Statistical properties of superimposed stationary spike trains.
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.
Statistical Complexity of Neural Spike Trains
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
Statistical technique for analysing functional connectivity of multiple spike trains.
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.
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.
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.
Test Statistics for the Identification of Assembly Neurons in Parallel Spike Trains
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
Staude, Benjamin; Grün, Sonja; Rotter, Stefan
2009-01-01
The extent to which groups of neurons exhibit higher-order correlations in their spiking activity is a controversial issue in current brain research. A major difficulty is that currently available tools for the analysis of massively parallel spike trains (N >10) for higher-order correlations typically require vast sample sizes. While multiple single-cell recordings become increasingly available, experimental approaches to investigate the role of higher-order correlations suffer from the limitations of available analysis techniques. We have recently presented a novel method for cumulant-based inference of higher-order correlations (CuBIC) that detects correlations of higher order even from relatively short data stretches of length T = 10–100 s. CuBIC employs the compound Poisson process (CPP) as a statistical model for the population spike counts, and assumes spike trains to be stationary in the analyzed data stretch. In the present study, we describe a non-stationary version of the CPP by decoupling the correlation structure from the spiking intensity of the population. This allows us to adapt CuBIC to time-varying firing rates. Numerical simulations reveal that the adaptation corrects for false positive inference of correlations in data with pure rate co-variation, while allowing for temporal variations of the firing rates has a surprisingly small effect on CuBICs sensitivity for correlations. PMID:20725510
Monitoring spike train synchrony.
Kreuz, Thomas; Chicharro, Daniel; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian
2013-03-01
Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.
The Computational Structure of Spike Trains
Haslinger, Robert; Klinkner, Kristina Lisa; Shalizi, Cosma Rohilla
2010-01-01
Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically-identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic information content (the information needed to describe the spike train exactly) can be split into three parts describing (1) the time-invariant structure (complexity) of the minimal spike-generating process, which describes the spike train statistically, (2) the randomness (internal entropy rate) of the minimal spike-generating process, and (3) a residual pure noise term not described by the minimal spike generating process. We use CSMs to approximate each of these quantities. The CSMs are inferred non-parametrically from the data, making only mild regularity assumptions, via the Causal State Splitting Reconstruction (CSSR) algorithm. The methods presented here complement more traditional spike train analyses by describing not only spiking probability, and spike train entropy, but also the complexity of a spike train’s structure. We demonstrate our approach using both simulated spike trains and experimental data recorded in rat barrel cortex during vibrissa stimulation. PMID:19764880
Kinetics of fast short-term depression are matched to spike train statistics to reduce noise.
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.
[Mathematical model of bursting spike train and its spectrum features].
Zhang, Dandan; Ding, Haiyan; Ye, Datian
2010-12-01
Bursting is an important firing mode of neurons. To propose a stochastic model of bursting spike train, the interspike interval (ISI) characteristics of single-spiking train and bursting spike train were analyzed and compared. In contrast with the exponential distribution of ISI in single-spiking train, normal distribution is supposed to be the ISI model of bursting spike train. Simulated neural spike trains were produced to investigate the spectrum features of the ISI model. The results showed that: (1) bursting spike train with normally distributed ISI held a low-pass spectrum while the spectrum of single-spiking train was flat; (2) the coefficient of variation of ISI in bursting train decided the bandwidth of its low-pass spectrum. Then neural activities from anesthetized rodent were used to check the validity of the model. 10 simultaneously recorded bursting spike trains and 10 single-spiking trains were selected during anesthesia and after pure-oxygen-washout period respectively. The spectrograms of these neural spike trains were analyzed and the results were matched with our mathematical model. It is believed that the bursting spike train model established in this paper will help to theoretically study the statistical characters of neural spike train and to add mathematical foundation in neural coding schemes.
Neuronal spike trains and stochastic point processes. I. The single spike train.
Perkel, D H; Gerstein, G L; Moore, G P
1967-07-01
In a growing class of neurophysiological experiments, the train of impulses ("spikes") produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains.
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.
Training Deep Spiking Neural Networks Using Backpropagation
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
Training Deep Spiking Neural Networks Using Backpropagation.
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.
Detecting joint pausiness in parallel spike trains.
Gärtner, Matthias; Duvarci, Sevil; Roeper, Jochen; Schneider, Gaby
2017-06-15
Transient periods with reduced neuronal discharge - called 'pauses' - have recently gained increasing attention. In dopamine neurons, pauses are considered important teaching signals, encoding negative reward prediction errors. Particularly simultaneous pauses are likely to have increased impact on information processing. Available methods for detecting joint pausing analyze temporal overlap of pauses across spike trains. Such techniques are threshold dependent and can fail to identify joint pauses that are easily detectable by eye, particularly in spike trains with different firing rates. We introduce a new statistic called pausiness that measures the degree of synchronous pausing in spike train pairs and avoids threshold-dependent identification of specific pauses. A new graphic termed the cross-pauseogram compares the joint pausiness of two spike trains with its time shifted analogue, such that a (pausiness) peak indicates joint pausing. When assessing significance of pausiness peaks, we use a stochastic model with synchronous spikes to disentangle joint pausiness arising from synchronous spikes from additional 'joint excess pausiness' (JEP). Parameter estimates are obtained from auto- and cross-correlograms, and statistical significance is assessed by comparison to simulated cross-pauseograms. Our new method was applied to dopamine neuron pairs recorded in the ventral tegmental area of awake behaving mice. Significant JEP was detected in about 20% of the pairs. Given the neurophysiological importance of pauses and the fact that neurons integrate multiple inputs, our findings suggest that the analysis of JEP can reveal interesting aspects in the activity of simultaneously recorded neurons. Copyright © 2017 Elsevier B.V. All rights reserved.
A simple algorithm for averaging spike trains.
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.
Measuring multiple spike train synchrony.
Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I
2009-10-15
Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.
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.
An Overview of Bayesian Methods for Neural Spike Train Analysis
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
Multiscale analysis of neural spike trains.
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.
Representing spike trains using constant sampling intervals.
Hirata, Yoshito; Aihara, Kazuyuki
2009-10-15
Sensory neurons encode external information by a series of times of action potentials, which is called a spike train. However, since it is a point process, it is hard to analyze. Here we propose a method for converting a spike train into a real-valued time series with a fixed sampling interval under the assumption of temporal codes. The proposed method yields time series that represent encoded signals. Especially when the method is applied to spike trains generated using integrate-and-fire models, the yielded time series look very similar to those of encoded information. The method works robustly even when a spike train is contaminated with noise. Since unlike filters it does not use its original signals for the conversion, the proposed method can be widely used for investigating spike train data in the real world.
Finding the event structure of neuronal spike trains.
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.
Spiking neural networks for cortical neuronal spike train decoding.
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.
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.
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.
Banerjee, Arunava
2016-05-01
We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.
Stochasticity, spikes and decoding: sufficiency and utility of order statistics.
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.
Unbiased estimation of precise temporal correlations between spike trains.
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.
Fitting Neuron Models to Spike Trains
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
Temporal Correlations and Neural Spike Train Entropy
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.
Detection of bursts and pauses in spike trains.
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.
Advantage of support vector machine for neural spike train decoding under spike sorting errors.
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.
Robust spike-train learning in spike-event based weight update.
Shrestha, Sumit Bam; Song, Qing
2017-09-12
Supervised learning algorithms in a spiking neural network either learn a spike-train pattern for a single neuron receiving input spike-train from multiple input synapses or learn to output the first spike time in a feedforward network setting. In this paper, we build upon spike-event based weight update strategy to learn continuous spike-train in a spiking neural network with a hidden layer using a dead zone on-off based adaptive learning rate rule which ensures convergence of the learning process in the sense of weight convergence and robustness of the learning process to external disturbances. Based on different benchmark problems, we compare this new method with other relevant spike-train learning algorithms. The results show that the speed of learning is much improved and the rate of successful learning is also greatly improved. Copyright © 2017 Elsevier Ltd. All rights reserved.
The stochastic properties of input spike trains control neuronal arithmetic.
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.
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.
Analyzing multiple spike trains with nonparametric Granger causality.
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.
Analysis of Neuronal Spike Trains, Deconstructed
Aljadeff, Johnatan; Lansdell, Benjamin J.; Fairhall, Adrienne L.; Kleinfeld, David
2016-01-01
As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods. PMID:27477016
Analysis of Neuronal Spike Trains, Deconstructed.
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.
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.
Neuronal spike train entropy estimation by history clustering.
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.
On the Mathematical Consequences of Binning Spike Trains.
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.
Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M.; Grün, Sonja
2017-01-01
Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis. PMID:28596729
Quaglio, Pietro; Yegenoglu, Alper; Torre, Emiliano; Endres, Dominik M; Grün, Sonja
2017-01-01
Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons). In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST). We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE) analysis.
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.
A new class of metrics for spike trains.
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.
Wavelet-based processing of neuronal spike trains prior to discriminant analysis.
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].
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).
SPIKY: a graphical user interface for monitoring spike train synchrony.
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.
SPIKY: a graphical user interface for monitoring spike train synchrony
Mulansky, Mario; Bozanic, Nebojsa
2015-01-01
Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. PMID:25744888
The time-rescaling theorem and its application to neural spike train data analysis.
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.
Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity
Gilson, Matthieu; Fukai, Tomoki; Burkitt, Anthony N.
2012-01-01
Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron “sees” the input correlations. We thus denote this unsupervised learning scheme as ‘kernel spectral component analysis’ (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a “linear” response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP. PMID:22792056
Multi-scale detection of rate changes in spike trains with weak dependencies.
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.
Improved similarity measures for small sets of spike trains.
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.
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.
Regular Patterns in Cerebellar Purkinje Cell Simple Spike Trains
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
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.
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.
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks
Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen
2016-01-01
The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001
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.
Local Variation of Hashtag Spike Trains and Popularity in Twitter
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
Local Variation of Hashtag Spike Trains and Popularity in Twitter.
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.
Balanced synaptic input shapes the correlation between neural spike trains.
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.
Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains
Litwin-Kumar, Ashok; Oswald, Anne-Marie M.; Urban, Nathaniel N.; Doiron, Brent
2011-01-01
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. PMID:22215995
Studying spike trains using a van Rossum metric with a synapse-like filter.
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.
Information content in cortical spike trains during brain state transitions.
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.
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
Quantifying Spike Train Oscillations: Biases, Distortions and Solutions
Matzner, Ayala; Bar-Gad, Izhar
2015-01-01
Estimation of the power spectrum is a common method for identifying oscillatory changes in neuronal activity. However, the stochastic nature of neuronal activity leads to severe biases in the estimation of these oscillations in single unit spike trains. Different biological and experimental factors cause the spike train to differentially reflect its underlying oscillatory rate function. We analyzed the effect of factors, such as the mean firing rate and the recording duration, on the detectability of oscillations and their significance, and tested these theoretical results on experimental data recorded in Parkinsonian non-human primates. The effect of these factors is dramatic, such that in some conditions, the detection of existing oscillations is impossible. Moreover, these biases impede the comparison of oscillations across brain regions, neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. We introduce a novel objective measure, the "modulation index", which overcomes these biases, and enables reliable detection of oscillations from spike trains and a direct estimation of the oscillation magnitude. The modulation index detects a high percentage of oscillations over a wide range of parameters, compared to classical spectral analysis methods, and enables an unbiased comparison between spike trains recorded from different neurons and using different experimental protocols. PMID:25909328
Detecting Multineuronal Temporal Patterns in Parallel Spike Trains
Gansel, Kai S.; Singer, Wolf
2012-01-01
We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept. PMID:22661942
Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms
Matthews, Brett A.; Clements, Mark A.
2014-01-01
This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset. PMID:24829568
Note on the coefficient of variations of neuronal spike trains.
Lengler, Johannes; Steger, Angelika
2017-08-01
It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.
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.
From the statistics of connectivity to the statistics of spike times in neuronal networks.
Ocker, Gabriel Koch; Hu, Yu; Buice, Michael A; Doiron, Brent; Josić, Krešimir; Rosenbaum, Robert; Shea-Brown, Eric
2017-08-29
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics. Copyright © 2017 Elsevier Ltd. All rights reserved.
Retention of lung distension information in pump cell spike trains.
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.
HOW COMPUTATIONAL TECHNIQUE AND SPIKE TRAIN PROPERTIES AFFECT COHERENCE DETECTION
Terry, K; Griffin, L
2008-01-01
Spike train coherence is used to characterize common inputs that drive motor unit synchronization. However, data segmentation, overlap, and taper can affect coherence magnitude, thereby influencing the incidence at which significant coherence is detected. Also, the effect of spike train firing rate and common input variability on the detection of significant coherence is unknown. We used a pool of simulated synchronized spike trains with various firing rates (7–19 Hz), coefficients of variation (CV) (0.05–0.50), common input frequencies (10, 20, and 30 Hz, CV: 0.05–0.50), trial durations (30, 60, 90 and 120 sec.), and synchronization strength to explore the effects of segment length (1024 and 2048 1-ms samples), tapering (Hann, Nuttall, and rectangular), and overlap (0, 37.5, 50, 62.5, and 75%). Tapered segments overlapped by at least 50% maximized coherence, regardless of taper type. Coherence for 30-second trials revealed significant coherence for less than half of the motor unit pairs, demonstrating the advantages of longer trails. 2048-sample segments produced similar coherence values with twice the frequency resolution. Increasing the common input variability from 0.15–0.50 reduced coherence incidence by approximately 60%, indicating that synchronized physiological motor unit pairs may fail to show significant coherence if the common input frequency is sufficiently unstable. PMID:17976736
An interactive tool for visualization of spike train synchronization.
