing rate, spike time variability, and filter properties of neurons. How Kv7 channels affect the joint spiking activity of populations of neurons is an important and open area of study. Using a combination of computational simulations and analytic calculations, we show that the activation of Kv7 conductances reduces the covariability between spike trains of pairs of neurons driven by common inputs. This reduction is beyond that explained by the lowering of firing rates and involves an active cancellation of common fluctuations in the membrane potentials of the cell pair. Our theory shows that the excess covariance reduction is due to a Kv7-induced shift from low-pass to band-pass filtering of the single neuron spike train response. Dysfunction of Kv7 conductances is related to a number of neurological diseases characterized by both elevated firing rates and increased network-wide correlations. We show how changes in the activation or strength of Kv7 conductances give rise to excess correlations that cannot be compensated for by synaptic scaling or homeostatic modulation of passive membrane properties. In contrast, modulation of Kv7 activation parameters consistent with pharmacological treatments for certain hyperactivity disorders can restore normal firing rates and spiking correlations. Our results provide key insights into how regulation of a ubiquitous potassium channel class can control the coordination of population spiking activity.

  6. An influence of spontaneous spike rates on information transmission in a spherical bushy neuron model with stochastic ion channels.

    PubMed

    Arata, Hiroki; Mino, Hiroyuki

    2012-01-01

    This article presents an effect of spontaneous spike firing rates on information transmission of the spike trains in a spherical bushy neuron model of antero-ventral cochlear nuclei. In computer simulations, the synaptic current stimuli ascending from auditory nerve fibers (ANFs) were modeled by a filtered inhomogeneous Poisson process modulated with sinusoidal functions, while the stochastic sodium and stochastic high- and low-threshold potassium channels were incorporated into a single compartment model of the soma in spherical bushy neurons. The information rates were estimated from the entropies of the inter-spike intervals of the spike trains to quantitatively evaluate information transmission in the spherical busy neuron model. The results show that the information rates increased, reached a maximum, and then decreased as the rate of spontaneous spikes from the ANFs increased, implying a resonance phenomenon dependent on the rate of spontaneous spikes from ANFs. In conclusion, this phenomenon similar to the stochastic resonance would be observed due to that spontaneous random spike firings coming from auditory nerves may act as an origin of fluctuation or noise, and these findings may play a key role in the design of better auditory prostheses.

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

    PubMed Central

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

    2015-01-01

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

  8. Role of precise spike timing in coding of dynamic vibrissa stimuli in somatosensory thalamus.

    PubMed

    Montemurro, Marcelo A; Panzeri, Stefano; Maravall, Miguel; Alenda, Andrea; Bale, Michael R; Brambilla, Marco; Petersen, Rasmus S

    2007-10-01

    Rats discriminate texture by whisking their vibrissae across the surfaces of objects. This process induces corresponding vibrissa vibrations, which must be accurately represented by neurons in the somatosensory pathway. In this study, we investigated the neural code for vibrissa motion in the ventroposterior medial (VPm) nucleus of the thalamus by single-unit recording. We found that neurons conveyed a great deal of information (up to 77.9 bits/s) about vibrissa dynamics. The key was precise spike timing, which typically varied by less than a millisecond from trial to trial. The neural code was sparse, the average spike being remarkably informative (5.8 bits/spike). This implies that as few as four VPm spikes, coding independent information, might reliably differentiate between 10(6) textures. To probe the mechanism of information transmission, we compared the role of time-varying firing rate to that of temporally correlated spike patterns in two ways: 93.9% of the information encoded by a neuron could be accounted for by a hypothetical neuron with the same time-dependent firing rate but no correlations between spikes; moreover, > or =93.4% of the information in the spike trains could be decoded even if temporal correlations were ignored. Taken together, these results suggest that the essence of the VPm code for vibrissa motion is firing rate modulation on a submillisecond timescale. The significance of such a code may be that it enables a small number of neurons, firing only few spikes, to convey distinctions between very many different textures to the barrel cortex.

  9. Reading Between the Spikes: Real-Time Signal Processing in Neural Systems

    NASA Astrophysics Data System (ADS)

    Warland, David Karsten

    This thesis discusses biological strategies for real-time signal processing in neural systems. Nearly all creatures encode information about the world as patterns of identically shaped action potentials, or "spikes". As a result, all the animal's knowledge of the world is contained in the occurrence times of these discrete events. Traditional approaches to the study of neural coding emphasize the encoding process, resulting in predictions of average neural responses to a limited class of stimuli. However, these studies fail to address the relevant biological question: What can the organism "learn" about the outside world from real-time observations of its own spike trains? Therefore, this thesis approaches neural coding from the point of view of the organism itself: We learn to decode neural spike trains to obtain real-time estimates of sensory stimuli. In particular, this ability to extract continuous signals from spiking cells, together with the definition of an equivalent spectral noise level for a spiking neuron allows characterization of the information contained in patterns of neural response as well as forming the basis for the prediction of optimal neural computation strategies with spike trains. These methods are applied to the design and analysis of experiments on a single wide field, movement -sensitive neuron (H1) in the visual system of the blowfly Calliphora erythrocephela and to the filiform hair receptors of the wind-sensing system of the cricket Acheta domestica. This thesis also discusses the generalization of these strategies to collections of neurons and the applications to future work in the context of neural computation in the retina.

  10. 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.

  11. Relationships between spike-free local field potentials and spike timing in human temporal cortex

    PubMed Central

    Zanos, Theodoros P.; Marmarelis, Vasilis Z.; Ojemann, George A.; Fetz, Eberhard E.

    2012-01-01

    Intracortical recordings comprise both fast events, action potentials (APs), and slower events, known as local field potentials (LFPs). Although it is believed that LFPs mostly reflect local synaptic activity, it is unclear which of their signal components are most closely related to synaptic potentials and would therefore be causally related to the occurrence of individual APs. This issue is complicated by the significant contribution from AP waveforms, especially at higher LFP frequencies. In recordings of single-cell activity and LFPs from the human temporal cortex, we computed quantitative, nonlinear, causal dynamic models for the prediction of AP timing from LFPs, at millisecond resolution, before and after removing AP contributions to the LFP. In many cases, the timing of a significant number of single APs could be predicted from spike-free LFPs at different frequencies. Not surprisingly, model performance was superior when spikes were not removed. Cells whose activity was predicted by the spike-free LFP models generally fell into one of two groups: in the first group, neuronal spike activity was associated with specific phases of low LFP frequencies, lower spike activity at high LFP frequencies, and a stronger linear component in the spike-LFP model; in the second group, neuronal spike activity was associated with larger amplitude of high LFP frequencies, less frequent phase locking, and a stronger nonlinear model component. Spike timing in the first group was better predicted by the sign and level of the LFP preceding the spike, whereas spike timing in the second group was better predicted by LFP power during a certain time window before the spike. PMID:22157112

  12. Spike timing control in retinal prosthetic

    NASA Astrophysics Data System (ADS)

    Werblin, Frank

    2005-03-01

    To restore meaningful vision to blind patients requires a retinal prosthetic device that can generate precise spiking patterns in retinal ganglion cells. We sought to develop a stimulus protocol that could reliably elicit one ganglion cell spike for every stimulation pulse over a broad frequency range. Small tipped platinum-iridium epiretinal electrodes were used to deliver biphasic cathodal electrical stimulus pulses at frequencies ranging from 10 to 125 Hz. We measured spiking responses with on-cell patch clamp from ganglion cells in the flat mount rabbit retina, identified by light response and morphology. Single electrical 30 pA cathodal pulses of 1 msec duration elicited both by direct electrical activation of ganglion cells and synaptic excitation and inhibition. Direct activation elicited a single spike that followed the onset of the cathodic pulse by about 100 μsec; presynaptic activation typically elicited multiple spikes which began after 10 msec and could persist for more than 50 ms depending on pulse amplitude levels. Limiting the pulse duration to 100 μsec eliminated all presynaptic activity: only ganglion cells were driven. Each pulse elicited a single pike for stimulation frequencies tested from 10 to125 Hz. Our ability to elicit one spike per pulse provides many important advantages: This protocol can be used to generate temporal patterns of activity in ganglion cells with precision. We can now mimic normal light evoked responses for either transient or sustained cells, and we can modulate spike frequency to simulate changes in intensity, contrast, motion and other essential cues in the visual environment.