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.
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.
Stochastic optimal control of single neuron spike trains.
Iolov, Alexandre; Ditlevsen, Susanne; Longtin, André
2014-08-01
External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain-machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access to the spike times (open-loop control). We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy of control degrades with increasing intensity of the noise. Simulations show that our algorithms produce the desired results for the LIF model, but also for the case where the neuron dynamics are given by more complex models than the LIF model. This is illustrated explicitly using the Morris-Lecar spiking neuron model, for which an LIF approximation is first obtained from a spike sequence using a previously published method. We further show that a related control strategy based on the assumption that there is no noise performs poorly in comparison to our noise-based strategies. The algorithms are numerically intensive and may require efficiency refinements to achieve real-time control; in particular, the open-loop context is more numerically demanding than the closed-loop one. Our main contribution is the
Stochastic optimal control of single neuron spike trains
NASA Astrophysics Data System (ADS)
Iolov, Alexandre; Ditlevsen, Susanne; Longtin, André
2014-08-01
Objective. External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain-machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy of control degrades with increasing intensity of the noise. Simulations show that our algorithms produce the desired results for the LIF model, but also for the case where the neuron dynamics are given by more complex models than the LIF model. This is illustrated explicitly using the Morris-Lecar spiking neuron model, for which an LIF approximation is first obtained from a spike sequence using a previously published method. We further show that a related control strategy based on the assumption that there is no noise performs poorly in comparison to our noise-based strategies. The algorithms are numerically intensive and may require efficiency refinements to achieve real-time control; in particular, the open-loop context is more numerically demanding than the closed
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
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
Single-trial Spike Trains in Parietal Cortex Reveal Discrete Steps During Decision-making
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
Parametric models to relate spike train and LFP dynamics with neural information processing
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
Training Spiking Neural Models Using Artificial Bee Colony
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
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.
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
Non-Euclidean properties of spike train metric spaces
NASA Astrophysics Data System (ADS)
Aronov, Dmitriy; Victor, Jonathan D.
2004-06-01
Quantifying the dissimilarity (or distance) between two sequences is essential to the study of action potential (spike) trains in neuroscience and genetic sequences in molecular biology. In neuroscience, traditional methods for sequence comparisons rely on techniques appropriate for multivariate data, which typically assume that the space of sequences is intrinsically Euclidean. More recently, metrics that do not make this assumption have been introduced for comparison of neural activity patterns. These metrics have a formal resemblance to those used in the comparison of genetic sequences. Yet the relationship between such metrics and the traditional Euclidean distances has remained unclear. We show, both analytically and computationally, that the geometries associated with metric spaces of event sequences are intrinsically non-Euclidean. Our results demonstrate that metric spaces enrich the study of neural activity patterns, since accounting for perceptual spaces requires a non-Euclidean geometry.
Highly variable spike trains underlie reproducible sensorimotor responses in the medicinal leech.
Zoccolan, Davide; Pinato, Giulietta; Torre, Vincent
2002-12-15
The nervous system of the leech is a particularly suitable model to investigate neural coding of sensorimotor responses because it allows both observation of behavior and the simultaneous measurement of a large fraction of its underlying neuronal activity. In this study, we used a combination of multielectrode recordings, videomicroscopy, and computation of the optical flow to investigate the reproducibility of the motor response caused by local mechanical stimulation of the leech skin. We analyzed variability at different levels of processing: mechanosensory neurons, motoneurons, muscle activation, and behavior. Spike trains in mechanosensory neurons were very reproducible, unlike those in motoneurons. The motor response, however, was reproducible because of two distinct biophysical mechanisms. First, leech muscles contract slowly and therefore are poorly sensitive to the jitter of motoneuron spikes. Second, the motor response results from the coactivation of a population of motoneurons firing in a statistically independent way, which reduces the variability of the population firing. These data show that reproducible spike trains are not required to sustain reproducible behaviors and illustrate how the nervous system can cope with unreliable components to produce reliable action.
Estimating the correlation between bursty spike trains and local field potentials.
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.
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.
Do neurons process information by relative intervals in spike trains?
Klemm, W R; Sherry, C J
1982-01-01
We suggest the possibility that neurons process information in terms of the relative duration of clusters of adjacent and successive inter-action potential intervals ("bytes" of intervals). If this concept is plausible, as is supported by research from several laboratories which have specifically addressed this possibility, one should be able to see evidence for such patterning in the published illustrations from studies in which this concept was not considered. We present some of this evidence here, along with some illustrations from the original publications. Byte patterns are evident in these examples, even though they went unrecognized by authors and readers alike. It is true that interval patterns are not obvious in all published illustrations of spike trains, and we suggest that this can be explained by one or more of the following: (1) some neurons may operate with an interval-pattern code while others do not, (2) a given neuron may use an interval-pattern code only under certain conditions, and (3) even when such a code exists, it may be difficult to detect for identifiable technical reasons. Therefore, we believe that the relative-internal-pattern concept is a valid scientific hypothesis which merits specific testing of its validity and range of applicability.
Time-resolved and time-scale adaptive measures of spike train synchrony.
Kreuz, Thomas; Chicharro, Daniel; Greschner, Martin; Andrzejak, Ralph G
2011-01-30
A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data. Copyright © 2010 Elsevier B.V. All rights reserved.
Long-term correlations in the spike trains of medullary sympathetic neurons.
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
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.
Reading spike timing without a clock: intrinsic decoding of spike trains.
Panzeri, Stefano; Ince, Robin A A; Diamond, Mathew E; Kayser, Christoph
2014-03-05
The precise timing of action potentials of sensory neurons relative to the time of stimulus presentation carries substantial sensory information that is lost or degraded when these responses are summed over longer time windows. However, it is unclear whether and how downstream networks can access information in precise time-varying neural responses. Here, we review approaches to test the hypothesis that the activity of neural populations provides the temporal reference frames needed to decode temporal spike patterns. These approaches are based on comparing the single-trial stimulus discriminability obtained from neural codes defined with respect to network-intrinsic reference frames to the discriminability obtained from codes defined relative to the experimenter's computer clock. Application of this formalism to auditory, visual and somatosensory data shows that information carried by millisecond-scale spike times can be decoded robustly even with little or no independent external knowledge of stimulus time. In cortex, key components of such intrinsic temporal reference frames include dedicated neural populations that signal stimulus onset with reliable and precise latencies, and low-frequency oscillations that can serve as reference for partitioning extended neuronal responses into informative spike patterns.
Quantification of clustering in joint interspike interval scattergrams of spike trains.
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.
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.
Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains
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
Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.
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
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.
Multisensory Interactions Influence Neuronal Spike Train Dynamics in the Posterior Parietal Cortex
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
Subthreshold Membrane-Potential Resonances Shape Spike-Train Patterns in the Entorhinal Cortex
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
Pillow, Jonathan W; Ahmadian, Yashar; Paninski, Liam
2011-01-01
One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present several applications of the encoding model framework to the problem of decoding stimulus information from population spike responses: (1) a tractable algorithm for computing the maximum a posteriori (MAP) estimate of the stimulus, the most probable stimulus to have generated an observed single- or multiple-neuron spike train response, given some prior distribution over the stimulus; (2) a gaussian approximation to the posterior stimulus distribution that can be used to quantify the fidelity with which various stimulus features are encoded; (3) an efficient method for estimating the mutual information between the stimulus and the spike trains emitted by a neural population; and (4) a framework for the detection of change-point times (the time at which the stimulus undergoes a change in mean or variance) by marginalizing over the posterior stimulus distribution. We provide several examples illustrating the performance of these estimators with simulated and real neural data.
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
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.
Xu, Song; Li, Yang; Guo, Qi; Yang, Xiao-Feng; Chan, Rosa H M
2017-02-15
Tracking the changes of neural dynamics based on neuronal spiking activities is a critical step to understand the neurobiological basis of learning from behaving animals. These dynamical neurobiological processes associated with learning are also time-varying, which makes the modeling problem challenging. We developed a novel multiwavelet-based time-varying generalized Laguerre-Volterra (TVGLV) modeling framework to study the time-varying neural dynamical systems using natural spike train data. By projecting the time-varying parameters in the TVGLV model onto a finite sequence of multiwavelet basis functions, the time-varying identification problem is converted into a time invariant linear-in-the-parameters one. An effective forward orthogonal regression (FOR) algorithm aided by mutual information (MI) criterion is then applied for the selection of significant model regressors or terms and the refinement of model structure. A generalized linear model fit approach is finally employed for parameter estimation from spike train data. The proposed multiwavelet-based TVGLV approach is used to identify both synthetic input-output spike trains and spontaneous retinal spike train recordings. The proposed method gives excellent the performance of tracking either sharply or slowly changing parameters with high sensitivity and accuracy regardless of the a priori knowledge of spike trains, which these results indicate that the proposed method is shown to deal well with spike train data. The proposed multiwavelet-based TVGLV approach was compared with several state-of-art parametric estimation methods like the steepest descent point process filter (SDPPF) or Chebyshev polynomial expansion method. The conventional SDPPF algorithm, or SDPPF with B-splines wavelet expansion method was shown to have the poor performance of tracking the time-varying system changes with the synthetic spike train data due to the slow convergence of the adaptive filter methods. Although the Chebyshev
Measures of complexity in neural spike-trains of the slowly adapting stretch receptor organs.
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.
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.
Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels
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
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.
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.
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
2015-01-01
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. PMID:26862574
A continuous entropy rate estimator for spike trains using a K-means-based context tree.
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.
Firing statistics and correlations in spiking neurons: a level-crossing approach.
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.
Population activity statistics dissect subthreshold and spiking variability in V1.
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.
Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G
2017-08-01
The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.
NASA Astrophysics Data System (ADS)
Malvestio, Irene; Kreuz, Thomas; Andrzejak, Ralph G.
2017-08-01
The detection of directional couplings between dynamics based on measured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons. One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L . Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes. We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data.
Synchronization phenomena in pulse-coupled networks driven by spike-train inputs.
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.
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.
Calculating mutual information for spike trains and other data with distances but no coordinates.
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.
A generative spike train model with time-structured higher order correlations.
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.
A generative spike train model with time-structured higher order correlations
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
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.
Reconstructing networks of pulse-coupled oscillators from spike trains
NASA Astrophysics Data System (ADS)
Cestnik, Rok; Rosenblum, Michael
2017-07-01
We present an approach for reconstructing networks of pulse-coupled neuronlike oscillators from passive observation of pulse trains of all nodes. It is assumed that units are described by their phase response curves and that their phases are instantaneously reset by incoming pulses. Using an iterative procedure, we recover the properties of all nodes, namely their phase response curves and natural frequencies, as well as strengths of all directed connections.
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.
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.
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
Functional connectivity among spike trains in neural assemblies during rat working memory task.
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.
NASA Astrophysics Data System (ADS)
Farkhooi, Farzad; Strube-Bloss, Martin F.; Nawrot, Martin P.
2009-02-01
The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50%, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency-adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors.
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.
Continuous functions determined by spike trains of a neuron subject to stimulation.
Awiszus, F
1988-01-01
Several ways of estimating a continuous function from the spike train output of a neuron subjected to repeated stimuli are compared: (i) the probability of firing function estimated by a PST-histogram (ii) the rate of discharge function estimated by a "frequencygram" (Bessou et al. 1968) and (iii) the interspike-interval function which is introduced in this paper. For a special class of neuronal responses, called deterministic, these functions may be expressed in terms of each other. It is shown that the current clamped Hodgkin-Huxley model of an action potential encoding membrane (Hodgkin and Huxley 1952) is able to generate such deterministic responses. As an experimental example, a deterministic response of a primary muscle spindle afferent is used to demonstrate the estimation of the functions. Interpretability and numerical estimatability of these spike train describing functions are discussed for deterministic neuronal responses.
Spike train generation and current-to-frequency conversion in silicon diodes
NASA Technical Reports Server (NTRS)
Coon, D. D.; Perera, A. G. U.
1989-01-01
A device physics model is developed to analyze spontaneous neuron-like spike train generation in current driven silicon p(+)-n-n(+) devices in cryogenic environments. The model is shown to explain the very high dynamic range (0 to the 7th) current-to-frequency conversion and experimental features of the spike train frequency as a function of input current. The devices are interesting components for implementation of parallel asynchronous processing adjacent to cryogenically cooled focal planes because of their extremely low current and power requirements, their electronic simplicity, and their pulse coding capability, and could be used to form the hardware basis for neural networks which employ biologically plausible means of information coding.
A hidden Markov model for decoding and the analysis of replay in spike trains.
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.
Schaette, Roland; Gollisch, Tim; Herz, Andreas V M
2005-06-01
Reliable accounts of the variability observed in neural spike trains are a prerequisite for the proper interpretation of neural dynamics and coding principles. Models that accurately describe neural variability over a wide range of stimulation and response patterns are therefore highly desirable, especially if they can explain this variability in terms of basic neural observables and parameters such as firing rate and refractory period. In this work, we analyze the response variability recorded in vivo from locust auditory receptor neurons under acoustic stimulation. In agreement with results from other systems, our data suggest that neural refractoriness has a strong influence on spike-train variability. We therefore explore a stochastic model of spike generation that includes refractoriness through a recovery function. Because our experimental data are consistent with a renewal process, the recovery function can be derived from a single interspike-interval histogram obtained under constant stimulation. The resulting description yields quantitatively accurate predictions of the response variability over the whole range of firing rates for constant-intensity as well as amplitude-modulated sound stimuli. Model parameters obtained from constant stimulation can be used to predict the variability in response to dynamic stimuli. These results demonstrate that key ingredients of the stochastic response dynamics of a sensory neuron are faithfully captured by a simple stochastic model framework.
ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains
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
ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains.
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.
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.
The Episodic Nature of Spike Trains in the Early Visual Pathway
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
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
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.
Holbrook, Andrew; Vandenberg-Rodes, Alexander; Fortin, Norbert; Shahbaba, Babak
2017-01-01
Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities-such as EEG, fMRI, LFP, and spike trains-offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modeling of LFP and spike train data, and present a novel Bayesian method for neural decoding to infer behavioral and experimental conditions. This model performs supervised dual-dimensionality reduction: it learns low-dimensional representations of two different sources of information that not only explain variation in the input data itself, but also predict extra-neuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential PCA and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference, and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone, and combined LFP and spike train data. We compare two methods for modeling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights.
Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.
Kuroda, S; Yamamoto, K; Miyamoto, H; Doya, K; Kawat, M
2001-03-01
Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike
Estimating Temporal Causal Interaction between Spike Trains with Permutation and Transfer Entropy
Li, Zhaohui; Li, Xiaoli
2013-01-01
Estimating the causal interaction between neurons is very important for better understanding the functional connectivity in neuronal networks. We propose a method called normalized permutation transfer entropy (NPTE) to evaluate the temporal causal interaction between spike trains, which quantifies the fraction of ordinal information in a neuron that has presented in another one. The performance of this method is evaluated with the spike trains generated by an Izhikevich’s neuronal model. Results show that the NPTE method can effectively estimate the causal interaction between two neurons without influence of data length. Considering both the precision of time delay estimated and the robustness of information flow estimated against neuronal firing rate, the NPTE method is superior to other information theoretic method including normalized transfer entropy, symbolic transfer entropy and permutation conditional mutual information. To test the performance of NPTE on analyzing simulated biophysically realistic synapses, an Izhikevich’s cortical network that based on the neuronal model is employed. It is found that the NPTE method is able to characterize mutual interactions and identify spurious causality in a network of three neurons exactly. We conclude that the proposed method can obtain more reliable comparison of interactions between different pairs of neurons and is a promising tool to uncover more details on the neural coding. PMID:23940662
What can spike train distances tell us about the neural code?
Chicharro, Daniel; Kreuz, Thomas; Andrzejak, Ralph G
2011-07-15
Time scale parametric spike train distances like the Victor and the van Rossum distances are often applied to study the neural code based on neural stimuli discrimination. Different neural coding hypotheses, such as rate or coincidence coding, can be assessed by combining a time scale parametric spike train distance with a classifier in order to obtain the optimal discrimination performance. The time scale for which the responses to different stimuli are distinguished best is assumed to be the discriminative precision of the neural code. The relevance of temporal coding is evaluated by comparing the optimal discrimination performance with the one achieved when assuming a rate code. We here characterize the measures quantifying the discrimination performance, the discriminative precision, and the relevance of temporal coding. Furthermore, we evaluate the information these quantities provide about the neural code. We show that the discriminative precision is too unspecific to be interpreted in terms of the time scales relevant for encoding. Accordingly, the time scale parametric nature of the distances is mainly an advantage because it allows maximizing the discrimination performance across a whole set of measures with different sensitivities determined by the time scale parameter, but not due to the possibility to examine the temporal properties of the neural code. Copyright © 2011 Elsevier B.V. All rights reserved.
A compound memristive synapse model for statistical learning through STDP in spiking neural networks
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
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.
Hu, Xiaogang; Suresh, Nina L; Jeon, Brian; Shin, Henry; Rymer, William Z
2014-01-01
Automated motor unit (MU) decomposition algorithms of surface electromyogram (EMG) have been developed recently. However, a routine estimate of the decomposition accuracy is still lacking. The objective of this preliminary study was to examine the statistics of the inter-spike intervals (ISIs) of the identified MUs as a measure of the decomposition accuracy, such that the ISI analysis can be used as a routine procedure to assess the accuracy of the surface identified MU spike timings. A surface EMG recording and decomposition system was used to record EMG signals and extract single MU activities from the first dorsal interosseous muscle of three healthy individuals. The estimated ISI statistics were cross-validated with decomposed MUs from simultaneous intramuscular EMG recordings. Our preliminary results reveal that the distribution of the ISIs, specifically the deviation from the Gaussian distribution as represented by secondary peaks at the short or long ISIs, can provide information regarding the spurious errors and missed firing errors in the decomposition. In addition, the variability (coefficient of variation) of the ISIs also correlated inversely with the decomposition accuracy. These findings show that the ISI statistics can be used to assess the spike timing accuracy of the identified MUs from surface EMG decomposition algorithms.
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.
Setubal, Maria Silvia Vellutini; Gonçalves, Andrea Vasconcelos; Rocha, Sheyla Ribeiro; Amaral, Eliana Martorano
2017-08-04
Objective Resident doctors usually face the task to communicate bad news in perinatology without any formal training. The impact on parents can be disastrous. The objective of this paper is to analyze the perception of residents regarding a training program in communicating bad news in perinatology based on video reviews and setting, perception, invitation, knowledge, emotion, and summary (SPIKES) strategy. Methods We performed the analysis of complementary data collected from participants in a randomized controlled intervention study to evaluate the efficacy of a training program on improving residents' skills to communicate bad news. Data were collected using a Likert scale. Through a thematic content analysis we tried to to apprehend the meanings, feelings and experiences expressed by resident doctors in their comments as a response to an open-ended question. Half of the group received training, consisting of discussions of video reviews of participants' simulated encounters communicating a perinatal loss to a "mother" based on the SPIKES strategy. We also offered training sessions to the control group after they completed participation. Twenty-eight residents who were randomized to intervention and 16 from the control group received training. Twenty written comments were analyzed. Results The majority of the residents evaluated training highly as an education activity to help increase knowledge, ability and understanding about breaking bad news in perinatology. Three big categories emerged from residents' comments: SPIKES training effects; bad news communication in medical training; and doctors' feelings and relationship with patients. Conclusions Residents took SPIKES training as a guide to systematize the communication of bad news and to amplify perceptions of the emotional needs of the patients. They suggested the insertion of a similar training in their residency programs curricula. Thieme Revinter Publicações Ltda Rio de Janeiro, Brazil.
A device for spike train sampling with built-in memory.
Schmid, K; Böhmer, G
1987-02-01
The described interface to a digital computer measures interspike interval durations with a resolution of 10 microseconds. A built-in first-in first-out (FIFO) memory relieves the host computer from frequent I/O intensive tasks. The internal FIFO buffer can store up to 512 data words (wordlength is 16 bit) and works on the dual-port principle. This way the acquisition of a neuronal spike train is completely independent of the computer's simultaneously ongoing data access. A simple handshake protocol between the interface and the computer prevents any overhead communication. The buffer architecture of the instrument releases the host computer from high speed I/O handling schemes like real-time, clock-controlled, polling or interrupt procedures, that would request assembly language support. The body of two software, driver routines in the BASIC and the PASCAL language is presented. A complete and detailed schematic diagram of the circuitry is included.
Sparse decoding of multiple spike trains for brain-machine interfaces
NASA Astrophysics Data System (ADS)
Tankus, Ariel; Fried, Itzhak; Shoham, Shy
2012-10-01
Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.
Neural Spike-Train Analyses of the Speech-Based Envelope Power Spectrum Model
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.
nSTAT: open-source neural spike train analysis toolbox for Matlab.
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.
nSTAT: Open-Source Neural Spike Train Analysis Toolbox for Matlab
Cajigas, I.; Malik, W.Q.; Brown, E.N.
2012-01-01
Over the last decade there has been a tremendous advance in the analytical tools available to neuroscientists to understand and model neural function. In particular, the point process - Generalized Linear Model (PPGLM) framework has been applied successfully to problems ranging from neuro-endocrine physiology to neural decoding. However, the lack of freely distributed software implementations of published PP-GLM algorithms together with problem-specific modifications required for their use, limit wide application of these techniques. In an effort to make existing PP-GLM methods more accessible to the neuroscience community, we have developed nSTAT – an open source neural spike train analysis toolbox for Matlab®. By adopting an Object-Oriented Programming (OOP) approach, nSTAT allows users to easily manipulate data by performing operations on objects that have an intuitive connection to the experiment (spike trains, covariates, etc.), rather than by dealing with data in vector/matrix form. The algorithms implemented within nSTAT address a number of common problems including computation of peri-stimulus time histograms, quantification of the temporal response properties of neurons, and characterization of neural plasticity within and across trials. nSTAT provides a starting point for exploratory data analysis, allows for simple and systematic building and testing of point process models, and for decoding of stimulus variables based on point process models of neural function. By providing an open-source toolbox, we hope to establish a platform that can be easily used, modified, and extended by the scientific community to address limitations of current techniques and to extend available techniques to more complex problems. PMID:22981419
Stochastic variational learning in recurrent spiking networks.
Jimenez Rezende, Danilo; Gerstner, Wulfram
2014-01-01
The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.
Stochastic variational learning in recurrent spiking networks
Jimenez Rezende, Danilo; Gerstner, Wulfram
2014-01-01
The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about “novelty” on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal. PMID:24772078
Wada, Yoshimasa; Mino, Hiroyuki
2006-01-01
This article presents a statistical analysis of neural spike trains in an auditory nerve fiber (ANF) model stimulated extracellularly by simulated vowel electric stimuli under the case where a high-rate pulsatile waveform is presented as a conditioner for increasing the across-fiber-independency, i.e., desynchronization. In the computer simulation, stimulus current waveforms were presented repeatedly to a stimulating electrode located 1 mm above the 26th node of Ranvier, in an ANF axon model having 50 nodes of Ranvier, each consisting of stochastic sodium and potassium channels. From spike firing times recorded at the 36th node of Ranvier, the raster plots were depicted to explore the temporal precision and reliability of spike trains. Then the period histograms were generated to obtain the synchronization index defined using Shannon's entropy as a distance between the period histogram and the vowel electric stimuli. In the present article, it is shown that at a specific amplitude of simulated vowel waveforms, the possibility to encode the vowel signals with various amplitudes became greater, as well as the synchronization index was found to be maximized. It was implied that setting the amplitude of vowel signals to the specific values which maximize the synchronization index might contribute to efficiently encoding information on vowel formants under the high-rate pulsatile stimulation in cochlear prostheses.
Ronacher, B; Franz, A; Wohlgemuth, S; Hennig, R M
2004-04-01
Object recognition and classification by sensory pathways is rooted in spike trains provided by sensory neurons. Nervous systems had to evolve mechanisms to extract information about relevant object properties, and to separate these from spurious features. In this review, problems caused by spike train variability and counterstrategies are exemplified for the processing of acoustic signals in orthopteran insects. Due to size limitations of their nervous system we expect to find solutions that are stripped to the computational basics. A key feature of auditory systems is temporal resolution, which is likely limited by spike train variability. Basic strategies to reduce such variability are to integrate over time, or to average across several neurons. The first strategy is constrained by its possible interference with temporal resolution. Grasshoppers do not seem to explore temporal integration much, in spite of the repetitive structure of their songs, which invites for 'multiple looks' at the signal. The benefits of averaging across neurons depend on uncorrelated responses, a factor that may be crucial for the performance and evolution of small nervous systems. In spite of spike train variability the temporal information necessary for the recognition of conspecifics is preserved to a remarkable degree in the auditory pathway.
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.
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.
The dependence of spike field coherence on expected intensity.
Lepage, Kyle Q; Kramer, Mark A; Eden, Uri T
2011-09-01
The coherence between neural spike trains and local-field potential recordings, called spike-field coherence, is of key importance in many neuroscience studies. In this work, aside from questions of estimator performance, we demonstrate that theoretical spike-field coherence for a broad class of spiking models depends on the expected rate of spiking. This rate dependence confounds the phase locking of spike events to field-potential oscillations with overall neuron activity and is demonstrated analytically, for a large class of stochastic models, and in simulation. Finally, the relationship between the spike-field coherence and the intensity field coherence is detailed analytically. This latter quantity is independent of neuron firing rate and, under commonly found conditions, is proportional to the probability that a neuron spikes at a specific phase of field oscillation. Hence, intensity field coherence is a rate-independent measure and a candidate on which to base the appropriate statistical inference of spike field synchrony.
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.
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.
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.
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
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
Reconstruction of audio waveforms from spike trains of artificial cochlea models
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
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.
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 . 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. PMID:24751599
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.
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis
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
Goodness-of-Fit Tests and Nonparametric Adaptive Estimation for Spike Train Analysis.
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.
Embedding theorem for spike trains and active processes in chaotic flows
NASA Astrophysics Data System (ADS)
Nishikawa, Takashi
2000-10-01
This thesis contains two separate topics. The first topic concerns proof of a theorem that justifies the method of reconstruction of dynamics using inter-event time intervals. In particular, we prove that the function from an invariant set of a typical dynamical system into R d, defined by successive inter-event time intervals from integrate-and-fire dynamics, is prevalently a topological embedding. This allows topological information about a dynamical attractor to be inferred from spike trains. The second topic is the active processes of particles advected by chaotic flows. While previous studies focused on the active processes of massless point particles, or an ideal tracer, we discuss the particles with finite mass and size. Their equations of motion are inherently dissipative, due to the Stokes drag. The dynamics of the advected particles can be chaotic even with a flow field that is simply time-periodic. Similarly to the case of ideal tracers, whose dynamics is Hamiltonian, chemical or biological activity involving such particles advected by fluid flows can be analyzed using the theory of chaotic dynamics. We choose the cellular vortex flow field with periodically varying vorticity as an example, and analyze the dynamics of the reaction of autocatalytic type, A + B → 2 B, and of coalescence type, B + B → B. Another assumption that the previous studies on the active processes had, was that the reaction of all particles in the system occurs simultaneously. Here we investigate the effect of asynchronism of the autocatalytic reaction taking place in an open hydrodynamical flow, by assigning each particle in the system with a phase to differentiate the timing of their reactions, but not their periodicity. The chaotic saddle in the flow dynamics acts as a catalyst and enhances the reaction in the same fashion as in the case of synchronous reaction that was studied previously. However, we show that, in certain range of a parameter, the group of particles with a
Time scales of spike-train correlation for neural oscillators with common drive.