  13. 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.

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

    NASA Astrophysics Data System (ADS)

    Fiete, Ila R.; Seung, H. Sebastian

    2006-07-01

    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.

  15. A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

    PubMed

    Dethier, Julie; Nuyujukian, Paul; Eliasmith, Chris; Stewart, Terry; Elassaad, Shauki A; Shenoy, Krishna V; Boahen, Kwabena

    2011-01-01

    Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

  16. Spike Train Driven Dynamical Models for Human Actions

    DTIC Science & Technology

    2010-06-01

    Toeplitz structure of the problem. The multipli- cation of a Toeplitz matrix with a vector can be performed in O(N logN) instead of O(N2). In our...formulation. In the situation where the output is multivariate, H can be represented with p Toeplitz matri- ces to maintain efficiency during multiplication

  17. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

    PubMed Central

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision. PMID:27857680

  18. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

    PubMed

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

  19. 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.

  20. 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.

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

    PubMed

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

    2016-12-28

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

  2. Statistical modelling of higher-order correlations in pools of neural activity

    NASA Astrophysics Data System (ADS)

    Montani, Fernando; Phoka, Elena; Portesi, Mariela; Schultz, Simon R.

    2013-07-01

    Simultaneous recordings from multiple neural units allow us to investigate the activity of very large neural ensembles. To understand how large ensembles of neurons process sensory information, it is necessary to develop suitable statistical models to describe the response variability of the recorded spike trains. Using the information geometry framework, it is possible to estimate higher-order correlations by assigning one interaction parameter to each degree of correlation, leading to a (2N-1)-dimensional model for a population with N neurons. However, this model suffers greatly from a combinatorial explosion, and the number of parameters to be estimated from the available sample size constitutes the main intractability reason of this approach. To quantify the extent of higher than pairwise spike correlations in pools of multiunit activity, we use an information-geometric approach within the framework of the extended central limit theorem considering all possible contributions from higher-order spike correlations. The identification of a deformation parameter allows us to provide a statistical characterisation of the amount of higher-order correlations in the case of a very large neural ensemble, significantly reducing the number of parameters, avoiding the sampling problem, and inferring the underlying dynamical properties of the network within pools of multiunit neural activity.

  3. Slow and Spike Potentials Recorded from Retinula Cells of the Honeybee Drone in Response to Light

    PubMed Central

    Baumann, Fritz

    1968-01-01

    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. PMID:5722083

  4. 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.

  5. Implementing Signature Neural Networks with Spiking Neurons

    PubMed Central

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

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

  6. Implementing Signature Neural Networks with Spiking Neurons.

    PubMed

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

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

  7. 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

  8. Function of specific K(+) channels in sustained high-frequency firing of fast-spiking neocortical interneurons.

    PubMed

    Erisir, A; Lau, D; Rudy, B; Leonard, C S

    1999-11-01

    Fast-spiking GABAergic interneurons of the neocortex and hippocampus fire high-frequency trains of brief action potentials with little spike-frequency adaptation. How these striking properties arise is unclear, although recent evidence suggests K(+) channels containing Kv3.1-Kv3.2 proteins play an important role. We investigated the role of these channels in the firing properties of fast-spiking neocortical interneurons from mouse somatosensory cortex using a pharmacological and modeling approach. Low tetraethylammonium (TEA) concentrations (spike trains evoked by steady depolarization revealed that, although TEA had little effect on the initial firing rate, it strongly reduced firing frequency later in the trains. These effects appeared to be specific to Kv3.1 and Kv3.2 channels, because blockade of dendrotoxin-sensitive Kv1 channels and BK Ca(2+)-activated K(+) channels, which also have high TEA sensitivity, produced opposite or no effects. Voltage-clamp experiments confirmed the presence of a Kv3.1-Kv3.2-like current in fast-spiking neurons, but not in other interneurons. Analysis of spike shape changes during the spike trains suggested that Na(+) channel inactivation plays a significant role in the firing-rate slowdown produced by TEA, a conclusion that was supported by computer simulations. These findings indicate that the unique properties of Kv3.1-Kv3.2 channels enable sustained high-frequency firing by facilitating the recovery of Na(+) channel inactivation and by minimizing the duration of the afterhyperpolarization in neocortical interneurons.

  9. Spike after-depolarization and burst generation in adult rat hippocampal CA1 pyramidal cells.

    PubMed Central

    Jensen, M S; Azouz, R; Yaari, Y

    1996-01-01

    1. Intracellular recordings in adult rat hippocampal slices were used to investigate the properties and origins of intrinsically generated bursts in the somata of CA1 pyramidal cells (PCs). The CA1 PCs were classified as either non-bursters or bursters according to the firing patterns evoked by intrasomatically applied long ( > or = 100 ms) depolarizing current pulses. Non-bursters generated stimulus-graded trains of independent action potentials, whereas bursters generated clusters of three or more closely spaced spikes riding on a distinct depolarizing envelope. 2. In all PCs fast spike repolarization was incomplete and ended at a potential approximately 10 mV more positive than resting potential. Solitary spikes were followed by a distinct after-depolarizing potential (ADP) lasting 20-40 ms. The ADP in most non-bursters declined monotonically to baseline ('passive' ADP), whereas in most bursters it remained steady or even re-depolarized before declining to baseline ('active' ADP). 3. Active, but not passive, ADPs were associated with an apparent increase in input conductance. They were maximal in amplitude when the spike was evoked from resting potential and were reduced by mild depolarization or hyperpolarization (+/- 2 mV). 4. Evoked and spontaneous burst firing was sensitive to small changes in membrane potential. In most cases maximal bursts were generated at resting potential and were curtailed by small depolarizations or hyperpolarizations (+/- 5 mV). 5. Bursts comprising clusters of spikelets ('d-spikes') were observed in 12% of the bursters. Some of the d-spikes attained threshold for triggering full somatic spikes. Gradually hyperpolarizing these neurones blocked somatic spikes before blocking d-spikes, suggesting that the latter are generated at more remote sites. 6. The data suggest that active ADPs and intrinsic bursts in the somata of adult CA1 PCs are generated by a slow, voltage-gated inward current. Bursts arise in neurones in which this current

  10. Spike timing and information transmission at retinogeniculate synapses

    PubMed Central

    Rathbun, Daniel L.; Warland, David K.; Usrey, W. Martin

    2010-01-01

    This study examines the rules governing the transfer of spikes between the retina and LGN with the goal of determining whether the most informative retinal spikes preferentially drive LGN responses and what role spike timing plays in the process. By recording from monosynaptically-connected pairs of retinal ganglion cells and LGN neurons in vivo in the cat, we show that relayed spikes are more likely than non-relayed spikes to be evoked by stimuli that match the recorded cells’ receptive fields and that an interspike interval (ISI)-based mechanism contributes to the process. Relayed spikes are also more reliable in their timing and number where they often achieve the theoretical limit of minimum variance. As a result, relayed spikes carry more visual information per spike. Based on these results, we conclude that retinogeniculate processing increases sparseness in the neural code by selectively relaying the highest fidelity spikes to the visual cortex. PMID:20943897

  11. 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

  12. 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.

  13. Higher-order correlations in common input shapes the output spiking activity of a neural population

    NASA Astrophysics Data System (ADS)

    Montangie, Lisandro; Montani, Fernando

    2017-04-01

    Recent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter q. We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.

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

    PubMed

    Fernando, Chrisantha

    2011-04-21

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

  15. 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)

  16. 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.

  17. 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.