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.
Australian Vocational Education and Training Statistics. Pocket Guide. Issued 2008
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2008
2008-01-01
This publication, presented in pocket guide format, contains data from 2007 vocational education and training (VET) statistics collections. It includes key data on students and courses, apprentices and trainees training activity, graduates, the financial operations of the VET system, and employers' use and views of the VET system. Among the…
Australian Vocational Education and Training Statistics Pocket Guide, Issued 2009
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2009
2009-01-01
This publication, presented in pocket guide format, contains data from 2008 vocational education and training (VET) statistics collections. It includes key data on students and courses, apprentices and trainees training activity, graduates, the financial operations of the VET system, and employers' use and views of the VET system. Among the…
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.
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…
Holmes, William R; Huwe, Janice A; Williams, Barbara; Rowe, Michael H; Peterson, Ellengene H
2017-05-01
Vestibular bouton afferent terminals in turtle utricle can be categorized into four types depending on their location and terminal arbor structure: lateral extrastriolar (LES), striolar, juxtastriolar, and medial extrastriolar (MES). The terminal arbors of these afferents differ in surface area, total length, collecting area, number of boutons, number of bouton contacts per hair cell, and axon diameter (Huwe JA, Logan CJ, Williams B, Rowe MH, Peterson EH. J Neurophysiol 113: 2420-2433, 2015). To understand how differences in terminal morphology and the resulting hair cell inputs might affect afferent response properties, we modeled representative afferents from each region, using reconstructed bouton afferents. Collecting area and hair cell density were used to estimate hair cell-to-afferent convergence. Nonmorphological features were held constant to isolate effects of afferent structure and connectivity. The models suggest that all four bouton afferent types are electrotonically compact and that excitatory postsynaptic potentials are two to four times larger in MES afferents than in other afferents, making MES afferents more responsive to low input levels. The models also predict that MES and LES terminal structures permit higher spontaneous firing rates than those in striola and juxtastriola. We found that differences in spike train regularity are not a consequence of differences in peripheral terminal structure, per se, but that a higher proportion of multiple contacts between afferents and individual hair cells increases afferent firing irregularity. The prediction that afferents having primarily one bouton contact per hair cell will fire more regularly than afferents making multiple bouton contacts per hair cell has implications for spike train regularity in dimorphic and calyx afferents.NEW & NOTEWORTHY Bouton afferents in different regions of turtle utricle have very different morphologies and afferent-hair cell connectivities. Highly detailed computational
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.
Luo, X.; Gee, S.; Sohal, V.; Small, D.
2015-01-01
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. PMID:26411923
Long temporal autocorrelations in tropical precipitation data and spike train prototypes
NASA Astrophysics Data System (ADS)
Abbott, Tristan H.; Stechmann, Samuel N.; Neelin, J. David
2016-11-01
Temporal precipitation autocorrelations drop slower than exponentially at long lags, and there is a range from tens to thousands of minutes where it is relevant to ask if a scale-free process might underlie the long autocorrelations. A simple stochastic model in which precipitation appears as variable-length spikes provides a reasonable prototype for this behavior. In both observations and the model, separating the component of the autocorrelation within wet events from the interevent contribution suggests long autocorrelation behavior is primarily associated with the latter. When precipitation spikes are short compared to dry events, a true power law is obtained with analytical exponent -0.5 and precipitation autocorrelation is determined by dry-spell model parameters. In more realistic cases, wet-spell termination is also important. Although a variety of apparent power law exponents can be obtained for different parameters, the fundamental long-lag process appears to be that of the interevent correlation.
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
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
NASA Astrophysics Data System (ADS)
Afeyan, Bedros
2013-10-01
We have recently introduced and extensively studied a new adaptive method of LPI control. It promises to extend the effectiveness of laser as inertial fusion drivers by allowing active control of stimulated Raman and Brillouin scattering and crossed beam energy transfer. It breaks multi-nanosecond pulses into a series of picosecond (ps) time scale spikes with comparable gaps in between. The height and width of each spike as well as their separations are optimization parameters. In addition, the spatial speckle patterns are changed after a number of successive spikes as needed (from every spike to never). The combination of these parameters allows the taming of parametric instabilities to conform to any desired reduced reflectivity profile, within the bounds of the performance limitations of the lasers. Instead of pulse shaping on hydrodynamical time scales, far faster (from 1 ps to 10 ps) modulations of the laser profile will be needed to implement the STUD pulse program for full LPI control. We will show theoretical and computational evidence for the effectiveness of the STUD pulse program to control LPI. The physics of why STUD pulses work and how optimization can be implemented efficiently using statistical nonlinear optical models and techniques will be explained. We will also discuss a novel diagnostic system employing STUD pulses that will allow the boosted measurement of velocity distribution function slopes on a ps time scale in the small crossing volume of a pump and a probe beam. Various regimes from weak to strong coupling and weak to strong damping will be treated. Novel pulse modulation schemes and diagnostic tools based on time-lenses used in both microscope and telescope modes will be suggested for the execution of the STUD pule program. Work Supported by the DOE NNSA-OFES Joint Program on HEDLP and DOE OFES SBIR Phase I Grants.
Functional differences between statistical learning with and without explicit training
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
Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity
Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon
2011-01-01
Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800
Australian Vocational Education and Training Statistics Pocket Guide, Issued 2012
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2012
2012-01-01
This pocket guide presents statistics about: (1) the public vocational education and training (VET) system, which includes activity undertaken at technical and further education (TAFE) institutes, other government providers, community education providers and publicly funded delivery by private providers; (2) apprentices and trainees, who are…
Toward reconstructing spike trains from large-scale calcium imaging data
Kwan, Alex C.
2010-01-01
Neural activity can be captured by state-of-the-art optical imaging methods although the analysis of the resulting data sets is often manual and not standardized. Therefore, laboratories using large-scale calcium imaging eagerly await software toolboxes that can automate the process of identifying cells and inferring spikes. An algorithm proposed and implemented in a recent paper by Mukamel et al. [Neuron 63, 747–760 (2009)] used independent component analysis and offers significant improvements over conventional methods. The approach should be widely applicable, as tested with data obtained from the mouse cerebellum, neocortex, and spinal cord. The emergence of analysis tools in parallel with the rapid advances in optical imaging is an exciting development that will stimulate new discoveries and further elucidate the functions of neural circuits. PMID:20676302
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.
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.
Training and technology statistical report, October 1979-September 1980
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.
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.
Australian Vocational Education and Training Statistics: Young People in Education and Training 2014
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2015
2015-01-01
The Australian education and training system offers a range of options for young people. This publication provides a summary of the statistics relating to young people aged 15 to 19 years who participated in an education and training activity during 2014. Information on participation is presented for school students, VET in Schools students,…
Young People in Education and Training 2015. Australian Vocational Education and Training Statistics
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2016
2016-01-01
The Australian education and training system offers a range of options for young people. This publication provides a summary of the statistics relating to young people aged 15 to 19 years who participated in an education and training activity during 2015. The report notes as of August 2015 there were 1.5 million young Australians were enrolled in…
Australian Vocational Education and Training Statistics: Young People in Education & Training 2013
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2014
2014-01-01
The Australian education and training system offers a range of options for young people. This publication provides a summary of the statistics relating to young people aged 15 to 19 years who participated in an education and training activity during 2013 Information on participation is presented for VET in Schools students, higher education…
Schuch, Klaus; Logothetis, Nikos K.; Maass, Wolfgang
2011-01-01
A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N-methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network
On the description of neuronal output properties using spike train data.
Awiszus, F
1989-01-01
Neuronal output properties for input stimuli that evoke a deterministic response can be efficiently described by the interspike-interval function (Awiszus 1988a). It is shown in this paper that there are stimuli for which both the Hodgkin-Huxley (HH-) model of an action potential encoding membrane (Hodgkin and Huxley 1952) and a muscle spindle primary afferent generate responses which violate the conditions for a deterministic one. Instead of being stochastic these responses follow systematic rules, namely those for a semi-deterministic response, a class of neuronal responses established in this paper that includes the deterministic one. Instead of being stochastic these output properties are best described by the interspike-interval curve. A phase plane analysis of the internal properties of the HH-model underlying such responses shows that it is reasonable to assume that responses of an HH-model and consequently, all neurons for which an HH-model is a valid description of the action potential encoding process, always fall into the class of semi-deterministic responses, regardless of the input current density time course as long as it is large enough to maintain spike activity. Consequences of this assumption for the analysis of neuronal output properties are discussed with respect to output measures and efficient input stimuli.
Neuronal spike-train responses in the presence of threshold noise.
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.
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.
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.
A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings
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
Consensus-Based Sorting of Neuronal Spike Waveforms
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
Predicting spike timing in highly synchronous auditory neurons at different sound levels
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
Comparing effects in spike-triggered averages of rectified EMG across different behaviors
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
Seven Pervasive Statistical Flaws in Cognitive Training Interventions
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
Statistical mentoring at early training and career stages
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.
Statistical mentoring at early training and career stages
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
Statistical mentoring at early training and career stages
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.
Estimating short-term synaptic plasticity from pre- and postsynaptic spiking.
Ghanbari, Abed; Malyshev, Aleksey; Volgushev, Maxim; Stevenson, Ian H
2017-09-01
Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful
Time resolution dependence of information measures for spiking neurons: scaling and universality.
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.
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
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…
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…
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…
Eglen, Stephen J.
2014-01-01
Correlations in neuronal spike times are thought to be key to processing in many neural systems. Many measures have been proposed to summarize these correlations and of these the correlation index is widely used and is the standard in studies of spontaneous retinal activity. We show that this measure has two undesirable properties: it is unbounded above and confounded by firing rate. We list properties needed for a measure to fairly quantify and compare correlations and we propose a novel measure of correlation—the spike time tiling coefficient. This coefficient, the correlation index, and 33 other measures of correlation of spike times are blindly tested for the required properties on synthetic and experimental data. Based on this, we propose a measure (the spike time tiling coefficient) to replace the correlation index. To demonstrate the benefits of this measure, we reanalyze data from seven key studies, which previously used the correlation index to investigate the nature of spontaneous activity. We reanalyze data from β2(KO) and β2(TG) mutants, mutants lacking connexin isoforms, and also the age-dependent changes in wild-type and β2(KO) correlations. Reanalysis of the data using the proposed measure can significantly change the conclusions. It leads to better quantification of correlations and therefore better inference from the data. We hope that the proposed measure will have wide applications, and will help clarify the role of activity in retinotopic map formation. PMID:25339742
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…
A Statistically Based Training Diagnostic Tool for Marine Aviation
2014-06-01
of training for SNAs is Primary Flight Training. The primary phase of training is conducted at NAS Whiting Field in Milton , Florida, NAS Corpus...use precise operational language (Swezey, 1981, p. 24). By decomposing objectives into three component parts, performances, conditions, and standards...a Second Language course was used to aid in the analysis of the responses (see Appendix E). The program conducts a count of unique words found in
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…
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…
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…
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…
Statistics of Nurse Training Schools 1926-1927. Bulletin, 1928, No. 2
ERIC Educational Resources Information Center
Phillips, Frank M.
1928-01-01
This report contains statistics of nurse-training schools for the year 1926-27. The principal items included are: Number of schools; number of nurse-training pupils; number of graduates; bed capacity of the hospitals maintaining the schools; average number of patients in these hospitals, length of the nurse-training course; admission requirements,…
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…
Statistical learning and auditory processing in children with music training: An ERP study.
Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Ibrahim, Ronny; Arciuli, Joanne
2017-07-01
The question whether musical training is associated with enhanced auditory and cognitive abilities in children is of considerable interest. In the present study, we compared children with music training versus those without music training across a range of auditory and cognitive measures, including the ability to detect implicitly statistical regularities in input (statistical learning). Statistical learning of regularities embedded in auditory and visual stimuli was measured in musically trained and age-matched untrained children between the ages of 9-11years. In addition to collecting behavioural measures, we recorded electrophysiological measures to obtain an online measure of segmentation during the statistical learning tasks. Musically trained children showed better performance on melody discrimination, rhythm discrimination, frequency discrimination, and auditory statistical learning. Furthermore, grand-averaged ERPs showed that triplet onset (initial stimulus) elicited larger responses in the musically trained children during both auditory and visual statistical learning tasks. In addition, children's music skills were associated with performance on auditory and visual behavioural statistical learning tasks. Our data suggests that individual differences in musical skills are associated with children's ability to detect regularities. The ERP data suggest that musical training is associated with better encoding of both auditory and visual stimuli. Although causality must be explored in further research, these results may have implications for developing music-based remediation strategies for children with learning impairments. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Spike history neural response model.
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.