  18. Automatic Spike Removal Algorithm for Raman Spectra.

    PubMed

    Tian, Yao; Burch, Kenneth S

    2016-11-01

    Raman spectroscopy is a powerful technique, widely used in both academia and industry. In part, the technique's extensive use stems from its ability to uniquely identify and image various material parameters: composition, strain, temperature, lattice/excitation symmetry, and magnetism in bulk, nano, solid, and organic materials. However, in nanomaterials and samples with low thermal conductivity, these measurements require long acquisition times. On the other hand, charge-coupled device (CCD) detectors used in Raman microscopes are vulnerable to cosmic rays. As a result, many spurious spikes occur in the measured spectra, which can distort the result or require the spectra to be ignored. In this paper, we outline a new method that significantly improves upon existing algorithms for removing these spikes. Specifically, we employ wavelet transform and data clustering in a new spike-removal algorithm. This algorithm results in spike-free spectra with negligible spectral distortion. The reduced dependence on the selection of wavelets and intuitive wavelet coefficient adjustment strategy enables non-experts to employ these powerful spectra-filtering techniques.

  19. 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?

  20. A Granger causality measure for point process models of ensemble neural spiking activity.

    PubMed

    Kim, Sanggyun; Putrino, David; Ghosh, Soumya; Brown, Emery N

    2011-03-01

    The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.

  1. A unified description of cerebellar inter-spike interval distributions and variabilities using summation of Gaussians.

    PubMed

    Chen, Yanqing; Nitz, Douglas A

    2011-01-01

    Neuronal inter-spike intervals (ISIs) have previously been described as Poisson, Gamma, inverse Gaussian or other unimodal distributions. We analyzed ISIs of rhythmic and arrhythmic neuronal spike trains in cerebellum recorded from freely behaving rats, and found that their distributions can be described as the summation or integration of multiple Gaussian distributions. The ISIs of rhythmic cerebellar Purkinje cells have a main Gaussian peak at a basic firing interval and exponentially reduced peaks at multiples of this firing period. ISIs of arrhythmic Purkinje cells can be modeled as the integration of multiple Gaussian distributions centered at continuous intervals with exponentially reduced peak amplitudes. The sources of variability are directly related to the relative timing of action potentials between neighboring cells since we show that irregularities of discharge in one cell are associated with the previous history of its discharge in time relative to another cell. Through relative phase analyses, we demonstrate that the shape and the mathematical form of the ISI distributions in cerebellum are direct result of dynamic interactions in the nearby neuronal network, in addition to intrinsic firing properties. The analysis in this paper provides a unified description of cerebellar inter-spike interval distributions which deviate from the usual Poisson assumptions. Our results suggest the existence of an intrinsic rhythmicity in cells exhibiting arrhythmic spike trains in cerebellum, and may identify an important source of variability in neuronal firing patterns that is relevant to the mechanism of neural computation in cerebellum.

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

    PubMed

    Litwin-Kumar, Ashok; Chacron, Maurice J; Doiron, Brent

    2012-01-01

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

  3. Hebbian based learning with winner-take-all for spiking neural networks

    NASA Astrophysics Data System (ADS)

    Gupta, Ankur; Long, Lyle

    2009-03-01

    Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

  4. Spike Ca2+ influx upmodulates the spike afterdepolarization and bursting via intracellular inhibition of KV7/M channels

    PubMed Central

    Chen, Shmuel; Yaari, Yoel

    2008-01-01

    In principal brain neurons, activation of Ca2+ channels during an action potential, or spike, causes Ca2+ entry into the cytosol within a millisecond. This in turn causes rapid activation of large conductance Ca2+-gated channels, which enhances repolarization and abbreviates the spike. Here we describe another remarkable consequence of spike Ca2+ entry: enhancement of the spike afterdepolarization. This action is also mediated by intracellular modulation of a particular class of K+ channels, namely by inhibition of KV7 (KCNQ) channels. These channels generate the subthreshold, non-inactivating M-type K+ current, whose activation curtails the spike afterdepolarization. Inhibition of KV7/M by spike Ca2+ entry allows the spike afterdepolarization to grow and can convert solitary spikes into high-frequency bursts of action potentials. Through this novel intracellular modulatory action, Ca2+ spike entry regulates the discharge mode and the signalling capacity of principal brain neurons. PMID:18187471

  5. The role of BK-type Ca2+-dependent K+ channels in spike broadening during repetitive firing in rat hippocampal pyramidal cells

    PubMed Central

    Shao, Li-Rong; Halvorsrud, Ragnhild; Borg-Graham, Lyle; Storm, Johan F

    1999-01-01

    The role of large-conductance Ca2+-dependent K+ channels (BK-channels; also known as maxi-K- or slo-channels) in spike broadening during repetitive firing was studied in CA1 pyramidal cells, using sharp electrode intracellular recordings in rat hippocampal slices, and computer modelling. Trains of action potentials elicited by depolarizing current pulses showed a progressive, frequency-dependent spike broadening, reflecting a reduced rate of repolarization. During a 50 ms long 5 spike train, the spike duration increased by 63·6 ± 3·4% from the 1st to the 3rd spike. The amplitude of the fast after-hyperpolarization (fAHP) also rapidly declined during each train. Suppression of BK-channel activity with (a) the selective BK-channel blocker iberiotoxin (IbTX, 60 nM), (b) the non-peptidergic BK-channel blocker paxilline (2–10 μM), or (c) calcium-free medium, broadened the 1st spike to a similar degree (≈60%). BK-channel suppression also caused a similar change in spike waveform as observed during repetitive firing, and eliminated (occluded) most of the spike broadening during repetitive firing. Computer simulations using a reduced compartmental model with transient BK-channel current and 10 other active ionic currents, produced an activity-dependent spike broadening that was strongly reduced when the BK-channel inactivation mechanism was removed. These results, which are supported by recent voltage-clamp data, strongly suggest that in CA1 pyramidal cells, fast inactivation of a transient BK-channel current (ICT), substantially contributes to frequency-dependent spike broadening during repetitive firing. PMID:10562340

  6. Flexible models for spike count data with both over- and under- dispersion.

    PubMed

    Stevenson, Ian H

    2016-08-01

    A key observation in systems neuroscience is that neural responses vary, even in controlled settings where stimuli are held constant. Many statistical models assume that trial-to-trial spike count variability is Poisson, but there is considerable evidence that neurons can be substantially more or less variable than Poisson depending on the stimuli, attentional state, and brain area. Here we examine a set of spike count models based on the Conway-Maxwell-Poisson (COM-Poisson) distribution that can flexibly account for both over- and under-dispersion in spike count data. We illustrate applications of this noise model for Bayesian estimation of tuning curves and peri-stimulus time histograms. We find that COM-Poisson models with group/observation-level dispersion, where spike count variability is a function of time or stimulus, produce more accurate descriptions of spike counts compared to Poisson models as well as negative-binomial models often used as alternatives. Since dispersion is one determinant of parameter standard errors, COM-Poisson models are also likely to yield more accurate model comparison. More generally, these methods provide a useful, model-based framework for inferring both the mean and variability of neural responses.

  7. Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

    PubMed

    Hwang, Eun Jung; Andersen, Richard A

    2011-04-01

    Local field potentials (LFPs) have shown diverse relations to the spikes across different brain areas and stimulus features, suggesting that LFP-spike relationships are highly specific to the underlying connectivity of a local network. If so, the LFP-spike relationship may vary even within one brain area under the same task condition if neurons have heterogeneous connectivity with the active input sources during the task. Here, we tested this hypothesis in the parietal reach region (PRR), which includes two distinct classes of motor goal planning neurons with different connectivity to the visual input, i.e., visuomotor neurons receive stronger visual input than motor neurons. We predicted that the visual stimulation would render both the spike response and the LFP-spike relationship different between the two neuronal subpopulations. Thus we examined how visual stimulations affect spikes, LFPs, and LFP-spike relationships in PRR by comparing their planning (delay) period activity between two conditions: with or without a visual stimulus at the reach target. Neurons were classified as visuomotor if the visual stimulation increased their firing rate, or as motor otherwise. We found that the visual stimulation increased LFP power in gamma bands >40 Hz for both classes. Moreover, confirming our prediction, the correlation between the LFP gamma power and the firing rate became higher for the visuomotor than motor neurons in the presence of visual stimulation. We conclude that LFPs vary with the stimulation condition and that the LFP-spike relationship depends on a given neuron's connectivity to the dominant input sources in a particular stimulation condition.

  8. Oxytocin enhances hippocampal spike transmission by modulating fast-spiking interneurons.

    PubMed

    Owen, Scott F; Tuncdemir, Sebnem N; Bader, Patrick L; Tirko, Natasha N; Fishell, Gord; Tsien, Richard W

    2013-08-22

    Neuromodulatory control by oxytocin is essential to a wide range of social, parental and stress-related behaviours. Autism spectrum disorders (ASD) are associated with deficiencies in oxytocin levels and with genetic alterations of the oxytocin receptor (OXTR). Thirty years ago, Mühlethaler et al. found that oxytocin increases the firing of inhibitory hippocampal neurons, but it remains unclear how elevated inhibition could account for the ability of oxytocin to improve information processing in the brain. Here we describe in mammalian hippocampus a simple yet powerful mechanism by which oxytocin enhances cortical information transfer while simultaneously lowering background activity, thus greatly improving the signal-to-noise ratio. Increased fast-spiking interneuron activity not only suppresses spontaneous pyramidal cell firing, but also enhances the fidelity of spike transmission and sharpens spike timing. Use-dependent depression at the fast-spiking interneuron-pyramidal cell synapse is both necessary and sufficient for the enhanced spike throughput. We show the generality of this novel circuit mechanism by activation of fast-spiking interneurons with cholecystokinin or channelrhodopsin-2. This provides insight into how a diffusely delivered neuromodulator can improve the performance of neural circuitry that requires synapse specificity and millisecond precision.

  9. Dopaminergic modulation of short-term synaptic plasticity in fast-spiking interneurons of primate dorsolateral prefrontal cortex.

    PubMed

    Gonzalez-Burgos, G; Kroener, S; Seamans, J K; Lewis, D A; Barrionuevo, G

    2005-12-01

    Dopaminergic regulation of primate dorsolateral prefrontal cortex (PFC) activity is essential for cognitive functions such as working memory. However, the cellular mechanisms of dopamine neuromodulation in PFC are not well understood. We have studied the effects of dopamine receptor activation during persistent stimulation of excitatory inputs onto fast-spiking GABAergic interneurons in monkey PFC. Stimulation at 20 Hz induced short-term excitatory postsynaptic potential (EPSP) depression. The D1 receptor agonist SKF81297 (5 microM) significantly reduced the amplitude of the first EPSP but not of subsequent responses in EPSP trains, which still displayed significant depression. Dopamine (DA; 10 microM) effects were similar to those of SKF81297 and were abolished by the D1 antagonist SCH23390 (5 microM), indicating a D1 receptor-mediated effect. DA did not alter miniature excitatory postsynaptic currents, suggesting that its effects were activity dependent and presynaptic action potential dependent. In contrast to previous findings in pyramidal neurons, in fast-spiking cells, contribution of N-methyl-D-aspartate receptors to EPSPs at subthreshold potentials was not significant and fast-spiking cell depolarization decreased EPSP duration. In addition, DA had no significant effects on temporal summation. The selective decrease in the amplitude of the first EPSP in trains delivered every 10 s suggests that in fast-spiking neurons, DA reduces the amplitude of EPSPs evoked at low frequency but not of EPSPs evoked by repetitive stimulation. DA may therefore improve detection of EPSP bursts above background synaptic activity. EPSP bursts displaying short-term depression may transmit spike-timing-dependent temporal codes contained in presynaptic spike trains. Thus DA neuromodulation may increase the signal-to-noise ratio at fast-spiking cell inputs.

  10. 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.

  11. 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...

  12. 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...

  13. 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...

  14. 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...

  15. 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...

  16. Is Season the Reason Why Heart Deaths Spike at Christmas?

    MedlinePlus

    ... 162691.html Is Season the Reason Why Heart Deaths Spike at Christmas? Increase isn't tied to ... winter that causes a spike in heart-related deaths at Christmas and New Year's -- it's the holiday ...

  17. 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

  18. Coding for stimulus velocity by temporal patterning of spike discharges in visual cells of cat superior colliculus.

    PubMed

    Mandl, G

    1993-07-01

    Statistical analyses, performed on extracellularly recorded spike trains generated by 69 single motion sensitive visual cells in the intermediate layers of superior colliculi of pretrigeminal cat preparations, revealed that--even in the unstimulated condition (38/69)--most neuronal spike discharge patterns tended to switch between two stochastically distinct states, in the form of rapidly alternating "bursting" (high frequency) and "resting" (low frequency) episodes. The numbers of consecutive interspike intervals within a given state were, as a rule, independent integer-valued random variables with discrete probability distributions, in essential agreement with the semi-Markov model proposed by Ekholm and Hyvärinen [(1970) Biophysical Journal, 10, 773-796]. The introduction of visual stimuli (47/69) moving with velocities of 2-160 deg/sec caused systematic and reproducible changes in the ratio of bursting to resting activities, decreases in overall discharge variability, and increases in signal transinformation flow. Moreover, with one group of stimulated cells (28/47), increasing stimulus velocity caused increasingly precise ("stimulus-forced") synchronization of bursting episodes with specific phases of stimulus movement; while for a smaller group (12/47), stimulus-related alternations between bursting and resting states assumed the form of semi-rhythmical burst discharges within the characteristic 60-80 Hz "gamma oscillation" range ("stimulus-induced" synchronization). For a minority of cells (7/47), switching between bursting and resting states--although characteristically modified by stimulus velocity--remained largely desynchronized with all phases of stimulus transit. It was argued that such temporal patterns of discharge may constitute elements of a candidate "distribution" code for movement detection by the cat visual system.

  19. Barbed micro-spikes for micro-scale biopsy

    NASA Astrophysics Data System (ADS)

    Byun, Sangwon; Lim, Jung-Min; Paik, Seung-Joon; Lee, Ahra; Koo, Kyo-in; Park, Sunkil; Park, Jaehong; Choi, Byoung-Doo; Seo, Jong Mo; Kim, Kyung-ah; Chung, Hum; Song, Si Young; Jeon, Doyoung; Cho, Dongil

    2005-06-01

    Single-crystal silicon planar micro-spikes with protruding barbs are developed for micro-scale biopsy and the feasibility of using the micro-spike as a micro-scale biopsy tool is evaluated for the first time. The fabrication process utilizes a deep silicon etch to define the micro-spike outline, resulting in protruding barbs of various shapes. Shanks of the fabricated micro-spikes are 3 mm long, 100 µm thick and 250 µm wide. Barbs protruding from micro-spike shanks facilitate the biopsy procedure by tearing off and retaining samples from target tissues. Micro-spikes with barbs successfully extracted tissue samples from the small intestines of the anesthetized pig, whereas micro-spikes without barbs failed to obtain a biopsy sample. Parylene coating can be applied to improve the biocompatibility of the micro-spike without deteriorating the biopsy function of the micro-spike. In addition, to show that the biopsy with the micro-spike can be applied to tissue analysis, samples obtained by micro-spikes were examined using immunofluorescent staining. Nuclei and F-actin of cells which are extracted by the micro-spike from a transwell were clearly visualized by immunofluorescent staining.

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

    PubMed Central

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

    2015-01-01

    SUMMARY 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

  1. 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

  2. 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.

  3. Hardware implementation of stochastic spiking neural networks.

    PubMed

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

    2012-08-01

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

  4. 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.

  5. 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.