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…
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…
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…
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…
VET in Schools 2015. Australian Vocational Education and Training Statistics
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2016
2016-01-01
This report presents information on VET in Schools, the vocational education and training (VET) undertaken by school students as part of their senior secondary certificate. The VET in Schools arrangement offers two main options: students can undertake school-based apprenticeships and traineeships; or they can take VET subjects and courses as part…
Australian Vocational Education and Training Statistics. VET in Schools, 2009
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2010
2010-01-01
This publication presents information on vocational education and training (VET) undertaken by school students as part of their senior secondary certificate, known as VET in Schools. The VET in Schools arrangement offers two main options: students can undertake school-based apprenticeships and traineeships; or VET subjects and courses (the latter…
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…
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…
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.
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.
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…
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…
Statistical Annex to Employee Training in the Federal Service, Fiscal Year 1968.
ERIC Educational Resources Information Center
Civil Service Commission, Washington, DC. Bureau of Training.
Tables in this statistical supplement are based on data submitted by Federal agencies in their annual training report to the Civil Service Commission for Fiscal Year 1968 (see document AC 004 019). The first table (Tab A) summarizes all training activity and expenditures for the year, with data arranged by occupational levels (GS01-04 through GS…
Students and Courses, 2002: In Detail. Australian Vocational Education and Training Statistics.
ERIC Educational Resources Information Center
National Centre for Vocational Education Research, Leabrook (Australia).
This document presents detailed statistical information about activity in Australia's public vocational education and training (VET) system in 2002. It contains information on VET students and courses for each of Australia's states and territories. The statistics included in the report were derived from the data collected from VET providers across…
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…
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.
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.
Time resolution dependence of information measures for spiking neurons: scaling and universality
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
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…
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
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. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events
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
Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.
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.
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…
People with a Disability in Vocational Education and Training: A Statistical Compendium
ERIC Educational Resources Information Center
Cavallaro, Toni; Foley, Paul; Saunders, John; Bowman, Kaye
2005-01-01
This statistical compendium examines, firstly, vocational education and training (VET) students with a disability as a whole group, focusing on their participation levels, achievements and outcomes from VET, and identifies gaps and/or issues with the existing data. This is followed by a section dealing with people with different types of…
Regulation of spike timing in visual cortical circuits
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
Reifman, J.; Vitela, E.J.; Lee, J.C.
1993-03-01
Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.
Reifman, J. . Reactor Analysis Div.); Vitela, E.J. . Inst. de Ciencias Nucleares); Lee, J.C. . Dept. of Nuclear Engineering)
1993-01-01
Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.
Methods of artificial enlargement of the training set for statistical shape models.
Koikkalainen, Juha; Tölli, Tuomas; Lauerma, Kirsi; Antila, Kari; Mattila, Elina; Lilja, Mikko; Lötjönen, Jyrki
2008-11-01
Due to the small size of training sets, statistical shape models often over-constrain the deformation in medical image segmentation. Hence, artificial enlargement of the training set has been proposed as a solution for the problem to increase the flexibility of the models. In this paper, different methods were evaluated to artificially enlarge a training set. Furthermore, the objectives were to study the effects of the size of the training set, to estimate the optimal number of deformation modes, to study the effects of different error sources, and to compare different deformation methods. The study was performed for a cardiac shape model consisting of ventricles, atria, and epicardium, and built from magnetic resonance (MR) volume images of 25 subjects. Both shape modeling and image segmentation accuracies were studied. The objectives were reached by utilizing different training sets and datasets, and two deformation methods. The evaluation proved that artificial enlargement of the training set improves both the modeling and segmentation accuracy. All but one enlargement techniques gave statistically significantly (p < 0.05) better segmentation results than the standard method without enlargement. The two best enlargement techniques were the nonrigid movement technique and the technique that combines principal component analysis (PCA) and finite element model (FEM). The optimal number of deformation modes was found to be near 100 modes in our application. The active shape model segmentation gave better segmentation accuracy than the one based on the simulated annealing optimization of the model weights.
Inference of neuronal network spike dynamics and topology from calcium imaging data.
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.
Bayesian population decoding of spiking neurons.
Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias
2009-01-01
The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.
Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.
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.
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.
Applied statistical training to strengthen analysis and health research capacity in Rwanda.
Thomson, Dana R; Semakula, Muhammed; Hirschhorn, Lisa R; Murray, Megan; Ndahindwa, Vedaste; Manzi, Anatole; Mukabutera, Assumpta; Karema, Corine; Condo, Jeanine; Hedt-Gauthier, Bethany
2016-09-29
To guide efficient investment of limited health resources in sub-Saharan Africa, local researchers need to be involved in, and guide, health system and policy research. While extensive survey and census data are available to health researchers and program officers in resource-limited countries, local involvement and leadership in research is limited due to inadequate experience, lack of dedicated research time and weak interagency connections, among other challenges. Many research-strengthening initiatives host prolonged fellowships out-of-country, yet their approaches have not been evaluated for effectiveness in involvement and development of local leadership in research. We developed, implemented and evaluated a multi-month, deliverable-driven, survey analysis training based in Rwanda to strengthen skills of five local research leaders, 15 statisticians, and a PhD candidate. Research leaders applied with a specific research question relevant to country challenges and committed to leading an analysis to publication. Statisticians with prerequisite statistical training and experience with a statistical software applied to participate in class-based trainings and complete an assigned analysis. Both statisticians and research leaders were provided ongoing in-country mentoring for analysis and manuscript writing. Participants reported a high level of skill, knowledge and collaborator development from class-based trainings and out-of-class mentorship that were sustained 1 year later. Five of six manuscripts were authored by multi-institution teams and submitted to international peer-reviewed scientific journals, and three-quarters of the participants mentored others in survey data analysis or conducted an additional survey analysis in the year following the training. Our model was effective in utilizing existing survey data and strengthening skills among full-time working professionals without disrupting ongoing work commitments and using few resources. Critical to our
Hierarchical spike coding of sound
Karklin, Yan; Ekanadham, Chaitanya; Simoncelli, Eero P.
2014-01-01
Natural sounds exhibit complex statistical regularities at multiple scales. Acoustic events underlying speech, for example, are characterized by precise temporal and frequency relationships, but they can also vary substantially according to the pitch, duration, and other high-level properties of speech production. Learning this structure from data while capturing the inherent variability is an important first step in building auditory processing systems, as well as understanding the mechanisms of auditory perception. Here we develop Hierarchical Spike Coding, a two-layer probabilistic generative model for complex acoustic structure. The first layer consists of a sparse spiking representation that encodes the sound using kernels positioned precisely in time and frequency. Patterns in the positions of first layer spikes are learned from the data: on a coarse scale, statistical regularities are encoded by a second-layer spiking representation, while fine-scale structure is captured by recurrent interactions within the first layer. When fit to speech data, the second layer acoustic features include harmonic stacks, sweeps, frequency modulations, and precise temporal onsets, which can be composed to represent complex acoustic events. Unlike spectrogram-based methods, the model gives a probability distribution over sound pressure waveforms. This allows us to use the second-layer representation to synthesize sounds directly, and to perform model-based denoising, on which we demonstrate a significant improvement over standard methods. PMID:25356065
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.
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
Inference of neuronal network spike dynamics and topology from calcium imaging data
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
Training site statistics from Landsat and Seasat satellite imagery registered to a common map base
NASA Technical Reports Server (NTRS)
Clark, J.
1981-01-01
Landsat and Seasat satellite imagery and training site boundary coordinates were registered to a common Universal Transverse Mercator map base in the Newport Beach area of Orange County, California. The purpose was to establish a spatially-registered, multi-sensor data base which would test the use of Seasat synthetic aperture radar imagery to improve spectral separability of channels used for land use classification of an urban area. Digital image processing techniques originally developed for the digital mosaics of the California Desert and the State of Arizona were adapted to spatially register multispectral and radar data. Techniques included control point selection from imagery and USGS topographic quadrangle maps, control point cataloguing with the Image Based Information System, and spatial and spectral rectifications of the imagery. The radar imagery was pre-processed to reduce its tendency toward uniform data distributions, so that training site statistics for selected Landsat and pre-processed Seasat imagery indicated good spectral separation between channels.
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.
Random Walk Models for the Spike Activity of a Single Neuron
Gerstein, George L.; Mandelbrot, Benoit
1964-01-01
Quantitative methods for the study of the statistical properties of spontaneously occurring spike trains from single neurons have recently been presented. Such measurements suggest a number of descriptive mathematical models. One of these, based on a random walk towards an absorbing barrier, can describe a wide range of neuronal activity in terms of two parameters. These parameters are readily associated with known physiological mechanisms. ImagesFigure 3 PMID:14104072
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
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.
Variability and coding efficiency of noisy neural spike encoders.
Steinmetz, P N; Manwani, A; Koch, C
2001-01-01
Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders--an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na(+) and K(+) channels obeying Hodgkin-Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones.
Dendritic spikes veto inhibition.
Stuart, Greg J
2012-09-06
How inhibition regulates dendritic excitability is critical to an understanding of the way neurons integrate the many thousands of synaptic inputs they receive. In this issue of Neuron, Müller et al. (2012) show that inhibition blocks the generation of weak dendritic spikes, leaving strong dendritic spikes intact. Copyright © 2012 Elsevier Inc. All rights reserved.
Neuronal communication: firing spikes with spikes.
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.
Evoking prescribed spike times in stochastic neurons
NASA Astrophysics Data System (ADS)
Doose, Jens; Lindner, Benjamin
2017-09-01
Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.
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…
Spike Code Flow in Cultured Neuronal Networks.
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.
Spike-Based Population Coding and Working Memory
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
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.
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.
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.
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
NASA Astrophysics Data System (ADS)
Keshtkaran, Mohammad Reza; Yang, Zhi
2017-06-01
Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.
Cao, Ying; Maran, Selva K.; Dhamala, Mukesh; Jaeger, Dieter; Heck, Detlef H.
2012-01-01
Purkinje cells (PCs) in the mammalian cerebellum express high frequency spontaneous activity with average spike rates between 30 and 200 Hz. Cerebellar nuclear (CN) neurons receive converging input from many PCs resulting in a continuous barrage of inhibitory inputs. It has been hypothesized that pauses in PC activity trigger increases in CN spiking activity. A prediction derived from this hypothesis is that pauses in PC simple spike activity represent relevant behavioral or sensory events. Here we asked whether pauses in the simple spike activity of PCs related to either fluid licking or respiration, play a special role in representing information about behavior. Both behaviors are widely represented in cerebellar PC simple spike activity. We recorded PC activity in the vermis and lobus simplex of head fixed mice while monitoring licking and respiratory behavior. Using cross correlation and Granger causality analysis we examined whether short ISIs had a different temporal relation to behavior than long ISIs or pauses. Behavior related simple spike pauses occurred during low-rate simple spike activity in both licking and breathing related PCs. Granger causality analysis revealed causal relationships between simple spike pauses and behavior. However, the same results were obtained from an analysis of surrogate spike trains with gamma ISI distributions constructed to match rate modulations of behavior related Purkinje cells. Our results therefore suggest that the occurrence of pauses in simple spike activity does not represent additional information about behavioral or sensory events that goes beyond the simple spike rate modulations. PMID:22723707
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.
A robust and biologically plausible spike pattern recognition network.
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.
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…
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…
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…
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.
A new supervised learning algorithm for spiking neurons.
Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming
2013-06-01
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.
A spiking neural network architecture for nonlinear function approximation.
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.
ERIC Educational Resources Information Center
Steinberg, Wendy J.
The purpose of this study was to examine the nature and degree of differences in expert versus novice knowledge structures, both before and after training, when judging the similarity of multiple-choice test items within a statistics and test theory (STT) domain. Subjects were employees of the Testing Division of the New York State Department of…
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)
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)
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…
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…
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…
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2017
2017-01-01
This publication presents estimates of apprentice and trainee activity in Australia for the September quarter 2016. Highlights include: (1) In-training as at 30 September 2016--There were 278,500 apprentices and trainees in-training as of 30 September 2016, a decrease of 5.7% from 30 September 2015; (2) Quarterly training activity--In the…
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…
A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings
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
A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.
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.
Temporal pairwise spike correlations fully capture single-neuron information
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
Gamma oscillations of spiking neural populations enhance signal discrimination.
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.
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…
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…
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…
Reliability of spike and burst firing in thalamocortical relay cells.
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.
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…
A method for decoding the neurophysiological spike-response transform.
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.
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
Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou
2013-01-01
A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe. PMID:24223789
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.
The possible role of spike patterns in cortical information processing.
Tiesinga, Paul H E; Toups, J Vincent
2005-06-01
When the same visual stimulus is presented across many trials, neurons in the visual cortex receive stimulus-related synaptic inputs that are reproducible across trials (S) and inputs that are not (N). The variability of spike trains recorded in the visual cortex and their apparent lack of spike-to-spike correlations beyond that implied by firing rate fluctuations, has been taken as evidence for a low S/N ratio. A recent re-analysis of in vivo cortical data revealed evidence for spike-to-spike correlations in the form of spike patterns. We examine neural dynamics at a higher S/N in order to determine what possible role spike patterns could play in cortical information processing. In vivo-like spike patterns were obtained in model simulations. Superpositions of multiple sinusoidal driving currents were especially effective in producing stable long-lasting patterns. By applying current pulses that were either short and strong or long and weak, neurons could be made to switch from one pattern to another. Cortical neurons with similar stimulus preferences are located near each other, have similar biophysical properties and receive a large number of common synaptic inputs. Hence, recordings of a single neuron across multiple trials are usually interpreted as the response of an ensemble of these neurons during one trial. In the presence of distinct spike patterns across trials there is ambiguity in what would be the corresponding ensemble, it could consist of the same spike pattern for each neuron or a set of patterns across neurons. We found that the spiking response of a neuron receiving these ensemble inputs was determined by the spike-pattern composition, which, in turn, could be modulated dynamically as a means for cortical information processing.