  6. Aurora Australis, Spiked, Sinuous Green Airglow

    NASA Technical Reports Server (NTRS)

    1991-01-01

    This distant view of the Aurora Australis or Southern Lights (location unknown) shows a spiked and sinuous band of green airglow above the Earth Limb. Calculated to be in the 80 - 120 km altitude region, auroral activity is due to exitation of atomic oxygen in the upper atmosphere by radiation from the van Allen Radiation Belts and is most common above the 65 degree north and south latitude range during the spring and fall of the year.

  7. 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.

  8. Stochastic synchronization in finite size spiking networks.

    PubMed

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

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

  9. Spiked instantons from intersecting D-branes

    NASA Astrophysics Data System (ADS)

    Nekrasov, Nikita; Prabhakar, Naveen S.

    2017-01-01

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

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

    PubMed Central

    Wallisch, Pascal; Ostojic, Srdjan

    2016-01-01

    Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. SIGNIFICANCE STATEMENT Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions. PMID:27807166

  11. Kernel bandwidth optimization in spike rate estimation.

    PubMed

    Shimazaki, Hideaki; Shinomoto, Shigeru

    2010-08-01

    Kernel smoother and a time-histogram are classical tools for estimating an instantaneous rate of spike occurrences. We recently established a method for selecting the bin width of the time-histogram, based on the principle of minimizing the mean integrated square error (MISE) between the estimated rate and unknown underlying rate. Here we apply the same optimization principle to the kernel density estimation in selecting the width or "bandwidth" of the kernel, and further extend the algorithm to allow a variable bandwidth, in conformity with data. The variable kernel has the potential to accurately grasp non-stationary phenomena, such as abrupt changes in the firing rate, which we often encounter in neuroscience. In order to avoid possible overfitting that may take place due to excessive freedom, we introduced a stiffness constant for bandwidth variability. Our method automatically adjusts the stiffness constant, thereby adapting to the entire set of spike data. It is revealed that the classical kernel smoother may exhibit goodness-of-fit comparable to, or even better than, that of modern sophisticated rate estimation methods, provided that the bandwidth is selected properly for a given set of spike data, according to the optimization methods presented here.

  12. 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

  13. Parameter Estimation of a Spiking Silicon Neuron

    PubMed Central

    Russell, Alexander; Mazurek, Kevin; Mihalaş, Stefan; Niebur, Ernst; Etienne-Cummings, Ralph

    2012-01-01

    Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model’s output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron’s parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron’s output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron’s parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed. PMID:23852978

  14. Millisecond precision spike timing shapes tactile perception.

    PubMed

    Mackevicius, Emily L; Best, Matthew D; Saal, Hannes P; Bensmaia, Sliman J

    2012-10-31

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems.

  15. Millisecond Precision Spike Timing Shapes Tactile Perception

    PubMed Central

    Mackevicius, Emily L.; Best, Matthew D.; Saal, Hannes P.

    2012-01-01

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems. PMID:23115169

  16. 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.

  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. Spikes and matter inhomogeneities in massless scalar field models

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

    We shall discuss the general relativistic generation of spikes in a massless scalar field or stiff perfect fluid model. We first investigate orthogonally transitive (OT) G 2 stiff fluid spike models both heuristically and numerically, and give a new exact OT G 2 stiff fluid spike solution. We then present a new two-parameter family of non-OT G 2 stiff fluid spike solutions, obtained by the generalization of non-OT G 2 vacuum spike solutions to the stiff fluid case by applying Geroch's transformation on a Jacobs seed. The dynamics of these new stiff fluid spike solutions is qualitatively different from that of the vacuum spike solutions in that the matter (stiff fluid) feels the spike directly and the stiff fluid spike solution can end up with a permanent spike. We then derive the evolution equations of non-OT G 2 stiff fluid models, including a second perfect fluid, in full generality, and briefly discuss some of their qualitative properties and their potential numerical analysis. Finally, we discuss how a fluid, and especially a stiff fluid or massless scalar field, affects the physics of the generation of spikes.

  19. Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

    PubMed Central

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

    2016-01-01

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

  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. Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

    PubMed Central

    2012-01-01

    Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved

  2. 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.

  3. 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…

  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. A systematic method for configuring VLSI networks of spiking neurons.

    PubMed

    Neftci, Emre; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney

    2011-10-01

    An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.

  6. 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.

  7. Spike count, spike timing and temporal information in the cortex of awake, freely moving rats

    NASA Astrophysics Data System (ADS)

    Scaglione, Alessandro; Foffani, Guglielmo; Moxon, Karen A.

    2014-08-01

    Objective. Sensory processing of peripheral information is not stationary but is, in general, a dynamic process related to the behavioral state of the animal. Yet the link between the state of the behavior and the encoding properties of neurons is unclear. This report investigates the impact of the behavioral state on the encoding mechanisms used by cortical neurons for both detection and discrimination of somatosensory stimuli in awake, freely moving, rats. Approach. Neuronal activity was recorded from the primary somatosensory cortex of five rats under two different behavioral states (quiet versus whisking) while electrical stimulation of increasing stimulus strength was delivered to the mystacial pad. Information theoretical measures were then used to measure the contribution of different encoding mechanisms to the information carried by neurons in response to the whisker stimulation. Main results. We found that the behavioral state of the animal modulated the total amount of information conveyed by neurons and that the timing of individual spikes increased the information compared to the total count of spikes alone. However, the temporal information, i.e. information exclusively related to when the spikes occur, was not modulated by behavioral state. Significance. We conclude that information about somatosensory stimuli is modulated by the behavior of the animal and this modulation is mainly expressed in the spike count while the temporal information is more robust to changes in behavioral state.

  8. Spike count, spike timing and temporal information in the cortex of awake, freely moving rats

    PubMed Central

    Scaglione, Alessandro; Foffani, Guglielmo; Moxon, Karen A.

    2014-01-01

    Objective Sensory processing of peripheral information is not stationary but is, in general, a dynamic process related to the behavioral state of the animal. Yet the link between the state of the behavior and the encoding properties of neurons is unclear. This report investigates the impact of the behavioral state on the encoding mechanisms used by cortical neurons for both detection and discrimination of somatosensory stimuli in awake, freely moving, rats. Approach Neuronal activity was recorded from the primary somatosensory cortex of five rats under two different behavioral states (quiet vs. whisking) while electrical stimulation of increasing stimulus strength was delivered to the mystacial pad. Information theoretical measures were then used to measure the contribution of different encoding mechanisms to the information carried by neurons in response to the whisker stimulation. Main Results We found that the behavioral state of the animal modulated the total amount of information conveyed by neurons and that the timing of individual spikes increased the information compared to the total count of spikes alone. However, the temporal information, i.e. information exclusively related to when the spikes occur, was not modulated by behavioral state. Significance We conclude that information about somatosensory stimuli is modulated by the behavior of the animal and this modulation is mainly expressed in the spike count while the temporal information is more robust to changes in behavioral state. PMID:25024291

  9. Communication through Resonance in Spiking Neuronal Networks

    PubMed Central

    Frégnac, Yves; Aertsen, Ad; Kumar, Arvind

    2014-01-01

    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. PMID:25165853

  10. Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses

    PubMed Central

    2013-01-01

    Background Repeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools. Results As a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern. We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature. Conclusions We therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking. PMID:24369773

  11. 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.

  12. 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

  13. 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

  14. Weak noise in neurons may powerfully inhibit the generation of repetitive spiking but not its propagation.

    PubMed

    Tuckwell, Henry C; Jost, Jürgen

    2010-05-27

    Many neurons have epochs in which they fire action potentials in an approximately periodic fashion. To see what effects noise of relatively small amplitude has on such repetitive activity we recently examined the response of the Hodgkin-Huxley (HH) space-clamped system to such noise as the mean and variance of the applied current vary, near the bifurcation to periodic firing. This article is concerned with a more realistic neuron model which includes spatial extent. Employing the Hodgkin-Huxley partial differential equation system, the deterministic component of the input current is restricted to a small segment whereas the stochastic component extends over a region which may or may not overlap the deterministic component. For mean values below, near and above the critical values for repetitive spiking, the effects of weak noise of increasing strength is ascertained by simulation. As in the point model, small amplitude noise near the critical value dampens the spiking activity and leads to a minimum as noise level increases. This was the case for both additive noise and conductance-based noise. Uniform noise along the whole neuron is only marginally more effective in silencing the cell than noise which occurs near the region of excitation. In fact it is found that if signal and noise overlap in spatial extent, then weak noise may inhibit spiking. If, however, signal and noise are applied on disjoint intervals, then the noise has no effect on the spiking activity, no matter how large its region of application, though the trajectories are naturally altered slightly by noise. Such effects could not be discerned in a point model and are important for real neuron behavior. Interference with the spike train does nevertheless occur when the noise amplitude is larger, even when noise and signal do not overlap, being due to the instigation of secondary noise-induced wave phenomena rather than switching the system from one attractor (firing regularly) to another (a stable

  15. 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.

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

    PubMed

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

    2012-06-01

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

  17. Multimodal Imaging of Spike Propagation: A Technical Case Report

    PubMed Central

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

    2012-01-01

    SUMMARY We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation. PMID:21960488

  18. 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.

  19. IMPORTANCE OF SPIKE TIMING IN TOUCH: AN ANALOGY WITH HEARING?

    PubMed Central

    Saal, Hannes P.; Wang, Xiaoqin; Bensmaia, Sliman J.

    2017-01-01

    Touch is often conceived as a spatial sense akin to vision. However, touch also involves the transduction and processing of signals that vary rapidly over time, inviting comparisons with hearing. In both sensory systems, first order afferents produce spiking responses that are temporally precise and the timing of their responses carries stimulus information. The precision and informativeness of spike timing in the two systems invites the possibility that both implement similar mechanisms to extract behaviorally relevant information from these precisely timed responses. Here, we explore the putative roles of spike timing in touch and hearing and discuss common mechanisms that may be involved in processing temporal spiking patterns. PMID:27504741

  20. Generalized activity equations for spiking neural network dynamics

    PubMed Central

    Buice, Michael A.; Chow, Carson C.

    2013-01-01

    Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales—the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances. PMID:24298252

  1. 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

  2. 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.

  3. 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

  4. 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

  5. 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

  6. Ambiguity and nonidentifiability in the statistical analysis of neural codes.

    PubMed

    Amarasingham, Asohan; Geman, Stuart; Harrison, Matthew T

    2015-05-19

    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.

  7. 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

  8. Space Weather Effects on Mid-Latitude Railways: a Statistical Study of Anomalies observed in the Operation of Signaling and Train Control Equipment on the East-Siberian Railway

    NASA Astrophysics Data System (ADS)

    Kasinskii, V. V.; Ptitsyna, N. G.; Lyahov, N. N.; Dorman, L. I.; Villoresi, G.; Iucci, N.

    The end result of a long chain of space weather events beginning on the Sun is the induction of currents in ground-based long conductors as power lines pipelines and railways Intense geomagnetically induced currents GIC can hamper rail traffic by disturbing signaling and train control systems In few cases induced voltages were believed to have affected signaling equipment in Sweden Jansen et al 2000 and in the North of Russia Belov et al 2005 GIC threats have been a concern for technological systems at high-latitude locations due to disturbances driven by electrojet intensifications However other geomagnetic storm processes such as SSC and ring current enhancement can also cause GIC concerns for the technological systems Objective of this report is to continue our research Ptitsyna et al 2005 on possible influence of geomagnetic storms on mid-latitude railways and to perform a statistical research in addition to case studies This will help in providing a basis for railway companies to evaluate the risk of disruption to signaling and train control equipment and devise engineering solutions In the present report we analyzed anomalies in operation of automatic signaling and train control equipment occurred in 2004-2005 on the East-Siberian Railway located at mid-latitudes latitudes 51N-56N longitudes 96E-114E The anomalies consist mainly in unstable functioning and false operations in traffic automatic control systems rail chain switches locomotive control devices etc often resulting in false engagement of railway

  9. 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

  10. 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.

  11. Presynaptic spike broadening reduces junctional potential amplitude.

    PubMed

    Spencer, A N; Przysiezniak, J; Acosta-Urquidi, J; Basarsky, T A

    1989-08-24

    Presynaptic modulation of action potential duration may regulate synaptic transmission in both vertebrates and invertebrates. Such synaptic plasticity is brought about by modifications to membrane currents at presynaptic release sites, which, in turn, lead to changes in the concentration of cytosolic calcium available for mediating transmitter release. The 'primitive' neuromuscular junction of the jellyfish Polyorchis penicillatus is a useful model of presynaptic modulation. In this study, we show that the durations of action potentials in the motor neurons of this jellyfish are negatively correlated with the amplitude of excitatory junctional potentials. We present data from in vitro voltage-clamp experiments showing that short duration voltage spikes, which elicit large excitatory junctional potentials in vivo, produce larger and briefer calcium currents than do long duration action potentials, which elicit small excitatory junctional potentials.

  12. [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.

  13. Descriptive statistics.

    PubMed

    Shi, Runhua; McLarty, Jerry W

    2009-10-01

    In this article, we introduced basic concepts of statistics, type of distributions, and descriptive statistics. A few examples were also provided. The basic concepts presented herein are only a fraction of the concepts related to descriptive statistics. Also, there are many commonly used distributions not presented herein, such as Poisson distributions for rare events and exponential distributions, F distributions, and logistic distributions. More information can be found in many statistics books and publications.

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

    PubMed Central

    Swindale, Nicholas V.; Spacek, Martin A.

    2014-01-01

    In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted) with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC) algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 min. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis (PCA). Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scalable to larger multi-electrode arrays (MEAs). PMID:24574979

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

    PubMed

    Ly, Cheng; Middleton, Jason W; Doiron, Brent

    2012-01-01

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

  16. 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

  17. Cellular and Circuit Mechanisms Maintain Low Spike Co-Variability and Enhance Population Coding in Somatosensory Cortex

    PubMed Central

    Ly, Cheng; Middleton, Jason W.; Doiron, Brent

    2012-01-01

    The responses of cortical neurons are highly variable across repeated presentations of a stimulus. Understanding this variability is critical for theories of both sensory and motor processing, since response variance affects the accuracy of neural codes. Despite this influence, the cellular and circuit mechanisms that shape the trial-to-trial variability of population responses remain poorly understood. We used a combination of experimental and computational techniques to uncover the mechanisms underlying response variability of populations of pyramidal (E) cells in layer 2/3 of rat whisker barrel cortex. Spike trains recorded from pairs of E-cells during either spontaneous activity or whisker deflected responses show similarly low levels of spiking co-variability, despite large differences in network activation between the two states. We developed network models that show how spike threshold non-linearities dilute E-cell spiking co-variability during spontaneous activity and low velocity whisker deflections. In contrast, during high velocity whisker deflections, cancelation mechanisms mediated by feedforward inhibition maintain low E-cell pairwise co-variability. Thus, the combination of these two mechanisms ensure low E-cell population variability over a wide range of whisker deflection velocities. Finally, we show how this active decorrelation of population variability leads to a drastic increase in the population information about whisker velocity. The prevalence of spiking non-linearities and feedforward inhibition in the nervous system suggests that the mechanisms for low network variability presented in our study may generalize throughout the brain. PMID:22408615

  18. Statistical Software.

    ERIC Educational Resources Information Center

    Callamaras, Peter

    1983-01-01

    This buyer's guide to seven major types of statistics software packages for microcomputers reviews Edu-Ware Statistics 3.0; Financial Planning; Speed Stat; Statistics with DAISY; Human Systems Dynamics package of Stats Plus, ANOVA II, and REGRESS II; Maxistat; and Moore-Barnes' MBC Test Construction and MBC Correlation. (MBR)

  19. Statistical Diversions

    ERIC Educational Resources Information Center

    Petocz, Peter; Sowey, Eric

    2008-01-01

    As a branch of knowledge, Statistics is ubiquitous and its applications can be found in (almost) every field of human endeavour. In this article, the authors track down the possible source of the link between the "Siren song" and applications of Statistics. Answers to their previous five questions and five new questions on Statistics are presented.