ERIC Educational Resources Information Center
National Centre for Vocational Education Research, Leabrook (Australia).
Of the 477,800 students reported to Australia's national vocational education and training (VET) data collection in 2000, 237,900 were enrolled in a vocational adult and community education (ACE) program. More than 70% of the latter individuals undertook informal training. Vocational ACE programs accounted for 49.8% of all ACE students and nearly…
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…
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…
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…
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…
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…
Spike correlations in a songbird agree with a simple markov population model.
Weber, Andrea P; Hahnloser, Richard H R
2007-12-01
The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.
Hayat, Matthew J.; Powell, Amanda; Johnson, Tessa; Cadwell, Betsy L.
2017-01-01
Statistical literacy and knowledge is needed to read and understand the public health literature. The purpose of this study was to quantify basic and advanced statistical methods used in public health research. We randomly sampled 216 published articles from seven top tier general public health journals. Studies were reviewed by two readers and a standardized data collection form completed for each article. Data were analyzed with descriptive statistics and frequency distributions. Results were summarized for statistical methods used in the literature, including descriptive and inferential statistics, modeling, advanced statistical techniques, and statistical software used. Approximately 81.9% of articles reported an observational study design and 93.1% of articles were substantively focused. Descriptive statistics in table or graphical form were reported in more than 95% of the articles, and statistical inference reported in more than 76% of the studies reviewed. These results reveal the types of statistical methods currently used in the public health literature. Although this study did not obtain information on what should be taught, information on statistical methods being used is useful for curriculum development in graduate health sciences education, as well as making informed decisions about continuing education for public health professionals. PMID:28591190
Hayat, Matthew J; Powell, Amanda; Johnson, Tessa; Cadwell, Betsy L
2017-01-01
Statistical literacy and knowledge is needed to read and understand the public health literature. The purpose of this study was to quantify basic and advanced statistical methods used in public health research. We randomly sampled 216 published articles from seven top tier general public health journals. Studies were reviewed by two readers and a standardized data collection form completed for each article. Data were analyzed with descriptive statistics and frequency distributions. Results were summarized for statistical methods used in the literature, including descriptive and inferential statistics, modeling, advanced statistical techniques, and statistical software used. Approximately 81.9% of articles reported an observational study design and 93.1% of articles were substantively focused. Descriptive statistics in table or graphical form were reported in more than 95% of the articles, and statistical inference reported in more than 76% of the studies reviewed. These results reveal the types of statistical methods currently used in the public health literature. Although this study did not obtain information on what should be taught, information on statistical methods being used is useful for curriculum development in graduate health sciences education, as well as making informed decisions about continuing education for public health professionals.
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…
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…
Spike sorting of synchronous spikes from local neuron ensembles
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
Multichannel sparse spike inversion
NASA Astrophysics Data System (ADS)
Pereg, Deborah; Cohen, Israel; Vassiliou, Anthony A.
2017-10-01
In this paper, we address the problem of sparse multichannel seismic deconvolution. We introduce multichannel sparse spike inversion as an iterative procedure, which deconvolves the seismic data and recovers the Earth two-dimensional reflectivity image, while taking into consideration the relations between spatially neighboring traces. We demonstrate the improved performance of the proposed algorithm and its robustness to noise, compared to competitive single-channel algorithm through simulations and real seismic data examples.
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.
Filter based phase distortions in extracellular spikes.
Yael, Dorin; Bar-Gad, Izhar
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.
Filter based phase distortions in extracellular spikes
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
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
Unsupervised spike sorting based on discriminative subspace learning.
Keshtkaran, Mohammad Reza; Yang, Zhi
2014-01-01
Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.
A supervised learning rule for classification of spatiotemporal spike patterns.
Lilin Guo; Zhenzhong Wang; Adjouadi, Malek
2016-08-01
This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.
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
Clark, Joseph F; Ellis, James K; Bench, Johnny; Khoury, Jane; Graman, Pat
2012-01-01
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. 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. 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. 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.
Wen, Dong; Peng, Ce; Ou-yang, Gao-xiang; Henderson, Zainab; Li, Xiao-li; Lu, Cheng-biao
2013-01-01
Aim: Spiking activities and neuronal network oscillations in the theta frequency range have been found in many cortical areas during information processing. The aim of this study is to determine whether nicotinic acetylcholine receptors (nAChRs) mediate neuronal network activity in rat medial septum diagonal band Broca (MSDB) slices. Methods: Extracellular field potentials were recorded in the slices using an Axoprobe 1A amplifier. Data analysis was performed off-line. Spike sorting and local field potential (LFP) analyses were performed using Spike2 software. The role of spiking activity in the generation of LFP oscillations in the slices was determined by analyzing the phase-time relationship between the spikes and LFP oscillations. Circular statistic analysis based on the Rayleigh test was used to determine the significance of phase relationships between the spikes and LFP oscillations. The timing relationship was examined by quantifying the spike-field coherence (SFC). Results: Application of nicotine (250 nmol/L) induced prominent LFP oscillations in the theta frequency band and both small- and large-amplitude population spiking activity in the slices. These spikes were phase-locked to theta oscillations at specific phases. The Rayleigh test showed a statistically significant relationship in phase-locking between the spikes and theta oscillations. Larger changes in the SFC were observed for large-amplitude spikes, indicating an accurate timing relationship between this type of spike and LFP oscillations. The nicotine-induced spiking activity (large-amplitude population spikes) was suppressed by the nAChR antagonist dihydro-β-erythroidine (0.3 μmol/L). Conclusion: The results demonstrate that large-amplitude spikes are phase-locked to theta oscillations and have a high spike-timing accuracy, which are likely a main contributor to the theta oscillations generated in MSDB during nicotine receptor activation. PMID:23474704
Statistical Methods for Tissue Array Images – Algorithmic Scoring and Co-training
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
Spiking neuron network Helmholtz machine.
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.
Spiking neuron network Helmholtz machine
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
Causal Inference and Explaining Away in a Spiking Network
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
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.
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.
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.
Chaos and Variability of Inter-Spike Intervals in Neuronal Models with Slow-Fast Dynamics
NASA Astrophysics Data System (ADS)
Doi, Shinji; Inoue, Junko
2011-04-01
A neuron generates action potentials or spikes in response to electric stimuli, and also produces a train of spikes (periodic oscillation) when a continuous stimulus current is injected. Using the extended Bonhoeffer-van der Pol (BVP) or FitzHugh-Nagumo (FHN) equations, which is a simplified version of the famous Hodgkin-Huxley neuronal model, we show that very slow spiking can appear near the (singular) Hopf bifurcation point in a certain generic situation. The patterns of the extraordinary slow spiking are phenomenologically classified into two types: a regular slow spiking and chaotic slow spiking. The variability of inter-spike intervals (ISI's) and the possible mechanism of slow spiking are discussed under slow-fast decomposition analysis. The noise effects on such variability of ISI's are also examined.
The neuronal response at extended timescales: a linearized spiking input–output relation
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
A Statistical Analysis of the Determinants of Naval Flight Officer Training Attrition
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
Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task
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
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
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…
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…
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…
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)…
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…
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…
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…
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…
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…
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…
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…
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…
ERIC Educational Resources Information Center
National Centre for Vocational Education Research (NCVER), 2017
2017-01-01
This publication presents estimates of apprentice and trainee activity in Australia for the December quarter 2016. The figures in this publication are derived from the National Apprentice and Trainee Collection no.91 (March 2017 estimates). The most recent figures in this publication are estimated (that is, for training activity from the June…
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…
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…
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'…
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.)…
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…
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…
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.
Rayleigh--Taylor spike evaporation
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.
Ponulak, Filip; Kasiński, Andrzej
2010-02-01
Learning from instructions or demonstrations is a fundamental property of our brain necessary to acquire new knowledge and develop novel skills or behavioral patterns. This type of learning is thought to be involved in most of our daily routines. Although the concept of instruction-based learning has been studied for several decades, the exact neural mechanisms implementing this process remain unrevealed. One of the central questions in this regard is, How do neurons learn to reproduce template signals (instructions) encoded in precisely timed sequences of spikes? Here we present a model of supervised learning for biologically plausible neurons that addresses this question. In a set of experiments, we demonstrate that our approach enables us to train spiking neurons to reproduce arbitrary template spike patterns in response to given synaptic stimuli even in the presence of various sources of noise. We show that the learning rule can also be used for decision-making tasks. Neurons can be trained to classify categories of input signals based on only a temporal configuration of spikes. The decision is communicated by emitting precisely timed spike trains associated with given input categories. Trained neurons can perform the classification task correctly even if stimuli and corresponding decision times are temporally separated and the relevant information is consequently highly overlapped by the ongoing neural activity. Finally, we demonstrate that neurons can be trained to reproduce sequences of spikes with a controllable time shift with respect to target templates. A reproduced signal can follow or even precede the targets. This surprising result points out that spiking neurons can potentially be applied to forecast the behavior (firing times) of other reference neurons or networks.
The dependence of all-atom statistical potentials on structural training database.
Zhang, Chi; Liu, Song; Zhou, Hongyi; Zhou, Yaoqi
2004-06-01
An accurate statistical energy function that is suitable for the prediction of protein structures of all classes should be independent of the structural database used for energy extraction. Here, two high-resolution, low-sequence-identity structural databases of 333 alpha-proteins and 271 beta-proteins were built for examining the database dependence of three all-atom statistical energy functions. They are RAPDF (residue-specific all-atom conditional probability discriminatory function), atomic KBP (atomic knowledge-based potential), and DFIRE (statistical potential based on distance-scaled finite ideal-gas reference state). These energy functions differ in the reference states used for energy derivation. The energy functions extracted from the different structural databases are used to select native structures from multiple decoys of 64 alpha-proteins and 28 beta-proteins. The performance in native structure selections indicates that the DFIRE-based energy function is mostly independent of the structural database whereas RAPDF and KBP have a significant dependence. The construction of two additional structural databases of alpha/beta and alpha + beta-proteins further confirmed the weak dependence of DFIRE on the structural databases of various structural classes. The possible source for the difference between the three all-atom statistical energy functions is that the physical reference state of ideal gas used in the DFIRE-based energy function is least dependent on the structural database.
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…
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…
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 (^{131m}Xe, ^{133m}Xe, ^{133}Xe and ^{135}Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This 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.
Use of Computer Statistical Packages to Generate Quality Control Reports on Training
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
Predictability of EEG interictal spikes.
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
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.
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
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.
Extracting information in spike time patterns with wavelets and information theory
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
Extracting information in spike time patterns with wavelets and information theory.
Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo
2015-02-01
We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.
Scalable hybrid computation with spikes.
Sarpeshkar, Rahul; O'Halloran, Micah
2002-09-01
We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be
Shrestha, Sumit Bam; Song, Qing
2017-02-01
Training a Spiking Neural Network using SpikeProp and its derivatives faces stability issues. Surges, marked by a sudden rise in learning cost, are a common occurrence during the learning process. They disrupt the learning process and often destabilize the process resulting in failure. A proper learning rate, which is neither too small nor too big, is important to minimize surges. Furthermore, external disturbances due to imperfection in sample data as well as internal disturbances are additional destabilizing source during the learning process. In this paper, we perform error system analysis incorporating external disturbance, followed by weight convergence analysis along with detailed robust stability analysis of SpikeProp learning process to ensure error bound of the learning process. Based on these results, we propose a robust adaptive learning rate scheme that aligns with the results of theoretical analysis. The performance of the proposed method has been compared with other prevalent methods based on different benchmark datasets and the results demonstrate that our method indeed has better performance in terms of convergence and learning speed as well. Copyright © 2016 Elsevier Ltd. All rights reserved.
Learning beyond finite memory in recurrent networks of spiking neurons.
Tino, Peter; Mills, Ashely J S
2006-03-01
We investigate possibilities of inducing temporal structures without fading memory in recurrent networks of spiking neurons strictly operating in the pulse-coding regime. We extend the existing gradient-based algorithm for training feedforward spiking neuron networks, SpikeProp (Bohte, Kok, & La Poutré, 2002), to recurrent network topologies, so that temporal dependencies in the input stream are taken into account. It is shown that temporal structures with unbounded input memory specified by simple Moore machines (MM) can be induced by recurrent spiking neuron networks (RSNN). The networks are able to discover pulse-coded representations of abstract information processing states coding potentially unbounded histories of processed inputs. We show that it is often possible to extract from trained RSNN the target MM by grouping together similar spike trains appearing in the recurrent layer. Even when the target MM was not perfectly induced in a RSNN, the extraction procedure was able to reveal weaknesses of the induced mechanism and the extent to which the target machine had been learned.
Neural cache: a low-power online digital spike-sorting architecture.
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.
Computing Complex Visual Features with Retinal Spike Times
Sompolinsky, Haim; Meister, Markus
2013-01-01
Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times. PMID:23301021
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
Dendritic Spikes in Sensory Perception.
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.
Dendritic Spikes in Sensory Perception
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
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.
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.
Unbiased and robust quantification of synchronization between spikes and local field potential.