  20. Reinforcement learning, spike-time-dependent plasticity, and the BCM rule.

    PubMed

    Baras, Dorit; Meir, Ron

    2007-08-01

    Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons and derive a spike-time-dependent plasticity rule that ensures convergence to a local optimum of the expected average reward. The approach is applicable to a broad class of neuronal models, including the Hodgkin-Huxley model. We demonstrate the effectiveness of the derived rule in several toy problems. Finally, through statistical analysis, we show that the synaptic plasticity rule established is closely related to the widely used BCM rule, for which good biological evidence exists.

  1. Stochastic inference with spiking neurons in the high-conductance state

    NASA Astrophysics Data System (ADS)

    Petrovici, Mihai A.; Bill, Johannes; Bytschok, Ilja; Schemmel, Johannes; Meier, Karlheinz

    2016-10-01

    The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.

  2. Parvalbumin tunes spike-timing and efferent short-term plasticity in striatal fast spiking interneurons.

    PubMed

    Orduz, David; Bischop, Don Patrick; Schwaller, Beat; Schiffmann, Serge N; Gall, David

    2013-07-01

      Striatal fast spiking interneurons (FSIs) modulate output of the striatum by synchronizing medium-sized spiny neurons (MSNs). Recent studies have broadened our understanding of FSIs, showing that they are implicated in severe motor disorders such as parkinsonism, dystonia and Tourette syndrome. FSIs are the only striatal neurons to express the calcium-binding protein parvalbumin (PV). This selective expression of PV raises questions about the functional role of this Ca(2+) buffer in controlling FSI Ca(2+) dynamics and, consequently, FSI spiking mode and neurotransmission. To study the functional involvement of FSIs in striatal microcircuit activity and the role of PV in FSI function, we performed perforated patch recordings on enhanced green fluorescent protein-expressing FSIs in brain slices from control and PV-/- mice. Our results revealed that PV-/- FSIs fired more regularly and were more excitable than control FSIs by a mechanism in which Ca(2+) buffering is linked to spiking activity as a result of the activation of small conductance Ca(2+)-dependent K(+) channels. A modelling approach of striatal FSIs supports our experimental results. Furthermore, PV deletion modified frequency-specific short-term plasticity at inhibitory FSI to MSN synapses. Our results therefore reinforce the hypothesis that in FSIs, PV is crucial for fine-tuning of the temporal responses of the FSI network and for the orchestration of MSN populations. This, in turn, may play a direct role in the generation and pathology-related worsening of motor rhythms.

  3. Parvalbumin tunes spike-timing and efferent short-term plasticity in striatal fast spiking interneurons

    PubMed Central

    Orduz, David; Bischop, Don Patrick; Schwaller, Beat; Schiffmann, Serge N; Gall, David

    2013-01-01

    Striatal fast spiking interneurons (FSIs) modulate output of the striatum by synchronizing medium-sized spiny neurons (MSNs). Recent studies have broadened our understanding of FSIs, showing that they are implicated in severe motor disorders such as parkinsonism, dystonia and Tourette syndrome. FSIs are the only striatal neurons to express the calcium-binding protein parvalbumin (PV). This selective expression of PV raises questions about the functional role of this Ca2+ buffer in controlling FSI Ca2+ dynamics and, consequently, FSI spiking mode and neurotransmission. To study the functional involvement of FSIs in striatal microcircuit activity and the role of PV in FSI function, we performed perforated patch recordings on enhanced green fluorescent protein-expressing FSIs in brain slices from control and PV−/− mice. Our results revealed that PV−/− FSIs fired more regularly and were more excitable than control FSIs by a mechanism in which Ca2+ buffering is linked to spiking activity as a result of the activation of small conductance Ca2+-dependent K+ channels. A modelling approach of striatal FSIs supports our experimental results. Furthermore, PV deletion modified frequency-specific short-term plasticity at inhibitory FSI to MSN synapses. Our results therefore reinforce the hypothesis that in FSIs, PV is crucial for fine-tuning of the temporal responses of the FSI network and for the orchestration of MSN populations. This, in turn, may play a direct role in the generation and pathology-related worsening of motor rhythms. PMID:23551945

  4. 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.

  5. 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...

  6. 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...

  7. 27 CFR 21.130 - Spike lavender oil, natural.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2014-04-01 2014-04-01 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...

  8. 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...

  9. 27 CFR 21.130 - Spike lavender oil, natural.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2012-04-01 2012-04-01 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. Probability distributions of the logarithm of inter-spike intervals yield accurate entropy estimates from small datasets.

    PubMed

    Dorval, Alan D

    2008-08-15

    The maximal information that the spike train of any neuron can pass on to subsequent neurons can be quantified as the neuronal firing pattern entropy. Difficulties associated with estimating entropy from small datasets have proven an obstacle to the widespread reporting of firing pattern entropies and more generally, the use of information theory within the neuroscience community. In the most accessible class of entropy estimation techniques, spike trains are partitioned linearly in time and entropy is estimated from the probability distribution of firing patterns within a partition. Ample previous work has focused on various techniques to minimize the finite dataset bias and standard deviation of entropy estimates from under-sampled probability distributions on spike timing events partitioned linearly in time. In this manuscript we present evidence that all distribution-based techniques would benefit from inter-spike intervals being partitioned in logarithmic time. We show that with logarithmic partitioning, firing rate changes become independent of firing pattern entropy. We delineate the entire entropy estimation process with two example neuronal models, demonstrating the robust improvements in bias and standard deviation that the logarithmic time method yields over two widely used linearly partitioned time approaches.

  11. 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

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

    PubMed

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

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

  13. Recent progress in multi-electrode spike sorting methods.

    PubMed

    Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

    2017-03-02

    In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

  14. Performing four basic arithmetic operations with spiking neural P systems.

    PubMed

    Zeng, Xiangxiang; Song, Tao; Zhang, Xingyi; Pan, Linqiang

    2012-12-01

    Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bio-inspired computing devices-spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information is encoded as the time interval between spikes). In this work, four SN P systems are constructed as adder, subtracter, multiplier, and divider, respectively. In these systems, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes.

  15. Preliminary Results from the QuietSpike Flight Test

    NASA Technical Reports Server (NTRS)

    Haering, Edward A., Jr.; Cliatt, Larry J., II; Howe, Don; Waithe, Kenrick

    2007-01-01

    This viewgraph presentation reviews the QuietSpike flight test results. It shows the previous tests from Nearfield probes. The presentation then reviews the approach to test the QuietSpike, and shows graphics of the positions of the test vehicles. It also shows the components of the Sonic Boom Probing Noseboom. A graph of the Pressure Over- Under-shoot (Shaped Sonic Boom Demonstration (SSBD)Data) is presented. It reviews the Shock Probing Orientations, explaining that the probing plane is always behind the tail of the QuietSpike jet. Graphs of the Shock Position Geometry (SSBD Data) and the QuietSpike signature as of the test on 12/13/06, Near-Field Probing Directly Under the QuietSpike jet, and Near-Field Probing to Side, Near-Field Probing Above and to Side. Several slides review the Computational Fluid Dynamic models, and results compared to the probe tests.