Li, Zhaohui; Cui, Dong; Li, Xiaoli
2016-08-30
In neuroscience, relating the spiking activity of individual neurons to the local field potential (LFP) of neural ensembles is an increasingly useful approach for studying rhythmic neuronal synchronization. Many methods have been proposed to measure the strength of the association between spikes and rhythms in the LFP recordings, and most existing measures are dependent upon the total number of spikes. In the present work, we introduce a robust approach for quantifying spike-LFP synchronization which performs reliably for limited samples of data. The measure is termed as spike-triggered correlation matrix synchronization (SCMS), which takes LFP segments centered on each spike as multi-channel signals and calculates the index of spike-LFP synchronization by constructing a correlation matrix. The simulation based on artificial data shows that the SCMS output almost does not change with the sample size. This property is of crucial importance when making comparisons between different experimental conditions. When applied to actual neuronal data recorded from the monkey primary visual cortex, it is found that the spike-LFP synchronization strength shows orientation selectivity to drifting gratings. In comparison to another unbiased method, pairwise phase consistency (PPC), the proposed SCMS behaves better for noisy spike trains by means of numerical simulations. This study demonstrates the basic idea and calculating process of the SCMS method. Considering its unbiasedness and robustness, the measure is of great advantage to characterize the synchronization between spike trains and rhythms present in LFP. Copyright © 2016 Elsevier B.V. All rights reserved.
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.
Spike-timing-dependent construction.
Lightheart, Toby; Grainger, Steven; Lu, Tien-Fu
2013-10-01
Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.
Wavelet analysis of epileptic spikes
NASA Astrophysics Data System (ADS)
Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.
2003-05-01
Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.
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
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.
Comparison of Classifier Architectures for Online Neural Spike Sorting.
Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood
2017-04-01
High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.
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.
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…
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…
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
Changes in complex spike activity during classical conditioning
Rasmussen, Anders; Jirenhed, Dan-Anders; Wetmore, Daniel Z.; Hesslow, Germund
2014-01-01
The cerebellar cortex is necessary for adaptively timed conditioned responses (CRs) in eyeblink conditioning. During conditioning, Purkinje cells acquire pause responses or “Purkinje cell CRs” to the conditioned stimuli (CS), resulting in disinhibition of the cerebellar nuclei (CN), allowing them to activate motor nuclei that control eyeblinks. This disinhibition also causes inhibition of the inferior olive (IO), via the nucleo-olivary pathway (N-O). Activation of the IO, which relays the unconditional stimulus (US) to the cortex, elicits characteristic complex spikes in Purkinje cells. Although Purkinje cell activity, as well as stimulation of the CN, is known to influence IO activity, much remains to be learned about the way that learned changes in simple spike firing affects the IO. In the present study, we analyzed changes in simple and complex spike firing, in extracellular Purkinje cell records, from the C3 zone, in decerebrate ferrets undergoing training in a conditioning paradigm. In agreement with the N-O feedback hypothesis, acquisition resulted in a gradual decrease in complex spike activity during the conditioned stimulus, with a delay that is consistent with the long N-O latency. Also supporting the feedback hypothesis, training with a short interstimulus interval (ISI), which does not lead to acquisition of a Purkinje cell CR, did not cause a suppression of complex spike activity. In contrast, observations that extinction did not lead to a recovery in complex spike activity and the irregular patterns of simple and complex spike activity after the conditioned stimulus are less conclusive. PMID:25140129
A stimulus-dependent spike threshold is an optimal neural coder.
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.
A stimulus-dependent spike threshold is an optimal neural coder
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
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.
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…
Incorporating spike-rate adaptation into a rate code in mathematical and biological neurons
Ralston, Bridget N.; Flagg, Lucas Q.; Faggin, Eric
2016-01-01
For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the “history”). To improve the decoding of spike trains produced by neurons that show spike-rate adaptation, we developed a simple scheme that incorporates “history” into a rate code. We utilized this rate-history code successfully to decode spike trains produced by 1) mathematical models of a neuron in which the mechanism for adaptation (IAHP) is specified, and 2) the gastropyloric receptor (GPR2), a stretch-sensitive neuron in the stomatogastric nervous system of the crab Cancer borealis, that exhibits long-lasting adaptation of unknown origin. Moreover, when we modified the spike rate either mathematically in a model system or by applying neuromodulatory agents to the experimental system, we found that changes in the rate-history code could be related to the biophysical mechanisms responsible for altering the spiking. PMID:26888106
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.
Event-driven contrastive divergence for spiking neuromorphic systems
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
Event-driven contrastive divergence for spiking neuromorphic systems.
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.
An online supervised learning method based on gradient descent for spiking neurons.
Xu, Yan; Yang, Jing; Zhong, Shuiming
2017-09-01
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.
Conditional modeling and the jitter method of spike resampling.
Amarasingham, Asohan; Harrison, Matthew T; Hatsopoulos, Nicholas G; Geman, Stuart
2012-01-01
The existence and role of fine-temporal structure in the spiking activity of central neurons is the subject of an enduring debate among physiologists. To a large extent, the problem is a statistical one: what inferences can be drawn from neurons monitored in the absence of full control over their presynaptic environments? In principle, properly crafted resampling methods can still produce statistically correct hypothesis tests. We focus on the approach to resampling known as jitter. We review a wide range of jitter techniques, illustrated by both simulation experiments and selected analyses of spike data from motor cortical neurons. We rely on an intuitive and rigorous statistical framework known as conditional modeling to reveal otherwise hidden assumptions and to support precise conclusions. Among other applications, we review statistical tests for exploring any proposed limit on the rate of change of spiking probabilities, exact tests for the significance of repeated fine-temporal patterns of spikes, and the construction of acceptance bands for testing any purported relationship between sensory or motor variables and synchrony or other fine-temporal events.
Sediment spiking for toxicity testing
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.
Interictal spikes in focal epileptogenesis.
de Curtis, M; Avanzini, G
2001-04-01
Interictal electroencephalography (EEG) potentials in focal epilepsies are sustained by synchronous paroxysmal membrane depolarization generated by assemblies of hyperexcitable neurons. It is currently believed that interictal spiking sets a condition that preludes to the onset of an ictal discharge. Such an assumption is based on little experimental evidence. Human pre-surgical studies and recordings in chronic and acute models of focal epilepsy showed that: (i) interictal spikes (IS) and ictal discharges are generated by different populations of neuron through different cellular and network mechanisms; (ii) the cortical region that generates IS (irritative area) does not coincide with the ictal-onset area; (iii) IS frequency does not increase before a seizure and is enhanced just after an ictal event; (iv) spike suppression is found to herald ictal discharges; and (v) enhancement of interictal spiking suppresses ictal events. Several experimental evidences indicate that the highly synchronous cellular discharge associated with an IS is generated by a multitude of mechanisms involving synaptic and non-synaptic communication between neurons. The synchronized neuronal discharge associated with a single IS induces and is followed by a profound and prolonged refractory period sustained by inhibitory potentials and by activity-dependent changes in the ionic composition of the extracellular space. Post-spike depression may be responsible for pacing interictal spiking periodicity commonly observed in both animal models and human focal epilepsies. It is proposed that the strong after-inhibition produced by IS protects against the occurrence of ictal discharges by maintaining a low level of excitation in a general condition of hyperexcitability determined by the primary epileptogenic dysfunction.
Sparse and powerful cortical spikes.
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.
Consequences and mechanisms of spike broadening of R20 cells in Aplysia californica.
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.
Optimization Methods for Spiking Neurons and Networks
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
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…
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…
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
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.
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…
Learning Universal Computations with Spikes
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
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…
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…
Generation of a comb electron beam to drive SASE FEL radiation spikes
NASA Astrophysics Data System (ADS)
Boscolo, M.; Boscolo, I.; Castelli, F.; Cialdi, S.; Ferrario, M.; Petrillo, V.; Vaccarezza, C.
2008-08-01
A radiofrequency electron gun followed by a compressor can generate trains of subpicosecond electron pulses by illuminating the photocathode with a comb laser pulse. This kind of electron beams can generate trains of single radiation spikes in a SASE-FEL. The dynamics of different electron beam trains traveling in an accelerator is investigated by PARMELA simulations. A set of parameters relative to the SPARC machine are studied with the intent of generating a train of single radiation spikes in a 500 nm SASE-FEL.
On the relation between encoding and decoding of neuronal spikes.
Koyama, Shinsuke
2012-06-01
Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response can be studied from two parallel points of view. The first, neural encoding, refers to the mapping from stimulus to response. It focuses primarily on understanding how neurons respond to a wide variety of stimuli and constructing models that accurately describe the stimulus-response relationship. Neural decoding refers to the reverse mapping, from response to stimulus, where the challenge is to reconstruct a stimulus from the spikes it evokes. Since neuronal response is stochastic, a one-to-one mapping of stimuli into neural responses does not exist, causing a mismatch between the two viewpoints of neural coding. Here we use these two perspectives to investigate the question of what rate coding is, in the simple setting of a single stationary stimulus parameter and a single stationary spike train represented by a renewal process. We show that when rate codes are defined in terms of encoding, that is, the stimulus parameter is mapped onto the mean firing rate, the rate decoder given by spike counts or the sample mean does not always efficiently decode the rate codes, but it can improve efficiency in reading certain rate codes when correlations within a spike train are taken into account.
Statistical Analysis of Membrane Potential Fluctuations
Levitan, H.; Segundo, J. P.; Moore, G. P.; Perkel, D. H.
1968-01-01
In a study of integration at the single neuron level, the relationships between the postsynaptic membrane potential and the presynaptic spike train were analyzed. Fluctuations in membrane potential of neurons in the visceral ganglion of Aplysia were measured and described by histograms. The histogram estimates the probability density function of the membrane potential. Comparisons were made among histograms when there was no synaptic input, and when there was a single input in which variations were made in the PSP (postsynaptic potential) sign, i.e. excitatory or inhibitory, and arrival statistics, e.g. slow or fast, regular, Poisson-like, or patterned. This was examined in cells where the membrane potential was constant and in cells in which there was spontaneous pacemaker activity. The form of the histogram depended on whether the neuron was spontaneously quiescent or a pacemaker, or whether it received presynaptic input and, if it did, on the sign and temporal characteristics of such input. From such histograms the mean firing rate of output spike trains can be predicted; additional information of a temporal nature is required, however, to predict features of the interval structure of the output train. Suggestions are made concerning the way the nervous system might utilize the information summarized in the membrane potential histogram. PMID:4301347
Error estimation for reconstruction of neuronal spike firing from fast calcium imaging.
Liu, Xiuli; Lv, Xiaohua; Quan, Tingwei; Zeng, Shaoqun
2015-02-01
Calcium imaging is becoming an increasingly popular technology to indirectly measure activity patterns in local neuronal networks. Calcium transients reflect neuronal spike patterns allowing for spike train reconstructed from calcium traces. The key to judging spiking train authenticity is error estimation. However, due to the lack of an appropriate mathematical model to adequately describe this spike-calcium relationship, little attention has been paid to quantifying error ranges of the reconstructed spike results. By turning attention to the data characteristics close to the reconstruction rather than to a complex mathematic model, we have provided an error estimation method for the reconstructed neuronal spiking from calcium imaging. Real false-negative and false-positive rates of 10 experimental Ca(2+) traces were within the estimated error ranges and confirmed that this evaluation method was effective. Estimation performance of the reconstruction of spikes from calcium transients within a neuronal population demonstrated a reasonable evaluation of the reconstructed spikes without having real electrical signals. These results suggest that our method might be valuable for the quantification of research based on reconstructed neuronal activity, such as to affirm communication between different neurons.
Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex
Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo
2015-01-01
The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537
Upper Limb Biomechanics During the Volleyball Serve and Spike
Reeser, Jonathan C.; Fleisig, Glenn S.; Bolt, Becky; Ruan, Mianfang
2010-01-01
Background: The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Hypothesis: Significant kinetic differences exist among specific types of volleyball serves and spikes. Study Design: Controlled laboratory study. Methods: Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were competent in jump serves (n, 5) performed 5 trials of that skill. A 240-Hz 3-dimensional automatic digitizing system captured each trial. Multivariate analysis of variance and post hoc paired t tests were used to compare kinetic parameters for the shoulder and elbow across all the skills (except the jump serve). A similar statistical analysis was performed for upper extremity kinematics. Results: Forces, torques, and angular velocities at the shoulder and elbow were lowest for the roll shot and second-lowest for the float serve. No differences were detected between the cross-body and straight-ahead spikes. Although there was an insufficient number of participants to statistically analyze the jump serve, the data for it appear similar to those of the cross-body and straight-ahead spikes. Shoulder abduction at the instant of ball contact was approximately 130° for all skills, which is substantially greater than that previously reported for female athletes performing tennis serves or baseball pitches. Conclusion: Because shoulder kinetics were greatest during spiking, the volleyball player with symptoms of shoulder overuse may wish to reduce the number of repetitions performed during practice. Limiting the number of jump serves may also reduce the athlete’s risk of overuse-related shoulder dysfunction. Clinical Relevance: Volleyball-specific overhead skills, such as the spike and serve, produce considerable upper extremity force and torque, which may contribute to the risk of
Upper limb biomechanics during the volleyball serve and spike.
Reeser, Jonathan C; Fleisig, Glenn S; Bolt, Becky; Ruan, Mianfang
2010-09-01
The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Significant kinetic differences exist among specific types of volleyball serves and spikes. Controlled laboratory study. Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were competent in jump serves (n, 5) performed 5 trials of that skill. A 240-Hz 3-dimensional automatic digitizing system captured each trial. Multivariate analysis of variance and post hoc paired t tests were used to compare kinetic parameters for the shoulder and elbow across all the skills (except the jump serve). A similar statistical analysis was performed for upper extremity kinematics. Forces, torques, and angular velocities at the shoulder and elbow were lowest for the roll shot and second-lowest for the float serve. No differences were detected between the cross-body and straight-ahead spikes. Although there was an insufficient number of participants to statistically analyze the jump serve, the data for it appear similar to those of the cross-body and straight-ahead spikes. Shoulder abduction at the instant of ball contact was approximately 130° for all skills, which is substantially greater than that previously reported for female athletes performing tennis serves or baseball pitches. Because shoulder kinetics were greatest during spiking, the volleyball player with symptoms of shoulder overuse may wish to reduce the number of repetitions performed during practice. Limiting the number of jump serves may also reduce the athlete's risk of overuse-related shoulder dysfunction. Volleyball-specific overhead skills, such as the spike and serve, produce considerable upper extremity force and torque, which may contribute to the risk of shoulder injury.
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns
Florian, Răzvan V.
2012-01-01
In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm. PMID:22879876
Mechanism for neuronal spike generation by small and large ion channel clusters
NASA Astrophysics Data System (ADS)
Zeng, Shangyou; Jung, Peter
2004-07-01
Neuronal action potentials are generated by clusters of ion channels between the Hillock and the first segment. If the clusters comprise a large number of sodium and potassium channels, action potentials are generated if the membrane potential exceeds a threshold of about -55mV . Such behavior is well described by an excitable model such as, for example, the Hodgkin-Huxley equations. In this paper we show through stochastic modeling that if the size of the generating ion channel cluster is small, action potentials are generated regardless of whether the membrane potential is below or above the excitation threshold. Action potential generation is then determined by single-channel kinetics. We further show that this switch in generation mechanism manifests itself in peculiar statistical properties of the generated spike trains at small cluster sizes.
Mechanism for neuronal spike generation by small and large ion channel clusters.
Zeng, Shangyou; Jung, Peter
2004-07-01
Neuronal action potentials are generated by clusters of ion channels between the Hillock and the first segment. If the clusters comprise a large number of sodium and potassium channels, action potentials are generated if the membrane potential exceeds a threshold of about -55 mV. Such behavior is well described by an excitable model such as, for example, the Hodgkin-Huxley equations. In this paper we show through stochastic modeling that if the size of the generating ion channel cluster is small, action potentials are generated regardless of whether the membrane potential is below or above the excitation threshold. Action potential generation is then determined by single-channel kinetics. We further show that this switch in generation mechanism manifests itself in peculiar statistical properties of the generated spike trains at small cluster sizes.
Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation.
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
Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation
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
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
Prospective Coding by Spiking Neurons
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
Electrophysiology of connection current spikes.
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.
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…
Kassambara, Alboukadel; Hose, Dirk; Moreaux, Jérôme; Walker, Brian A.; Protopopov, Alexei; Reme, Thierry; Pellestor, Franck; Pantesco, Véronique; Jauch, Anna; Morgan, Gareth; Goldschmidt, Hartmut; Klein, Bernard
2012-01-01
Background Genetic abnormalities are common in patients with multiple myeloma, and may deregulate gene products involved in tumor survival, proliferation, metabolism and drug resistance. In particular, translocations may result in a high expression of targeted genes (termed spike expression) in tumor cells. We identified spike genes in multiple myeloma cells of patients with newly-diagnosed myeloma and investigated their prognostic value. Design and Methods Genes with a spike expression in multiple myeloma cells were picked up using box plot probe set signal distribution and two selection filters. Results In a cohort of 206 newly diagnosed patients with multiple myeloma, 2587 genes/expressed sequence tags with a spike expression were identified. Some spike genes were associated with some transcription factors such as MAF or MMSET and with known recurrent translocations as expected. Spike genes were not associated with increased DNA copy number and for a majority of them, involved unknown mechanisms. Of spiked genes, 36.7% clustered significantly in 149 out of 862 documented chromosome (sub)bands, of which 53 had prognostic value (35 bad, 18 good). Their prognostic value was summarized with a spike band score that delineated 23.8% of patients with a poor median overall survival (27.4 months versus not reached, P<0.001) using the training cohort of 206 patients. The spike band score was independent of other gene expression profiling-based risk scores, t(4;14), or del17p in an independent validation cohort of 345 patients. Conclusions We present a new approach to identify spike genes and their relationship to patients’ survival. PMID:22102711
To sort or not to sort: the impact of spike-sorting on neural decoding performance
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
On the Analytical Solution of Firing Time for SpikeProp.
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.
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity
Albers, Christian; Westkott, Maren; Pawelzik, Klaus
2016-01-01
Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns. PMID:26900845
Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.
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.
A memristive spiking neuron with firing rate coding
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
Frequency transfer properties of the spike generating mechanism of cat retinal ganglion cells.
Lankheet, M J; Molenaar, J; van de Grind, W A
1989-01-01
The dynamic properties of the spike generating (SG) mechanism of retinal ganglion cells have been studied from intracellular recordings in the cat eye. Intracellularly recorded light flicker responses were separated by computer into spike trains and corresponding generator potentials. Both the spike train and the generator potential responses to temporally modulated light spots were analysed in terms of amplitude and phase plots. The differences in dynamic properties between the two response measures reveal that the SG-mechanism affects the temporal frequency transfer properties of retinal ganglion cells to a considerable extent. With respect to the transfer of the amplitude of the first harmonic the SG-mechanism has differentiating (or high-pass) properties. This means that the responses to high temporal stimulus frequencies are amplified relatively much more than are the responses to lower frequencies. Furthermore, the SG-mechanism causes a phase lead of the spike train response relative to the generator potential by, on average, 37 degrees. The measured frequency responses, among other things, have been used to verify and to quantify the SG-model that we proposed in a previous paper (Lankheet, Molenaar & van de Grind, 1989). With this model it proved possible to reproduce the spike train responses as model output from the corresponding measured generator potentials as model input. A good qualitative and quantitative correspondence between model output and the measured spike trains was obtained for a wide range of stimulus frequencies and with fixed values of the model parameters. With parameter values that optimized this correspondence the model allowed us to investigate the dynamic behaviour of the SG-mechanism in more detail. It also provides a reliable and validated method to predict the shape of the generator potential from the spike train (the "inversion problem").
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.
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/.
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
A Markovian event-based framework for stochastic spiking neural networks.
Touboul, Jonathan D; Faugeras, Olivier D
2011-11-01
In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.
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.
Caputi, Theodore L
2017-06-22
Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status 1-8. Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase 9-10. However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes." Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing powerContributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.
ERIC Educational Resources Information Center
Bureau of Labor Statistics (DOL), Washington, DC.
This paper describes briefly the following surveys that have been conducted to determine the amount and thrust of employee training in the United States: (1) household surveys including the Current Population Survey, the National Longitudinal Surveys of Labor Market Experience, the Survey of Income and Program Participation, and the University of…
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.
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
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.
Independent component analysis in spiking neurons.
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.
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…
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…
ERIC Educational Resources Information Center
Storms, Doris M.
The training program for nurse practitioners is concerned with the issue of educational preparation of nurses so that they might be more effective providers of health care. The focus is primarily on expansion of nurses' responsibilities and its effect on the nurse-patient relationship, the level of performance and independent decision-making…
Effects of phase on homeostatic spike rates.
Fisher, Nicholas; Talathi, Sachin S; Carney, Paul R; Ditto, William L
2010-05-01
Recent experimental results by Talathi et al. (Neurosci Lett 455:145-149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.
Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings
Mokri, Yasamin; Salazar, Rodrigo F.; Goodell, Baldwin; Baker, Jonathan; Gray, Charles M.; Yen, Shih-Cheng
2017-01-01
One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91% of the overlapping waveforms with a false positive rate of 0.19% for single-electrode data, and 95% of the overlapping waveforms with a false positive rate of 0.27% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods. PMID:28860985
The spike timing dependence of plasticity
Feldman, Daniel E.
2012-01-01
In spike timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression (LTD). STDP is widely utilized in models of circuit-level plasticity, development, and learning. However, spike timing is just one of several factors (including firing rate, synaptic cooperativity, and depolarization) that govern plasticity induction, and its relative importance varies across synapses and activity regimes. This review summarizes the forms, cellular mechanisms, and prevalence of STDP, and evaluates the evidence that spike timing is an important determinant of plasticity in vivo. PMID:22920249
Supervised learning with decision margins in pools of spiking neurons.
Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre
2014-10-01
Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.
A spiking network model of decision making employing rewarded STDP.
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.
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.
Spiking and LFP activity in PRR during symbolically instructed reaches.
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.
Spiking and LFP activity in PRR during symbolically instructed reaches
Andersen, Richard A.
2012-01-01
The spiking activity in the parietal reach region (PRR) represents the spatial goal of an impending reach when the reach is directed toward or away from a visual object. The local field potentials (LFPs) in this region also represent the reach goal when the reach is directed to a visual object. Thus PRR is a candidate area for reading out a patient's intended reach goals for neural prosthetic applications. For natural behaviors, reach goals are not always based on the location of a visual object, e.g., playing the piano following sheet music or moving following verbal directions. So far it has not been directly tested whether and how PRR represents reach goals in such cognitive, nonlocational conditions, and knowing the encoding properties in various task conditions would help in designing a reach goal decoder for prosthetic applications. To address this issue, we examined the macaque PRR under two reach conditions: reach goal determined by the stimulus location (direct) or shape (symbolic). For the same goal, the spiking activity near reach onset was indistinguishable between the two tasks, and thus a reach goal decoder trained with spiking activity in one task performed perfectly in the other. In contrast, the LFP activity at 20–40 Hz showed small but significantly enhanced reach goal tuning in the symbolic task, but its spatial preference remained the same. Consequently, a decoder trained with LFP activity performed worse in the other task than in the same task. These results suggest that LFP decoders in PRR should take into account the task context (e.g., locational vs. nonlocational) to be accurate, while spike decoders can robustly provide reach goal information regardless of the task context in various prosthetic applications. PMID:22072511
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.
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 computer programs, demonstrating that such supports are not essential to the effectiveness of equivalence-based instruction. © Society for the Experimental Analysis of Behavior.
Movement generation with circuits of spiking neurons.
Joshi, Prashant; Maass, Wolfgang
2005-08-01
How can complex movements that take hundreds of milliseconds be generated by stereotypical neural microcircuits consisting of spiking neurons with a much faster dynamics? We show that linear readouts from generic neural microcircuit models can be trained to generate basic arm movements. Such movement generation is independent of the arm model used and the type of feedback that the circuit receives. We demonstrate this by considering two different models of a two-jointed arm, a standard model from robotics and a standard model from biology, that each generates different kinds of feedback. Feedback that arrives with biologically realistic delays of 50 to 280 ms turns out to give rise to the best performance. If a feedback with such desirable delay is not available, the neural microcircuit model also achieves good performance if it uses internally generated estimates of such feedback. Existing methods for movement generation in robotics that take the particular dynamics of sensors and actuators into account (embodiment of motor systems) are taken one step further with this approach, which provides methods for also using the embodiment of motion generation circuitry, that is, the inherent dynamics and spatial structure of neural circuits, for the generation of movement.
Spike voltage topography in temporal lobe epilepsy.
Asadi-Pooya, Ali A; Asadollahi, Marjan; Shimamoto, Shoichi; Lorenzo, Matthew; Sperling, Michael R
2016-07-15
We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology. Copyright © 2016 Elsevier B.V. All rights reserved.
Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series
Zeldenrust, Fleur; de Knecht, Sicco; Wadman, Wytse J.; Denève, Sophie; Gutkin, Boris
2017-01-01
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., “the neural code”) lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing “sensory stimulus”: the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the “Bayesian neuron” (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant. PMID:28663729
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.
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.
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)
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)
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
Noise-robust speech recognition through auditory feature detection and spike sequence decoding.
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.
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.
Asymptotics of empirical eigenstructure for high dimensional spiked covariance
Wang, Weichen
2017-01-01
We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies. PMID:28835726
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
Spiking neural P systems with multiple channels.
Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian
2017-11-01
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.
An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation.
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.
An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation
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
Uroguanylin induces electroencephalographic spikes in rats.
Teixeira, M D A; Nascimento, N R F; Fonteles, M C; Vale, O C
2013-08-01
Uroguanylin (UGN) is an endogenous peptide that acts on membrane-bound guanylate cyclase receptors of intestinal and renal cells increasing cGMP production and regulating electrolyte and water epithelial transport. Recent research works demonstrate the expression of this peptide and its receptor in the central nervous system. The current work was undertaken in order to evaluate modifications of electroencephalographic spectra (EEG) in anesthetized Wistar rats, submitted to intracisternal infusion of uroguanylin (0.0125 nmoles/min or 0.04 nmoles/min). The current observations demonstrate that 0.0125 nmoles/min and 0.04 nmoles/min intracisternal infusion of UGN significantly enhances amplitude and frequency of sharp waves and evoked spikes (p = 0.03). No statistical significance was observed on absolute alpha and theta spectra amplitude. The present data suggest that UGN acts on bioelectrogenesis of cortical cells by inducing hypersynchronic firing of neurons. This effect is blocked by nedocromil, suggesting that UGN acts by increasing the activity of chloride channels.
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.
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.
NASA Astrophysics Data System (ADS)
Stemmler, Martin
1998-03-01
Information from the senses must be compressed into the limited range of firing rates generated by spiking nerve cells. By adjusting the diverse set voltage-dependent conductances in the cell membrane, nerve cells can, in principle, match their response to the statistics of naturally occurring stimuli. By treating the activation and inactivation functions of voltage-dependent conductances as time-dependent basis functions, one can map Hodgkin-Huxley models of single neurons onto a standard neural network model with feedback. Adaptation rules are proposed that change the parameters of these time-dependent basis functions based on maximum entropy/maximum information principles. I explore how such learning rules can transform a non-spiking neuron into one that produces action potentials.
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
Emergent Properties of Interacting Populations of Spiking Neurons
Cardanobile, Stefano; Rotter, Stefan