  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. 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

  18. 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

  19. Metatarsophalangeal joint function during sprinting: a comparison of barefoot and sprint spike shod foot conditions.

    PubMed

    Smith, Grace; Lake, Mark; Lees, Adrian

    2014-04-01

    The metatarsophalangeal joint is an important contributor to lower limb energetics during sprint running. This study compared the kinematics, kinetics and energetics of the metatarsophalangeal joint during sprinting barefoot and wearing standardized sprint spikes. The aim of this investigation was to determine whether standard sprinting footwear alters the natural motion and function of the metatarsophalangeal joint exhibited during barefoot sprint running. Eight trained sprinters performed maximal sprints along a runway, four sprints in each condition. Three-dimensional high-speed (1000 Hz) kinematic and kinetic data were collected at the 20 m point. Joint angle, angular velocity, moment, power and energy were calculated for the metatarsophalangeal joint. Sprint spikes significantly increase sprinting velocity (0.3 m/s average increase), yet limit the range of motion about the metatarsophalangeal joint (17.9% average reduction) and reduce peak dorsiflexion velocity (25.5% average reduction), thus exhibiting a controlling affect over the natural behavior of the foot. However, sprint spikes improve metatarsophalangeal joint kinetics by significantly increasing the peak metatarsophalangeal joint moment (15% average increase) and total energy generated during the important push-off phase (0.5 J to 1.4 J). The results demonstrate substantial changes in metatarsophalangeal function and potential improvements in performance-related parameters due to footwear.

  20. 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.

  1. Two functional inhibitory circuits are comprised of a heterogeneous population of fast-spiking cortical interneurons.

    PubMed

    Li, P; Huntsman, M M

    2014-04-18

    Cortical fast spiking (FS) interneurons possess autaptic, synaptic, and electrical synapses that serve to mediate a fast, coordinated response to their postsynaptic targets. While FS interneurons are known to participate in numerous and diverse actions, functional subgroupings within this multi-functional interneuron class remain to be identified. In the present study, we examined parvalbumin-positive FS interneurons in layer 4 of the primary somatosensory (barrel) cortex - a brain region well-known for specialized inhibitory function. Here we show that FS interneurons fall into two broad categories identified by the onset of the first action potential in a depolarizing train as: "delayed firing FS interneurons (FSD) and early onset firing FS interneurons (FSE). Subtle variations in action potential firing reveal six subtypes within these two categories: delayed non-accommodating (FSD-NAC), delayed stuttering (FSD-STUT), early onset stuttering (FSE-STUT), early onset-late spiking (FSE-LS), early onset early-spiking (FSE-ES), and early onset accommodating (FSE-AC). Using biophysical criteria previously employed to distinguish neuronal cell types, the FSD and FSE categories exhibit several shared biophysical and synaptic properties that coincide with the notion of specificity of inhibitory function within the cortical FS interneuron class.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. Coronavirus Spike Protein and Tropism Changes.

    PubMed

    Hulswit, R J G; de Haan, C A M; Bosch, B-J

    2016-01-01

    Coronaviruses (CoVs) have a remarkable potential to change tropism. This is particularly illustrated over the last 15 years by the emergence of two zoonotic CoVs, the severe acute respiratory syndrome (SARS)- and Middle East respiratory syndrome (MERS)-CoV. Due to their inherent genetic variability, it is inevitable that new cross-species transmission events of these enveloped, positive-stranded RNA viruses will occur. Research into these medical and veterinary important pathogens-sparked by the SARS and MERS outbreaks-revealed important principles of inter- and intraspecies tropism changes. The primary determinant of CoV tropism is the viral spike (S) entry protein. Trimers of the S glycoproteins on the virion surface accommodate binding to a cell surface receptor and fusion of the viral and cellular membrane. Recently, high-resolution structures of two CoV S proteins have been elucidated by single-particle cryo-electron microscopy. Using this new structural insight, we review the changes in the S protein that relate to changes in virus tropism. Different concepts underlie these tropism changes at the cellular, tissue, and host species level, including the promiscuity or adaptability of S proteins to orthologous receptors, alterations in the proteolytic cleavage activation as well as changes in the S protein metastability. A thorough understanding of the key role of the S protein in CoV entry is critical to further our understanding of virus cross-species transmission and pathogenesis and for development of intervention strategies.

  8. 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

  9. 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

  10. 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.

  11. 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

  12. On the simulation of nonlinear bidimensional spiking neuron models.

    PubMed

    Touboul, Jonathan

    2011-07-01

    Bidimensional spiking models are garnering a lot of attention for their simplicity and their ability to reproduce various spiking patterns of cortical neurons and are used particularly for large network simulations. These models describe the dynamics of the membrane potential by a nonlinear differential equation that blows up in finite time, coupled to a second equation for adaptation. Spikes are emitted when the membrane potential blows up or reaches a cutoff θ. The precise simulation of the spike times and of the adaptation variable is critical, for it governs the spike pattern produced and is hard to compute accurately because of the exploding nature of the system at the spike times. We thoroughly study the precision of fixed time-step integration schemes for this type of model and demonstrate that these methods produce systematic errors that are unbounded, as the cutoff value is increased, in the evaluation of the two crucial quantities: the spike time and the value of the adaptation variable at this time. Precise evaluation of these quantities therefore involves very small time steps and long simulation times. In order to achieve a fixed absolute precision in a reasonable computational time, we propose here a new algorithm to simulate these systems based on a variable integration step method that either integrates the original ordinary differential equation or the equation of the orbits in the phase plane, and compare this algorithm with fixed time-step Euler scheme and other more accurate simulation algorithms.

  13. 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

  14. Cytosolic pro-apoptotic SPIKE induces mitochondrial apoptosis in cancer.

    PubMed

    Nikolic, Ivana; Kastratovic, Tatjana; Zelen, Ivanka; Zivanovic, Aleksandar; Arsenijevic, Slobodan; Mitrovic, Marina

    2010-04-30

    Proteins of the BCL-2 family are important regulators of apoptosis. The BCL-2 family includes three main subgroups: the anti-apoptotic group, such as BCL-2, BCL-XL, BCL-W, and MCL-1; multi-domain pro-apoptotic BAX, BAK; and pro-apoptotic "BH3-only" BIK, PUMA, NOXA, BID, BAD, and SPIKE. SPIKE, a rare pro-apoptotic protein, is highly conserved throughout the evolution, including Caenorhabditis elegans, whose expression is downregulated in certain tumors, including kidney, lung, and breast. In the literature, SPIKE was proposed to interact with BAP31 and prevent BCL-XL from binding to BAP31. Here, we utilized the Position Weight Matrix method to identify SPIKE to be a BH3-only pro-apoptotic protein mainly localized in the cytosol of all cancer cell lines tested. Overexpression of SPIKE weakly induced apoptosis in comparison to the known BH3-only pro-apoptotic protein BIK. SPIKE promoted mitochondrial cytochrome c release, the activation of caspase 3, and the caspase cleavage of caspase's downstream substrates BAP31 and p130CAS. Although the informatics analysis of SPIKE implicates this protein as a member of the BH3-only BCL-2 subfamily, its role in apoptosis remains to be elucidated.

  15. Statistical Diversions

    ERIC Educational Resources Information Center

    Petocz, Peter; Sowey, Eric

    2008-01-01

    In this article, the authors focus on hypothesis testing--that peculiarly statistical way of deciding things. Statistical methods for testing hypotheses were developed in the 1920s and 1930s by some of the most famous statisticians, in particular Ronald Fisher, Jerzy Neyman and Egon Pearson, who laid the foundations of almost all modern methods of…

  16. 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

  17. Spike synchronization of chaotic oscillators as a phase transition.

    PubMed

    Ciszak, M; Montina, A; Arecchi, F T

    2009-02-01

    We study how a locally coupled array of spiking chaotic systems synchronizes to an external driving in a short time. Synchronization means spike separation at adjacent sites much shorter than the average inter-spike interval; a local lack of synchronization is called a defect. The system displays sudden spontaneous defect disappearance at a critical coupling strength suggesting an existence of a phase transition. Below critical coupling, the system reaches order at a definite amplitude of an external input; this order persists for a fixed time slot. Thus, the array behaves as an excitable-like system, even though the single element lacks such a property.

  18. 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.

  19. 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.

  20. 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.